Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

Nouri, Houssem Eddine; Driss, Olfa Belkahla; Ghédira, Khaled

doi:10.1016/j.cie.2016.02.024

Cited by 88 publications

(23 citation statements)

References 28 publications

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“…A machine selection heuristic and a vehicle assignment heuristic were incorporated in the differential evolution approach to assign tasks to appropriate machines and vehicles and to minimize cycle time. Similar research works can be found in Nouri et al [15], Ahmadi-Javid and Hooshangi-Tabrizi [16], Lin et al [17], Lacomme et al [18], and Zhang and Li et al [19]. Ahmadi-Javid and Hooshangi-Tabrizi [20] addressed a ternary integration scheduling problem, which incorporated employee timetabling into the schedules of machines and transportation systems in a job shop environment.…”

Section: Introductionsupporting

confidence: 65%

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Xiao

Zeng

et al. 2020

Mathematical Problems in Engineering

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In the current industrial fields, automatic guided vehicles (AGVs) are widely employed to constitute the flexible manufacturing system (FMS), owing to their great advantages of routing flexibility and high efficiency. However, one main challenge lies in the coupling process of the design problem of the unidirectional guide-path network (UGN) and the task scheduling problem of AGVs. To reduce the complexity, most pertinent literatures only handle these problems one by one, based on the stepwise design methods, thereby neglecting the constraint conditions and the optimization objectives caused by the FMS environment. The motivation of the paper is to bring the coupling factors into the integrated design and solution process. Firstly, an integrated design model of designing UGN and scheduling AGVs simultaneously is proposed, with the objective of minimizing the makespan (i.e., the maximum completion time of all handling tasks), in the consideration of the practical constraints, e.g., the job handling and processing sequence constraints and the AGV number constraint. Secondly, a dual-population collaborative evolutionary genetic algorithm (CEGA) is developed to solve the problems of designing and scheduling in a parallel way. The solutions of the integrated model, i.e., the potential strongly connected UGN and the feasible processing and handling sequence, are, respectively, coded as two different subpopulations with independent and concurrent evolution processes. The neighbourhood search operation, the niche technique, and the elitism strategy are combined to improve the convergence speed and maintain the population diversity. The experimental results show that the integrated design model can formulate the problem more accurately, and the CEGA algorithm is computationally efficient with high solution quality.

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Section: Introductionsupporting

confidence: 65%

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Xiao

Zeng

et al. 2020

Mathematical Problems in Engineering

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“…The algorithms are applied for test problems taken from the literature and the results obtained using the two algorithms are compared. Nouri et al [6] introduced the clustered holonic multiagent model using metaheuristic for simultaneous scheduling of machines and transport robot in FMS. Computational results are presented using three sets of benchmark instances in the literature.…”

Section: A Simultaneous Scheduling In Fmsmentioning

confidence: 99%

Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

Rao¹,

Kumari²,

Manohar³

et al. 2020

IJEAT

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High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems-a new metaheuristic Ant Colony Optimization (ACO) algorithm is proposed.

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“…The algorithms are implemented for bench mark problems taken from the literature and the comparision is also done. Nouri et al [6] introduced the clustered holonic multiagent model using metaheuristic for simultaneous scheduling of machines and transport robot in FMS. Computational results are presented using three sets of benchmark instances in the literature.…”

Section: A Simultaneous Scheduling In Fmsmentioning

confidence: 99%

Scheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm

Babu¹,

Babu²,

Rao³

2019

IJEAT

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The most complex problem in FMS is scheduling task, due to this complexity it has created interest among many researchers. Even though FMS scheduling problem was considered earlier, material handling systems like (AGVs) scheduling was not done effectively. As transportation times cannot be neglected in an FMS, a carefully managed and designed material handling system is important in achieving the required integration in flexible manufacturing environment. Hence there is a need for scheduling both the machines and material handling system simultaneously for the successful implementation of an FMS, which makes the scheduling of FMS more complex. Metaheuristic Algorithms are mostly received by the researchers, because of their capability to tackle more complex problems. Hybridization of the metaheuristics may further improve their performance. In the present work a new hybrid metaheuristic Teaching Learning based optimization(HTLBO) is proposed to solve simultaneous scheduling problems.

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Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

Cited by 88 publications

References 28 publications

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

Scheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm

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