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AGV Scheduling and Energy Consumption Optimization in Automated Container Terminals Based on variable neighborhood search algorithm

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JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第4期13卷 647-647页

作者： Zhao, Ning Li, Rongao Yang, Xiaoming Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

Automated Guided Vehicles (AGVs) for automated container terminals are mainly used for horizontal transportation at the forefront of the terminal. They shoulder the responsibility of container transportation between the quay cranes and yard cranes. Optimizing their scheduling can not only improve operational efficiency, but also help reduce energy consumption and promote green development of the port. This article first constructs a mathematical model with the goal of minimizing the total energy consumption of AGVs, considering the impact of different states of AGVs on energy consumption during operation. Secondly, by using the variable neighborhood search algorithm, the AGV allocation for container operation tasks is optimized, and the operation sequence is adjusted to reduce energy consumption. The algorithm introduces five types of operators and a random operator usage order to expand the search range and avoid local optima. Finally, the influence of the number and speed of AGVs on the total energy consumption is discussed, and the optimization performance of the variable neighborhood search algorithm and genetic algorithm is compared through computational experiments. The research results show that the model and variable neighborhood search algorithm proposed in this paper have a significant effect on reducing the total energy consumption of AGVs and show good stability and practical application potential.

关键词： automated container terminal AGV scheduling variable neighborhood search algorithm energy consumption optimization

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A modified variable neighborhood search algorithm for manufacturer selection and order acceptance in distributed virtual manufacturing network

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OPTIMIZATION LETTERS 2022年第1期16卷 117-135页

作者： Kong, Min Zhou, Jing Pei, Jun Liu, Xinbao Pardalos, Panos M. Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77840 USA Univ Florida Ctr Appl Optimizat Dept Ind & Syst Engn Gainesville FL 32611 USA Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China Zhongnan Univ Econ & Law Sch Publ Finance & Taxat Wuhan 430073 Peoples R China

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufacturing company (MC) receives a series of order requests from its customers. Due to the limited production capacity, the MC is not able to fulfill all the orders requested by the customers on time. To satisfy the order demand as much as possible, one of the MC's choices is to establish a collaborative manufacturing platform (i.e., DVMN) that leverages the advantages of a social manufacturing network to expand their production capabilities. However, the increase in production capacity does not mean that all orders from the customers are able to be accepted and produced. In fact, standing at the profit-maximizing point of view, the order requests that do not bring profits to the company need to be rejected at the beginning of planning horizon. For the accepted orders, the MC will produce a part of them by its plants in the DVMN, and the remaining orders will be outsourced to other co-manufacturers in the DVMN. Based on some structural properties, we develop a novel Reduced variable neighborhood search (RVNS) algorithm incorporated multiple random mutations neighborhood structures (NSs-MRM) to determine the composition of the DVMN, and further determine which orders should be rejected, which accepted orders should be processed by the company-owned plants in the DVMN, and which accepted orders should be outsourced to the co-manufacturers in the DVMN. In particular, the processing sequence of the orders produced in the company-owned plants are also determined. Finally, a reasonable composition of the DVMN and corresponding order acceptance as well as order scheduling on each member of the DVMN are given. To evaluate the proposed a

关键词： variable neighborhood search algorithm Order acceptance Outsourcing selection Production scheduling Dynamic programming algorithm

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The multi-route location-routing problem and zone price decision-making using a tabu and variable neighborhood search algorithm

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2018年第5期35卷 277-297页

作者： Setak, Mostafa Sadeghi-Dastaki, Mohsen Karimi, Hossein KN Toosi Univ Technol Fac Ind Engn Tehran Iran Univ Bojnord Dept Ind Engn Bojnurd Iran

Zone pricing is one of the most important pricing policies that are prevalently used by very companies. This paper considers integrating zone pricing and location-routing problems for profit maximization. Hence, a node-based model is proposed as mixed integer nonlinear programming to solve the problem. In order to handle the nonlinearity elements and change the model to a mixed integer linear programming, a piecewise linearization method is employed to approximate the problem. Moreover, we introduce a new heuristic variable neighborhood search (VNS) and tabu search algorithm for large-scale problems. The proposed algorithms consist of four intra-tour and seven inter-tour operators in its local search. Finally, some test instances with 20, 200, 400, and 600 nodes are solved and the results of them confirm the efficiency of the proposed algorithms and comparative superiority of the VNS algorithm over the tabu search algorithm.

关键词： Location-routing problem pricing zone pricing variable neighborhood search algorithm tabu search algorithm

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Computation of optimum Type-II progressively hybrid censoring schemes using variable neighborhood search algorithm

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TEST 2017年第4期26卷 802-821页

作者： Bhattacharya, Ritwik Pradhan, Biswabrata Ctr Invest Matemat CIMAT Monterrey 66629 Mexico Indian Stat Inst SQC & OR Unit 203 BT Rd Kolkata 700108 India

Type-II progressively hybrid censoring scheme is a mixture of Type-II progressive censoring and Type-I censoring schemes. In this article, we first derive the expression of Fisher information matrix based on Type-II progressively hybrid censored data for multi-parameter distribution. We then propose a cost minimization-based optimality criterion to determine optimum Type-II progressively hybrid censoring schemes. Determination of optimum schemes through exhaustive search within the set of all admissible censoring schemes for large sample sizes is not feasible in practice. We propose a meta-heuristic algorithm based on variable neighborhood search approach for large sample sizes. A sensitivity analysis is also carried out in order to study the effect of mis-specification of parameter values or cost coefficients on the optimum solution. Finally, we also discuss A-, D- and T- optimum censoring schemes.

关键词： Cost function A-optimality D-optimality Information matrix variable neighborhood search algorithm Near-optimal solution

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Solving fuzzy number linear programming problems using a variable neighborhood search algorithm 6

Solving fuzzy number linear programming problems using a var...

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6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)

作者： Ghorbani-Moghadam, Khatere Ghanbari, Reza Mahdavi-Amiri, Nezam Sharif Univ Technol Fac Math Sci Tehran Iran Ferdowsi Univ Mashhad Dept Appl Math Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781538628362

We want to solve fuzzy linear programming problems with fuzzy coefficients in the objective function using a new variable neighborhood search algorithm. In our proposed algorithm, the local search is defined based on descent directions. We make use of our recently proposed modified Kerre's method for finding descent directions. The fuzzy optimization problem is solved directly, without changing it to a crisp program. We show the effectiveness of our proposed method in comparison with some available methods by using a non-parametric statistical sign test. The objective function values obtained by our proposed method turn to be more accurate the ones obtained by other methods.

关键词： Fuzzy linear programming problem Modified Kerre's method variable neighborhood search algorithm

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REsearch ON SIMULATION OF EMERGENCY EVACUATION OUTSIDE NUCLEAR POWER PLANTS BASED ON GENETIC algorithm AND variable neighborhood search algorithm 31

RESEARCH ON SIMULATION OF EMERGENCY EVACUATION OUTSIDE NUCLE...

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31st International Conference on Nuclear Engineering (ICONE) - 24/7 Clean Nuclear Energy - Solution for Global Warming

作者： Yan, Jin Zhang, Jiemin Cao, Qun Wang, Mengxi Long, Liang Xue, Na China Nucl Power Engn Co Ltd Beijing 100840 Peoples R China

ISBN: (纸本)9780791888254

This paper focuses on the evacuation process of the public during off-site emergency, and studies a simulation method for off-site emergency evacuation based on genetic algorithm and variable neighborhood search algorithm. And this model is used to calculate and analyze the off- site emergency evacuation plan for a nuclear power plant, based on information such as population, vehicles, assembly points, settle places, transportation networks, and radiation dose fields around the nuclear power plant. The optimization objectives are evacuation time, collective dose, or individual maximum effective dose. The consumption time of the evacuation process and the dose exposure received by personnel during the evacuation process are provided. And compare the results with traditional evacuation simulation algorithm. The results indicate that emergency evacuation plans should not only consider the evacuation time, but also comprehensively consider the dose received by personnel during the evacuation process. In addition, this paper analyzed the impact of adding private cars on the evacuation process, and the results showed that when adding more than 50% of private cars to the number of buses, the impact on evacuation time, collective dose, and individual maximum effective dose significantly increased. In addition, when the evacuation time is taken as the optimization objective, the evacuation time of the optimized evacuation plan through genetic algorithm combined with variable neighborhood search algorithm is shorter than that of the traditional simulated evacuation plan. This algorithm can generate a more optimized evacuation plan.

关键词： Nuclear power plant genetic algorithm variable neighborhood search algorithm emergency evacuation evacuation simulation

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A variable neighborhood search algorithm for transshipment scheduling of multi products at a single station

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APPLIED SOFT COMPUTING 2021年 98卷 106736-106736页

作者： Ranjbar, Mohammad Saber, Reza Ghorbani Ferdowsi Univ Mashhad Fac Engn Dept Ind Engn Mashhad Razavi Khorasan Iran

In this research, we investigate a transshipment scheduling problem in which a set of loading and unloading jobs must be performed at a single station of a transshipment terminal. We assume that there is a set of product types and an inventory storage center with a constant capacity for each. Each loading job decreases the inventory level of a particular product type and has a predetermined due date, while each unloading job increases the inventory level of a product type and has a given release date. An inbound truck delivers a set of products, which have the same release dates and must be unloaded, to the inventory storage center. In contrast, an outbound truck conveys a set of goods, which have identical completion times and must be loaded. We aim to minimize the total freight cost of trucks as well as the total weighted tardiness of products. To this end, we develop a linear integer programming model and a variable neighborhood search algorithm including a set of efficient local search procedures. Moreover, we run the two previously developed metaheuristics, i.e. a genetic algorithm and a particle swarm algorithm so as to solve the problem. Using a set of randomly generated test instances, we perform extensive computational experiments and statistical analyses to highlight the efficiency and superiority of the developed algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Scheduling Transshipment terminal variable neighborhood search algorithm

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Research of Order Batching variable neighborhood search algorithm based on Saving Mileage

Research of Order Batching Variable Neighborhood Search Algo...

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3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Zeping Pei Zhuan Wang Yiwen Yang School of Mechanical Engineering University of Science and Technology Beijing

Basing on a picker-to-parts batch picking system of full container load, an order batching model takes picking equipment and commodity packaging volume into consideration is constructed, the objective of the model is to maximize the saving mileage. To solve the model, an order batching variable neighborhood search algorithm is proposed. With the data from a specific logistic center, a simulation experiment has been carried out. The results show that, the performance of VNS-Deco is superior to FCFS, SBBM and S&U-Deco under 4 different order pool situations, the total picking mileage optimized by VNS-Deco is reduced by 13.2%, 3.3% and 1.6% compared with FCFS, SBBM and S&U-Deco.

关键词： order batching mileage saving routing optimization variable neighborhood search algorithm

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Automated Course Timetabling Optimization Using Tabu-variable neighborhood search Based Hyper-Heuristic algorithm 5

Automated Course Timetabling Optimization Using Tabu-Variabl...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Muklason, Ahmad Irianti, Redian Galih Marom, Ahsanul Inst Teknol Sepuluh Nopember Dept Informat Syst Surabaya Indonesia

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers' requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Course Timetabling variable neighborhood search algorithm Tabu search algorithm Hyper-Heuristic algorithm

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Automated Course Timetabling Optimization Using Tabu-variable neighborhood search Based Hyper-Heuristic algorithm

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Procedia Computer Science 2019年 161卷 656-664页

作者： Ahmad Muklason Redian Galih Irianti Ahsanul Marom Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers’ requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works.

关键词： Course Timetabling variable neighborhood search algorithm Tabu search algorithm Hyper-Heuristic algorithm

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