A matheuristic for parallel machine scheduling with tool replacements

Dang, Quang-Vinh; Diessen, Thijs van; Martagan, Tugçe; Adan, I.J.B.F.

doi:10.1016/j.ejor.2020.09.050

Cited by 24 publications

(11 citation statements)

References 44 publications

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“…Wang, Zou, and Wang (2020) found the optimal solutions to a parallel machine scheduling problem combining operation scheduling, tool scheduling and restrained resources are obtained by a Tabu-Genetic algorithm. Dang et al (2021) proposed a mathematical model and combination of a genetic algorithm and an integer linear programming formulation to solve industry-size instances of parallel machine scheduling with tool replacements.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

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Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices-the "tool-indexing problem". Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation toolindexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

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“…ird, an excellent variant of GP in the scheduling domain [41], genetic programming hyperheuristic (GP-HH), is chosen to be compared with GP-SS. Finally, two state-of-the-art algorithms designed for PMS, greedy randomized adaptive search procedure with variable neighborhood search (GRASP-VNS) [42] and improved genetic algorithm (IGA) [43], are also chosen to evaluate the performance of our proposed GP-SS.…”

Section: Comparison Experimentsmentioning

confidence: 99%

“…GRASP-VNS [42] and IGA [43] are two recent state-of-theart algorithms designed for the PMS problem. ese two algorithms are compared with GP-SS, and the results are shown in Table 8.…”

Section: Comparison With Other Pms Scheduling Algorithmsmentioning

confidence: 99%

An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

Zhan

Zhang

Cui³

et al. 2021

Discrete Dynamics in Nature and Society

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In current environments, production systems need the ability of quick response to face the volatile markets. Seru production systems, as a new mode of the production system, have the advantages of quick response, high flexibility, and high efficiency. Seru scheduling, which refers to constructing serus with an exact sequence in limited workspace, is an important decision problem in the operational management of seru production systems and can reflect the reconfiguration nature of seru production systems. This study investigates a seru scheduling problem with resource conflicts, whose objective is to minimize the makespan. An automatic heuristic design approach that combines a genetic programming algorithm and structure similarity-based operators is proposed. Comparative experiments are conducted with human-made rules, basic genetic programming, genetic programming-based algorithm, and with some state-of-the-art scheduling algorithms. The results show the effectiveness and efficiency of the proposed algorithm.

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“…Moreover, the authors propose a combination of a GA and a dispatching rule called Apparent Tardiness Cost (GA_ATC). Dang et al [23] also deal with the JSP of parallel machines with tool replacements to schedule a set of jobs with tool requirements on identical parallel machines in a work center. To do so, the authors propose a mathematical model for the problem and a matheuristic that combines a GA and an integer linear programming (ILP) formulation to solve large datasets.…”

Section: Literature Review: Matheuristic Resolution Approachesmentioning

confidence: 99%

Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

2021

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This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.

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A matheuristic for parallel machine scheduling with tool replacements

Cited by 24 publications

References 44 publications

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

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