scholarly journals Solving Flexible Job-Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Behnam Barzegar ◽  
Homayun Motameni ◽  
Hossein Bozorgi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Gravitational Search

Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN) that is based on gravitational search algorithm (GSA). In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.

scholarly journals A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

2021 ◽  
Vol 7 ◽  
pp. e574
Author(s):  
Nayeli Jazmin Escamilla Serna ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Joselito Medina-Marin ◽  
Norberto Hernandez-Romero ◽  
Irving Barragan-Vite ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

scholarly journals Two Improved Cuckoo Search Algorithms for Solving The Flexible Job-Shop Scheduling Problem

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Ahmed T. Saadeq Al-Obaidi ◽  
Samer Alaa Hussein
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The Cuckoo Search (CS) is heuristic search algorithm which inspired from cuckoo bird behavior. In this paper, we proposed two improvements for the cuckoo search algorithm of solving Flexible Job-Shop Scheduling problem (FJSP); the first one depends on Best Neighbors Generation (CS-BNG) and the second one based on Iterative Levy Flight (CS-ILF). Some adaptation for the key points of CS algorithm has been done to enhance searching in the discrete state space. The proposed algorithms have increased solutions quality and convergence rate. The improved algorithms have been tested on some FJSP benchmark instances for performance examination. The experimental results demonstrate the effectiveness of the improved algorithms in comparison to the basic cuckoo search algorithm.

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