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Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure

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TOP 2023年第1期31卷 67-109页

作者： Edis, Emrah B. Araz, Ozlem Uzun Eski, Ozgur Edis, Rahime Sancar Manisa Celal Bayar Univ Dept Ind Engn Sehit Prof Dr Ilhan Varank Kampusu TR-45140 Manisa Turkey

Warehouse operations have a significant role to survive in today's competitive world. Hence, companies introduce various solutions to improve efficiency of warehouses which is mainly affected by the performance of storage operations. This study deals with a real-life storage location and assignment problem encountered in a fastener company where several orders consisting steel coils are to be assigned into storage areas. Three individual objective functions;minimizing the number of lanes to be used, minimizing area usage, and maximizing volume utilization are considered. For the investigated problem, first, an integer linear programming (ILP) model is developed. Then, a greedy randomized adaptive search procedure (GRASP) which provides quick and efficient solutions is proposed. The proposed methods are applied to the real problem case and the results are compared with the current storage assignment. Moreover, through an extensive computational study, the performances of proposed methods are evaluated on a set of test problems with different range of characteristics. The computational results show that the ILP model proves optimality in most of the problem instances within reasonable computation times, while the GRASP gives quick solutions with small optimality gaps.

关键词： Storage location assignment problem Round item Integer linear programming greedy randomized adaptive search procedure

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greedy randomized adaptive search procedure for Close Enough Orienteering Problem 20

Greedy Randomized Adaptive Search Procedure for Close Enough...

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35th Annual ACM Symposium on Applied Computing (SAC)

作者： Stefanikova, Petra Vana, Petr Faigl, Jan Czech Tech Univ Fac Elect Engn Dept Comp Sci Computat Robot Lab Artificial Intelligence Ctr AI Prague Czech Republic

ISBN: (纸本)9781450368667

In this paper, we address the Close Enough Orienteering Problem (CEOP) that is motivated to find the most rewarding route visiting disk-shaped regions under the given travel budget. The CEOP differs from the regular OP in the continuous optimization of finding the most suitable waypoint locations to collect the reward associated with each region of interest in addition to the selection of the subset of the regions and sequence of their visits as in the OP. We propose to employ the greedy randomized adaptive search procedure (GRASP) combinatorial metaheuristic to solve the addressed CEOP, in particular, the GRASP with Segment Remove. The continuous optimization is addressed by the newly introduced heuristic search that is applied in the construction phase and also in the local search phase of the GRASP. The proposed approach has been empirically evaluated using existing benchmarks, and based on the reported comparison with existing algorithms, the proposed GRASP-based approach provides solutions with the competitive quality while its computational requirements are low.

关键词： GRASP greedy randomized adaptive search procedure CEOP Close Enough Orienteering Problem OP Orienteering Problem

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Genetic algorithms and greedy-randomized adaptive search procedure for router placement problem in wireless networks

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JOURNAL OF HIGH SPEED NETWORKS 2019年第3期25卷 273-300页

作者： Sylejmani, Kadri Kadriu, Admir Ilazi, Endrit Krasniqi, Bujar Univ Prishtina Fac Elect & Comp Engn Prishtina 10000 Kosovo

Wireless networks are present in all the large buildings or sites, and they are expected to provide high speed Internet for the connected users. This can be achieved by connecting wireless routers to the Internet backbone through fast connection cables (e.g. fiber optics), as well finding optimal distribution of the routers along the building, so that the targeted area is covered with Internet access as much as possible, provided that the cost constraints of routers and the cost of their mutual interconnection are satisfied. Therefore, in this paper, we present two approaches for solving the problem of router placement in a two-dimensional area, where the goal is to provide optimal Internet coverage by meeting the cost constraints. Our first approach is based on Genetic Algorithms, and the second one employs the greedy-randomized adaptive search procedure. Both approaches utilize a neighborhood exploration mechanism that consists of operators which are dealing with adding, removing, jumping and shifting routers in the target area. Furthermore, an advanced operator for intelligent shifting of routers inside a predefined neighborhood is implemented. The computational experiments, performed over a data set of four large instances, indicate that both proposed approaches can obtain competitive results to the ones that are presently available in the literature. Consequently, the results indicate that both approaches can be easily adapted for application in practice for designing the topography of wireless networks, in terms of router placement and distribution.

关键词： Router placement wireless networks genetic algorithms greedy randomized adaptive search procedure

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A greedy randomized adaptive search procedure for Solving the Uncapacitated Plant Cycle Problem 1

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15th International Conference on Computer Aided Systems Theory

作者： Lopez-Plata, Israel Exposito-Izquierdo, Christopher Lalla-Ruiz, Eduardo Melian-Batista, Belen Marcos Moreno-Vega, J. Univ La Laguna Dept Comp & Syst Engn San Cristobal la Laguna 38271 Spain

ISBN: (数字)9783319273402

ISBN: (纸本)9783319273402;9783319273396

The Uncapacitated Plant Cycle Problem seeks to select a subset of potential locations in which to open plants dedicated to provide service to customers scattered abroad upon the field. The locations are known and each plant can serve an unlimited number of customers through a vehicle route. The objective of this problem is to (i) determine the number of plants to open, (ii) select the subset of locations in which to open the plants, (iii) assign a non-empty subset of customers to each plant, and (iv) determine a vehicle route dedicated to serve the subset of customers assigned to each plant. With the goal of solving this problem from an approximate point of view, a greedy randomized adaptive search procedure is proposed in this paper. The computational experiments disclose the suitable performance of this algorithmic approach, which allows to reach high-quality solutions in reasonable computational times.

关键词： greedy randomized adaptive search procedure Uncapacitated plant cycle problem Logistics

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New greedy randomized adaptive search procedure based on Differential Evolution algorithm for solving no-wait flowshop scheduling problem

New Greedy Randomized Adaptive Search Procedure based on Dif...

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International Conference on Advanced Logistics and Transport (ICALT)

作者： Akrout, Hanen Jarboui, Bassem Rebai, Abdelwaheb Siarry, Patrick MODILS Fac Sci Econ & Gest Sfax Tunisia

ISBN: (纸本)9781479903139;9781479903146

Scheduling in a production workshop consists of assigning a number of different tasks to the existing machines with respect to the constraints of the problem in order to optimise the objective function. In this paper, we focus on the No-Wait Flow Shop Scheduling Problem (NWFSSP) with the makespan objective. We present a new hybrid algorithm NEW-GRASP-DE that combines greedy Randomised adaptive search procedure (GRASP) with Differential Evolution Algorithm (DEA). Furthermore, we used an Iterative Local search (ILS) as an improvement phase in the GRASP method. Our algorithm is tested on instances that have been proposed in the literature. We compared our results with those of different authors. The experimental results show the effectiveness of our algorithm.

关键词： no-wait flowshop greedy randomized adaptive search procedure differential evolution algorithm makespan

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New greedy randomized adaptive search procedure based on Differential Evolution algorithm for solving no-wait flowshop scheduling problem

New Greedy Randomized Adaptive Search Procedure based on Dif...

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International Conference on Advanced Logistics and Transport

作者： Hanen Akrout Bassem Jarboui Abdelwaheb Rebai Patrick Siarry Faculte des Sciences Economiques et de Gestion MODILS Universite de Paris-Est Creteil LiSSi

ISBN: (纸本)9781479903146

关键词： No-wait flowshop greedy randomized adaptive search procedure Differential evolution algorithm Makespan

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Optimizing quay crane scheduling using deep reinforcement learning with hybrid metaheuristic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Long, Le Ngoc Bao You, Sam-Sang Cuong, Truong Ngoc Kim, Hwan-Seong Korea Maritime & Ocean Univ Dept Logist 727 Taejong Ro Busan 49112 South Korea Korea Maritime & Ocean Univ Northeast Asia Shipping & Port Logist Res Ctr 727 Taejong ro Busan 49112 South Korea Korea Maritime & Ocean Univ Div Mech Engn 727 Taejong Ro Busan 49112 South Korea Ho Chi Minh City Univ Technol HCMUT Fac Mech Engn Dept Mechatron 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam

As global trade via maritime transport increases annually, competition among seaports necessitates dynamic scheduling in operational management to optimize port performance. Quay crane scheduling problem (QCSP) is a typical optimization problem for container terminal operations on the quayside. This paper proposes a modern approach based on model-free deep reinforcement learning (DRL) named proximal policy optimization (PPO), a gradient-based framework with a shared actor-critic network structure. Besides, a novel hybrid metaheuristic combined with a greedy randomized adaptive search procedure (GRASP) and ant colony optimization (ACO) is employed as an alternative approach. The performance of the artificial intelligence (AI) -powered solutions is verified through numerical simulation for single- and multi-agent scenarios with different starting positions. The results highlight the intelligent approach's remarkable performance in solving complex optimization problems and its flexibility compared to traditional metaheuristics, as it does not require clear insights into the system's dynamics.

关键词： Quay crane scheduling problem Gradient-based policy learning Deep reinforcement learning Proximal policy optimization greedy randomized adaptive search procedure ant colony optimization

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The multiobjective traveling salesman-repairman problem with profits: design and implementation of a variable neighborhood descent algorithm for a real scenario

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL REsearch 2025年第1期32卷 221-243页

作者： Morante-Gonzalez, R. Lopez-Sanchez, A. D. Sanchez-Oro, J. Hernandez-Diaz, A. G. Univ Rey Juan Carlos Tulipán S-N Madrid 28933 Spain Univ Pablo Olavide Ctra Utrera Km 1 Seville 41013 Spain

This paper introduces a problem that can be seen as a combination of the traveling salesman problem with profits and the traveling repairman problem with profits, coined as the multi-objective traveling salesman-repairman problem with profits (Mo-TSRPP). The objective of the Mo-TSRPP is to simultaneously optimize three objectives: the total cost, total latency, and total profit. Indirectly, the number of nodes visited is also considered although not as an objective itself since it is determined by the size of every efficient solution in the Pareto front. The Mo-TSRPP emerges as a real-world problem in which a freelancer, which repairs appliances, wants to plan the daily route. Moreover, the daily plan does not require to visit all customers. To solve the problem, first, a greedy randomized adaptive procedure is designed to generate a set of high-quality nondominated solutions and then, a variable neighborhood descent algorithm is applied for further improving the initial set. This procedure allows us to attain a good approximation of the Pareto front. To prove the performance of the proposal a comparison is done against three well-known evolutionary algorithms: NSGA-II, SPEA-2, and MOEA/D. Finally, a realistic problem is shown and solved to illustrate the potential of the algorithm.

关键词： traveling salesman problem traveling repairman problem profit multiobjective optimization problem greedy randomized adaptive search procedure variable neighborhood descent

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A two-stage solution approach for scheduling flexible manufacturing systems with multi-fixturing pallets

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年 1-25页

作者： Sim, Dong-Gi Lee, Dong-Ho Hanyang Univ Dept Ind Engn Seoul 04763 South Korea

This study addresses a scheduling problem for flexible manufacturing systems with multi-fixturing pallets that can load multiple parts under the practical constraints of central buffer capacity, limited number of pallets and pallet setup worker availability. The problem consists of four dependent sub-problems that determines: (a) the parts to be loaded onto each pallet, i.e. palletizing;(b) the sequence of pallets to be initially released into the system, i.e. pallet input sequencing;(c) the route of each pallet over machines to process its loaded parts, i.e. pallet routing;and (d) the sequence of pallets to process the corresponding parts on each machine, i.e. pallet process sequencing. After showing that pallet routing and process sequencing is an open shop scheduling problem, a two-stage solution approach is proposed for the objective of minimizing makespan. In the two-stage solution approach, an initial solution is obtained by a greedy randomized adaptive search procedure (GRASP) based construction method for the parts released into the system using a palletizing/pallet input sequencing rule, and then it is improved by an extended disjunctive graph based local search method. Computational results show that the two-stage solution approach outperforms the existing one significantly.

关键词： Flexible manufacturing systems Multi-fixturing pallets Scheduling Heuristics greedy randomized adaptive search procedure

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Hybrid meta-heuristic solving no-wait flow shop scheduling minimizing maximum tardiness

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3935-3959页

作者： Nejjarou, Omar Aqil, Said Lahby, Mohamed Hassan II Univ Higher Normal Sch Lab Appl Math & Comp Sci Casablanca 52400 Morocco Hassan II Univ Natl Higher Sch Arts & Crafts Lab Artificial Intelligence & Complex Syst Engn Casablanca 20670 Morocco

The non-waiting constraint is a crucial factor in industries with continuous production flows. Recognizing the significance of this constraint in the manufacturing process, we propose several approaches to minimize the maximum tardiness in the no-wait manufacturing flow shop scheduling problem. This research presents two main approaches: an exact method that utilizes mixed integer linear programming and an approximate method based on constructive heuristics and hybrid meta-heuristics. To tackle the problem, we introduce multiple heuristics and hybrid improved meta-heuristics based Nawaz Enscore Ham, greedy randomized adaptive search procedure and genetic algorithm. Furthermore, we propose three hybrid meta-heuristics based on the simulated annealing approach. To validate the effectiveness and robustness of our methods, we conducted experiments on multiple instances of the flow shop problem proposed by Taillard. The results demonstrate that the hybrid algorithm, which combines a greedy randomized adaptive search procedure with the insertion procedure and simulated annealing, exhibits strong performance. In fact, this algorithm achieved a success rate of 72%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$72\%$$\end{document} across 200 test instances. It outperformed the other two meta-heuristics, with a minimum average relative percentage deviation of only 0.0039%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.0039\%$$\end{document}.

关键词： Permutation flow shop scheduling problem No-wait Maximum tardiness Genetic algorithm greedy randomized adaptive search procedure Simulated annealing

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