Two-agent scheduling on a single parallel-batching machine to minimize the weighted sum of the agents’ makespans

Zhang, Cui-Lin; Wang, Jun Qiang; Zhang, Cheng-wu

doi:10.1007/s12652-018-0741-3

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…We examine the DJSP characteristics and solution approach in the field. The interested readers are referred to recent review papers [26][27][28][29][30][31][32][33][34][35] in the field. The relevant studies in the literature are summarized under three main categories as the static problem, the dynamic problem, and DJSP.…”

Section: Literature Summarymentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

et al. 2023

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In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in–first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 × 3, 5 × 5, and 10 × 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.

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Section: Literature Summarymentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

et al. 2023

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“…Further, the weighted objective method adopts the classic rules, the construction rules or sublots [4,5]. To illustrate, Zhang et al [6] scheduled the jobs from two agents with equal processing times and non-identical job sizes on a single parallel-batching machine by using the objective of minimize the weighted sum of the two Makespan of the jobs from two agents. Speci cally, Zhang et al proved that both the absolute worst-case ratio and the asymptotic worst-case ratio was the weight of one agent [7].…”

Section: E Description Ofmentioning

confidence: 99%

“…C max f − c 1 + 2 × c 2 + c 3 /4 c 1 : e best Makespan; c 2 : e most possible Makespan; c 3 : e worst Makespan. 6e robustness of the Makespan[65],C max r C max r − max (c 2 − c 1 ), (c 3 − c 1 ) 􏼈 􏼉7e expected weighted sum of Makespan[6],C max w− s C max w− s − αC max A + (1 − α)C max B αC max A : Equipment A's Makesapn; (1 − α)C max B : Equipment B's C max m− l − 􏽐t 2e d− p + t mat t 2e d− p : Secondary manufacturer's completion time; t mat : e secondary manufacturer matches the transport time of the primary manufacturer.…”

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confidence: 99%

Critical Review on the Objective Function of Flexible Job Shop Scheduling

Pei

Zhang

Mei

et al. 2022

Mathematical Problems in Engineering

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The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP.

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Two-agent scheduling on bounded parallel-batching machines with an aging effect of job-position-dependent

et al. 2019

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Two-agent scheduling on a single parallel-batching machine to minimize the weighted sum of the agents’ makespans

Cited by 8 publications

References 33 publications

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

Critical Review on the Objective Function of Flexible Job Shop Scheduling

Two-agent scheduling on bounded parallel-batching machines with an aging effect of job-position-dependent

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