A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem

Ahmadi, Ehsan; Goldengorin, Boris; Süer, Gürsel A.; Mosadegh, Hadi

doi:10.1016/j.asoc.2017.12.045

Cited by 40 publications

(31 citation statements)

References 40 publications

(106 reference statements)

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“…Additionally, the use of a heuristic approach to solve the job sequencing and tool-switching problem (SSP) has been reported in Paiva and Carvalho (2017). In another study (Ahmadi et al 2018), SSP has been modelled as a secondorder travelling-salesman problem, and the same has been solved using a learning-based GA.…”

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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“…Population-based methods have also been successful. Hybrid methods combining genetic algorithms (GA) with other search procedures can be found in [2,4,5,6,7,8,18]. Amaya et al [4,6,7] combined GA with local search-based procedures such as hill climbing, simulated annealing and tabu search, leading to hybrid methods which are also known under the name of memetic algorithms.…”

Section: Related Studiesmentioning

confidence: 99%

“…Amaya et al [5,6,8] combined genetic algorithms with a multi-agent approach or cross-entropy methods. Finally, Chaves et al [18] combined clustering search (CS) with a biased random-key genetic algorithm (BRKGA), while Ahmadi et al [2] combined a 2-TSP scheme with a novel learning-based GA. Table 1 summarizes the SSP studies in chronological order, lists their main contributions as well as the origin of the benchmark instances considered.…”

Section: Related Studiesmentioning

confidence: 99%

A simple and effective hybrid genetic search for the job sequencing and tool switching problem

Mecler¹,

Subramanian²,

Vidal³

2019

Preprint

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The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.

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“…Recently, Ahmadi et al (2018) showed that the SSP can be formulated as a TSP of second order (2-TSP). They present a dynamic Q-learning-based GA that is seeded by the solutions obtained from solving the SSP as 2-TSP.…”

Section: The Uniform Sspmentioning

confidence: 99%

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

Calmels

2018

International Journal of Production Research

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The job sequencing and tool switching problem is a combinatorial optimisation problem that appears in various industries, mainly in the manufacturing sector. Although tool switching is only part of a much larger decision-making process in manufacturing systems, it has a major impact on the overall performance of the system by affecting the total set-up time as well as the machine and tool utilisation. Over the past few decades, various approaches have been applied to tool switching problems. This study provides a comprehensive review of the literature on the job sequencing and tool switching problem. Studies are identified and compared through a structured literature review and classified according to a new framework which allows the identification of problem perspectives and solution methods. The results show that current research does not fully exploit real-world situations and studies are often limited to a single-machine and uniform tool size and set-up time. Finally, this literature review summarises the current research results and provides directions for future research.. comparatively review, in an organised fashion, the existing unclassified tool switching literature with respect to problem formulation development and applied solution methods, . develop a classification scheme that may help to identify aspects of the tool switching problem that have been insufficiently explored and show how far the existing modelling efforts have progressed towards real-world needs, . synthesise the past trends of tool switching studies, and . offer some suggestions and directions for future research.The remainder of this paper is structured as follows. Section 2 introduces the generic tool switching problem, its subproblems, and related optimisation problems. Subsequently, the research approach is described in section 3, and the classification framework is described in section 4. The literature review is presented in section 5. Section 6 outlines the research gaps and implications for future research. Section 7 concludes the paper with a short summary.

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A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem

Cited by 40 publications

References 40 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

A simple and effective hybrid genetic search for the job sequencing and tool switching problem

The job sequencing and tool switching problem: state-of-the-art literature review, classification, and trends

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