An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem

Giovanni, Luigi De; Pezzella, Ferdinando

doi:10.1016/j.ejor.2009.01.008

Cited by 259 publications

(90 citation statements)

References 24 publications

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“…, m, which has been proved to be NP-hard (Papadimitriou 1977). In this situation, meta-heuristics are good alternatives as it has been demonstrated to be able to obtain competitive solutions within a reasonable computational budget for various NP-hard combinatorial optimization problems (Mei et al 2009a(Mei et al , 2011aTang et al 2009;Fuellerer et al 2010;Bolduc et al 2010;De Giovanni and Pezzella 2010;Sbihi 2010). In the following, two metaheuristic approaches are proposed for solving TTP.…”

Section: Solving Ttp With Meta-heuristicsmentioning

confidence: 99%

On investigation of interdependence between sub-problems of the Travelling Thief Problem

Mei

Yao

2014

Soft Comput

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In this paper, the interdependence between subproblems in a complex overall problem is investigated using a benchmark problem called Travelling Thief Problem (TTP), which is a combination of Travelling Salesman Problem (TSP) and Knapsack Problem (KP). First, the analysis on the mathematical formulation shows that it is impossible to decompose the problem into independent sub-problems due to the non-linear relationship in the objective function. Therefore, the algorithm for TTP is not straightforward although each sub-problem alone has been investigated intensively. Then, two meta-heuristics are proposed for TTP. One is the Cooperative Co-evolution (CC) that solves the sub-problems separately and transfers the information between them in each generation. The other is the Memetic Algorithm (MA) that solves TTP as a whole. The comparative results showed that MA consistently obtained much better results than both the standard and dynamic versions of CC within comparable computational budget. This indicates the importance of considering the interdependence between sub-problems in an overall problem like TTP.

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Section: Solving Ttp With Meta-heuristicsmentioning

confidence: 99%

On investigation of interdependence between sub-problems of the Travelling Thief Problem

Mei

Yao

2014

Soft Comput

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“…Besides, its usefulness made it a problem worth to scrutinize. Due to its complexity, several alternative presentations of the problem have been tried (Cheng & Smith, 1997), (Sadeh & Fox, 1995), (Crawford & Baker, 1994), (De Giovanni & Pezzella, 2010), in order to apply particular algorithms like Clonal Selection (Cortés Rivera et al, 2003), Taboo Search (Armentano & Scrich, 2000), Ant Colony Optimization (Merkle & Middendorf, 2001), Genetic Algorithms (Zalzala & Flemming, 1997), Priority Rules (Panwalker & Iskander, 1977), Shifting Bottlenecks (Adams et al, 1998, etc. The performance of these meta-heuristic procedures varies, and some seem fitter than others (Chinyao & Yuling, 2009).…”

Section: Jssp Treatments: State Of the Artmentioning

confidence: 99%

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes

Frutos¹,

Olivera²,

Tohmé³

2012

Real-World Applications of Genetic Algorithms

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“…In order to solve the flexible job shop scheduling problem, many algorithms have been proposed, such as tabu search algorithm [8], genetic algorithm [9], ant colony algorithm [10], particle swarm optimization algorithm [11] and so on. Genetic algorithm shows great applicability in solving similar problems, so this paper uses the genetic algorithm to solve the problem [12][13][14].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Flexible job shop scheduling model with parallel processes based on genetic algorithm

Bao¹,

Zhang²,

Zhang³

2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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This paper studies the flexible job shop scheduling problem under the condition that with parallel processes. In this paper, we analyses the characteristics of the process parallelism in the job shop scheduling problem, puts forward the concept of parallel efficiency, establishes the flexible job shop scheduling model with parallel processes based on the objective criteria of minimizing the makespan, and designs the model solution algorithm based on genetic algorithm. The feasibility of the scheduling model and the effectiveness of the algorithm are verified by simulation experiments. The parameters of process parallel rate are compared and analyzed. The experimental results show that it is effective to improve the scheduling efficiency by improving the parallelism of the process route.

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An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem

Cited by 259 publications

References 24 publications

On investigation of interdependence between sub-problems of the Travelling Thief Problem

On investigation of interdependence between sub-problems of the Travelling Thief Problem

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes

Flexible job shop scheduling model with parallel processes based on genetic algorithm

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