Um algoritmo enumerativo baseado em ordenamento parcial para resolução do problema de minimização de trocas de ferramentas

Yanasse, Horácio Hideki; Rodrigues, Rita de Cássia Meneses; Senne, Edson Luiz França

doi:10.1590/s0104-530x2009000300005

Cited by 12 publications

(35 citation statements)

References 21 publications

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“…Examples of the four neighborhood structures for MTSP. [19,16]). The ILS [16] does not find the optimal solution for two instances of C and one instance of E. For group D (Table 6), the CS þBRKGA found optimal solutions for 189 of 260 instances (enumeration proved optimality of only those 189 instances) while ILS [16] did not find optimal solutions for 13 of these 189 instances.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The instances of Crama et al [2] were solved by enumeration [18,19], the ILS [16], the BRKGA, and CS þBRKGA. For each problem size ðN; M; CÞ, 10 instances were generated.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Table 8 also shows the number of tool switches required by the best sequence found by the heuristics of Crama et al [2]. The enumeration algorithm [18,19], limited to 3600 s of computational time, was able to get the optimal solution only for instances with 10 and 15 jobs. For these instances, the ILS and CS þBRKGA found all optimal solutions.…”

Section: Computational Resultsmentioning

confidence: 99%

“…This paper reports results found by BRKGA and CS on over 1510 instances for the MTSP. The CS þ BRKGA found optimal solutions for 1360 instances (proven by [19,16]) and the best known solutions for the others. The results show that the CS þ BRKGA is competitive for solving the MTSP.…”

Section: Discussionmentioning

confidence: 99%

“…Laporte et al [17] report that just a few instances among a set of 25 jobs instances were solved to optimality using the branch-and-bound scheme they proposed. Yanasse and Rodrigues [18], Yanasse et al [19] present an enumeration algorithm based on partial orders that obtained good results on instances in which the algorithm of Laporte et al [17] failed. The computational results of Chaves et al [16] show that the hybrid heuristic contributes to a significant reduction in the number of nodes in the tree of the enumeration algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Chaves

Lorena

Senne

et al. 2016

Computers & Operations Research

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The minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA.

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Section: Computational Resultsmentioning

confidence: 99%

“…The instances of Crama et al [2] were solved by enumeration [18,19], the ILS [16], the BRKGA, and CS þBRKGA. For each problem size ðN; M; CÞ, 10 instances were generated.…”

Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Chaves

Lorena

Senne

et al. 2016

Computers & Operations Research

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

Ahmadi

Goldengorin

Süer

et al. 2018

Applied Soft Computing

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One of the well-known problems in single machine scheduling context is the Job Sequencing and Tool Switching Problem (SSP). The SSP is optimally sequencing a finite set of jobs and loading restricted subset of tools to a magazine with the aim of minimizing the total number of tool switches. It has been proved in the literature that the SSP can be reduced to the Job Sequencing Problem (JSeP). In the JSeP, the number of tool switches from the currently processed job to the next job depends on the sequencing of all predecessors. In this paper, the JSeP is modeled as a Traveling Salesman Problem of Second Order (2-TSP). We call the induced JSeP by 2-TSP as the Job Sequencing Problem of Second Order (2-JSeP) with a different objective function formulation from JSeP and prove that 2-JSeP is NP-hard. Then the Assignment Problem of Second Order (2-AP) and Karp-Steele patching heuristic are incorporated to solve 2-JSeP. The obtained solution, however, does not guarantee the optimal sequence and are used to seed a Dynamic Q-learning-based Genetic Algorithm (DQGA) to improve the solution quality. Q-learning, which is a kind of reinforcement learning method, is used to learn from the experience of selecting the order of mutation and crossover operators in each generation of the genetic algorithm. The computational results on 320 benchmark instances show that the proposed DQGA is comparable to the state-of-the-art methods in the literature. The DQGA even outperforms the existing methods for some instances, as could improve the reported "best-known solutions" in notably less time. Finally, through the statistical analysis, the performance of DQGA is compared with those of non-learning genetic algorithms.

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Improved heuristic algorithms for the Job Sequencing and Tool Switching Problem

Paiva

Carvalho

2017

Computers & Operations Research

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Um algoritmo enumerativo baseado em ordenamento parcial para resolução do problema de minimização de trocas de ferramentas

Cited by 12 publications

References 21 publications

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

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

Improved heuristic algorithms for the Job Sequencing and Tool Switching Problem

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