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1
Dika Prasisti and Yohanes Anton Nugroho. "Optimasi Penjadwalan Produksi untuk Meminimalkan Makespan dengan Pendekatan Particle Swarm Optimization dan Genetic Algorithm." Jurnal Teknologi dan Manajemen Industri Terapan 2, no. 2 (2023): 111–18. http://dx.doi.org/10.55826/tmit.v2i2.134.
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PT Adi Satria Abadi merupakan perusahaan manufaktur yang bergerak dalam produksi sarung tangan golf. Permasalahan yang terjadi adalah tanggal 21 Maret terdapat produk work in process di bagian sewing sejumlah 1.205 unit sarung tangan golf, dari total keseluruhan order sebanyak 11.880 unit sarung tangan golf. Produk harus dikirimkan pada tanggal 22 Maret dan jumlah produk work in process melebihi target produksi harian. Tujuan dari penelitian ini adalah mendapatkan hasil penjadwalan produksi dengan particle swarm optimization algorithm dan algoritma genetika untuk mendapatkan nilai makespan minimum. Perhitungan penjadwalan produksi dengan kedua metode tersebut dilakukan sesuai dengan prosedur masing-masing metode menggunakan software Matlab R2020a. Hasil penjadwalan particle swarm optimization mempunyai urutan job, yaitu J1, J3, J2, J4, J5, J6 dengan makespan sebesar 273 menit. Sedangkan hasil penjadwalan produksi dengan metode algoritma genetika diperlukan waktu proses total (makespan) untuk memproduksi job dengan urutan J1, J4, J6, J5, J2, J3 sebesar 281 menit. Berdasarkan hasil makespan masing-masing metode menunjukkan bahwa hasil penjadwalan produksi dengan metode particle swarm optimization memiliki waktu lebih cepat 8 menit dibandingkan dengan metode genetic algorithm. Hal ini menyimpulkan bahwa metode particle swarm optimization merupakan metode yang paling optimal untuk penjadwalan produksi karena memiliki nilai makespan yang paling minimum.
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Saini, Nisha, and Jitender Kumar. "Mean makespan task scheduling approach for the edge computing environment." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4714. http://dx.doi.org/10.11591/ijece.v14i4.pp4714-4720.
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Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.
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Saini, Nisha, and Jitender Kumar. "Mean makespan task scheduling approach for the edge computing environment." Mean makespan task scheduling approach for the edge computing environment 14, no. 4 (2024): 4714–20. https://doi.org/10.11591/ijece.v14i4.pp4714-4720.
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Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.
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Yunus, Muhammad Ahladi, Marwan Marwan, and Muhammad Rijal Alfian. "Optimization of production process scheduling at Mataram Convection using the Campbell-Dudek and Smith method and the Ho and Chang method." Majalah Ilmiah Matematika dan Statistika 23, no. 1 (2023): 80. http://dx.doi.org/10.19184/mims.v23i1.36403.
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Konveksi Mataram (Djagoan Kaos dan Seragam) is one of the industries engaged in the manufacture of various types of clothing models with fabric as the basic material. So far, the scheduling method used by the company is the First Come First Serve method, in which the completion of production is based on order-to-order data. In this case, with high order intensity, companies often experience difficulties in completing orders according to a predetermined pick-up time. The problems experienced by the company were caused by the production process scheduling that was not optimal. Based on the problems encountered, the purpose of this research is to obtain the optimal scheduling sequence by determining the smallest makespan (minimum total completion time) of the application of the method to the production process. The methods used in this study are the Campbell-Dudek and Smith method and the Ho and Chang method and from these two methods, it is known that the smallest production process is optimal. Based on the results of calculations using the Campbell-Dudek and Smith method, the optimal scheduling sequence with the smallest makespan is 39163 minutes or the production process will be completed in 73 working days. While the results of calculations using the Ho and Chang method obtained the optimal scheduling sequence with the smallest makespan of 38660.50 minutes or the production process will be completed in 72 working days. From the makespans of the two methods, the Ho and Chang method is superior to the Campbell-Dudek and Smith method with a difference of 502.50 minutes or about 1 working day, whereas when compared to the company's initial method, namely First Serve First Come with a makespan of 43025.50 minutes, the HC method can make completion time efficient with a difference of 4365 minutes or about 8 working days.
 Keywords: Campbell-Dudek and Smith methods, first come first serve, Ho and Chang, makespan, production schedulingMSC2020: 90B30
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Putra, Andika Prima, Zeny Fatimah Hunusalela, and Hermanto Ruslan. "Usulan Penjadwalan Produksi Menggunakan Metode Algoritma Tabu Search dan Ant Colony Optimization Untuk Meminimasi Makespan di PT. Raja Ampat Indotim." Jurnal KaLIBRASI - Karya Lintas Ilmu Bidang Rekayasa Arsitektur, Sipil, Industri. 5, no. 2 (2022): 139–47. http://dx.doi.org/10.37721/kalibrasi.v5i2.1022.
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Pentingnya penjadwalan produksi untuk mendapatkan waktu penyelesaian pekerjaan yang optimal, yaitu waktu yang dibutuhkan secara wajar oleh pekerja normal untuk menyelesaikan suatu pekerjaan yang dijalankan dalam sistem kerja terbaik. Terdapat suatu permasalahan yang sering terjadi pada PT. Raja Ampat Indotim seperti nilai makespan perusahaan terlalu tinggi pada pembuatan mesin parutan kelapa, pelet lokal dan pemipil jagung dengan nilai makespan sebesar 757,97 menit. Sehingga menyebabkan terlambatnya target pemesanan produk. Tujuan dari penelitian ini adalah untuk mencari alternatif penjadwalan terbaik untuk menggurangi makespan selama proses produksi, sehingga dalam sehari didapatkan waktu proses yang lebih optimal. Metode yang digunakan Algoritma Tabu Search (TS) dan Algoritma Ant Colony Optimization (ACO). Hasil penelitian yang didapatkan dari metode yang digunakan yaitu metode Algoritma Tabu Search pada job 3-1-2 dengan nilai makespan sebesar 621,11 menit. Kemudian metode Algoritma Ant Colony Optimization didapatkan hasil pada job 3-1-2 dengan total makespan terendah yaitu 683,27 menit. Dengan demikian perusahaan dapat menggunakan penjadwalan produksi menggunakan metode Algoritma Tabu Search guna untuk mengatasi target keterlambatan produk pada penjadwalan produksinya.
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Mangngenre, Saiful, A. Besse Indah, Diniary Syamsul, Azran Arief, and Olyvia Novawanda. "Performance analysis of production scheduling in Toyota simulation." Acta logistica 12, no. 1 (2025): 91–102. https://doi.org/10.22306/al.v12i1.592.
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This research analyzes production scheduling performance in the context of sustainable manufacturing using Toyota Production System (TPS) simulation. The primary focus of this study is to study scheduling performance based on the makespan value and job order for each method. To reduce makespan, two metaheuristic techniques are employed: the tabu search (TS) method and the simulated annealing (SA) method. This research fills the literature gap by exploring makespan optimization methods, combining computer simulation with metaheuristics, and considering TPS scheduling constraints. Data obtained from a miniature car simulation based on the Toyota Production System concept. The research method includes SA and TS implementation using Python and Visual Basic 6.0. The results show that SA and TS produce makespan 2.2-3.2% lower than the Initial Method. SA shows flexibility with different job sequences for each level of demand, while TS produces consistent sequences. The increase in makespan as demand increases is consistent across all methods (14.1-16.4%). In conclusion, SA and TS are effective optimization methods for production scheduling, with the selection depending on the preference for flexibility or consistency.
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Mashuri, Chamdan, Ahmad Heru Mujianto, and Hadi Sucipto. "Comparative analysis of the Campbell Dudek Smith (CDS) and GUPTA Methods for Optimization of Production Scheduling." Generation Journal 5, no. 1 (2021): 1–10. http://dx.doi.org/10.29407/gj.v5i1.13954.
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Abstrak – Penelitian optimalisasi waktu produksi menggunakan algoritma campbell dudek smith (CDS) pada penjadwalan proses produksi bertujuan untuk optimasi makespan untuk pengoperasian mesin untuk memproduksi produk wajan ukuran 12, wajan ukuran 14, wajan ukuran 16, wajan ukuran 18 dan wajan ukuran 20 sehingga didapat nilai makespan yang optimal. Metode yang diterapkan algoritma Campbell Dudek and Smith (CDS), CDS merupakan metode yang digunakan dalam penjadwalan bersifat flowshop dikembangkan dari aturan Johnson yang mampu meminimalkan makespan 2 mesin yang disusun seri. Metode CDS sangat cocok pada karakter produksi yang menerapkan urutan mesin untuk proses produksi. CDS menghasilkan beberapa iterasi yang memiliki nilai makespan, dari beberapa iterasi tersebut didapat nilai makespan yang paling minimal untuk menentukan urutan produk yang akan diproduksi. Penelitian ini menghasilakan aplikasi yang dapat menjadwalkan produk yang akan diproduksi oleh mesin secara otomatis. Dari hasil pengujian dengan jumlah produksi 12 buah pada setiap produk dengan perulangan sebanyak 6 kali, maka didapatkan nilai makespan paling minimal yaitu 210,12 menit dengan urutan pengerjaan produk wajan 20, wajan 18, wajan 16, wajan 14, dan wajan 12. Akurasi hasil pengujian aplikasi menunjukkan 99,99% untuk waktu pertama dan 99,96% untuk waktu kedua jika dibandingkan dengan perhitungan manual.
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Sahputra, Iwan Halim, Tanti Octavia, and Agus Susanto Chandra. "TABU SEARCH SEBAGAI LOCAL SEARCH PADA ALGORITMA ANT COLONY UNTUK PENJADWALAN FLOWSHOP." Jurnal Teknik Industri 11, no. 2 (2009): 188–94. http://dx.doi.org/10.9744/jti.11.2.188-194.
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Ant colony optimization (ACO) is one of the meta-heuristic methods developed for finding solutions to optimization problems such as scheduling. Local search method is one part of the ACO which determines the quality of the resulting solution. In this paper, Tabu Search was proposed as a method of local search in ACO to solve the problem of flowshop scheduling. The purpose of this scheduling was to minimize the makespan. Makespan and computation time of the proposed method were compared to the ACO that implemented Job-Index as local search method. Using proposed algorithm, makespan values obtained were not significantly different than solutions of ACO using Job-Index method, and had computation time shorter.
 
 
 Abstract in Bahasa Indonesia:
 
 Ant colony optimization (ACO) adalah salah satu metode meta-heuristic yang dikembangkan untuk mencari solusi bagi permasalahan optimasi seperti penjadwalan. Metode local search merupakan salah satu bagian dari ACO yang menentukan kualitas solusi yang dihasilkan. Dalam makalah ini Tabu Search diusulkan sebagai metode local search dalam algoritma ACO untuk menyelesaikan masalah penjadwalan flowshop. Tujuan dari penjadwalan ini adalah untuk meminimalkan makespan. Hasil makespan dan computation time dari metode usulan ini akan dibandingkan dengan algoritma ACO yang menggunakan Job-Index sebagai metode local search. Dengan menggunakan algoritma Tabu Search sebagai local search didapat nilai makespan yang tidak berbeda secara signifikan dibandingkan yang menggunakan metode Job-Index, dengan kelebihan computation time yang lebih singkat.
 
 Kata kunci: Tabu Search, Ant Colony Algorithm, Local Search, Penjadwalan Flowshop.
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Mashuri, Chamdan, Ahmad Heru Mujianto, Hadi Sucipto, Rinaldo Yudianto Arsam, and Ginanjar Setyo Permadi. "Production Time Optimization using Campbell Dudek Smith (CDS) Algorithm for Production Scheduling." E3S Web of Conferences 125 (2019): 23009. http://dx.doi.org/10.1051/e3sconf/201912523009.
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The production time optimization study used the Campbell Dudek smith (CDS) algorithm in the production process scheduling aimed at makespan optimization for engine operation to produce 12-size pan products, 14-size griddle, 16-size griddle, 18-size griddle, and 20-size griddle. The method applied by the Campbell Dudek and Smith (CDS) algorithm, CDS is a method used in flowshop-type scheduling developed from Johnson's rule that is able to minimize makespan 2 machines arranged in series. The CDS method is very suitable for production characters who apply the machine sequence to the production process. CDS produces several iterations that have makespan values, from the few iterations the most minimum makespan value is obtained to determine the order of products to be produced. This research produces an application that can schedule products to be produced by the machine automatically. From the results of testing with a total production of 12 pieces on each product with repetitions of 6 times, the minimum makespan value is 210.12 minutes with a work order of 20, grid 18, griddle 16, griddle 14, and griddle 12. Accuracy of results Application testing showed 99.99% for the first time and 99.96% for the second time when compared to manual calculations.
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Lahza, Husam, Sreenivasa B R, Hassan Fareed M. Lahza, and Shreyas J. "Adaptive Multi-Objective Resource Allocation for Edge-Cloud Workflow Optimization Using Deep Reinforcement Learning." Modelling 5, no. 3 (2024): 1298–313. http://dx.doi.org/10.3390/modelling5030067.
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This study investigates the transformative impact of smart intelligence, leveraging the Internet of Things and edge-cloud platforms in smart urban development. Smart urban development, by integrating diverse digital technologies, generates substantial data crucial for informed decision-making in disaster management and effective urban well-being. The edge-cloud platform, with its dynamic resource allocation, plays a crucial role in prioritizing tasks, reducing service delivery latency, and ensuring critical operations receive timely computational power, thereby improving urban services. However, the current method has struggled to meet the strict quality of service (QoS) requirements of complex workflow applications. In this study, these shortcomings in edge-cloud are addressed by introducing a multi-objective resource optimization (MORO) scheduler for diverse urban setups. This scheduler, with its emphasis on granular task prioritization and consideration of diverse makespans, costs, and energy constraints, underscores the complexity of the task and the need for a sophisticated solution. The multi-objective makespan–energy optimization is achieved by employing a deep reinforcement learning (DRL) model. The simulation results indicate consistent improvements with average makespan enhancements of 31.6% and 70.09%, average cost reductions of 62.64% and 73.24%, and average energy consumption reductions of 25.02% and 17.77%, respectively, by MORO over-reliability enhancement strategies for workflow scheduling (RESWS) and multi-objective priority workflow scheduling (MOPWS) for SIPHT workflow. Similarly, consistent improvements with average makespan enhancements of 37.98% and 74.44%, average cost reductions of 65.53% and 74.89%, and average energy consumption reductions of 29.52% and 24.73%, respectively, by MORO over RESWS and MOPWS for CyberShake workflow, highlighting the proposed model’s efficiency gains. These findings substantiate the model’s potential to enhance computational efficiency, reduce costs, and improve energy conservation in real-world smart urban scenarios.
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Chandrasiri, Sunera, and Dulani Meedeniya. "Energy-Efficient Dynamic Workflow Scheduling in Cloud Environments Using Deep Learning." Sensors 25, no. 5 (2025): 1428. https://doi.org/10.3390/s25051428.
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Dynamic workflow scheduling in cloud environments is a challenging task due to task dependencies, fluctuating workloads, resource variability, and the need to balance makespan and energy consumption. This study presents a novel scheduling framework that integrates Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) using the Proximal Policy Optimization (PPO) algorithm to achieve multi-objective optimization, focusing on minimizing makespan and reducing energy consumption. By leveraging GNNs to model task dependencies within workflows, the framework enables adaptive and informed resource allocation. The agent was evaluated within a CloudSim-based simulation environment using synthetic datasets. Experimental results across benchmark datasets demonstrate the proposed framework’s effectiveness, achieving consistent improvements in makespan and energy consumption over traditional heuristic methods. The framework achieved a minimum makespan of 689.22 s against the second best of 800.72 s in moderate-sized datasets, reducing makespan significantly with improvements up to 13.92% over baseline methods such as HEFT, Min–Min, and Max–Min, while maintaining competitive energy consumption of 10,964.45 J. These findings highlight the potential of combining GNNs and DRL for dynamic task scheduling in cloud environments, effectively balancing multiple objectives.
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Gulbaz, Rohail, Abdul Basit Siddiqui, Nadeem Anjum, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, and Naeem Ramzan. "Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing." Applied Sciences 11, no. 14 (2021): 6244. http://dx.doi.org/10.3390/app11146244.
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Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.
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Khiat, Abdelhamid, and Abdelkamel Tari. "InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments." MENDEL 25, no. 1 (2019): 179–88. http://dx.doi.org/10.13164/mendel.2019.1.179.
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The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.
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Mangalampalli, Sudheer, Sangram Keshari Swain, Tulika Chakrabarti, et al. "Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization." Sensors 23, no. 13 (2023): 6155. http://dx.doi.org/10.3390/s23136155.
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Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.
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Y. Hamed, Ahmed, M. Kh Elnahary, and Hamdy H. El-Sayed. "Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm." International Journal of Advanced Networking and Applications 14, no. 06 (2023): 5686–95. http://dx.doi.org/10.35444/ijana.2023.14605.
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Cloud computing is now dominant in high-performance distributed computing, offering resource polling and ondemand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.
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Karim, Faten, Sara Ghorashi, Salem Alkhalaf, Ishak Ben, and Sameer Alshetewi. "Modelling of horse herd optimization based multi objective task scheduling approach in cloud computing environment." Thermal Science 29, no. 2 Part B (2025): 1583–95. https://doi.org/10.2298/tsci2502583k.
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Cloud computing, which offers scalable and flexible resources, faces a key challenge in task scheduling, directly impacting system performance and user satisfaction. Effective scheduling is crucial for optimizing resource use and reducing makespan. The NP-completeness of the task scheduling problem complicates achieving optimal outcomes. Scheduling applications is critical in cloud computing due to the need to map future tasks to resources in real time. Many existing methods focus on makespan and resource consumption but overlook factors like energy usage and migration time, which affect web services. This study proposes a horse herd optimization-based multi-objective task scheduling approach (HHO-MOTSA) to address these gaps. The HHO-MOTSA aims to minimize makespan, energy usage, and cost by modelling the social behaviors of horses, including grazing, hierarchy, sociability, and defense mechanisms. A fitness function helps evaluate solutions, where a low value indicates minimized energy, makespan, and cost. Performance tests using CloudSim show that HHO-MOTSA outperforms other methods in effec?tive task scheduling.
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MOHAMMED, SHARIF, HUSAIN AZHAR, LATEEF MOHAMMED, and DAYOUB M. "Optimization of Makespan of Container Loading -Unloading Problem Using Mixed Integer Programming." International Journal of Earth Sciences and Engineering 10, no. 01 (2017): 53–57. http://dx.doi.org/10.21276/ijee.2017.10.0108.
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Isa, Noor Amira, Noor Azizah Sidek, Salleh Ahmad Bareduan, Azli Nawawi, and Muhammad Marsudi. "OPTIMIZATION OF NEW CONSTRUCTIVE HEURISTIC ALGORITHMS FOR PERMUTATION FLOW SHOP SCHEDULING PROBLEM." Journal of Information System and Technology Management 9, no. 37 (2024): 203–19. https://doi.org/10.35631/jistm.937016.
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This paper presents a new heuristic designed specifically for minimizing makespan in scheduling problems. The proposed approach incorporates a dual bottleneck phase combined with a pre-initial arrangement to enhance optimization of new heuristic. By introducing both major and minor bottleneck identification phases, the heuristic effectively identifies critical processing machines with significant completion times. To evaluate the performance, this study employed the Taillard benchmark and the upper bound (UB) makespan as comparative tools, assessing the new heuristic against the well-known NEH heuristic. Computational results clearly demonstrate that the new heuristic significantly outperforms the NEH heuristic in reducing the total completion time. The consistent lower RPD values and negative percentage errors indicate that ICHA is more effective in approaching the optimal makespan as indicated by the Taillard UB.
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Wang, Bing Gang. "Solving Lot-Sizing and Sequencing Integrated Optimization Problems in Mixed-Model Production Systems." Advanced Materials Research 472-475 (February 2012): 3335–38. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3335.
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This paper is concerned about the lot-sizing and sequencing integrated optimization problems in mixed-model production systems composed of one mixed-model assembly line and one fabrication flow line. The optimization objective is minimizing the total makespan cost in regular hour, the overtime makespan cost and the holding cost in the whole production system. The mathematic models are presented and an adaptive genetic algorithm is developed for solving this problem. A traditional genetic algorithm is also designed for testing the optimization performance of the adaptive genetic algorithm. Computational experiments are conducted and the optimization results are compared between the above two algorithms. The comparison results show that the adaptive genetic algorithm is a feasible and effective method for solving this problem.
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Xing, Lining, Jun Li, Zhaoquan Cai, and Feng Hou. "Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds." Mathematics 11, no. 9 (2023): 2126. http://dx.doi.org/10.3390/math11092126.
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Making sound trade-offs between the energy consumption and the makespan of workflow execution in cloud platforms remains a significant but challenging issue. So far, some works balance workflows’ energy consumption and makespan by adopting multi-objective evolutionary algorithms, but they often regard this as a black-box problem, resulting in the low efficiency of the evolutionary search. To compensate for the shortcomings of existing works, this paper mathematically formulates the cloud workflow scheduling for an infrastructure-as-a-service (IaaS) platform as a multi-objective optimization problem. Then, this paper tailors a knowledge-driven energy- and makespan-aware workflow scheduling algorithm, namely EMWSA. Specifically, a critical task adjustment-based local search strategy is proposed to intelligently adjust some critical tasks to the same resource of their successor tasks, striving to simultaneously reduce workflows’ energy consumption and makespan. Further, an idle gap reuse strategy is proposed to search the optimal energy consumption of each non-critical task without affecting the operation of other tasks, so as to further reduce energy consumption. Finally, in the context of real-world workflows and cloud platforms, we carry out comparative experiments to verify the superiority of the proposed EMWSA by significantly outperforming 4 representative baselines on 19 out of 20 workflow instances.
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Sachin Karadgi, Rashmi Benni, Shashikumar Totad, Karibasappa K. G,. "A Comparative Study of Evolutionary and Swarm Intelligence Algorithms for Job Scheduling on Identical Parallel Machines." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 3734–45. http://dx.doi.org/10.52783/tjjpt.v44.i4.1531.
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In parallel computing systems, job scheduling plays a crucial role in enhancing system efficiency and minimizing the makespan. In recent years, evolutionary and swarm intelligence algorithms have gained prominence as effective approaches for solving combinatorial optimization problems. In the present work, we have considered genetic algorithm (GA) for evolutionary algorithms and particle swarm optimization (PSO) for swarm intelligence algorithms. Evolutionary algorithms (EA) and swarm intelligence algorithms (SIA) have shown promising results in solving job scheduling challenges. In this study, we collate the performance of EA and SIA approaches for job scheduling on parallel machines. We use different benchmark instances to evaluate the algorithms' makespan and computational time performance. The results show that SIA algorithms outperform EA algorithms regarding makespan and computational time for all benchmark instances. Furthermore, the study provides insights into the strengths and weaknesses of EA and SIA algorithms for job scheduling on parallel machines. Our findings provide useful insights for researchers and practitioners interested in applying optimization techniques to solve job scheduling problems on parallel machines.
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Pulansari, Farida, and Triyono Dwi Retno M. "The Unrelated Parallel Machine Scheduling with a Dependent Time Setup using Ant Colony Optimization Algorithm." Jurnal Teknik Industri 23, no. 1 (2021): 65–74. http://dx.doi.org/10.9744/jti.23.1.65-74.
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The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving scheduling problems quickly and precisely, the company can get more profit. On that note, this paper focused on unrelated PMS problems, which did not consider the inherent uncertainty in processing time and set up time by minimizing the makespan and tardiness. This paper aimed to minimize the makespan and tardiness using timing considerations. This paper described how to schedule unrelated parallel machines using the Ant Colony Optimization (ACO) Algorithm approach. The ACO is beneficial for inherent parallelism problems and can provide fast and reasonable solutions. This study revealed that the results of ACO Algorithm scheduling were obtained under a steady condition in iteration 30467. This condition can be interpreted that the makespan and tardiness value is close to 2.75%. By minimizing the makespan and tardiness, the delay of product delivery to consumers can be anticipated. Moreover, a company can maintain customer satisfaction and increase its profit.
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Prassetiyo, Hendro, and Firda Heryati. "The Effectiveness of Genetic Algorithm, And the CDS Method In Solving Flowshop Scheduling Problems." E3S Web of Conferences 484 (2024): 01008. http://dx.doi.org/10.1051/e3sconf/202448401008.
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Flow shop scheduling problem is considered NP-hard for m machines and n jobs. For such NP-hard combinatorial optimization problems, heuristics play a major role in searching for near-optimal solutions. In this paper we used Genetic Algorithm, and the CDS method for solving flow shop scheduling problem with makespan as the criteria. The objective of this model is to obtain a sequence of jobs and the minimization of the total completion time (makespan). To test the effectiveness of the method, a dataset of case studies is used to compare the makespan values obtained for each method.
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Wang, Jinlong, Zhezhuang Xu, Mingxing He, Liang Xue, and Hongjie Xu. "Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage." Applied Sciences 14, no. 9 (2024): 3628. http://dx.doi.org/10.3390/app14093628.
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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.
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Mary, J. Magelin, and D. I. George Amalarethinam. "Modified Sunflower Optimization Algorithm for Task Scheduling in Cloud Computing." Indian Journal Of Science And Technology 18, no. 17 (2025): 1365–76. https://doi.org/10.17485/ijst/v18i17.537.
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Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation
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Widyawati, Widyawati. "PENERAPAN ALGORITMA ANT COLONY OPTIMIZATION (ACO) PADA JOB SHOP SCHEDULING PROBLEM (JSSP) DI PT. SIEMENS INDONESIA (CILEGON FACTORY)." Jurnal Sistem Informasi dan Informatika (Simika) 1, no. 01 (2018): 35–51. http://dx.doi.org/10.47080/simika.v1i01.37.
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Fabrication process is often disrupted by non–deterministic job, this create a problem in the Pre-Fabrication department schedule because often the manufacture of raw material for non-deterministic job should given priority. This problem also affected by the existing system which is not yet fully developed to solve the problem of optimize rescheduling master line (seen from total makespan time). Ant Colony Optimization (ACO) variant Ant System (AS) was proposed to solve Job Shop Scheduling Problem (JSSP) with the objective to propose the best schedule that give shortest makespan. The algorithm tested to perform scheduling of 5 projects (consist of 10 parts) as the initial job, and another 2 projects (consist of 4 parts) as the non-deterministic job. For the initial job, makespan was 287 days and after the arrival of non-deterministic job, makespan was 362 days compare with the actual manufacturing time (7 project consist of 14 parts) which is ± 511 days
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Mangalampalli, Sudheer, Kiran Sree Pokkuluri, G. Naga Satish, and K. Varada Raj Kumar. "An Effective Workflow Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization." ECS Transactions 107, no. 1 (2022): 2523–30. http://dx.doi.org/10.1149/10701.2523ecst.
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Effective workflow scheduling approach is needed, as it is huge challenge in cloud computing as variable and heterogeneous workflows comes onto cloud console. To handle these complex workflows and to schedule them onto appropriate virtual resources an effective workflow scheduler is necessary. Earlier authors used various nature inspired algorithms to solve this workflow scheduling by minimizing makespan and maximizing resource utilization, but still there is a chance to minimize makespan and improve resource utilization. In this paper, we have calculated the task priorities of all tasks, which are incoming onto cloud console, and thereby mapping tasks onto VMs to effectively map tasks. Cat swarm optimization used to solve scheduling problem and finally we have addressed parameters named as makespan and resource utilization. Simulation was carried out on workflowsim and we evaluated the efficacy of our algorithm by comparing with existing algorithms PSO and CS. From simulation results we observed that our algorithm shows great improvement over existing algorithms for mentioned parameters.
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Prakash, Shiv, and Deo Prakash Vidyarthi. "A Hybrid GABFO Scheduling for Optimal Makespan in Computational Grid." International Journal of Applied Evolutionary Computation 5, no. 3 (2014): 57–83. http://dx.doi.org/10.4018/ijaec.2014070104.
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Scheduling in Computational Grid (CG) is an important but complex task. It is done to schedule the submitted jobs onto the nodes of the grid so that some characteristic parameter is optimized. Makespan of the job is an important parameter and most often scheduling is done to optimize makespan. Genetic Algorithm (GA) is a search procedure based on the evolutionary technique that is able to solve a class of complex optimization problem. However, GA takes longer to converge towards its near optimal solution. Bacteria Foraging Optimization (BFO), also derived from nature, is a technique to optimize a given function in a distributed manner. Due to limited availability of bacteria, BFO is not suitable to optimize the solution for the problem involving a large search space. Characteristics of both GA and BFO are combined so that their benefits can be reaped. The hybrid approach is referred to as Genetic Algorithms Bacteria Foraging Optimization (GABFO) algorithm. The proposed GABFO has been applied to optimize makespan of a given schedule in a computational grid. Results of the simulation, conducted to evaluate the performance of the proposed model, reveal the effectiveness of the proposed model.
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Manalu, Haposan Vincentius, Fatiha Widyanti, Nur Mayke Eka Normasari, Andiny Trie Oktavia, and Achmad Pratama Rifai. "Permutation Flowshop Scheduling in ED Aluminium Using Metaheuristic Approaches." Journal of Industrial Engineering and Halal Industries 4, no. 2 (2024): 49–56. http://dx.doi.org/10.14421/jiehis.3003.
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This study proposes metaheuristics to solve the permutation flowshop scheduling problem in ED Aluminium which produces kitchen utensils. The aim is to find the processing sequence of products that results in the shortest total completion time, minimizing makespan and total flowtime. Three metaheuristics are developed, which are Simulated Annealing (SA), Large Neighborhood Search (LNS), and Ant Colony Optimization (ACO). Experiments are performed in this research to evaluate the three algorithms. The result using the simulated annealing algorithm is considered better because it has a shorter makespan. The contribution of this study is developing Simulated Annealing, Large Neighborhood Search, and Ant Colony Optimization to solve the problem.
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NSV, Kiran Kumar, and Manish KR Singh Dr. "Desert sparrow optimization algorithm." International Journal of Trends in Emerging Research and Development 1, no. 1 (2023): 167–74. https://doi.org/10.5281/zenodo.12663258.
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The Desert Sparrow Optimization (DSO) algorithm is a nature-inspired metaheuristic optimization technique that mimics the foraging behavior of sparrows in desert environments. It leverages the strategies employed by sparrows to survive and thrive in harsh, resource-constrained habitats. The algorithm is characterized by its simplicity, efficiency, and effectiveness in solving complex optimization problems across various domains. This research aims to better understand desert sparrows by Analysing their cooperative work allocation behaviour and developing an algorithm to Minimise Makespan.
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Azad, Poopak, and Nima Jafari Navimipour. "An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm." International Journal of Cloud Applications and Computing 7, no. 4 (2017): 20–40. http://dx.doi.org/10.4018/ijcac.2017100102.
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In a cloud environment, computing resources are available to users, and they pay only for the used resources. Task scheduling is considered as the most important issue in cloud computing which affects time and energy consumption. Task scheduling algorithms may use different procedures to distribute precedence to subtasks which produce different makespan in a heterogeneous computing system. Also, energy consumption can be different for each resource that is assigned to a task. Many heuristic algorithms have been proposed to solve task scheduling as an NP-hard problem. Most of these studies have been used to minimize the makespan. Both makespan and energy consumption are considered in this paper and a task scheduling method using a combination of cultural and ant colony optimization algorithm is presented in order to optimize these purposes. The basic idea of the proposed method is to use the advantages of both algorithms while avoiding the disadvantages. The experimental results using C# language in cloud azure environment show that the proposed algorithm outperforms previous algorithms in terms of energy consumption and makespan.
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Sissodia, Rajeshwari, ManMohan Singh Rauthan, and Varun Barthwal. "A Multi-Objective Optimization Scheduling Method Based on the Genetic Algorithm in Cloud Computing." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijcac.305217.
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For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing. This paper propose a resource cost model that defines the demand of tasks on resources with more details. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan, wall clock time , execution time and the costs as constraints of the optimization problem. This paper proposed a multi-objective improved genetic algorithm (MOIGA) to address multi-objective task scheduling problems. The experiment results showed that the MOIGA algorithm minimizes makespan, wall clock time, execution time and cost when compared with First Come First Serve (FCFS), Round Robin (RR) and Shortest Job First (SJF).
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Babor, Majharulislam, Line Pedersen, Ulla Kidmose, Olivier Paquet-Durand, and Bernd Hitzmann. "Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study." Processes 10, no. 8 (2022): 1623. http://dx.doi.org/10.3390/pr10081623.
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Minimizing the makespan is an important research topic in manufacturing engineering because it accounts for significant production expenses. In bakery manufacturing, ovens are high-energy-consuming machines that run throughout the production time. Finding an optimal combination of makespan and oven idle time in the decisive objective space can result in substantial financial savings. This paper investigates the hybrid no-wait flow shop problems from bakeries. Production scheduling problems from multiple bakery goods manufacturing lines are optimized using Pareto-based multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), and a random search algorithm. NSGA-II improved NSGA, leading to better convergence and spread of the solutions in the objective space, by removing computational complexity and adding elitism and diversity strategies. Instead of a single solution, a set of optimal solutions represents the trade-offs between objectives, makespan and oven idle time to improve cost-effectiveness. Computational results from actual instances show that the solutions from the algorithms significantly outperform existing schedules. The NSGA-II finds a complete set of optimal solutions for the cases, whereas the random search procedure only delivers a subset. The study shows that the application of multi-objective optimization in bakery production scheduling can reduce oven idle time from 1.7% to 26% while minimizing the makespan by up to 12%. Furthermore, by penalizing the best makespan a marginal amount, alternative optimal solutions minimize oven idle time by up to 61% compared to the actual schedule. The proposed strategy can be effective for small and medium-sized bakeries to lower production costs and reduce CO2 emissions.
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Wilda, Erina Mu’rivatul, and Sumiati. "Optimization of Production Scheduling to Minimize Makespan in the 54-Ton Flat Carriage Project at PT INKA Multi Solusi." JKIE (Journal Knowledge Industrial Engineering) 12, no. 1 (2025): 30–39. https://doi.org/10.35891/jkie.v12i1.5912.
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Efficient production scheduling is essential to achieve optimal operational goals in the manufacturing industry. This study aims to optimize production scheduling on a 54 Ton Flat Carriage manufacturing project at PT INKA Multi Solusi using the Nawaz, Enscore, and Ham (NEH) Method. The main focus of this study is to minimize the total completion time (makespan) in order to improve operational efficiency, reduce costs, and meet deadlines and quality standards that have been set. The NEH method is applied to compare the results with the method commonly used by the company, namely First Come First Serve (FCFS). The results show that the NEH method produces a smaller makespan (1.39 hours) compared to the FCFS method which produces a makespan of 1.41 hours. Thus, the NEH method is proven to be more efficient in reducing makespan and can be used as a reference for better production scheduling. This study also contributes to the allocation of limited resources such as machines, labor, and raw materials, in order to increase the company's productivity and competitiveness amidst the tight competition in the manufacturing industry
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Ahmadi, Parham. "Enhancing Online Job Shop Scheduling Efficiency: Simulation-Based Optimization Incorporating Human Productivity Factors and TOPSIS Ranking." Smart and Sustainable Manufacturing Systems 8, no. 1 (2024): 119–35. https://doi.org/10.1520/ssms20230042.
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Abstract The job shop scheduling problem is a classical optimization challenge aimed at determining the optimal processing order by assigning a set of resources to a corresponding set of operations. This article investigates various approaches to address the online job shop scheduling problem, employing a simulation-based study. Dispatching rules are applied to allocate resources to operations, with discrete event simulation used for problem assessment. The study also incorporates human productivity factors, specifically investigating the shortest processing time (SPT) dispatching rule in a separate scenario. Five distinct scenarios are simulated, including four dispatching rules (first in, first out; last in, first out; longest processing time; and SPT) and an additional scenario integrating the SPT rule and human productivity factors. The simulation results are used to compare makespan across these scenarios, revealing that the scenario involving the SPT dispatching rule and human productivity factors represents the shortest makespan. Through a TOPSIS technique-based ranking, considering makespan and cost as criteria, the study identifies the SPT rule and human productivity factors as the most efficient scenario. The findings imply that employing human productivity factors with effective dispatching rules, such as SPT, can significantly improve job shop scheduling operational efficiency and lead to more optimal results in both makespan and overall operational costs.
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Wu, Weiyuan. "Dispatching Rules-based Optimization of the No-wait Flow Shop Scheduling Problem." Journal of Physics: Conference Series 2587, no. 1 (2023): 012066. http://dx.doi.org/10.1088/1742-6596/2587/1/012066.
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Abstract To improve the productivity of the flow shop, this paper proposes a new mathematical model to describe the scheduling problem of the no-wait flow shop (NWFS) with the minimization makespan as the scheduling objective. In addition, a new dispatching rule PCA-AS is created using the principal component analysis (PCA) algorithm, and PCA-AS is applied to the process of solving the scheduling problem to gain a scheduling strategy with excellent performance. Finally, the paper numerically verifies that PCA-AS outperforms the shortest processing time (SPT) dispatching rule and the first-in-first-out (FIFO) dispatching rule in minimizing the makespan.
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Vijayalakshmi, R., V. Vasudevan, Seifedine Kadry, and R. Lakshmana Kumar. "Optimization of makespan and resource utilization in the fog computing environment through task scheduling algorithm." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (2020): 1941025. http://dx.doi.org/10.1142/s021969131941025x.
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The Fog computing is rising as a dominant and modern computing model to deliver Internet of Things (IoT) computations, which is an addition to the cloud computing standard to get it probable to perform the IoT requests in the network of edge. In those above independent and dispersed environment, resource allocation is vital. Therefore, scheduling will be a test to enhance potency and allot resources properly to the tasks. This paper offers a distinct task scheduling algorithm in the fog computing environment that tries to depreciate the makespan and maximize resource utilization. This algorithm catalogues the task based on the mean Suffrage value. The suggested algorithm gives much resource utilization and diminishes makespan. Our offered algorithm is compared with different alive scheduling for performance investigation, and test results confirm that our algorithm has a more significant resource utilization rate and low makespan than other familiar algorithms.
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Cho, Young In, So Hyun Nam, Ki Young Cho, Hee Chang Yoon, and Jong Hun Woo. "Minimize makespan of permutation flowshop using pointer network." Journal of Computational Design and Engineering 9, no. 1 (2021): 51–67. http://dx.doi.org/10.1093/jcde/qwab068.
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ABSTRACT During the shipbuilding process, a block assembly line suffers a bottleneck when the largest amount of material is processed. Therefore, scheduling optimization is important for the productivity. Currently, sequence of inbound products is controlled by determining the input sequence using a heuristic or metaheuristic approach. However, the metaheuristic algorithm has limitations in that the computation time increases exponentially as the number of input objects increases, and separate optimization calculations are required for every problem. Also, the heuristic such as dispatching algorithm has the limitation of the exploring the problem domain. Therefore, this study tries a reinforcement learning algorithm based on a pointer network to overcome these limitations. Reinforcement learning with pointer network is found to be suitable for permutation flowshop problem, including input-order optimization. A trained neural network is applied without re-learning, even if the number of inputs is changed. The trained model shows the meaningful results compared with the heuristic and metaheuristic algorithms in makespan and computation time. The trained model outperforms the heuristic and metaheuristic algorithms within a limited range of permutation flowshop problem.
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Büke, Burak, John J. Hasenbein, and David P. Morton. "MINIMIZING MAKESPAN IN A MULTICLASS FLUID NETWORK WITH PARAMETER UNCERTAINTY." Probability in the Engineering and Informational Sciences 23, no. 3 (2009): 457–80. http://dx.doi.org/10.1017/s026996480900028x.
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We introduce and investigate a new type of decision problem related to multiclass fluid networks. Optimization problems arising from fluid networks with known parameters have been studied extensively in the queueing, scheduling, and optimization literature. In this article, we explore the makespan problem in fluid networks, with the assumption that the parameters are known only through a probability distribution. Thus, the decision maker does not have complete knowledge of the parameters in advance. This problem can be formulated as a stochastic nonlinear program. We provide necessary and sufficient feasibility conditions for this class of problems. We also derive a number of other structural results that can be used in developing effective computational procedures for solving stochastic fluid makespan problems.
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Liu, Zhimeng, Shuguang Li, Muhammad Ijaz Khan, Shaimaa A. M. Abdelmohsen, and Sayed M. Eldin. "Bicriteria multi-machine scheduling with equal processing times subject to release dates." Networks and Heterogeneous Media 18, no. 3 (2023): 1378–92. http://dx.doi.org/10.3934/nhm.2023060.
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<abstract><p>This paper addresses the problem of scheduling $ n $ equal-processing-time jobs with release dates non-preemptively on identical machines to optimize two criteria simultaneously or hierarchically. For simultaneous optimization of total completion time (and makespan) and maximum cost, an algorithm is presented which can produce all Pareto-optimal points together with the corresponding schedules. The algorithm is then adapted to solve the hierarchical optimization of two min-max criteria, and the final schedule has a minimum total completion time and minimum makespan among the hierarchical optimal schedules. The two algorithms provided in this paper run in $ O(n^3) $ time.</p></abstract>
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Amanullah, Wahidatul Fatin, Sapti Wahyuningsih, and Lucky Tri Oktoviana. "Ant colony optimization (ACO) pada job shop scheduling problem (JSSP)." Jurnal MIPA dan Pembelajarannya 2, no. 11 (2023): 9. http://dx.doi.org/10.17977/um067v2i112022p9.
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Job Shop Scheduling Problem (JSSP) merupakan permasalahan dalam menentukan makespan yang minimum pada suatu jadwal dengan n jobs dan m mesin. Salah satu algoritma yang dapat digunakan dalam penyelesaian permasalahan ini adalah ant colony optimization (ACO). ACO adalah metode yang terinspirasi oleh perilaku makhluk hidup yaitu perilaku dari sekumpulan semut yang keluar dari sarangnya menuju sumber makanan dengan meninggalkan zat pheromone. Dalam algoritma ACO terdapat beberapa tahapan penyelesaian yaitu inisialisasi parameter, aturan transisi status, tahap pembaharuan jejak pheromone, dan menemukan solusi terbaik. Parameter yang dibutuhkan yaitu m (banyaknya mesin), α (tetapan pengendali intensitas jejak semut), β (tetapan pengendali visibilitas), τ_ij (t) (intensitas pheromone), k (banyaknya semut), ρ (evaporasi pheromone), Q (konstanta), dan Cmax (banyaknya iterasi) yang digunakan untuk mencari rute dan makespan.
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Suvarna, N. A., and Deepak Bharadwaj. "Optimization of System Performance through Ant Colony Optimization: A Novel Task Scheduling and Information Management Strategy for Time-Critical Applications." Indian Journal of Information Sources and Services 14, no. 2 (2024): 167–77. http://dx.doi.org/10.51983/ijiss-2024.14.2.24.
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Optimization of task scheduling and information storage/retrieval is crucial for managing resource utilization, which enhances system performance and ultimately impacts provider productivity and customer satisfaction. Efficient task scheduling aims to optimize computing time, while efficient information management focuses on maximizing memory usage. This paper presents a novel approach to task scheduling using Ant Colony Optimization (ACO) to improve time-critical objectives such as makespan and network latency, while maintaining balanced load distribution across systems. By enhancing makespan, we aim to maximize CPU utilization, and by optimizing information storage/retrieval, we target minimizing network latency. Performance across these multiple objectives is achieved by modifying the heuristic and visibility functions to guide ants toward specific solutions. The effectiveness of the proposed algorithm, Resource-Aware Load-Balancing for Time-Critical Applications (RALB-TCA), is demonstrated through implementation in the CloudSim simulation platform and benchmarking against existing techniques.
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Amallynda, Ikhlasul, and Bhisma Hutama. "The Moth-Flame Optimization Algorithm for Flow Shop Scheduling Problem with Travel Time." Jurnal Teknik Industri 22, no. 2 (2021): 224–35. http://dx.doi.org/10.22219/jtiumm.vol22.no2.224-235.
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This article examined the flow shop scheduling problem by considering the travel time between machines. The objective function of this problem was to provide a makespan. The Moth Flame Optimization (MFO) algorithm was proposed to solve the flow shop problem. The MFO experiment was carried out with a combination of iteration parameters and the population of the MFO algorithm to solve the flow shop scheduling problem. The computational results showed that MFO could produce a better solution than the actual scheduling method. Furthermore, the MFO Proposal Algorithm was able to reduce the makespan by up to 3%.
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Makhija, Divya, Posham Bhargava Reddy, Chapram Sudhakar, and Varsha Kumari. "Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization." Computer Science & Engineering: An International Journal 12, no. 6 (2022): 01–10. http://dx.doi.org/10.5121/cseij.2022.12601.
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Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by improving the exploitation (local search) of PSO algorithm and exploration (global search) of GWO algorithm. This leads to better balance between exploration and exploitation, consequently it minimizes the makespan with 5.52% compared to GWO and 3.68% compared to PSO. The degree of imbalance reduced upto 33.22% compared to GWO and 17.61% compared to PSO, improves the convergence rate as well depending on number tasks and iterations. CloudSim tool is used to evaluate the proposed algorithm. The simulation results confirmed that the proposed method performs better than both of the standard PSO and GWO in terms of makespan, degree of imbalance and convergence rate
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Aupy, Guillaume, and Anne Benoit. "Approximation Algorithms for Energy, Reliability, and Makespan Optimization Problems." Parallel Processing Letters 26, no. 01 (2016): 1650001. http://dx.doi.org/10.1142/s0129626416500018.
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We consider the problem of scheduling an application on a parallel computational platform. The application is a particular task graph, either a linear chain of tasks, or a set of independent tasks. The platform is made of identical processors, whose speed can be dynamically modified. It is also subject to failures: if a processor is slowed down to decrease the energy consumption, it has a higher chance to fail. Therefore, the scheduling problem requires us to re-execute or replicate tasks (i.e., execute twice the same task, either on the same processor, or on two distinct processors), in order to increase the reliability. It is a tri-criteria problem: the goal is to minimize the energy consumption, while enforcing a bound on the total execution time (the makespan), and a constraint on the reliability of each task. Our main contribution is to propose approximation algorithms for linear chains of tasks and independent tasks. For linear chains, we design a fully polynomial-time approximation scheme. However, we show that there exists no constant factor approximation algorithm for independent tasks, unless P=NP, and we propose in this case an approximation algorithm with a relaxation on the makespan constraint.
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Baskar, A., and M. Anthony Xavior. "Optimization of Makespan in Job and Machine Priority Environment." Procedia Engineering 97 (2014): 22–28. http://dx.doi.org/10.1016/j.proeng.2014.12.220.
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Panggabean, Jonas Franky R. "Hybrid Ant Colony Optimization-Genetics Algorithm to Minimize Makespan Flow Shop Scheduling." International Journal of Engineering & Technology 7, no. 2.2 (2018): 40. http://dx.doi.org/10.14419/ijet.v7i2.2.11868.
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Flow shop scheduling is a scheduling model in which the job to be processed entirely flows in the same product direction / path. In other words, jobs have routing work together. Scheduling problems often arise if there is n jobs to be processed on the machine m, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. In research of Zini, H and ElBernoussi, S. (2016) NEH Heuristic and Stochastic Greedy Heuristic (SG) algorithms. This paper presents modified harmony search (HS) for flow shop scheduling problems with the aim of minimizing the maximum completion time of all jobs (makespan). To validate the proposed algorithm this computational test was performed using a sample dataset of 60 from the Taillard Benchmark. The HS algorithm is compared with two constructive heuristics of the literature namely the NEH heuristic and stochastic greedy heuristic (SG). The experimental results were obtained on average for the dataset size of 20 x 5 to 50 x 10, that the ACO-GA algorithm has a smaller makespan than the other two algorithms, but for large-size datasets the ACO-GA algorithm has a greater makespan of both algorithms with difference of 1.4 units of time.
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Kaur, Avinash, Pooja Gupta, Parminder Singh, and Manpreet Singh. "Data Placement Oriented Scheduling Algorithm for Scheduling Scientific Workflow in Cloud: A Budget-Aware Approach." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 871–83. http://dx.doi.org/10.2174/2666255813666190925141324.
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Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposed a novel workflow scheduling scheme. Also, data placement is included in the proposed algorithm. Results: In this scheme, DPO-HEFT (Data Placement Oriented HEFT) algorithm is developed which closely integrates the data placement mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflow demonstrate the efficacy of our scheme. Conclusion: Our scheme can achieve significantly better cost and makespan trade-off fronts with remarkably higher hypervolume and can run up to hundreds times faster than the state-of-the-art algorithms.
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Ullah, Md Rahamat, Selim Molla, Iqtiar Md Siddique, Anamika Ahmed Siddique, and Md Minhajul Abedin. "Utilization of Johnson's Algorithm for Enhancing Scheduling Efficiency and Identifying the Best Operation Sequence: An Illustrative Scenario." Journal of Recent Activities in Production 8, no. 3 (2023): 11–20. http://dx.doi.org/10.46610/jorap.2023.v08i03.002.
Full textAbstract:
This study's primary objective is the enhancement of makespan optimization, thereby minimizing unproductive time for both machinery and tasks. The research employs a case study methodology, focusing on job scheduling within anElectronicsmanufacturing facility, with a particular emphasis on resource availability. It implements Johnson's algorithm and its expanded versions designed for both two-machine and three-machine scenarios within the context of flow shop scheduling. The principal aim is to identify optimal sequences for scheduling. Within this framework, the investigation computes idle time and makespan metrics for individual machines, utilizing task processing durations and in-out timestamps. The findings reveal anoptimal idle time of 6.21 minutes and a makespan of 142.06minutes for scenarios involving two machines. Furthermore, the research extends its analysis to scenarios with three machines, where two machines are combined in each group, resulting in an optimal idle time of 5.22minutes for combined machine A2, 14.98minutes for combined machine A3, and a makespan of 192minutes. This study provides valuable insights applicable to industries dealing with diverse machinery and components, ultimately contributing to improved scheduling and productivity.
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K. Sathya Sundari. "Makespan Minimization in Job Shop Scheduling." International Journal of Engineering and Management Research 11, no. 1 (2021): 228–30. http://dx.doi.org/10.31033/ijemr.11.1.31.
Full textAbstract:
In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.