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A variable Group Parallel Flexible Job Shop Scheduling in a SMEs Manufacturing Platform

IEEE ACCESS 2023年 11卷 79531-79541页

作者： Choi, Yeonjee Hwang, Hyun Suk Kim, Chang Soo Pukyong Natl Univ Dept Informat Syst Busan 608737 South Korea Hanyoung DS Inc Res Ctr Seoul 07600 South Korea

Manufacturing is a broad field with different types of production processes. Therefore, specific processes can accommodate multiple parallel machines operating simultaneously in some production environments. This assumption is particularly crucial in factory scheduling for industries such as textile, aircraft, and semiconductor manufacturing. The assumption proposed in this paper is differ from FJSP in that they are closer to the real world by allowing different machines to perform the same multiple processes. In this paper, a new approach for solving the flexible job shop scheduling problem has been proposed, which is referred to as the flexible job shop scheduling problem in a parallel machine environment with a variable group. We proposed a hybrid genetic algorithm-based variable neighborhood search algorithm (GA-VNS) to solve this problem, where the multiple parallel machines can operate the same operations simultaneously and are grouped as a variable group. The GA-VNS algorithm combines the global searching ability of GA with the local searching ability of VNS to fully reflect the condition of the parallel machines with variable groups. The objective functions of the algorithm are to minimize the production makespan and to improve facility usage. Multiple simulation experiments are conducted using forty-seven test instances to assess the feasibility and effectiveness of the new approach. This study is expected to reduce processing time and production costs for Small and Medium Enterprises (SMEs) with low cost and high efficiency compared to existing systems, such as the manufacturing execution system, enterprise resource planning, and job shop scheduling system.

关键词： Genetic algorithms variable neighborhood search algorithm flexible job shop scheduling problem global and local searching algorithm parallel machines small and medium sized enterprises manufacturing platform

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A location-inventory model considering a strategy to mitigate disruption risk in supply chain by substitutable products

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 108卷 213-224页

作者： Farahani, Maryam Shavandi, Hassan Rahmani, Donya Sharif Univ Technol Dept Ind Engn Tehran Iran KN Toosi Univ Technol Dept Ind Engn Tehran Iran

In this paper we consider a multiple products inventory location problem with disruption. Disruptions may occur in any supply chain due to several reasons such as earthquakes, floods, fires, labor strikes, terrorist attacks, equipment breakdowns and economic crises. We develop a model by assuming that facilities may fail partially in case of disruption and considering substitutable products as a strategy to mitigate the risk of disruption. The developed model is a non-linear integer programming model and NP-Hard. To solve the model, a hybrid algorithm including Tabu search (TS) and variable neighborhood search (VNS) is proposed. Numerical results show reasonable performance of the algorithm and signify the importance of multiple substitutable products strategy to manage arising disruptions. Finally, conclusion and future researches are presented. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Inventory-location model Disruption risk Substitute product Tabu search algorithm 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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I-Optimal Design of Mixture Experiments in the Presence of Ingredient Availability Constraints

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JOURNAL OF QUALITY TECHNOLOGY 2015年第3期47卷 220-234页

作者： Syafitri, Utami Sartono, Bagus Goos, Peter Univ Antwerp Fac Appl Econ B-2020 Antwerp Belgium Univ Antwerp StatUa Ctr Stat Antwerp Belgium Bogor Agr Univ Dept Stat Bogor Indonesia Katholieke Univ Leuven Fac Biosci Engn Leuven Belgium Katholieke Univ Leuven Leuven Stat Res Ctr Leuven Belgium

Mixture experiments usually involve various constraints on the proportions of the ingredients of the mixture under study. In this paper, inspired by the fact that the available stock of certain ingredients is often limited, we focus on a new type of constraint, which we refer to as an ingredient availability constraint. This type of constraint substantially complicates the search for optimal designs for mixture experiments. One difficulty, for instance, is that the optimal number of experimental runs is not known a priori. To deal with this complication, we propose a variable neighborhood search algorithm to find I-optimal designs for mixture experiments in case there is a limited stock of certain ingredients.

关键词： variable neighborhood search algorithm V-Optimality

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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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Statistical inference and optimum life-testing plans with joint progressively type-II censoring scheme

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2023年第3期20卷 279-306页

作者： Lin, Chien-Tai Chen, Yen-Chou Yeh, Tzu-Chi Ng, Hon Keung Tony Tamkang Univ Dept Math New Taipei Taiwan Bentley Univ Dept Math Sci Waltham MA USA

This article discusses the estimation of model parameters and the determination of optimal life-testing plans for Weibull-distributed lifetimes under a joint progressive Type-II censoring scheme. We obtain the maximum likelihood estimates numerically and propose a modified variable-neighborhood-search-based approach for determining the optimum joint progressive Type-II censoring scheme. Different optimal criteria, including a cost minimization criterion, the A-optimality criterion, and the D-optimality criterion, are considered. A sensitivity analysis is used to study the effect of misspecification of parameter values on the optimal schemes.

关键词： Maximum likelihood estimation monte carlo simulation optimal criteria variable neighborhood search algorithm

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Hybrid multi-objective metaheuristic algorithms for solving airline crew rostering problem with qualification and language

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 1460-1487页

作者： Deng, Bin Ding, Ran Li, Jingfeng Huang, Junfeng Tang, Kaiyi Li, Weidong Yunnan Univ Sch Math & Stat Kunming 650000 Peoples R China China Eastern Yunnan Airlines Kunming 650200 Peoples R China

In order to cope with the rapid growth of flights and limited crew members, the rational allocation of crew members is a strategy to greatly alleviate scarcity. However, if there is no appropriate allocation plan, some flights may be canceled because there is no pilot in the scheduling period. In this paper, we solved an airline crew rostering problem (CRP). We model the CRP as an integer programming model with multiple constraints and objectives. In this model, the schedule of pilots takes into account qualification restrictions and language restrictions, while maximizing the fairness and satisfaction of pilots. We propose the design of two hybrid metaheuristic algorithms based on a genetic algorithm, variable neighborhood search algorithm and the Aquila optimizer to face the trade-off between fairness and crew satisfaction. The simulation results show that our approach preserves the fairness of the system and maximizes the fairness at the cost of crew satisfaction.

关键词： airline crew rostering problem multi-objective optimization genetic algorithm variable neighborhood search algorithm Aquila optimizer

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Hybrid artificial immune algorithm for energy-efficient distributed flexible job shop in semiconductor manufacturing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3075-3098页

作者： Chen, Xiaolong Du, Yu Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China HengXing Univ Sch Informat Engn Qingdao Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

In semiconductor manufacturing, constraints, including multiple scheduling routes, complex production processes, and robotic transportation, particularly bring difficult challenges to production schedules. To address this issue, we investigated an energy-efficient distributed flexible job shop scheduling problem with robotic transportation. In the considered problem, two objectives, i.e., the maximum completion time and total energy consumption, are minimized simultaneously. First, two mixed integer linear programming models are constructed, including the sequence-based and position-based models. Then, a hybrid algorithm combining the artificial immune algorithm and variable neighborhood search algorithm is proposed. To generate a population with better performance and diversity, five initialization strategies and an active decoding method are designed. In addition, a novel clone mechanism and an improved suppression process are being developed to improve the exploration abilities. Furthermore, two distinct neighborhood structures and two object-oriented local search heuristics are designed to balance exploration and exploitation capabilities. Eventually, the proposed algorithm achieved better performance with the comparison of three other state-of-the-art algorithms with different scale instances.

关键词： Distributed flexible job shop scheduling problem Robotic transportation Mixed integer linear programming models Artificial immune algorithm variable neighborhood search algorithm

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A bi-level optimization of storage allocation and shelf sequencing in a goods-to-person picking system 5

A bi-level optimization of storage allocation and shelf sequ...

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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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VNS-Based Heuristic for Identical Parallel Machine Scheduling Problem

VNS-Based Heuristic for Identical Parallel Machine Schedulin...

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Bathrinath, S. Sankar, S. Saravana Ponnambalam, S. G. Leno, I. Jerin Kalasalingam Univ Virudunagar Tamil Nadu India Monash Univ Malaysia Bandar Sunway 46150 Selangor Malaysia Natl Coll Engn Tirunelveli Tamil Nadu India

ISBN: (纸本)9788132221265;9788132221258

Minimization of make span and minimization of number of tardy jobs in identical parallel machine scheduling problems are proved to be NP-hard problems. Many researchers have attempted to solve these combinatorial optimization problems by employing different heuristic algorithms. While providing a satisfactory solution to the production environment for each of the above-said objectives, still remains as a challenge, most of the time, the need has been to have satisfactory solutions optimizing simultaneously the above-said two objectives. In this research work, an attempt is made to address this issue and heuristic algorithms using simulated annealing algorithm (SA) and variable neighborhood search algorithm (VNS) have been developed to provide near-optimal solutions. The developed heuristics are tested for their efficiency on a very large data sets generated as per the prescribed procedure found in the literature. Based on the results of experiments, it is inferred that the VNS-based heuristics outperforms the SA-based heuristics consistently both in terms of solution quality and consistency.

关键词： Identical parallel machine scheduling variable neighborhood search algorithm Simulated annealing algorithm Make span Number of tardy jobs

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