Wyniki wyszukiwania - BazTech

1

A genetic algorithm-based approach for flexible job shop rescheduling problem with machine failure interference

Liang Zhongyuan, Zhong Peisi, Zhang Chao, Yang Wenlei, Xiong Wei, Yang Shihao, Meng Jing

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 171784

EN

Rescheduling is the guarantee to maintain the reliable operation of production system process. In production system, the original scheduling scheme cannot be carried out when machine breaks down. It is necessary to transfer the production tasks in the failure cycle and replan the production path to ensure that the production tasks are completed on time and maintain the stability of production system. To address this issue, in this paper, we studied the event-driven rescheduling policy in dynamic environment, and established the usage rules of right-shift rescheduling and complete rescheduling based on the type of interference events. And then, we proposed the rescheduling decision method based on genetic algorithm for solving flexible job shop scheduling problem with machine fault interference. In addition, we extended the "mk" series of instances by introducing the machine fault interference information. The solution data show that the complete rescheduling method can respond effectively to the rescheduling of flexible job shop scheduling problem with machine failure interference.

2

Research study of state-of-the-art algorithms for flexible job-shop scheduling problem

Wojakowski P., Warżołek D.

Czasopismo Techniczne. Mechanika

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2013

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R. 110, z. 1-M

381--388

EN

The paper discusses various approaches used to solve flexible job-shop scheduling problem concentrating on formulations proposed in the last ten years. It mainly refers to the applied metaheuristic techniques which have been exploited in this research area. A comparison of presented approaches is attempted, some concluding insights are highlighted. Finally future research directions are suggested.

PL

W artykule opisano różne podejścia stosowane do rozwiązania problemu harmonogramowania zadań z maszynami alternatywnymi. Skoncentrowano się na opracowaniach opublikowanych w ostatnich dziesięciu latach. Głównie skupiono uwagę na podejściach wykorzystujących algorytmy metaheurystyczne. Dokonano próby porównania merytorycznego dostępnych w literaturze rozwiązań oraz wskazano kierunki dalszych prac.

3

A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems

Liu H., Abraham A., Wang Z.

Fundamenta Informaticae

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2009

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Vol. 95, nr 4

465-489

EN

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. In this paper, we model the scheduling problem for the multi-objective Flexible Job-shop Scheduling Problems (FJSP) and attempt to formulate and solve the problem using a Multi Particle Swarm Optimization (MPSO) approach. MPSO consists of multi-swarms of particles, which searches for the operation order update and machine selection. All the swarms search the optima synergistically and maintain the balance between diversity of particles and search space. We theoretically prove that the multi-swarm synergetic optimization algorithm converges with a probability of 1 towards the global optima. The details of the implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP, especially for large scale problems.

4

Lower bounds for the scheduling problem with uncertain demands

Berkoune D., Mesghouni K., Rabenasolo B.

International Journal of Applied Mathematics and Computer Science

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2006

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Vol. 16, no 2

263-269

EN

This paper proposes various lower bounds to the makespan of the flexible job shop scheduling problem (FJSP). The FJSP is known in the literature as one of the most difficult combinatorial optimisation problems (NP-hard). We will use genetic algorithms for the optimisation of this type of problems. The list of the demands is divided in two sets: the actual demand, which is considered as certain (a list of jobs with known characteristics), and the predicted demand, which is a list of uncertain jobs. The actual demand is scheduled in priority by the genetic algorithm. Then, the predicted demand is inserted using various methods in order to generate different scheduling solutions. Two lower bounds are given for the makespan before and after the insertion of the predicted demand. The performance of solutions is evaluated by comparing the real values obtained on many static and dynamic scheduling examples with the corresponding lower bounds.