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A Job Sequence Optimization Approach for parallel machine scheduling problem in Printing Manufacturing Systems

IEEE ACCESS 2024年 12卷 63462-63476页

作者： Li, Huailin Zheng, Yingying Sun, Bangyong Du, Bin Xian Univ Technol Fac Printing Packaging Engn & Digital Media Techno Xian 710048 Peoples R China Shaanxi Prov Key Lab Printing & Packaging Engn Xian 710048 Peoples R China

This paper investigated a parallel machine scheduling problem in the printing manufacturing systems due to operational requirements on the color-batching of the printed matter and sequential requirements on the sequence adherence of a printing press. Resequencing printing jobs as color-oriented batches reduced the costs of color changes and operational costs for printing shop. Also, post press workshop required printing shops to print jobs with minimal makespan so that high sequence adherence with its demand is assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and minimal makespan-in a parallel high-fidelity printing press scheduling environment. A job sequence optimization approach is proposed. Moving interpolation algorithm and color sequence mapping algorithm are designed to reduce frequency of replacing ink. Base on iterative greedy algorithm, color sequence job groups with the same or similar color sequence are scheduled to the printing presses. Three experiments are conducted and showed that the proposed approach has good feasibility and effectiveness, and convergence in solving minimum completion time. Therefore, the proposed approach can be used in printing shops to improve scheduling efficiency and reduce production costs.

关键词： Printing manufacturing systems parallel machine scheduling problem minimal makespan color sequence comparison iterated greedy algorithm

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Simulation-based deep reinforcement learning for multi-objective identical parallel machine scheduling problem

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INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING 2024年 16卷

作者： Nam, Sohyun Cho, Young-in Woo, Jong Hun Seoul Natl Univ Dept Naval Architecture & Ocean Engn Seoul South Korea Seoul Natl Univ Res Inst Marine Syst Engn Seoul South Korea

In the shipbuilding industry, traditional optimization studies based on linear programming and constraint programming have been conducted to solve mid-term or long-term scheduling problems. However, due to the extensive computational time, these methods face limitations in addressing short-term scheduling problems for the unit production systems of shipbuilding processes, where various environmental uncertainties must be considered. This study employs a deep reinforcement learning approach to develop a dynamic scheduling algorithm for the welding process in profile shops, considering the random arrival of materials and variability in processing time. The scheduling problems of the welding process are formulated as multi-objective identical parallel machine scheduling problems, aimed at minimizing both setup time and tardiness. This study proposes a novel Markov decision process model for the multi-objective scheduling problems for the welding process, incorporating setup requirements and due date-related constraints into the state representation, action modelling, and reward design. Additionally, based on the proposed Markov decision process model, this study develops a learning environment in which a discrete-event simulation model of the welding process is integrated for state transition considering the uncertainties in the welding process. In the training phase of the scheduling agent, the Proximal Policy Optimization algorithm is applied to learn the scheduling policy, which is approximated by deep neural networks. The performance of the proposed algorithm is validated in comparison to four priority rules (SSPT, ATCS, MDD, and COVERT) for various test scenarios with different workloads and levels of variability in processing time.

关键词： parallel machine scheduling problem Deep reinforcement learning Discrete-event simulation Proximal policy optimization

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The parallel machine scheduling problem with Different Speeds and Release Times in the Ore Hauling Operation

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ALGORITHMS 2024年第8期17卷 348页

作者： Tarazona-Torres, Luis Amaya, Ciro Paipilla, Alvaro Gomez, Camilo Alvarez-Martinez, David Univ Andes Ind Engn Dept Cra 1 N18A12 Bogota 111711 Colombia

Ore hauling operations are crucial within the mining industry as they supply essential minerals to production plants. Conducted with sophisticated and high-cost operational equipment, these operations demand meticulous planning to ensure that production targets are met while optimizing equipment utilization. In this study, we present an algorithm to determine the minimum amount of hauling equipment required to meet the ore transport target. To achieve this, a mathematical model has been developed, considering it as a parallel machine scheduling problem with different speeds and release times, focusing on minimizing both the completion time and the costs associated with equipment use. Additionally, another algorithm was developed to allow the tactical evaluation of these two variables. These procedures and the model contribute significantly to decision-makers by providing a systematic approach to resource allocation, ensuring that loading and hauling equipment are utilized to their fullest potentials while adhering to budgetary constraints and operational schedules. This approach optimizes resource usage and improves operational efficiency, facilitating continuous improvement in mining operations.

关键词： parallel machine scheduling problem ore hauling equipment mathematical model mining industry

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Multiobjective pseudo-variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2020年第3期27卷 1478-1500页

作者： de Queiroz, Thiago Alves Mundim, Leandro Resende Fed Univ Goias Catalao Inst Math & Technol Av Dr Lamartine P Avelar 1120 BR-75704020 Catalao Go Brazil Univ Sao Paulo Inst Math & Comp Sci Av Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil

A multiobjective variable neighborhood descent (VND) based heuristic is developed to solve a bicriteria parallel machine scheduling problem. The problem considers two objectives, one related to the makespan and the other to the flow time, where the setup time depends on the sequence, and the machines are identical. The heuristic has a set of neighborhood structures based on swap, remove, and insertion moves. We propose changing the local search inside the VND to a sequential search through the neighborhoods to obtain nondominated points for the Pareto-front quickly. In the numerical tests, we consider a single-objective version of the heuristic, comparing the results on 510 benchmark instances to show that it is quite effective. Moreover, new instances are generated in accordance with the literature for the bicriteria problem, showing the ability of the proposed heuristic to return an efficient set of nondominate solutions compared with the well-known nondominated sorting genetic algorithm II.

关键词： multiobjective variable neighborhood descent parallel machine scheduling problem sequence-dependent setup time makespan flow time

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Modeling and solving for bi-objective cutting parallel machine scheduling problem

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ANNALS OF OPERATIONS RESEARCH 2020年第1-2期285卷 223-245页

作者： Meng, Ronghua Rao, Yunqing Luo, Qiang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Key Lab Hydroelectr Machinery Design & Main Yichang 443002 Peoples R China

This paper addresses a bi-objective cutting parallel machine scheduling problem aiming to minimize the total makespan and total tardiness. This problem is inspired from a structural metal-cutting plant that combines identical and unrelated parallel machine scheduling problems. To formulate this complicated problem, a new mixed-integer programming (MIP) model is presented in consideration of total makespan and total tardiness. The machine-job-dependent processing times are considered along with the setup times, pickup times, different delivery times, and machine eligibility constraints. Owing to the complex characteristics of the problem, an appropriate non-dominated sorting Genetic Algorithm III (NSGAIII) with an embedded variable neighborhood structure strategy (VNSGAIII) is developed. A number of randomly generated datasets are used to test the performance of VNSGAIII in comparison with NSGAII, and NSGAIII on solving the engineering problem addressed herein. The experimental results demonstrate that the suggested VNSGAIII statistically outperforms the compared algorithms, especially in the distribution of Pareto solutions. The epsilon-constrained method is implemented in the direct MIP model by CPLEX for comparison with the proposed evolutionary algorithms. The proposed algorithm performs efficiently when obtaining the Pareto solutions.

关键词： parallel machine scheduling problem NSGA III Bi-objective problem Makespan Tardiness

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Hybrid Discrete Teaching-Learning-Based Optimization Algorithm for Solving parallel machine scheduling problem with Multiple Constraints 14th

Hybrid Discrete Teaching-Learning-Based Optimization Algorit...

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14th International Conference on Intelligent Computing (ICIC)

作者： He, Yu-Jie Qian, Bin Liu, Bo Hu, Rong Deng, Chao Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9783319959290;9783319959306

This paper proposes a hybrid discrete teaching-learning-based optimization algorithm (HDTLBO) for solving the parallel machine scheduling problem with arrival time, multiple operations and process restraints (PMSP_AMP), which widely exists in the various manufacturing process. The criterion is to minimize the maximum completion time (i.e., makespan). Firstly, a discretization method is designed to remold the standard teaching-learning-based optimization algorithm, which enhances its global exploration ability and makes it can execute the global search directly in the discrete solution space. Then, the swap-based and the insert-based neighborhood are utilized to construct the local search for performance improvement. Simulation results and comparisons based on s set of random instances demonstrate the effectiveness and searching quality of the presented HDTLBO.

关键词： parallel machine scheduling problem Multiple operations Arrival times Discrete teaching-learning-based optimization

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parallel machine scheduling problem of Flexible Maintenance Activities with Fuzzy Random Time Windows 10th

Parallel Machine Scheduling Problem of Flexible Maintenance ...

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10th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Nie, Ling Chengdu Univ Sch Tourism Econ & Management Chengdu 610106 Sichuan Peoples R China

ISBN: (纸本)9789811018374;9789811018367

In this paper, a scheduling problem on parallel machines subject to fuzzy random time windows was studied. The main purpose was to meet demands of both production scheduling and maintenance planning. Then, there were two objectives considered, minimizing the makespan and maximizing average timeliness level. First, a fuzzy random variable for maintenance time windows was considered, and this model was then transformed using the expected value. Then, an instance of the algorithm was demonstrated to illustrate the applicability of the proposed approach and the results obtained were highly favourable.

关键词： parallel machine scheduling problem Fuzzy random Maintenance Timeless level

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An ant colony optimization approach for the parallel machine scheduling problem with outsourcing allowed

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JOURNAL OF INTELLIGENT MANUFACTURING 2015年第3期26卷 527-538页

作者： Tavares Neto, Roberto Fernandes Godinho Filho, Moacir da Silva, Fabio Molina Univ Fed Sao Carlos Dept Prod Engn BR-13565905 Sao Carlos SP Brazil

Several manufacturing environments can be represented as a set of identical parallel machines. Moreover, some industries uses third-part manufacturing to increase the production capacity for short periods. This paper proposes, implements and evaluates an ACO algorithm to solve the parallel machine scheduling problem with outsourcing allowed. The goal is to minimize the sum of outsource and delay costs (since, in many practical situations, the delay generates fine). To the best of our knowledge, this work is the first to address this problem. In order to evaluate the algorithm proposed, a mathematical programming model of the problem is also presented and implemented. The ACO algorithm proposed is composed of three sequential transition rules, each one responsible for one different decision: the first one decides the next job to be scheduled;the second decides the machine to schedule a job and the third decides if the job must be outsourced or not. Computational results show that this algorithm, for instances of size larger or equal to 20 jobs, could reach better solutions than the ones found using the mathematical programming method when the commercial solver used has its running time limited by 1 h. Moreover, the times required to reach a solution were significantly smaller when the ACO is executed.

关键词： Ant colony optimization parallel machine scheduling problem Outsourcing

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An evolution strategies-based reinforcement learning algorithm for multi-objective dynamic parallel machine scheduling problems

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Chen, Yarong Zhang, Junjie Mumtaz, Jabir Huang, Shenquan Zhou, Shengwei Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The multi-objective dynamic parallel machine scheduling (PMS) problem is a complex combinatorial optimization challenge encountered in manufacturing systems. Various uncertainties exist in the real-world dynamic PMS problem, such as job release time, processing time, and flexible preventive maintenance for machines. The goal is simultaneously optimizing multiple objectives under dynamic and uncertain environments, such as makespan, total tardiness, and energy consumption. This paper proposes an evolution strategies-based reinforcement learning (ESRL) algorithm to address the current multi-objective dynamic PMS problem. The proposed algorithm leverages the exploration capabilities of evolution strategies to evolve effective policies for reinforcement learning in dynamic scheduling. Moreover, the efficiency of the ESRL algorithm is enhanced by implanting three features: a) train the policy to iteratively produce the sequence directly and mitigate the sparse reward issue resulting from the symmetry inherent in the given problem;b) a multi-agent system with independent interaction and centralized training to generate the PMS policy simultaneously;c) a non-dominated sorting mechanism to determine fitness function. Extensive computational experimental results show that the ESRL algorithm outperforms the comparison state-of-the-art evolutionary algorithms and priority dispatching rules in terms of solution quality, convergence, and efficiency, with the advantage of the C-matrix exceeding 60 %, and the advantages in GD and NR surpassing 50 %. Furthermore, ablation experiments demonstrate the significant contributions of additional features in ESRL in enhancing the algorithm's performance. Meanwhile, the results of generalization experiments indicate that the ESRL quickly generates Pareto optimal solutions allowing the trained model to make optimal scheduling decisions.

关键词： parallel machine scheduling problem Evolution strategies Multi-agent, reinforcement learning Multi-objective optimization Energy consumption

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New representation schemes for identical parallel machine scheduling problems with sequence dependent setup times

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第2期38卷 1041-1054页

作者： Takan, Arda Sarac, Tugba Eskisehir Osmangazi Univ Fac Engn & Architecture Dept Ind Engn TR-26480 Eskisehir Turkiye

In this study, an identical parallel machine scheduling problem (IPMSP) with sequence-dependent setup times, which is significantly crucial in literature, is studied. There are different heuristics and metaheuristics for the problem in the literature. The representation method used in these studies is usually the permutation type representation, where each number corresponds to a job. The order of these numbers represents the order of the jobs in the machines. In this study, new solution representations are presented. A classical genetic algorithm and two new genetic algorithms using the proposed solution representations are compared by using randomly generated instances to show the success of the proposed representations. Diversification of the solution space is expanded, and the same results are eliminated with these solution representations. Specifically, the new algorithms generate better results than the classical genetic algorithm for large sized problems.

关键词： parallel machine scheduling problem metaheuristic algorithms genetic algorithms representation scheme

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