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Journal articles on the topic 'Scheduling objectives'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Castillo, Ignacio, Tarja Joro, and Yong Yue Li. "Workforce scheduling with multiple objectives." European Journal of Operational Research 196, no. 1 (July 2009): 162–70. http://dx.doi.org/10.1016/j.ejor.2008.02.038.

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2

Epstein, Sheldon, Yonah Wilamowsky, and Bernard Dickman. "Deterministic multiprocessor scheduling with multiple objectives." Computers & Operations Research 19, no. 8 (November 1992): 743–49. http://dx.doi.org/10.1016/0305-0548(92)90013-u.

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3

Franke, Carsten, Joachim Lepping, and Uwe Schwiegelshohn. "Greedy scheduling with custom-made objectives." Annals of Operations Research 180, no. 1 (December 6, 2008): 145–64. http://dx.doi.org/10.1007/s10479-008-0491-2.

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4

Važan, Pavel, Michal Škamla, Dominika Jurovatá, and Vladimír Ľupták. "Effect of Selected Priority Rules on Manufacturing Objectives in Scheduling." Advanced Materials Research 488-489 (March 2012): 1125–29. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1125.

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In this paper, the effect of selected priority rules of scheduling is presented. This effect has been studied for chosen scheduling objectives by simulation method. The authors used the simulator Witness to experimentation with priority rules in a designed manufacturing system. The results of experiments have been processed into the synthesis of knowledge. The implementation of scheduling procedure is described in this paper.

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5

Brezoňáková, Andrea. "Realistic scheduling agreement: Defining principles and objectives." Transportation Research Procedia 43 (2019): 156–64. http://dx.doi.org/10.1016/j.trpro.2019.12.030.

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6

Monteiro, Thibaud, Nadine Meskens, and Tao Wang. "Surgical scheduling with antagonistic human resource objectives." International Journal of Production Research 53, no. 24 (September 11, 2015): 7434–49. http://dx.doi.org/10.1080/00207543.2015.1082040.

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7

Ozturkoglu, Yucel. "An efficient time algorithm for makespan objectives." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 5, no. 2 (July 1, 2015): 75–80. http://dx.doi.org/10.11121/ijocta.01.2015.00260.

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This paper focuses on a single machine scheduling subject to machine deterioration with rate-modifying activities (RMA). The motivation for this study stems from the automatic-production line problem with one machine. The main question is to find the sequence in which jobs should be scheduled, how many maintenance activity (RMA) to use, if any, and where to insert them in the schedule during the time interval with optimal makespan objective. This problem is known to be NP-hard and we give concise analyses of the problem and provide polynomial time algorithms to solve the makespan problem. We also propose an algorithm which can be applied to some scheduling problems with the actual processing time of job nonlinearly based on its position.This paper focuses on a single machine scheduling subject to machine deterioration with rate-modifying activities (RMA). The motivation for this study stems from the automatic-production line problem with one machine. The main question is to find the sequence in which jobs should be scheduled, how many maintenance activity (RMA) to use, if any, and where to insert them in the schedule during the time interval with optimal makespan objective. This problem is known to be NP-hard and we give concise analyses of the problem and provide polynomial time algorithms to solve the makespan problem. We also propose an algorithm which can be applied to some scheduling problems with the actual processing time of job nonlinearly based on its position.

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8

Bhattacharyya, Siddhartha, and Gary J. Koehler. "Learning by Objectives for Adaptive Shop-Floor Scheduling." Decision Sciences 29, no. 2 (March 1998): 347–75. http://dx.doi.org/10.1111/j.1540-5915.1998.tb01580.x.

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9

Salman, Saad Mohsin. "Scheduling Critical Activities on Multi-objectives Stochastic Projects." Journal of Al-Nahrain University Science 16, no. 3 (September 1, 2013): 246–51. http://dx.doi.org/10.22401/jnus.16.3.34.

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10

Hellerstein, J. L. "Achieving service rate objectives with decay usage scheduling." IEEE Transactions on Software Engineering 19, no. 8 (1993): 813–25. http://dx.doi.org/10.1109/32.238584.

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11

Rajendran, Chandrasekharan. "Heuristics for scheduling in flowshop with multiple objectives." European Journal of Operational Research 82, no. 3 (May 1995): 540–55. http://dx.doi.org/10.1016/0377-2217(93)e0212-g.

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12

Lebacque, Vassilissa, Vincent Jost, and Nadia Brauner. "Simultaneous optimization of classical objectives in JIT scheduling." European Journal of Operational Research 182, no. 1 (October 2007): 29–39. http://dx.doi.org/10.1016/j.ejor.2006.07.019.

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13

Elmaghraby, Salah. "Mathematical Programming and Financial Objectives for Scheduling Projects." Journal of Scheduling 8, no. 6 (December 2005): 543–46. http://dx.doi.org/10.1007/s10951-005-4783-y.

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14

Chandrasekharam, R., V. V. Vinod, and S. Subramanian. "Genetic algorithm for test scheduling with different objectives." Integration 17, no. 2 (October 1994): 153–61. http://dx.doi.org/10.1016/0167-9260(94)00009-3.

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15

Johnston, Mark D., and Mark E. Giuliano. "Multi-Objective Scheduling for Space Science Missions." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1140–48. http://dx.doi.org/10.20965/jaciii.2011.p1140.

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We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to reuse existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives aremodeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angularmomentumthat would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

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Liu, Peng Fei, and Shou Bin Dong. "Multi-Objective Scheduling for Parallel Jobs on Grid." Key Engineering Materials 439-440 (June 2010): 1281–86. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1281.

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Focused on the complexity of the parallel job scheduling on heterogeneous Grid, the paper proposes a multi-objective optimization based scheduling algorithm. The algorithm first splits the parallel job up into a series of independent processes with constraints, and then adopts particles to represent the mapping of job-resource. Multi-objective PSO is employed to simultaneously optimize the scheduling objectives of throughput and average turnaround time. Experimental result indicates that the proposed approach is effective while dealing with large scale parallel jobs scheduling on heterogeneous Grid and outperforms other conventional algorithms.

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17

Xie, Zhiqiang, Lirong Pei, Qing Jia, and Xu Yu. "A Process Migration Oriented Multi-Shop Integrated Scheduling Algorithm for Double Objectives." International Journal of Cooperative Information Systems 29, no. 01n02 (March 2020): 2040008. http://dx.doi.org/10.1142/s0218843020400080.

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The characteristic of multi-shop scheduling is that the processing equipment is scattered in multiple workshops in different geographical locations. To solve this problem, a multi-shop comprehensive scheduling algorithm considering migration dual-objective is proposed of migration of a single complex product in multi-shop equipment, and the workpiece is migrated from one device to another during the processing. The algorithm first uses the leaf nodes of the process tree as a set of schedulable operations; second, the pre-scheduled operation set is determined according to the long path first strategy and the short time strategy; third, the actual scheduling set of the same idle equipment is determined according to the same equipment procedure selection strategy in the workshop; finally, considering the migration time and cost of the process to be processed, a two-objective optimization strategy for process shop selection is proposed. The example results show that the algorithm in this paper consumes less time and cost during the migration process, and can obtain a relatively compromised solution of the total time and total cost of product scheduling.

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18

Nie, Li, Yue Wei Bai, Xin Jiang, and Chang Tao Pang. "An Approach for Level Scheduling Mixed Models on an Assembly Line in a JIT Production System." Applied Mechanics and Materials 697 (November 2014): 473–77. http://dx.doi.org/10.4028/www.scientific.net/amm.697.473.

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A mixed-model assembly line (MMAL) is a type of production system that is capable of producing different models of a common base product simultaneously. Mixed-model assembly line level scheduling problem (MMALSP) is a challenge for Just-in-time (JIT) production systems. In the paper, a mixed-model assembly line level scheduling model is proposed which considers multiple objectives simultaneously. The considered objectives include the variation in parts consumption considering the batch part supply, inventory cost and maximum transportation load. An approach based on genetic algorithm is proposed to solve the multiple objectives problem. In order to translate individuals in the GA population into candidate scheduling schemes a delivery scheduling algorithm (DSA) is proposed. In addition, dimensionless processing technique is employed in the design of the fitness function in order to comprehensively evaluate different individual considering three objectives simultaneously. The approach’s performance is validated through comprehensive experiment.

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19

Ze, Tao, Di Liang, and Zhou Qun. "Study on Job Shop Scheduling Optimization with Multi-Objective." Advanced Materials Research 217-218 (March 2011): 326–29. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.326.

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A new multi-objective scheduling method based on the GA is proposed to the job-shop scheduling problem (JSP) constrained by machines, workers. Function objectives of the proposed method are to minimize the completion time, the maximum load of machines and the total expense of machines and workers. Firstly, the mathematical model is constructed. Then, on the basis of the mathematical model, the genetic algorithm (GA) based on Pareto is applied, and an optimal or suboptimal scheduling plan can be obtained. The optimal solutions are not unique due to the multi-objective of JSP. Finally, a scheduling example is employed to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.

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20

Zhang, Lianmin, Lei Guan, and Ke Zhou. "Stochastic Machine Scheduling to Minimize Waiting Time Related Objectives with Emergency Jobs." Discrete Dynamics in Nature and Society 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/837910.

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We consider a new scheduling model where emergency jobs appear during the processing of current jobs and must be processed immediately after the present job is completed. All jobs have random processing times and should be completed on a single machine. The most common case of the model is the surgery scheduling problem, where some elective surgeries are to be arranged in an operation room when emergency cases are coming during the operating procedure of the elective surgeries. Two objective functions are proposed to display this practice in machine scheduling problem. One is the weighted sum of the waiting times and the other is the weighted discounted cost function of the waiting times. We address some optimal policies to minimize these objectives.

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21

Vakhania, Nodari. "Scheduling a Single Machine with Primary and Secondary Objectives." Algorithms 11, no. 6 (June 5, 2018): 80. http://dx.doi.org/10.3390/a11060080.

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22

Ji, Haoran, Weidong Bao, and Xiaomin Zhu. "Adaptive workflow scheduling for diverse objectives in cloud environments." Transactions on Emerging Telecommunications Technologies 28, no. 2 (April 23, 2015): e2941. http://dx.doi.org/10.1002/ett.2941.

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23

Oyetunji, E. O. "Assessing solution methods to mixed multi-objectives scheduling problems." International Journal of Industrial and Systems Engineering 9, no. 2 (2011): 213. http://dx.doi.org/10.1504/ijise.2011.042836.

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24

Hof, John, and Michael Bevers. "Optimal timber harvest scheduling with spatially defined sediment objectives." Canadian Journal of Forest Research 30, no. 9 (September 1, 2000): 1494–500. http://dx.doi.org/10.1139/x00-065.

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This note presents a simple model formulation that focuses on the spatial relationships over time between timber harvesting and sediment levels in water runoff courses throughout the watershed being managed. A hypothetical example is developed to demonstrate the formulation and show how sediment objectives can be spatially defined anywhere in the watershed. Spatial sensitivity in the example is shown, indicating that, at least in some cases, tracking sediment levels below a watershed may be inadequate for achieving sediment objectives within the watershed itself.

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25

Boros, Endre, Lei Lei, Yao Zhao, and Hua Zhong. "Scheduling vessels and container-yard operations with conflicting objectives." Annals of Operations Research 161, no. 1 (November 17, 2007): 149–70. http://dx.doi.org/10.1007/s10479-007-0260-7.

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26

Gupta, Amit Kumar, and Appa Iyer Sivakumar. "Single machine scheduling with multiple objectives in semiconductor manufacturing." International Journal of Advanced Manufacturing Technology 26, no. 9-10 (July 21, 2004): 950–58. http://dx.doi.org/10.1007/s00170-004-2074-y.

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27

Low, Chinyao, Yukling Yip, and Tai-Hsi Wu. "Modelling and heuristics of FMS scheduling with multiple objectives." Computers & Operations Research 33, no. 3 (March 2006): 674–94. http://dx.doi.org/10.1016/j.cor.2004.07.013.

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28

Prasad, S. Deva. "A genetic algorithm for flowshop scheduling with multiple objectives." OPSEARCH 44, no. 1 (March 2007): 1–16. http://dx.doi.org/10.1007/bf03398787.

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29

Cao, Ya, Zhenzhong Zhang, and Zhaoxia Guo. "Multi-objective flexible job shop scheduling problem with triple bottom line theory-based sustainable objectives." Journal of Physics: Conference Series 1633 (September 2020): 012151. http://dx.doi.org/10.1088/1742-6596/1633/1/012151.

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30

Wang, W. L., S. X. Chen, Yan Wei Zhao, J. L. Zhang, and H. Y. Wang. "Bi-Level Quantum Evolutionary Algorithm for Scheduling of Dynamic Alliance." Materials Science Forum 626-627 (August 2009): 783–88. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.783.

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The plan scheduling is an important part for forming a dynamic alliance on the ASP platform. In order to solve this problem, in the first place, a scheduling model of multi-objectives and multi-enterprise has been developed. The scheduling objectives of this model considered the enterprise credibility, production tasks, production process and the relationship between them. Secondly, a bi-level quantum evolutionary algorithm is provided for this model. One level is the coding of processes that determines the sequence of process. The other level is the coding of bidding enterprise that chooses each procedure of bidding enterprise. This scheduling approach in an ASP platform realizing dynamic alliance production scheduling system has been applied actually. And the approach can get faster convergence and better characteristics.

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31

Horng, Sophea, and Pisal Yenradee. "Multi-Objectives Finite Capacity Scheduling of Make-and-Pack Production with Options to Adjust Processing Time." Journal of Engineering, Project, and Production Management 5, no. 1 (January 31, 2015): 48–58. http://dx.doi.org/10.32738/jeppm.201501.0006.

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32

Kumar, Sampath, and M. Sushama. "Strategic demand response framework for energy management in distribution system based on network loss sensitivity." Energy & Environment 31, no. 8 (January 21, 2020): 1385–402. http://dx.doi.org/10.1177/0958305x19893041.

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This paper discusses an energy management system–based demand response scheduling strategy in distribution system. The proposed strategy includes customer payment minimization and network loss minimization as responsive load scheduling objectives through centralized approach. Two types of optimization strategies each based on payment minimization and network loss sensitivity are discussed in this paper. Thus, the proposed scheduling strategy can effectively resolve the optimality issue between different objectives of the distribution system scheduling under demand response penetration. The demand response scheduling strategies are simulated using standard IEEE 37 bus distribution test system through different cases of scheduling and optimization scenarios. The simulation results are presented, discussed, and compared with the base test cases without demand response penetration and without optimization strategies under demand response penetration to demonstrate the effectiveness of network loss, sensitivity consideration and optimization strategies in carrying out distribution system scheduling. In addition, sensitivity analysis is performed. The variation of distribution network performance is analyzed for various test cases and scenarios at different penetration levels.

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33

Xhafa, Fatos, Junzi Sun, Admir Barolli, Alexander Biberaj, and Leonard Barolli. "Genetic Algorithms for Satellite Scheduling Problems." Mobile Information Systems 8, no. 4 (2012): 351–77. http://dx.doi.org/10.1155/2012/717658.

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Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

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Rosemarin, Hanan, Ariel Rosenfeld, and Sarit Kraus. "Emergency Department Online Patient-Caregiver Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10013–14. http://dx.doi.org/10.1609/aaai.v33i01.330110013.

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Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED’s objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospitalspecific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED’s performance metrics.

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Rosemarin, Hanan, Ariel Rosenfeld, and Sarit Kraus. "Emergency Department Online Patient-Caregiver Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 21, 2019): 695–701. http://dx.doi.org/10.1609/aaai.v33i01.3301695.

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Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.

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36

Salido, Miguel A., Joan Escamilla, Federico Barber, Adriana Giret, Dunbing Tang, and Min Dai. "Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, no. 3 (June 9, 2015): 300–312. http://dx.doi.org/10.1017/s0890060415000335.

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AbstractMany real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

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37

Thenata, Angelina Pramana, and Andhika Galuh Prabawati. "EVALUATION INFORMATION TECHNOLOGY UTILIZATION OF SCHOOL SUBJECT SCHEDULING USING PIECES FRAMEWORK." Jurnal Terapan Teknologi Informasi 2, no. 1 (July 23, 2018): 53–63. http://dx.doi.org/10.21460/jutei.2018.21.97.

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Abstract— Information technology has been widely used by several aspects, such as in the scope of business, industry, government, education, and service. The use of information technology in various aspects of spatial planning can help in its business process. One of the use of information technology in the field of education is e-learning, scheduling, financial information systems, and others. SMK N 1 Kaliwungu is one of the schools that already utilize information technology, such as Program Scheduling Subject Schedule. The program helps scheduling in its implementation need to be evaluated to improve more accurate objectives. In this study, will be discussed about system scheduling program Lesson using PIECES Framework. The Framework section is one way of ordering systems or applications related to Performance, Information, Economics, Control, Efficiency, Application Services. Theuse of this framework aims to determine theimplementation of scheduling aids program at SMKN1 Kaliwungu works as a support, enabler, ordriver in the utilization of Information Technology. This research using data from interviews,observations on the curriculum and provision ofquestionnaires to 30 teachers SMKN 1 Kaliwungu. The result of the evaluation obtained from this research is the utilization of scheduling aidsprogram can improve the curriculum performance program can improve the curriculum performance and teaching process to be smoothly without anycollision schedule. The results of evaluation in this research can be said that the utilization of auxiliary programscheduling at SMKN 1 Kaliwungu role as an enabler. Keyword ----- Evaluation, Technology Information, PIECESFramework, Schedulling

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38

Shu, Jian, Hemant Jain, and Changyong Liang. "Business Process Driven Trust-Based Task Scheduling." International Journal of Web Services Research 16, no. 3 (July 2019): 1–28. http://dx.doi.org/10.4018/ijwsr.2019070101.

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The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.

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39

Almeida, Ana, and Goreti Marreiros. "An Approach to Collaborative Scheduling Through Group Decision Support." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 4 (July 20, 2006): 479–85. http://dx.doi.org/10.20965/jaciii.2006.p0479.

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The model we present supporting collaborative scheduling in complex dynamic manufacturing environments, considers the interaction between an agent-based scheduling module (ASM) and a group decision support module (GDSM). The ASM outputs a set of candidate scheduling solutions, each generated based on specific criteria and/or by a particular method. Scheduling is a multicriteria decision problem in practice where different schedulers may agree on key objectives but differ greatly on their relative importance in any given situation. Interaction among scheduling actors is supported by the GDSM selecting a scheduling solution.

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40

Alabbadi, Afra A., and Maysoon F. Abulkhair. "Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing." Algorithms 14, no. 3 (February 27, 2021): 77. http://dx.doi.org/10.3390/a14030077.

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Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.

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41

Hwang, Wook-Yeon, and Jong-Seok Lee. "Compromising Multiple Objectives in Production Scheduling: A Data Mining Approach." Management Science and Financial Engineering 20, no. 1 (May 30, 2014): 1–9. http://dx.doi.org/10.7737/msfe.2014.20.1.001.

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42

Angel, Eric, Evripidis Bampis, and Aleksei V. Fishkin. "A note on scheduling to meet two min-sum objectives." Operations Research Letters 35, no. 1 (January 2007): 69–73. http://dx.doi.org/10.1016/j.orl.2006.01.004.

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43

Koulamas, Christos, and George J. Kyparisis. "Single machine scheduling with release times, deadlines and tardiness objectives." European Journal of Operational Research 133, no. 2 (January 2001): 447–53. http://dx.doi.org/10.1016/s0377-2217(00)00207-1.

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44

Golenko-Ginzburg, Dimitri, and Aharon Gonik. "Optimal job-shop scheduling with random operations and cost objectives." International Journal of Production Economics 76, no. 2 (March 2002): 147–57. http://dx.doi.org/10.1016/s0925-5273(01)00140-2.

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45

Canca, David, Alejandro Zarzo, Encarnación Algaba, and Eva Barrena. "Confrontation of Different Objectives in the determination of train scheduling." Procedia - Social and Behavioral Sciences 20 (2011): 302–12. http://dx.doi.org/10.1016/j.sbspro.2011.08.036.

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Rachuba, Sebastian, and Brigitte Werners. "A robust approach for scheduling in hospitals using multiple objectives." Journal of the Operational Research Society 65, no. 4 (April 2014): 546–56. http://dx.doi.org/10.1057/jors.2013.112.

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Maigha and Mariesa L. Crow. "Electric Vehicle Scheduling Considering Co-optimized Customer and System Objectives." IEEE Transactions on Sustainable Energy 9, no. 1 (January 2018): 410–19. http://dx.doi.org/10.1109/tste.2017.2737146.

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OSMAN, Hesham, Marwan AMMAR, and Moheeb EL-SAID. "OPTIMAL SCHEDULING OF WATER NETWORK REPAIR CREWS CONSIDERING MULTIPLE OBJECTIVES." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 23, no. 1 (January 19, 2017): 28–36. http://dx.doi.org/10.3846/13923730.2014.948911.

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Abstract:

Water main breaks disrupt water services and impact traffic flow along congested city roads. Dispatching water pipe repair crews needs to consider several factors that include: 1) the priority of repair site; 2) the suitability and efficiency of the construction crew in repairing a particular break type; and 3) the time required for crews to travel between break sites. This paper presents a simulation-based multi-objective optimization model to schedule repair crews across water network break sites in an urban setting. Discrete-event simulation models for the water pipe repair process are developed to account for various repair methods. These models are subsequently integrated within a GA-based multi-objective optimization model that considers the following objectives: 1) minimizing the total repair time required to complete all breaks; 2) minimizing the total cost to complete the breaks; and 3) minimizing the cumulative impact of all breaks incident on road users and water customers. A case study for the water network on the City of Damietta, Egypt is used to demonstrate the capabilities of the model. Results show a 21% reduction in repair time and 50% reduction in user impact compared to heuristic crew allocation methods used by the water utility.

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Gupta, A. K., and A. I. Sivakumar. "Optimization of due-date objectives in scheduling semiconductor batch manufacturing." International Journal of Machine Tools and Manufacture 46, no. 12-13 (October 2006): 1671–79. http://dx.doi.org/10.1016/j.ijmachtools.2005.08.017.

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Pandelis, Dimitrios G. "Optimal preemptive scheduling on uniform machines with discounted flowtime objectives." European Journal of Operational Research 177, no. 1 (February 2007): 630–37. http://dx.doi.org/10.1016/j.ejor.2005.09.039.

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