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An iterated greedy algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem

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Computers, Materials & Continua 2025年第1期82卷 371-388页

作者： Binhui Wang Hongfeng Wang College of Information Science and Engineering Northeastern UniversityShenyang110819China

The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent *** iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution ***,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale *** a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the ***,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous *** addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory ***,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom *** last,a discrete adaptive learning rate is employed to enhance the stability of the memory and *** ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large ***,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale *** indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.

关键词： Distributed permutation flow shop scheduling makespan iterated greedy algorithm memory mechanism cooperative reinforcement learning

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A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Miao, Zhonghua Guo, Hengwei Pan, Quan-ke Peng, Chen Xu, Ziyu Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

With technological advancements, robots have been widely used in various fields and play a vital role in the production execution system of a smart farm. However, the operation scheduling problem of robots within production execution systems has not received much attention so far. To enable efficient management, this paper develops a multi-objective mathematical model concerning both the efficiency and economic indicators. We propose a population-based iterated greedy algorithm enhanced with Q-learning (Q_DPIG) for a multi-weedingrobots operation scheduling problem. An index-based heuristic (IBH) is designed to generate a diverse set of initial solutions, while an adaptive destruction phase, guided by the Q-learning framework, ensures effective neighborhood search and solution optimization. Additionally, a local search method focusing on the high-load and the critical robots is employed to further optimize the two objectives. Finally, Q_DPIG is demonstrated to be effective and significantly outperform the state-of-the-art algorithms through comprehensive test datasets and a real case study from a farmland management center.

关键词： Task allocation Operation scheduling Multi-objective optimization iterated greedy algorithm Smart farms

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iterated greedy algorithm for Solving a New Single Machine Scheduling Problem 16

Iterated Greedy Algorithm for Solving a New Single Machine S...

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16th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Zhao, ZiYan Liu, ShiXin Zhou, MengChu Guo, XiWang Xue, JiaLun Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China

ISBN: (纸本)9781728100845

This paper studies a new single machine scheduling problem with sequence-dependent setup time, release time, due time and group technology assumption originated from a wire rod and bar rolling process in steel plants. The objective is to find an optimal batch sequence and job sequences of all batches to minimize the number of late jobs. A two-stage mixed integer program is created to describe and solve this problem. The first stage can be solved in a short time by CPLEX while the second one is time-consuming when dealing with large-scale cases. Thus, an iterated greedy algorithm able to solve the second stage fast is developed. The experimental results demonstrate that the proposed two-stage mixed integer program can be solved optimally by CPLEX for small-scale cases and the proposed algorithm can effectively solve the second stage for large-scale cases.

关键词： Single machine scheduling two-stage integer program iterated greedy algorithm CPLEX wire rod and bar rolling process

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iterated greedy algorithm for Solving a Hybrid Flow Shop Scheduling Problem with Reentrant Jobs 32

Iterated Greedy Algorithm for Solving a Hybrid Flow Shop Sch...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang, Qi Tian, Zheng Wang, Sen Liu, Shixin Northeast Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Shanghai Baosight Software Co Ltd Shanghai 201900 Peoples R China

ISBN: (纸本)9781728158556

This paper studies a hybrid flow shop scheduling problem with reentrant jobs from a copper strip cold rolling process in copper plants. The objective is to find a near-optimal job sequences to minimize makespan. A mixed integer program is created to describe this problem. An iterated greedy heuristic algorithm is proposed to solve this problem. Practical instances are collected from a Chinese copper plant to test the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the large-scale instances.

关键词： Hybrid flow shop scheduling Reentrant jobs iterated greedy algorithm

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Effective iterated greedy algorithm for Flow-Shop Scheduling Problems with Time lags

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Chinese Journal of Mechanical Engineering 2017年第3期30卷 652-662页

作者： Ning ZHAO Song YE Kaidian LI Siyu CHEN school of mechanical engineering university of science and technology beijingBeijing 100083China

Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem（PFSP with time lags） is concentrated but non-permutation problem（non-PFSP with time lags） seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algo- rithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation（IGTLP） and iterated greedy algorithm for non-permutation （IGTLNP）. The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% com- putational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.

关键词： Permutation Non-permutation Flow shopTime lags . Makespan iterated greedy algorithm

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A Population-Based iterated greedy algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第5期19卷 6692-6705页

作者： Zhao, Fuqing Xu, Zesong Wang, Ling Zhu, Ningning Xu, Tianpeng Jonrinaldi, J. Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine, which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Finally, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this article.

关键词： Production facilities Job shop scheduling Statistics Sociology greedy algorithms Search problems Processor scheduling Distributed assembly scheduling iterated greedy algorithm no-wait flow-shop total flowtime

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An iterated greedy algorithm integrating job insertion strategy for distributed job shop scheduling problems

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 77卷 746-763页

作者： Huang, Lin Tang, Dunbing Zhang, Zequn Zhu, Haihua Cai, Qixiang Zhao, Shikui Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210000 Peoples R China Univ Jinan Sch Mech Engn Jinan 250022 Peoples R China

The distributed scheduling problem (DSP) becomes particularly important with the popularization of the distributed manufacturing mode. The distributed job shop scheduling problem (DJSP) is a typical representative of the DSP. It consists of two subproblems, assigning jobs to factories and determining the operation sequence on machines. Some benchmark instances have been proposed to test the performance of the DJSP approach, but most instances have not found the optimal solution. In this paper, an iterated greedy algorithm integrating job insertion (IGJI) is proposed to solve the DJSP. Firstly, a job insertion strategy based on idle time (JIIT) is designed for the insertion of a job into a factory. Secondly, JIIT is used in the reconstruction phase of IGJI, while three destruction-reconstruction methods are designed to balance the makespan among factories. Finally, tabu search is adopted in the local search phase of IGJI to improve the solution quality further. The performance of IGJI is tested on 240 benchmark instances, and the experimental results show that the solution quality of IGJI outperforms the other four state-of-the-art algorithms. In particular, IGJI has found 231 new solutions for these benchmark instances.

关键词： Distributed job shop scheduling Job insertion strategy iterated greedy algorithm Tabu search Makespan

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A multi-start iterated greedy algorithm for the minimum weight vertex cover P3 problem

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APPLIED MATHEMATICS AND COMPUTATION 2019年 349卷 359-366页

作者： Zhang, Wenjie Tu, Jianhua Wu, Lidong Beijing Univ Chem Technol Dept Math Beijing 100029 Peoples R China Univ Texas Tyler Dept Comp Sci Tyler TX 75799 USA

Given a vertex-weighted graph G = (V, E) and a positive integer k >= 2, the minimum weight vertex cover P-k (MWVCPk) problem is to find a vertex subset F subset of V with minimum total weight such that every path of order k in G contains at least one vertex in F. For any integer k >= 2, the MWVCPk problem for general graphs is NP-hard. In this paper, we restrict our attention to the MWVCP3 problem and present a multi-start iterated greedy algorithm to solve the MWVCP3 problem. The experiments are carried out on randomly generated instances with up to 1000 vertices and 250000 edges. Our work is the first one to adopt heuristic algorithms to solve the MWVCP3 problem, and the experimental results show that our algorithm performs reasonably well in practice. (C) 2019 Elsevier Inc. All rights reserved.

关键词： iterated greedy algorithm Minimum weight vertex cover P-3 problem Heuristic algorithms Combinatorial optimization problems

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AN iterated greedy algorithm WITH VARIABLE NEIGHBORHOOD DESCENT FOR THE PLANNING OF SPECIALIZED DIAGNOSTIC SERVICES IN A SEGMENTED HEALTHCARE SYSTEM

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2020年第2期16卷 857-885页

作者： Mendoza-Gomez, Rodolfo Rios-Mercado, Roger Z. Valenzuela-Ocana, Karla B. Tecnol Monterrey Dept Ind Engn Campus TolucaAv Eduardo Monroy Cardenas 2000 San Antonio Buenavista 50110 Toluca Mexico Univ Autonoma Nuevo Leon Grad Program Syst Engn Av Univ S-N San Nicolas De Los Garza 66455 Nuevo Leon Mexico

In this paper, a problem arising in the planning of specialized diagnostic services in a segmented public healthcare system is addressed. The problem consists of deciding which hospitals will provide the service and their capacity levels, the allocation of demand in each institution, and the reallocation of uncovered demand to other institutions or private providers, while minimizing the total equivalent annual cost of investment and operating cost required to satisfy all the demand. An associated mixed-integer linear programming model can be solved by conventional branch and bound for relatively small instances;however, for larger instances the problem becomes intractable. To effectively address larger instances, a hybrid metaheuristic framework combining iterated greedy (IGA) and variable neighborhood descent (VND) components for this problem is proposed. Two greedy construction heuristics are developed, one starting with an infeasible solution and iteratively adding capacity and the other starting with a feasible, but expensive, solution and iteratively decrease capacity. The iterated greedy algorithm includes destruction and reconstruction procedures. Four different neighborhood structures are designed and tested within a VND procedure. In addition, the computation of local search components benefit from an intelligent exploitation of problem structure since, when the first-level location variables (hospital location and capacity) are fixed, the remaining subproblem can be solved efficiently as it is very close to a transshipment problem. All components and different strategies were empirically assessed both individually and within the IGA-VND framework. The resulting metaheuristic is able to obtain near optimal solutions, within 3% of optimality, when tested over a data base of 60- to 300-hospital instances.

关键词： Healthcare planning integer programming hybrid metaheuristics iterated greedy algorithm variable neighborhood descent

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An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 152卷 107021-107021页

作者： Huang, Ying-Ying Pan, Quan-Ke Huang, Jiang-Ping Suganthan, P. N. Gao, Liang Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representation, we present an effective initialization procedure by combining the well-known NEH heuristic and Palmer method. Secondly, in order to improve the efficiency of the algorithm, both the destruction reconstruction process and the local search process are elaborately designed for the products and jobs separately. Next, our algorithm adaptively extracts jobs in the destruction stage with regard to the size of instances. In addition, we employ a novel selection method that is based on the objective values and the ages of the individuals in the population. Through a total of 810 benchmark instances, the proposed algorithm is compared with seven state-of-the-art algorithms in the literature. The experimental results show that the proposed algorithm performs significantly better than the other algorithms in comparison by three analytical methods for solving the DAPFSP with TF criterion.

关键词： Distributed assembly permutation flowshop scheduling Total flowtime iterated greedy algorithm Heuristic Local search

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