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Composite multi-objective optimization on a new collaborative vehicle routing problem with shared carriers and depots

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JOURNAL OF CLEANER PRODUCTION 2020年 274卷 122593-122593页

作者： Zhang, Wenyu Chen, Zixuan Zhang, Shuai Wang, Weirui Yang, Shuiqing Cai, Yishuai Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China

The collaborative vehicle routing problem for logistic optimization is a fundamental part of the sustainable supply chain, and has increasingly been of concern in recent years. This study proposes a novel logistics collaboration model to address the collaborative vehicle routing problem that involves shared carriers and depots (CVRP-SCD). This is inspired by the fact that a depot often has orders for multiple carriers, and a carrier often delivers orders to multiple depots. This problem involves decreasing transportation distances and improving capacity utilization by extending supply-side unilateral logistics collaborations to simultaneously consider collaborations between supplyand demand-sides. Further, the proposed CVRP-SCD model uses a composite objective that is a weighted sum of four objectives including quality, reliability, cost, and time to accurately evaluate and analyze the efficiency improvement. An extended variable neighborhood search algorithm is presented based on three matrices, including the carrier collaboration matrix, depot collaboration matrix, and transportation sequence matrix. This algorithm aims to address the trade-offs among multiple objectives for the CVRPSCD model by incorporating new operators based on specific features involved in the problem. Simulation experiments are performed on two sets of instances to evaluate the proposed algorithm's effectiveness as well as the savings produced from the proposed model. Further, a comparison experiment between different objective optimization also verifies the rationality of the proposed composite multi objective optimization. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Vehicle routing problem Carrier collaboration Depot collaboration Composite multi-objective optimization variable neighborhood search algorithm

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Effective metaheuristic and rescheduling strategies for the multi-AGV scheduling problem with sudden failure

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Wang, Xue Zou, Wenqiang Meng, Leilei Zhang, Biao Li, Junqing Sang, Hongyan Liaocheng Univ Sch Comp Sci 1 Hunan Rd Liaocheng 252000 Peoples R China

This paper investigates a novel scheduling problem, the multi-AGV scheduling problem with sudden failure (MASP-SF), which is of great importance in modern intelligent manufacturing systems. The problem considers how to assign tasks to multiple AGVs with the objective of minimizing the total cost under two events of normal production and sudden failure in the workshop. In the event of normal production, a population-based variable neighborhood search (PVNS) algorithm with effective strategies such as rule-based stochastic method in the initialization stage, pancake flipping strategy in the shaking stage, improved neighborhood structures in the variable neighborhood descent stage is presented to solve the multi-AGV scheduling problem. In the event of sudden failure, a mixed-integer linear programming model and two rescheduling strategies are proposed to solve MASP-SF. The two rescheduling strategies are the rapid repair rescheduling strategy and a rescheduling strategy based on an improved NNH. To validate the above ideas, we conduct comprehensive and in-depth experiments on a practical plant and statistical analysis of the resulting data. The results show that the scheduling solutions generated by the rescheduling strategies are feasible, and the proposed PVNS algorithm has a superior performance compared to existing algorithms in multi-AGV scheduling problem.

关键词： Automated guided vehicle Rescheduling Matrix manufacturing workshop Sudden failure variable neighborhood search algorithm

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Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer's time availability in cloud manufacturing

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年

作者： Zhao, Zian Zhou, Hong Zheng, Weibo Beihang Univ Sch Econ & Management 37 Xueyuan Rd Beijing 100191 Peoples R China Beihang Univ Beijing Key Lab Emergency Support Simulat Technol Beijing Peoples R China China Aerosp Standardizat Inst Beijing Peoples R China

As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer's capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

关键词： Cloud manufacturing scheduling vehicle scheduling collaborative optimization time window variable neighborhood search algorithm

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Optimal life-testing plans for joint progressively Type-II censored Birnbaum-Saunders distributions with compound optimal criteria

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2024年

作者： Lin, Chien-Tai Chen, Zhi-Wei Li, Yun-Wei Ng, Hon Keung Tony Tamkang Univ Dept Math New Taipei City 25137 Taiwan Bentley Univ Dept Math Sci Waltham MA USA

In this paper, we first derive the maximum likelihood estimates (MLEs) of the parameters of Birnbaum-Saunders (BISA) distributions based on the joint progressive Type-II censored (JPC) samples. We also discuss using the EM algorithm to obtain the MLEs of the model parameters. Then, we determine the single-objective-criterion optimal JPC schemes for BISA distributions based on the cost minimization criterion, $A$A-optimality criterion, and $D$D-optimality criterion by the complete search method, variable neighborhood search (VNS) algorithm and modified VNS (MVNS) algorithm. These algorithms are compared in terms of accuracy and computation time. In addition, we determine the compound-criterion optimal JPC schemes based on different optimal criteria and the reasonably efficient compound optimal JPC schemes for two competing statistical models, such as the inverse Gaussian and BISA models. The advantages of using the compound optimal scheme over the single-objective-criterion optimal scheme are demonstrated through a real-life data set on the micro-indentation test about the hardness of polymeric bone cement.

关键词： EM algorithm maximum likelihood estimation Monte Carlo simulation optimal criteria variable neighborhood search algorithm

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MAXIMUM COMPLETION TIME UNDER LEARNING EFFECT IN PERMUTATION FLOWSHOP SCHEDULING PROBLEM

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2018年第2期25卷 156-174页

作者： Mustu, Settar Eren, Tamer SDL Chain Ind Kirikkale Turkey Kirikkale Univ Dept Ind Engn Kirikkale Turkey

The permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Furthermore, meta-heuristics are developed to achieve effective solutions for large-sized problems encountered in real applications. These meta-heuristics are the genetic algorithm which is a population-based solution approach, the kangaroo and the variable neighborhood search algorithms which both are single-solution-based solution approaches. In addition, different solution methods, which are in the literature for similar problem structures, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance differences between each other for large-sized problems.

关键词： learning effect flowshop make span genetic algorithm kangaroo algorithm variable neighborhood search algorithm

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Optimization of energy-efficiency train schedule considering passenger demand and rolling stock circulation plan of subway line

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ENERGY 2023年第1期275卷

作者： Huang, Yu Zhou, Wenliang Qin, Jin Deng, Lianbo Cent South Univ Sch Traff & Transportat Engn Changsha 410000 Hunan Peoples R China

An energy-efficiency train schedule greatly contributes to alleviating some environmental issues such as carbon emissions. However, the pursuit of reducing train energy consumption often leads to passengers' longer travel time, and more operation cost of rolling stocks. Thus, it is necessary to consider costs of passengers and rolling stocks when optimizing the energy-efficiency schedule for better satisfying passenger demand and reducing trains' total operating cost. In this paper, a mixed integer nonlinear programming model of energy-efficiency train schedule considering passenger demand and rolling stock circulation plan is established for minimizing passengers' travel cost and trains' operating cost. More specifically, this model devotes to simultaneously optimizing: (1) passengers' travel choices to reduce passengers' waiting time and travel time, (2) the selection of train traction strategy in the section to reduce the traction energy consumption, and (3) the train connection scheme to ensure the economy of the rolling stock circulation plan. This paper designs a variable neighborhood search algorithm combined with CPLEX solver to solve the model efficiently. Based on the data of Guangzhou Metro Line 9, the optimization method proposed in this paper can reduce the train operation cost by 9.73% and the total passenger travel cost by 18.11%.

关键词： Subway line Energy-efficient train scheduling Passenger demand Rolling stock circulation planning variable neighborhood search algorithm

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Green logistics oriented tug scheduling for inland waterway logistics

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ADVANCED ENGINEERING INFORMATICS 2021年 49卷 101323-101323页

作者： Zhu, Shengyan Gao, Jiajing He, Xueting Zhang, Shuanglu Jin, Ying Tan, Zheyi Yibin Univ Sanjiang Res Inst Artificial Intelligence & Robot Chengdu Sichuan Peoples R China Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China

Under the global consensus on reducing emissions, shipping companies are undertaking the social responsibility of greenhouse gas emission reduction. Meanwhile, it is a top priority for shipping companies to arrange the tugs and barges reasonably and achieve the goal of energy saving as well as emission reduction. Based on the above problem, this paper proposes a Mixed Integer Programming (MIP) model to jointly optimize the transport routes of tugs considering the barge transshipment, with the objective of minimizing the sum of the carbon emissions for barges handling, tugs travelling and waiting. In the view of specific problem, a variable neighborhood search (VNS) algorithm is designed to solve this MIP model effectively. Numerical experiments based on different sizes of instances are implemented to validate the effectiveness of the proposed algorithm. Computational results indicate that the proposed model reduces carbon emissions by about 46.93% compared to the dispatching rule. Moreover, the consideration of barge transshipment in the model can reduce carbon emissions by about 10.46%. In addition, the VNS algorithm yields solutions with optimality gaps about 0.29% in a short time.

关键词： Inland waterway logistics Sustainable logistics Transshipment of barges Mixed Integer Programming variable neighborhood search algorithm

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Ring hierarchical hub network design problem: Exact and heuristic solution methods

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2022年 11卷

作者： Eydi, Alireza Khaleghi, Amir Barzegar, Khaled Univ Kurdistan Dept Ind Engn Sanandaj Iran Univ Kurdistan Ind Engn Sanandaj Iran Univ Kurdistan Ind Engn Sanandaj Iran

In this paper, we study the hierarchical hub network design problem with a ring-star-star structure. In this problem, the hubs are located in two layers. In the first layer, central hubs (main hubs) are located in a ring structure, and in the second layer, secondary hubs are located. Each secondary hub is allocated to a central hub. Other demand points can also be allocated to each of these hubs (central hubs or secondary hubs). The objective is to minimize transportation costs on the network. This problem applies to communication networks when establishing direct links between demand nodes is not cost-effective, and there are two levels of service for customers. Also, the ring structure is used to reduce costs associated with full communication between central hubs. We present a mixed-integer programming model for the problem and report the results of the problem solving for a numerical example on the CAB dataset. Also, we present three solution methods for the problem. First, by exploiting the decomposable structure of the proposed model, we introduce an accelerated Benders decomposition algorithm. Then, we present a hybrid genetic algorithm that uses the Dijkstra algorithm to evaluate solutions. Next, we present a hybrid variable neighborhood search algorithm that uses the Dijkstra algorithm to calculate the cost of each solution. Also, a relax-and-fix algorithm is proposed to find near-optimal feasible solutions for the problem. Computational results are presented on the USA423 dataset. The results show that the proposed relax-and-fix algorithm can solve instances with up to 100 nodes. Also, the proposed hybrid algorithms can solve instances with up to 423 nodes. The performance of the proposed hybrid variable neigh-borhood search is better than the proposed hybrid genetic algorithm in solving large-sized instances.

关键词： Transportation Ring hierarchical hub network Ring network Benders decomposition Genetic algorithm variable neighborhood search algorithm Relax-and-fix algorithm

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Production routing in perishable and quality degradable supply chains

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HELIYON 2020年第2期6卷 e03376-e03376页

作者： Manouchehri, Fateme Nookabadi, Ali Shahandeh Kadivar, Mahdi Isfahan Univ Technol Isfahan Iran Dept Ind & Syst Engn Esfahan Iran Isfahan Univ Technol Coll Agr Dept Food Sci Esfahan Iran

Generally, the major goal in perishable supply chains is preserving the product's quality along with improving its logistic performance. In this regard, temperature seems to be the most important and most sensitive factor, since uncontrolled temperature has shown to have great impact on reducing product quality. In this study, an integrated production routing model for a perishable product was developed;considering the production, inventory, storage temperature, routing and vehicle temperature. To solve the problem, a hybrid search algorithm combining the variable of neighborhood search algorithm and the mechanism of the simulated annealing algorithm was designed. In order to evaluate the validity of the proposed algorithm, its results were compared with that of the CPLEX solver in the GAMZ software environment. Comparison of the results of the two methods shows the efficiency of the proposed algorithm to solve this problem. As a case study, the proposed model was also applied in a real industrial case. According to the results, this company can greatly reduce its distribution and inventory costs and also avoid waste by using this program.

关键词： Applied mathematics Industrial engineering Systems engineering Food engineering Food quality Production routing Perishable product Product quality Temperature of vehicle and storage variable neighborhood search algorithm

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RETRACTED: A Hybrid Intelligent Optimization algorithm for Solving the Producing Work Order Group Furnace Problem (Retracted Article)

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2579-2579页

作者： Liu, Yefeng Li, Shaowu Pang, Xinfu Zhao, Yuan Yu, Shengping Shenyang Inst Technol Liaoning Key Lab Informat Phys Fusion & Intellige Fushun 113122 Liaoning Peoples R China Hubei Univ Nationalities Sch Informat Engn Enshi Peoples R China Shenyang Inst Engn Sch Automat Shenyang 110136 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

A hybrid optimization method for the production of work order group furnaces was proposed in this paper. First, a working order group furnace model was constructed including performance index, the constraint condition and the decision variable to fit the problems in working order group furnaces. The hybrid optimization method consists of an optimal priority and a variable neighborhood search algorithm. In the algorithm, we have adopted a lot of rules and corresponding grade limits on stock production. Based on the proposed algorithm's calculation results, the delivery time deviation, grade deviation and priority deviation of the 20 group furnace production orders are reduced from 58 to 42 with reduction rate of 27.59%. The satisfaction rate for grade preparation is increased from 4 to 6, which has a rate of increase of 50%. In order to prove the effectiveness of the proposed algorithm, the proposed algorithm is compared with other literature algorithms such as a discrete particle swarm algorithm, a variable neighborhood search algorithm and an adaptive variable neighborhood search algorithm.

关键词： Furnace group problem Hybrid optimization method Best priority Manufacturing Model variable neighborhood search algorithm Discrete particle swarm algorithm Adaptive variable neighborhood search algorithm

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