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Journal articles on the topic 'Initial solution heuristic'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ursani, Ziauddin, and Ahsan Ahmad Ursani. "Augmented tour construction heuristics for the travelling salesman problem." International Journal of Industrial Optimization 4, no. 2 (2023): 131–44. http://dx.doi.org/10.12928/ijio.v4i2.7875.

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Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study

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Saksuriya, Payakorn, and Chulin Likasiri. "Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System." Applied Sciences 12, no. 13 (2022): 6486. http://dx.doi.org/10.3390/app12136486.

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This work involves a heuristic for solving vehicle routing problems with time windows (VRPTW) with general compatibility-matching between customer/patient and server/caretaker constraints to capture the nature of systems such as caretakers’ home visiting systems or home healthcare (HHC) systems. Since any variation of VRPTW is more complicated than regular VRP, a specific, custom-made heuristic is needed to solve the problem. The heuristic proposed in this work is an efficient hybrid of a novice Local Search (LS), Ruin and Recreate procedure (R&R) and Particle Swarm Optimization (PSO). The

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Seipp, Jendrik, Florian Pommerening, and Malte Helmert. "New Optimization Functions for Potential Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 193–201. http://dx.doi.org/10.1609/icaps.v25i1.13714.

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Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (rea

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Jardón, Edgar, Marcelo Romero, and José-Raymundo Marcial-Romero. "Application of Optimization Algorithms in Voter Service Module Allocation." Information 16, no. 6 (2025): 506. https://doi.org/10.3390/info16060506.

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Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. This study evaluates several heuristics to address an allocation problem involving young individuals reaching voting age. A five-step methodology was implemented: defining variables, executing heuristics, compiling results, evaluating outcomes, and selecting the most effective heuristic. Using experimental data from the Mexican National Electoral Institute (INE), the study focuses on 88,107 individuals aged 17–18 in the 16 municipalities

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Iqbal, Zahid, Rafia Ilyas, Huah Yong Chan, and Naveed Ahmed. "Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach." Baghdad Science Journal 18, no. 4(Suppl.) (2021): 1465. http://dx.doi.org/10.21123/bsj.2021.18.4(suppl.).1465.

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The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a high

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Castro, Eduardo Meca. "Two Neighbourhood-based Approaches for the Set Covering Problem." U.Porto Journal of Engineering 5, no. 1 (2019): 1–15. http://dx.doi.org/10.24840/2183-6493_005.001_0001.

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The Set Covering Problem is a well-known NP-complete problem which we address in this work. Due to its combinatorial nature heuristic methods, namely neighbourhood-based meta-heuristics, were used.Based on the well-known algorithms GRASP, Simulated Annealing and Variable Neighbourhood Descend, along with a constructive heuristic based on a dynamic dispatching rule to generate initial feasible solutions, two approaches to the problem were formulated. The performance of both methods was assessed in 42 instances of the problem. Our best approach has an average deviation from the best-known soluti

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Wang, Lingya, and Dean S. Oliver. "Efficient Optimization of Well-Drilling Sequence with Learned Heuristics." SPE Journal 24, no. 05 (2019): 2111–34. http://dx.doi.org/10.2118/195640-pa.

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Summary When preparing a field–development plan, the forecast value of the development can be sensitive to the order in which the wells are drilled. Determining the optimal drilling sequence generally requires many simulation runs. In this paper, we formulate the sequential decision problem of a drilling schedule as one of finding a path in a decision tree that is most likely to generate the highest net present value (NPV). A nonparametric online–learning methodology is developed to efficiently compute the sequence of drilling wells that is optimal or near optimal. The main ideas behind the ap

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Li, Bing, Xinyu Yang, and Hua Xuan. "A Hybrid Simulated Annealing Heuristic for Multistage Heterogeneous Fleet Scheduling with Fleet Sizing Decisions." Journal of Advanced Transportation 2019 (January 10, 2019): 1–19. http://dx.doi.org/10.1155/2019/5364201.

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This paper deals with multistage heterogeneous fleet scheduling with fleet sizing decisions (MHFS-FSD). This MHFS-FSD attempts to integrate vehicles allocation and fleet sizing decisions considering the vehicle routing of multiple vehicle types. The problem is formulated as mixed integer programming model. The matrix formulation denoting vehicle allocation scheme is explored according to the characteristic of this problem. Generating vehicle allocation scheme with greedy heuristic procedure (VA-GHP) as initial solution of problem is presented. The USP-IVA method to update the initial solution

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John Oyewole, Gbeminiyi, and Olufemi Adetunji. "A HYBRID ALGORITHM TO SOLVE THE FIXED CHARGE SOLID LOCATION AND TRANSPORTATION PROBLEM." Engineering Heritage Journal 5, no. 1 (2021): 01–11. http://dx.doi.org/10.26480/gwk.01.2021.01.11.

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In this paper, we propose a Hybrid Algorithm (HA) to solve the Fixed Charge Solid Location and Transportation problem (FCSLTP). The FCSLTP considers the cost of facility location and route fixed costs during transportation planning or load consolidation. The HA integrates two heuristics into the Genetic Algorithm framework to solve the FCSLTP. Genetic operations are used to select the best combination of facility locations while a greedy heuristic which uses some cost relaxations are used for the initial load allocation. An improvement heuristic, a modified stepping stone method, is then used

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Yang, Jie, and Yangsheng Jiang. "Application of Modified NSGA-II to the Transit Network Design Problem." Journal of Advanced Transportation 2020 (August 1, 2020): 1–24. http://dx.doi.org/10.1155/2020/3753601.

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The transit network design problem involves determining a certain number of routes to operate in an urban area to balance the costs of the passengers and the operator. In this paper, we simultaneously determine the route structure of each route and the number of routes in the final solution. A novel initial route set generation algorithm and a route set size alternating heuristic are embedded into a nondominated sorting genetic algorithm-II- (NSGA-II-) based solution framework to produce the approximate Pareto front. The initial route set generation algorithm aims to generate high-quality init

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Jeya Mala D. and Ramalakshmi Prabha M. "On the Design and Optimization of Test Cases Using an Improved Artificial Bee Colony Algorithm-Based Swarm Intelligence Approach." International Journal of Swarm Intelligence Research 13, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijsir.309941.

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In this research work, a swarm intelligence-based approach, namely an improved artificial bee colony (IABC), has been proposed to design and optimize the test cases during the software testing process. The novelty of the proposed IABC algorithm is that it has three major improvement heuristics over the general ABC algorithm: (1) it replaces random population generation during the initial phase into a systematic initial solution generation by means of a novel heuristic, namely ‘Chaotic Map'; (2) to eliminate the redundant test cases, another novel heuristic, namely ‘Euclidean Distance', is appl

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Icasia, Gabriella, Raras Tyasnurita, and Etria Sepwardhani Purba. "Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 4 (2020): 664–71. http://dx.doi.org/10.29207/resti.v4i4.2066.

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Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-h

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Richter, Silvia, Jordan Thayer, and Wheeler Ruml. "The Joy of Forgetting: Faster Anytime Search via Restarting." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 137–44. http://dx.doi.org/10.1609/icaps.v20i1.13412.

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Anytime search algorithms solve optimisation problems by quickly finding a (usually suboptimal) first solution and then finding improved solutions when given additional time. To deliver an initial solution quickly, they are typically greedy with respect to the heuristic cost-to-go estimate h. In this paper, we show that this low-h bias can cause poor performance if the greedy search makes early mistakes. Building on this observation, we present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via

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Müller, Felipe Martins, and Iaê Santos Bonilha. "Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems." Algorithms 15, no. 1 (2021): 9. http://dx.doi.org/10.3390/a15010009.

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal with Dynamic Optimization Problems (DOPs) properly. Despite the good results obtained by the integration of local search operators with ACO, little has been done to tackle DOPs. In this research, one of the most reliable ACO schemes, the MAX-MIN Ant System (MMAS), has been integrated with advanced and effective local search operators, resulting in a

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Ma, Rong Hua. "Cross Warehouse Scheduling of Logistics Distribution and Cycle Re-Claimer Based on Heuristics Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 2606–10. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2606.

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In the uncertain environment, the cycle re-claimer and logistics distribution cross warehouse scheduling is important and it should be optimized. The mathematical programming model is constructed, and the proposed two-stage heuristic algorithm is proposed, the optimal solution of heuristic algorithm is used as the initial value. And the taboo search algorithm is designed to improve the initial solution. In order to verify the availability of the method, the Monte Carlo simulation method is used for numerical experiment. The experiment results show that the new method can solve the suboptimal s

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Burke, Edmund K., James P. Newall, and Rupert F. Weare. "Initialization Strategies and Diversity in Evolutionary Timetabling." Evolutionary Computation 6, no. 1 (1998): 81–103. http://dx.doi.org/10.1162/evco.1998.6.1.81.

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This document seeks to provide a scientific basis by which different initialization algorithms for evolutionary timetabling may be compared. Seeding the initial population may be used to improve initial quality and provide a better starting point for the evolutionary algorithm. This must be tempered against the consideration that if the seeding algorithm produces very similar solutions, then the loss of genetic diversity may well lead to a worse final solution. Diversity, we hope, provides a good indication of how good the final solution will be, although only by running the evolutionary algor

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Hu, Shicheng, Tengjiao Liu, Song Wang, Yonggui Kao, and Xuedong Sun. "A Hybrid Heuristic Algorithm for Ship Block Construction Space Scheduling Problem." Discrete Dynamics in Nature and Society 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/841637.

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Ship block construction space is an important bottleneck resource in the process of shipbuilding, so the production scheduling optimization is a key technology to improve the efficiency of shipbuilding. With respect to ship block construction space scheduling problem, a hybrid heuristic algorithm is proposed in this paper. Firstly, Bottom-Left-Fill (BLF) process is introduced. Next, an initial solution is obtained by guiding the sorting process with corners. Then on the basis of the initial solution, the simulated annealing arithmetic (SA) is used to improve the solution by offering a possibil

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Zhang, Zhen, Junfeng Yang, Limei Liu, Xuesong Xu, Guozhen Rong, and Qilong Feng. "Towards a Theoretical Understanding of Why Local Search Works for Clustering with Fair-Center Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16953–60. http://dx.doi.org/10.1609/aaai.v38i15.29638.

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The representative k-median problem generalizes the classical clustering formulations in that it partitions the data points into several disjoint demographic groups and poses a lower-bound constraint on the number of opened facilities from each group, such that all the groups are fairly represented by the opened facilities. Due to its simplicity, the local-search heuristic that optimizes an initial solution by iteratively swapping at most a constant number of closed facilities for the same number of opened ones (denoted by the O(1)-swap heuristic) has been frequently used in the representative

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Wang, Chao, Deguang Wang, and Chun Jin. "A quick Heuristic and a general search algorithm for traveling salesman problem." E3S Web of Conferences 360 (2022): 01097. http://dx.doi.org/10.1051/e3sconf/202236001097.

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This paper puts forward a constructive heuristic algorithm called the method of inserting the minimum neighbor edge from outside to the center (IMNEFOTC) that can be applied to solve large-scale and ultra-large-scale travelling salesman problems. Through it and the randomized greedy heuristic algorithm (RGH) which greedy heuristic algorithm is modified, a general meta-heuristic search algorithm framework is built. The general search algorithm (GSA) is based on a set of initial solutions, and continuous 2-opt operations are performed, so as to search for solutions in better quality. The data fr

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Shi, Yanjun, Lingling Lv, Fanyi Hu, and Qiaomei Han. "A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems." Applied Sciences 10, no. 7 (2020): 2403. http://dx.doi.org/10.3390/app10072403.

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This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectiv

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Li, Xiang, Christophe Claramunt, Xihui Zhang, and Yingping Huang. "A Fast and Deterministic Approach to a Near Optimal Solution for the p-Median Problem." International Journal of Operations Research and Information Systems 3, no. 3 (2012): 1–14. http://dx.doi.org/10.4018/joris.2012070101.

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Finding solutions for the p-median problem is one of the primary research issues in the field of location theory. Since the p-median problem has proved to be a NP-hard problem, several heuristic and approximation methods have been proposed to find near optimal solutions with acceptable computational time. This study introduces a computationally efficient and deterministic algorithm whose objective is to return a near optimal solution for the p-median problem. The merit of the proposed approach, called Relocation Median (RLM), lies in solving the p-median problem in superior computational time

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Kirca, Omer, and Ahmet Satir. "A Heuristic for Obtaining and Initial Solution for the Transportation Problem." Journal of the Operational Research Society 41, no. 9 (1990): 865. http://dx.doi.org/10.2307/2583502.

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Kirca, Ömer, and Ahmet Şatir. "A Heuristic for Obtaining an Initial Solution for the Transportation Problem." Journal of the Operational Research Society 41, no. 9 (1990): 865–71. http://dx.doi.org/10.1038/sj/jors/0410909.

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Kirca, Ömer, and Ahmet Şatir. "A Heuristic for Obtaining and Initial Solution for the Transportation Problem." Journal of the Operational Research Society 41, no. 9 (1990): 865–71. http://dx.doi.org/10.1057/jors.1990.124.

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Chen, Jiawei, Ming Chen, Jun Wen, Lei He, and Xiaolu Liu. "A Heuristic Construction Neural Network Method for the Time-Dependent Agile Earth Observation Satellite Scheduling Problem." Mathematics 10, no. 19 (2022): 3498. http://dx.doi.org/10.3390/math10193498.

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The agile earth observation satellite scheduling problem (AEOSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficien

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Boonphakdee, Warut, Duangrat Hirunyasiri, and Peerayuth Charnsethikul. "Effective Heuristics for Solving the Multi-Item Uncapacitated Lot-Sizing Problem Under Near-Minimal Storage Capacities." Computation 13, no. 6 (2025): 148. https://doi.org/10.3390/computation13060148.

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In inventory management, storage capacity constraints complicate multi-item lot-sizing decisions. As the number of items increases, deciding how much of each item to order without exceeding capacity becomes more difficult. Dynamic programming works efficiently for a single item, but when capacity constraints are nearly minimal across multiple items, novel heuristics are required. However, previous heuristics have mainly focused on inventory bound constraints. Therefore, this paper introduces push and pull heuristics to solve the multi-item uncapacitated lot-sizing problem under near-minimal ca

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Bech, K. H. "A fast, heuristic method for generating offshore wind farm turbine layouts." Journal of Physics: Conference Series 2265, no. 2 (2022): 022025. http://dx.doi.org/10.1088/1742-6596/2265/2/022025.

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Abstract This paper describes a heuristic method for generating turbine layouts for offshore wind farms, applying a physical analogy between particle motion in a potential field and turbine placement in a wind kinetic energy field. The method is fast, and relies on industry-standard wake models to account for internal and external wake. The method is heuristic in several senses; it is based on physical analogy, it does not solve the wake model explicitly during the iterative process, and it does not necessarily provide an optimal solution at the end of iteration. The current method does not ai

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Pokalas, Tal Mark, Anayo Charles Iwuji, Uchendu Kingsley, Chisimkwuo John, and Ajaegbu Henry. "A NEW DUAL HEURISTIC ALGORITHM FOR FINDING THE INITIAL BASIC FEASIBLE SOLUTION FOR A TRANSPORTATION PROBLEM." FUDMA JOURNAL OF SCIENCES 8, no. 4 (2024): 382–90. http://dx.doi.org/10.33003/fjs-2024-0804-2330.

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Because determining the best initial basic feasible solution (IBFS) for a transportation problem is so crucial, numerous authors have expended a great deal of energy developing effective algorithms that will result in the lowest possible cost of moving products from a given source to a destination. The goal of this work was to develop an efficient dual algorithm for finding an initial basic feasible solution to a transportation problem (TP). Two distinct algorithms that produce the same IBFS make up our suggested approach. Compared to some popular methods in the literature, Using four numerica

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Liang, Tao, Xiaodong Yin, and Qun Ge. "A new meta-heuristic for economic dispatch of integrated energy system with multi-type energy storages." E3S Web of Conferences 233 (2021): 01026. http://dx.doi.org/10.1051/e3sconf/202123301026.

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Dispatch optimization of the integrated energy system with multi-type energy storages, as an essential issue to optimum energy management, is a high-dimensional complex nonlinear problem with time-series constraints. To overcome the problem, a new meta-heuristic method is presented in this paper. The proposed method introduces several heuristic rules, called as maximum storage utilization heuristic rules, and embeds them into an adaptive GA to improve the quality of initial feasible solutions and new solutions in each iteration. In contrast to existing methods, the new method guarantees the co

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Phan, Thomy, Benran Zhang, Shao-Hung Chan, and Sven Koenig. "Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23286–94. https://doi.org/10.1609/aaai.v39i22.34495.

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Anytime multi-agent path finding (MAPF) is a promising approach to scalable and collision-free path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a fast initial solution is iteratively optimized by destroying and repairing selected paths of the solution. Current MAPF-LNS variants commonly use an adaptive selection mechanism to choose among multiple destroy heuristics. However, to determine promising destroy heuristics, MAPF-LNS requires a considerable amount of exploration time. As common destroy heuristi

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Eyerich, Patrick, and Malte Helmert. "Stronger Abstraction Heuristics Through Perimeter Search." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 303–7. http://dx.doi.org/10.1609/icaps.v23i1.13604.

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Perimeter search is a bidirectional search algorithm consisting of two phases. In the first phase, a limited regression search computes the perimeter, a region which must necessarily be passed in every solution. In the second phase, a heuristic forward search finds an optimal plan from the initial state to the perimeter. The drawback of perimeter search is the need to compute heuristic estimates towards every state on the perimeter in the forward phase. We show that this limitation can be effectively overcome when using pattern database (PDB) heuristics in the forward phase. The combination of

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Jackson, Kimberly F., Zahar Prasov, Emily C. Vincent, and Eric M. Jones. "A Heuristic Based Framework for Improving Design of Unmanned Systems by Quantifying and Assessing Operator Trust." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1696–700. http://dx.doi.org/10.1177/1541931213601390.

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Operator trust in autonomous capability is a critical factor affecting the success of fielded unmanned systems. However, current means of identifying trusted system behavior do not sufficiently inform guidelines for designing an engineering solution. We present a novel Trust Assessment Framework for providing actionable and formative feedback for building an unmanned system that promotes operators’ initial adoption of and reliance on its autonomous capabilities. Based on a review of the unmanned systems literature and a series of inquiries focusing on operators of unmanned aerial vehicles, we

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Botea, Adi, and Vadim Bulitko. "Scaling Up Search with Partial Initial States in Optimization Crosswords." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (2021): 20–27. http://dx.doi.org/10.1609/socs.v12i1.18547.

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Heuristic search remains a leading approach to difficult combinatorial optimization problems. Search algorithms can utilize pruning based on comparing a target score with an admissible (optimistic) estimate of the best score that can be achieved from a given state. If the former is larger they prune the state. However, when the target score is too high the search can fail by exhausting the space without finding a solution. In this paper we show that such failed searches can still be valuable. Specifically, best partial solutions encountered in such failed searches can often bear a high similar

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Escobar Z., Antonio H., Ramón A. Gallego R., and Rubén A. Romero L. "Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem." Ingeniería e Investigación 31, no. 1 (2011): 127–43. http://dx.doi.org/10.15446/ing.investig.v31n1.20534.

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This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found i

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Vaez, Parinaz, Armin Jabbarzadeh, and Nader Azad. "Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 13 (2020): 1640–55. http://dx.doi.org/10.1177/0954405420927578.

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In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This st

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Singh, Richa, Suraiya Parveen, and Aparna . "Meta Heuristic Optimization of TSP using Genetic Algorithm." International Journal of Advance Research and Innovation 2, no. 1 (2014): 42–45. http://dx.doi.org/10.51976/ijari.211406.

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Initiation of this paper demarcates the limits of artificial intelligence, as it calls artificial intelligence a science for its extensibility and genetic algorithm to provide an affable decision to solve a popular routing problem named as Travelling Salesman Problem. This study will help more in moving to a world where a computer will be able to program based on natural selection and evolution. The travelling salesperson (or, salesman) problem (TSP) is an important combinatorial optimization problem. A combinatorial optimization problem helps to find an optimal object from a finite set of obj

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Sun, Manqiong, Yang Xu, Feng Xiao, Hao Ji, Bing Su, and Fei Bu. "Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints." Mathematics 12, no. 23 (2024): 3845. https://doi.org/10.3390/math12233845.

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As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. Th

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Babu, Md Ashraful, M. A. Hoque, and Md Sharif Uddin. "A heuristic for obtaining better initial feasible solution to the transportation problem." OPSEARCH 57, no. 1 (2019): 221–45. http://dx.doi.org/10.1007/s12597-019-00429-5.

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Pelletier, Patrice M., and Slobodan P. Simonovic. "Initial attempt to optimize the operation of a hydrometric network using branch and bound algorithm." Canadian Journal of Civil Engineering 15, no. 4 (1988): 717–25. http://dx.doi.org/10.1139/l88-092.

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The paper presents a new approach developed to formulate and solve the problem of field operation of a hydrometric network. In present practice, optimization techniques are not used for solving this problem. In the initial attempt, documented in the paper, the traveling salesman algorithm is used to find the optimal solution. Assumptions used and experience gained during the research are addressed in the paper. For the Dauphin hydrometric field area in Manitoba, an "optimal" solution has been obtained and presented. Based on the analysis of the results, conclusions were drawn about future rese

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Gir ̃ao-Silva, Rita, Jos ́e Craveirinha, and Jo ̃ao Cl ́ımaco. "Hierarchical Multiobjective Routing in MPLS Networks with Two Service Classes – A Meta-Heuristic Solution." Journal of Telecommunications and Information Technology, no. 3 (June 26, 2023): 20–37. http://dx.doi.org/10.26636/jtit.2009.3.935.

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The paper begins by reviewing a two-level hierarchical multicriteria routing model for MPLS networks with two service classes (QoS and BE services) and alternative routing, as well as the foundations of a heuristic resolution approach, previously proposed by the authors. Afterwards a new approach, of meta-heuristic nature, based on the introduction of simulated annealing and tabu search techniques, in the structure of the dedicated heuristic, is described. The application of the developed procedures to a benchmarking case study will show that, in certain initial conditions, this approach provi

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RAZKI, Hanaa, and Ahmed MOUSSA. "Production Lot Sizing Problem with the Lead Time." Journal of Research in Business, Economics and Management 11, no. 2 (2018): 2141–51. https://doi.org/10.5281/zenodo.3953571.

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The issue in lot sizing problem is to plan production processes, so that mean the production quantities must be equal to customer demand quantities such that the inventory cost and setup production cost is minimized. In this work, we use the model Multi Level Capacitated Lot Sizing problem with consideration the Lead times, which means that the problem of finding a feasible solution is complex. For this, we propose a new formula in comparison with the classic model. The efficiency of the new formula is demonstrated and infeasible solutions are solved by a heuristic method that's based on L

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Zhang, Houshan. "A Graph-Induced Neighborhood Search Heuristic for the Capacitated Multicommodity Network Design Problem." Mathematics 13, no. 4 (2025): 588. https://doi.org/10.3390/math13040588.

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In this work, an efficient graph-induced neighborhood search heuristic is proposed to address the capacitated multicommodity network design problem. This problem, which commonly arises in transportation and telecommunication, is well known for its inherent complexity and is often classified as NP-hard. Our approach commences with an arbitrary feasible solution and iteratively improves it by solving a series of small-scale auxiliary mixed-integer programming problems. These small-scale problems are closely tied to the cycles inherent in the network topology, enabling us to reroute the flow more

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Lin, Bi Liang, Ray Shyan Wu, and Shu Liang Liaw. "A heuristic approach algorithm for the optimization of water distribution networks." Water Science and Technology 36, no. 5 (1997): 219–26. http://dx.doi.org/10.2166/wst.1997.0202.

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Many optimization models to determine cost-effectiveness and obtain the least-cost system designs for water distribution networks have been developed. An algorithm used for solving an optimization model, such as the branch-and-bound or enumeration method, has deficiencies that may include efficiency problems and local optimal solution problems. A heuristic approach, with a bounded implicit enumeration (BIE) algorithm was developed in this study to improve searching efficiency. The heuristic approach first treats the system as a sub-system so that the sub-system's solution must spread widely at

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Zhai, Xuejun, Xiaonan Niu, Hong Tang, Lixin Wu, and Yonglin Shen. "Robust Satellite Scheduling Approach for Dynamic Emergency Tasks." Mathematical Problems in Engineering 2015 (2015): 1–20. http://dx.doi.org/10.1155/2015/482923.

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Earth observation satellites play a significant role in rapid responses to emergent events on the Earth’s surface, for example, earthquakes. In this paper, we propose a robust satellite scheduling model to address a sequence of emergency tasks, in which both the profit and robustness of the schedule are simultaneously maximized in each stage. Both the multiobjective genetic algorithm NSGA2 and rule-based heuristic algorithm are employed to obtain solutions of the model. NSGA2 is used to obtain a flexible and highly robust initial schedule. When every set of emergency tasks arrives, a combined

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Guo, Huiping, and Hongru Li. "An efficient Bayesian network structure learning algorithm using the strategy of two-stage searches." Intelligent Data Analysis 24, no. 5 (2020): 1087–106. http://dx.doi.org/10.3233/ida-194844.

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It is important for Bayesian network (BN) structure learning, a NP-problem, to improve the accuracy and hybrid algorithms are a kind of effective structure learning algorithms at present. Most hybrid algorithms adopt the strategy of one heuristic search and can be divided into two groups: one heuristic search based on initial BN skeleton and one heuristic search based on initial solutions. The former often fails to guarantee globality of the optimal structure and the latter fails to get the optimal solution because of large search space. In this paper, an efficient hybrid algorithm is proposed

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Xiong, Zhenhua, Yan Chen, Guihua Ban, Yixin Zhuo, and Kui Huang. "A Hybrid Algorithm for Short-Term Wind Power Prediction." Energies 15, no. 19 (2022): 7314. http://dx.doi.org/10.3390/en15197314.

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Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the “best parameters” (high-quality inferior solutions). Finall

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Wang, Zheng, and Chunyue Zhou. "A Three-Stage Saving-Based Heuristic for Vehicle Routing Problem with Time Windows and Stochastic Travel Times." Discrete Dynamics in Nature and Society 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7841297.

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This paper presents a saving-based heuristic for the vehicle routing problem with time windows and stochastic travel times (VRPTWSTT). One of the basic ideas of the heuristic is to advance the latest service start time of each customer by a certain period of time. In this way, the reserved time can be used to cope with unexpected travel time delay when necessary. Another important idea is to transform the VRPTWSTT to a set of vehicle routing problems with time windows (VRPTW), each of which is defined by a given percentage used to calculate the reserved time for customers. Based on the above t

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Vangipurapu, Bapi Raju, and Rambabu Govada. "A construction heuristic for finding an initial solution to a very large-scale capacitated vehicle routing problem." RAIRO - Operations Research 55, no. 4 (2021): 2265–83. http://dx.doi.org/10.1051/ro/2021100.

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In this paper, a deterministic heuristic method is developed for obtaining an initial solution to an extremely large-scale capacitated vehicle routing problem (CVRP) having thousands of customers. The heuristic has three main objectives. First, it should be able to withstand the computational and memory problems normally associated with extremely large-scale CVRP. Secondly, the outputs should be reasonably accurate and should have a minimum number of vehicles. Finally, it should be able to produce the results within a short duration of time. The new method, based on the sweep algorithm, minimi

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Jalali, Maryam, Morteza Zahedi, Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, and João Manuel R. S. Tavares. "A Hybrid Hierarchical Mathematical Heuristic Solution of Sparse Algebraic Equations in Sentiment Analysis." Information 15, no. 9 (2024): 513. http://dx.doi.org/10.3390/info15090513.

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Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation system

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Lin, Yu, Zheyong Bian, Shujing Sun, and Tianyi Xu. "A Two-Stage Simulated Annealing Algorithm for the Many-to-Many Milk-Run Routing Problem with Pipeline Inventory Cost." Mathematical Problems in Engineering 2015 (2015): 1–22. http://dx.doi.org/10.1155/2015/428925.

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In recent years, logistics systems with multiple suppliers and plants in neighboring regions have been flourishing worldwide. However, high logistics costs remain a problem for such systems due to lack of information sharing and cooperation. This paper proposes an extended mathematical model that minimizes transportation and pipeline inventory costs via the many-to-many Milk-run routing mode. Because the problem is NP hard, a two-stage heuristic algorithm is developed by comprehensively considering its characteristics. More specifically, an initial satisfactory solution is generated in the fir

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