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An effective population-based iterated greedy algorithm for solving the multi-AGV scheduling problem with unloading safety detection

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INFORMATION SCIENCES 2024年 657卷

作者： Zou, Wenqiang Zou, Jiazhen Sang, Hongyan Meng, Leilei Pan, Quanke Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

With the increasing prevalence of automated guided vehicles (AGVs), the multi-AGV scheduling problem has become a hot research topic in recent years. However, little attention has been devoted to unloading safety detection for multi-AGV scheduling. This paper investigates a multiAGV scheduling problem with unloading safety detection (MAGVSUSD) in a matrix manufacturing workshop. The aim is to minimize the total cost composed of travel cost, penalty cost and cost of AGV used. To address the MAGVSUSD, a mixed-integer linear programming model and a population-based iterated greedy (PIG) algorithm are proposed. In the PIG, a hyper-heuristic based on neighborhood operators and a population-based initialization method are proposed to obtain multiple high-quality initial solutions. The two-stage destruction strategy is designed to avoid falling into the local optimal solution and to enhance its ability to explore a larger solution space. The two-choice reconstruction strategy is developed to obtain better neighbor solutions by switching the search landscape. The local search based on the winning neighbor operators is proposed to dynamically adapt to each distribution situation in the workshop. A large number of experimental results demonstrate the proposed algorithm's superiority over existing algorithms in addressing the considered problem.

关键词： Automated guided vehicle iterated greedy algorithm Safety detection Unloading setup time Scheduling

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Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第5期16卷

作者： Li, Wenhan Li, Junqing Gao, Kaizhou Han, Yuyan Niu, Ben Liu, Zhengmin Sun, Qun Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Shandong Peoples R China Liaocheng Univ Sch Mech & Automot Engn Liaocheng Shandong Peoples R China

In this study, we propose an improved iterated greedy algorithm for solving the distributed permutation flowshop problem, where there is a single robot in each factory and the makespan needs to be minimized. In the problem considered, the robot is used to transfer each job from the predecessor machine to the successor machine. A blocking constraint between machines is considered, thus jobs should remain on the completed machine while waiting for the robot. The loading and unloading times are considered and different for all of the jobs conducted by the robot, and the deteriorating time is also considered. In the proposed algorithm, first, four types of neighborhood structures are developed. Then, the simulated annealing algorithm is embedded in the proposed algorithm to enhance the exploration abilities. Furthermore, a problem-specific destruction and construction strategies are investigated. Finally, several realistic instances were generated to test the proposed algorithm, and its competitive performance was verified based on detailed experimental comparisons.

关键词： Distributed flowshop problem deteriorating processing time iterated greedy algorithm robotic transportation

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A tabu memory based iterated greedy algorithm for the distributed heterogeneous permutation flowshop scheduling problem with the total tardiness criterion

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Feng, Xiaobing Zhao, Fei Jiang, Gedong Tao, Tao Mei, Xuesong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Distributed scheduling problems have been extensively studied due to their critical roles in industrial applications. Most research focuses on identical distributed factories and the objective of minimizing the makespan, ignoring the widely existing heterogeneous factories and delivery targets in reality. This paper aims to establish a distributed heterogeneous flowshop scheduling scenario with the objective of minimizing total tardiness (DHPFSP-t), and solve it using an improved iterative greedy algorithm. A mixed-integer programming model and several problem-specific properties are proposed for this novel scheduling scenario. Based on the distributed heterogeneous characteristics of the problem, four constructive algorithms are established to quickly generate initial solutions. Among them, the better solution is selected as the initial solution of the iterative greedy algorithm. To enhance the performance of the iterative greedy algorithm, the concept of tabu memory is incorporated and novel destruction and local search processes are devised. Thorough experiments are conducted to verify the effectiveness of the mixed-integer programming model of the scenario, the constructive heuristics and the improved iterative greedy algorithm. The result confirms the validity of the proposed model and the good performance of the proposed method.

关键词： Distributed heterogeneous Tabu memory iterated greedy algorithm Total tardiness

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Intelligent optimization under blocking constraints: A novel iterated greedy algorithm for the hybrid flow shop group scheduling problem

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Qin, Haoxiang Han, Yuyan Wang, Yuting Liu, Yiping Li, Junqing Pan, Quanke Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 250014 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

This paper introduces a new flow shop combinatorial optimization problem, called the blocking hybrid flow shop group scheduling problem (BHFGSP). In the problem, no buffers exist between any adjacent machines, and a set of jobs with different sequence-dependent setup times needs to be scheduled and processed at organized manufacturing cells. We verify the correctness of the mathematical model of BHFGSP by using CPLEX. In this paper, we proposed a novel iterated greedy algorithm to solve the problem. The proposed algorithm has two key techniques. One is the decoding procedure that calculates the makespan of a job sequence, and the other is the neighborhood probabilistic selection strategies with families and blocking-based jobs. The performance of the proposed algorithm is investigated through a large number of numerical experiments. Comprehensive results show that the proposed algorithm is effective in solving BHFGSP. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Blocking iterated greedy algorithm Makespan Hybrid flow shop group scheduling problem Neighborhood probabilistic selection strategies

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An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 169卷 114495-114495页

作者： Mao, Jia-yang Pan, Quan-ke Miao, Zhong-hua Gao, Liang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration.

关键词： Distributed permutation flowshop scheduling problem Makespan Preventive maintenance iterated greedy algorithm Heuristic methods

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An iterated greedy algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第4期30卷 1241-1252页

作者： Yuan, Shuaipeng Wang, Bailin Li, Tike Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry.

关键词： group processing iterated greedy algorithm parallel machine scheduling programming model re-entrant

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An improved iterated greedy algorithm for distributed robotic flowshop scheduling with order constraints

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 164卷

作者： Li, Wenhan Chen, Xiaolong Li, Junqing Sang, Hongyan Han, Yuyan Du, Shubo Shandong Normal Univ Sch Informat Jinan 250014 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China

Research in robotic scheduling has gained significant focus, especially for multi-factory manufacturing systems. In addition, production orders should be considered during the scheduling procedure. Therefore, this study considers an extension of the distributed permutation flow shop problem (DPFSP) with order constraints, in which the jobs of the same production order must be assigned to the same factory. Each factory has a single robot responsible for transporting jobs, and the deterioration time constraint is also considered. The objective is to minimize the maximum completion time of all factories. To this end, an improved iterated greedy (IIG) algorithm is investigated to solve the DPFSP with both robotic transportation and order constraints. In the proposed algorithm, each solution is represented by a two-dimensional vector to report the order assignment and the factory assignment. Next, an efficient decoding heuristic is developed to consider the robot routing during the transportation process. Then, an improved destruction and construction approach is embedded in the proposed algorithm to improve the computational complexity. Four problem-specific neighbourhood search operators are designed to enhance the local search abilities. Finally, the simulated annealing (SA) algorithm is embedded as the acceptance criterion to improve the exploration search abilities. The IIG algorithm is compared with several efficient algorithms in the literature, and the experimental results show the competitive performance of the proposed algorithm.

关键词： Distributed flowshop problem iterated greedy algorithm Order constraint Robotic transportation

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An iterated greedy algorithm for distributed flowshops with tardiness and rejection costs to maximize total profit

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Lin, Zhen Jing, Xue-Lei Jia, Bao-Xian Liaocheng Univ Sch Comp Sci & Technol Liaocheng 252000 Peoples R China Liaocheng Univ Network Informat Ctr Liaocheng 252000 Peoples R China

Distributed permutation flowshop scheduling problems (DPFSP) have become research hotspots. However, a DPFSP with tardiness and rejection costs (DPTR), which has important practical significance, has not yet been addressed. As manufacturers are most concerned about the maximization of the total profit, this study addresses the DPFSP to maximize the total profit. A mixed-integer linear programming model is used to describe the DPTR. An iterated greedy algorithm named IG_TR is proposed. IG_TR generates initial solutions using an improved NEH heuristic using four rules based on the deadline, due date, tardiness and rejection costs. For IG_TR, a dynamic number of jobs are removed from factories in the destruction phase. In the reconstruction phase, the removed jobs and those waiting to be processed are selected to be inserted into appropriate positions in the factories. Two local search methods are designed to improve the quality of solutions generated in the reconstruction phase. During the acceptance phase, a restart mechanism is proposed to avoid falling into local optimal. The experimental results of the component analysis demonstrate the effectiveness of IG_TR. Several comprehensive experiments show that IG_TR outperforms five related algorithms.

关键词： Distributed flowshops iterated greedy algorithm Total profit Local search

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An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 100992-100992页

作者： Qin, Hao-Xiang Han, Yu-Yan Zhang, Biao Meng, Lei-Lei Liu, Yi-Ping Pan, Quan-Ke Gong, Dun-Wei Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the continuous development of national economies, problems of various energy consumption levels and pollution emissions in manufacturing have attracted attention from researchers. Most existing research has focused on reducing economic costs and energy consumption. However, the Hybrid Flow Shop Scheduling Problem with energy-efficient criteria has not yet been well studied, especially with blocking constraints. This paper is the first to present a mathematical model of the blocking hybrid flow shop problem with an energy-efficient criterion and a modified Iterative greedy algorithm based on a swap strategy designed to optimize the constructed model. In the proposed algorithm, first, a heuristic is adopted to generate the initial solution. Second, a local perturbation strategy based on a swap operator is designed to ensure the convergence of the algorithm. Third, a simple global perturbation strategy based on a half-swap operator is proposed as a means to further search for the potentially best solution with the traditional simulated annealing criterion. The proposed algorithm is applied to 150 test instances at different scales and compared to state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms and can obtain a better solution.

关键词： Blocking Hybrid flow shop scheduling problem iterated greedy algorithm Energy efficiency Local perturbation

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A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Lu, Chao Liu, Qiao Zhang, Biao Yin, Lvjiang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed coproduction mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (TEC). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions' quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments.

关键词： Distributed hybrid flowshop scheduling Multi-objective optimization Energy-efficiency iterated greedy algorithm

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