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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Feng, Jinhan Qin, Kun Chen, Zhongwei Kou, Hongrui Sun, Shulin Wang, Ting Civil Aviat Univ China Sch Elect Informat & Automat Tianjin Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the rapid development of e-commerce economy, Robotic Mobile Fulfillment Systems based on "goods-to-person" warehouse order picking mode are widely used in large e-commerce platforms such as Amazon and Alibaba. A Bi-level optimization algorithm is proposed to jointly optimize storage allocation and shelf sorting for the shelf scheduling problem with multiple storage locations in the "goods-to-person" picking system. The upper-layer optimization uses an improved genetic algorithm to allocate storage locations for the shelves to be picked, adopts a problem-specific knowledge strategy to generate an elite initial population, and introduces crossover and neighborhood search operators with adaptive transformation probabilities to enhance the local search and global optimization capabilities. According to the given storage location, shelf sorting optimization is carried out at the lower level, which is abstracted into the variant traveling salesman problem for modeling and analysis, based on which two neighborhood structures are designed and solved with the variable neighborhood search algorithm. The experimental results show that the performance of the improved genetic algorithm is improved by 2.31% compared with the ordinary genetic algorithm, the combined algorithm has certain advantages over the optimization solver in terms of speed and solution quality, which is of great significance to improve the operational efficiency of the "goods-to-person" picking system.

关键词： "goods-to-person" picking system Bi-level optimization genetic algorithm variable neighborhood search algorithm

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Churn Prediction Using Mathematical Programming via a Linear Classification algorithm

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Mathematical Models and Computer Simulations 2024年第3期16卷 504-519页

作者： Barhdadi, Mohamed Benyacoub, Badreddine Ouzineb, Mohamed National Institute for Statistics and Applied Economics Rabat Morocco

Abstract: The initial idea in developing customer churn prediction was to use statistical analysis of a sample of previous customers to help operators predict which existing customers were likely migrate to other operators. In this paper, a nonstatistical approach to this problem can also be envisaged by formulating the problem as a linear program. The main concept of our proposed algorithm is to hybridize the Jackknife resampling technique with an efficient heuristic based on a variable neighborhood search algorithm to construct a new approach that can present an alternative to black-box machine learning methods. The goal is to find possible solutions (or weights) to minimize error distances and the number of misclassified points. We tested the suggested algorithm with an imbalanced instance and the comparisons show that the proposed model outperforms the most existing machine learning classifiers, both in terms of the solution quality and the execution time. The numerical results indicate that the best solution is achieved in 85% over G-mean of all instances tested. The average gap between our solution and the best solution is still quite small (1.15%). © Pleiades Publishing, Ltd. 2024. ISSN 2070-0482, Mathematical Models and Computer Simulations, 2024, Vol. 16, No. 3, pp. 504–519. Pleiades Publishing, Ltd., 2024.

关键词： customer churn prediction linear classification mathematical modeling resampling technique variable neighborhood search algorithm

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Robust assembly line balancing problem considering preventive maintenance scenarios with interval time

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期116卷

作者： Meng, Kai Tang, Qiuhua Zhang, Zikai Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Mfg Inst Wuhan 430081 Peoples R China

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning -based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances.

关键词： Robust assembly line balancing problem Preventive maintenance scenarios Q-learning variable neighborhood search algorithm

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Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Duan, Shuang Zheng, Hong-Xing Wang, Zhao-Yang Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Peoples R China

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

关键词： Truck appointment quota Transshipment container Yard crane Scheduling Block relocation variable neighborhood search algorithm

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Solution approaches for the integrated production and outbound distribution scheduling problem with multiple plants and perishable items

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Atasagun, Gozde Can Karaoglan, Ismail Konya Tech Univ Fac Engn & Nat Sci Dept Ind Engn TR-42250 Konya Turkiye

In this manuscript, we consider the integrated production and outbound distribution system in which there are several geographically dispersed facilities. These facilities are responsible for producing and delivering timesensitive customer demands. We propose a mixed integer programming (MIP) formulation, MIP with an initial solution using Clarke and Wright (MIPCW) and a metaheuristic algorithm (variable neighborhood search-VNS) for the problem whose objective is to minimize the maximum delivery time whereby satisfying all customer demands. We conduct a two-stage experimental study to determine the effectiveness of the proposed metaheuristic algorithm. We compare the VNS algorithm with the best-known solutions for the single plant version of the problem, in the first stage. These results indicate that the VNS algorithm yields promising results. The second stage includes the results for the MIP formulation and the VNS algorithm on a set of newly generated test instances. The average percentage gap between the VNS algorithm and the MIPCW formulation is about 5% which means that the VNS algorithm gives better results in average about 70 s computation time. These results show how effective the suggested VNS algorithm is at solving the test instances.

关键词： Time -sensitive products Machine scheduling Vehicle routing problem Mixed integer programming formulation 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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Routing Design and Departure Time Determination of Customized Buses Based on Reserved Trip Request 7

Routing Design and Departure Time Determination of Customize...

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IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Cheng, Zhuxuan Xiong, Jie Li, Tongfei Chen, Yanyan Minist Transport Key Lab Technol Intelligent Transportat Syst Beijing Peoples R China Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China

ISBN: (纸本)9781665460071

This paper investigates an integrated optimization problem of routing design and departure time determination of customized buses based on demand-responsive service. The problem aims to optimize both a set of customized routes and the departure time of each route based on a set of reserved trip requests. A mixed integer programming model is formulated to address the problem. The model aims to minimize the sum of the operator cost of the bus company and the total travel cost of passengers so that the buses can be efficiently used to provide high-quality services to passengers. To solve the model, a departure time determination method based on a time window backpropagation process is first proposed to optimize the bus departure time for a given route. Then a variable neighborhood search (VNS) algorithm with customized operators is designed to generate the optimized routes. Finally, different numerical tests are conducted to verify the effectiveness of the proposed model and the algorithm, then the sensitivity analysis on key parameters follows.

关键词： customized bus routing design departure time determination variable neighborhood search algorithm

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Train timetabling with dynamic and random passenger demand: A stochastic optimization method

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2021年 123卷 102963-102963页

作者： Gong, Congcong Shi, Jungang Wang, Yanhui Zhou, Housheng Yang, Lixing Chen, Dewang Pan, Hanchuan Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China East China Jiaotong Univ Coll Transportat & Logist Nanchang 330013 Jiangxi Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Shanghai Univ Engn Sci Sch Urban Rail Transportat Shanghai 201620 Peoples R China

Considering the dynamics and randomness of passenger demand, this paper investigates a train timetabling problem in the stochastic environment for an urban rail transit system. With the scenario-based representation of passenger distribution, an integer nonlinear programming (INLP) model is first formulated to simultaneously optimize the total number of train services, headway settings and speed profile selection decision during the planning time horizon, in which the expected total service cost is treated as the objective function. Through an analysis of the features of the nonlinear constraints, a reformulation method is proposed to develop an equivalent integer linear programming (ILP) model that can be easily solved by commercial software. Moreover, a variable neighborhood search algorithm is developed to find the approximate optimal solutions for large-scale problems within the tolerable computing time. Finally, two sets of numerical experiments, with the operation environments of a simple urban rail transit line and Fuzhou Metro Line 1, are implemented to verify the solution quality and effectiveness of the proposed methods.

关键词： Urban rail transit Train timetable Stochastic optimization variable neighborhood search algorithm

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