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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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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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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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An iterated greedy algorithm for the planning of yarn-dyeing boilers

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第1期31卷 115-139页

作者： Demir, Yunus Bursa Tech Univ Dept Ind Engn Bursa Turkiye

Yarn dyeing is a critical link in the textile production chain that consumes the most time and energy. Today's dyeing shops receive hundreds of demands with thousands of different colors, different due dates, and different production requirements. This situation has made it very difficult for the human brain to create a minimum-cost production plan by complying with the due dates. In this study, a real-life problem of a company operating in the textile industry is discussed and a solution has been developed for the planning of yarn-dyeing boilers. The application was held in Bursali Textile, which is the major towel manufacturer operating in Turkey. The problem dealt with is basically in the nature of the variable-size bin-packing problem (VSBPP). The limited availability of bins (boilers) of different sizes and the packing of the items (yarn work orders) with due date constraints are the original aspects of this study. Multi-objective mixed integer programming model is developed to minimize two objectives. For the solution, the preemptive method called the lexicographic approach, in which the objectives are solved in order, is preferred. As the first objective, the overcapacity usage is minimized and then the second objective, which is the boiler usage cost, is minimized. Given that the VSBPP is strongly NP-hard, an iterated greedy algorithm with two different decoding approaches is proposed. Computational experiments were conducted on 20 randomly generated benchmark instances and a real-world industrial dataset. The numerical results show that good solutions can be obtained in seconds using the proposed approaches.

关键词： yarn bobbin dyeing mixed integer programming iterated greedy algorithm variable-sized bin packing

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An iterated greedy algorithm with acceleration of job allocation probability for distributed heterogeneous permutation flowshop scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 88卷

作者： Li, Haoran Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl Key Lab Intelligent Mfg Equipment & Technol State Key Lab Digital Man Wuhan 430074 Hubei Peoples R China

Distributed manufacturing paradigm is becoming one of the dominant manufacturing models today. Heterogeneities between factories exist in fact due to the differences in process flexibility and machine performance. Most research focuses on identical distributed factories and overlooks the influence of heterogeneous factories on the subproblem of job allocation. This paper investigates a distributed heterogeneous permutation flowshop scheduling problem (DHPFSP) to minimize the makespan, and a mixed -integer linear programming (MILP) model is established. Building upon a special phenomenon of job assignment to a specific heterogeneous factory in DHPFSP, an iterated greedy algorithm with acceleration of job allocation probability (IGP) is proposed. To utilize this phenomenon, a job allocation probability matrix is introduced to describe the trend of job allocation, and a fast pass strategy with job allocation probability is suggested to speed up the algorithm search process. Furthermore, to generate a high -quality initial solution quickly for IGP, an NEH-based heuristic framework for distributed heterogeneous factory environment (NEHDHF) is proposed. In NEHDHF, several distinct initial job sequence generation strategies and a new first -f job allocation strategy are proposed for obtain a better initial solution. In computational experiments, there are 720 test instances designed and publicized. The IGP achieves 535 best solutions out of 720 instances. Statistically sound results demonstrate the effectiveness of the presented algorithms for the considered problem.

关键词： Distributed heterogeneous permutation flowshop Acceleration of job allocation probability NEH-based heuristic framework iterated greedy algorithm

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Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Yu, Fei Lu, Chao Zhou, Jiajun Yin, Lvjiang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (TTD). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality;(2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution;(3) A knowledge-based destruction and construction is designed to improve the exploration capability;(4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors.

关键词： Distributed hybrid flow shop scheduling problem Assembly shop problem Dual-resource constraints iterated greedy algorithm Mixed-integer linear programming model

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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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Solving Stochastic Online Food Delivery Problem via iterated greedy algorithm With Decomposition-Based Strategy

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第2期53卷 957-969页

作者： Zheng, Jie Wang, Ling Wang, Li Wang, Shengyao Chen, Jing-Fang Wang, Xing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Meituan Dept Delivery Technol Beijing 100102 Peoples R China

Online food delivery (OFD) service has developed rapidly due to its great convenience for customers, the enormous markets for restaurants and the abundant job openings for riders. However, OFD platforms are encountering enormous challenges, such as massive demand, inevitable uncertainty and short delivery time. This article addresses an OFD problem with stochastic food preparation time. It is a complex NP-hard problem with uncertainty, large search space, strongly coupled subproblems, and high timeliness requirements. To solve the problem, we design an iterated greedy algorithm with a decomposition-based strategy. Concretely speaking, to cope with the large search space due to massive demands, a filtration mechanism is designed by preliminarily selecting suitable riders. To reduce the risk affected by the uncertainty, we introduce a risk-measuring criterion into the objective function and employ a scenario-sampling method. For timeliness requirements caused by short delivery time, we design two time-saving strategies via mathematical analysis, i.e., an adaptive selection mechanism to choose the method with less computational effort and a fast evaluation mechanism based on the small-scale sampling and machine learning model to speed up evaluation. We also prove an upper bound of the stochastic time cost under risk measurement as one of the baseline features to improve the prediction accuracy. The experiments on real-world data sets demonstrate the effectiveness of the proposed algorithm.

关键词： Stochastic processes Uncertainty Costs greedy algorithms Computational modeling Search problems Prediction algorithms Decomposition-based strategy fast evaluation mechanism iterated greedy algorithm machine learning model stochastic online food delivery problem (SOFDP)

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A novel iterated greedy algorithm for no-wait permutation flowshop scheduling to minimize weighted quadratic tardiness

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ENGINEERING OPTIMIZATION 2023年第12期55卷 2070-2083页

作者： Prata, Bruno de Athayde Nagano, Marcelo Seido Univ Fed Ceara Dept Ind Engn Fortaleza Brazil Univ Sao Paulo Dept Prod Engn Sao Carlos Brazil

This article addresses the no-wait permutation flowshop layout where the jobs must be processed continuously in the available machines without interruption. The objective function is weighted quadratic tardiness minimization. Since the problem under study is NP-hard, an iterated greedy algorithm is introduced where the parameters are selected according to a variable neighbourhood descent framework. In the authors' innovative algorithm, in the first iteration of the search process, the parameters are initialized with a fixed number. In the remaining iterations, such parameters are reduced deterministically, aiming to explore distinct values throughout the search process. The proposal is compared with five other algorithms proposed for closely related problems, considering two performance measures: the Relative Deviation Index (RDI) and the Success Rate (SR). The proposed algorithm produced average values of RDI and SR of 1.2% and 86.9%, respectively. The computational results demonstrated that the authors' proposal outperformed all the other five algorithms under comparison.

关键词： Production scheduling weighted quadratic tardiness no-wait flowshop iterated greedy algorithm

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Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced iterated greedy algorithm

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Complex System Modeling and Simulation 2023年第4期3卷 282-306页

作者： Yong Wang Yuting Wang Yuyan Han Junqing Li Kaizhou Gao Yusuke Nojima School of Computer Science Liaocheng UniversityLiaocheng 252000China School of Computer Science Shandong Normal UniversityJinan 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant ***,in actual scheduling,some adjacent machines do not have buffers between them,resulting in *** paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production *** solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi ***,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking *** balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are ***,each factory is mutually independent,and the movement within one factory does not affect the *** view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the ***,two shaking strategies are incorporated into the algorithm to mitigate premature *** advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

关键词： blocking distributed hybrid flow shop neighborhood search iterated greedy algorithm

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