scholarly journals Analysis of usage of genetic and tabu search algorithms in shop scheduling

2008 ◽  
Vol 48 ◽  
Author(s):  
Edgaras Šakurovas ◽  
Narimantas Listopadskis
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Real World ◽  
Job Shop ◽  
Flow Shop ◽  
Proper Time ◽  
Search Algorithms ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

A wide area of scheduling problem is industrial so-called shop scheduling (Job Shop, Flow Shop and Open Shop) which has important applications in real world industrial problems. Metaheuristic algorithms(Genetic and Tabu search algorithms in this case) seem to be one of the best candidates for finding nearbyoptima in proper time. In this work we implemented several genetic algorithms (separated by values oftheir parameters) and several Tabu search algorithms (separated by neighborhood of solution). Finally, implemented eight algorithms are examined for random shop scheduling problems in terms of variouscriteria.

A New Neighborhood for the Job Shop Scheduling Problem

2012 ◽  
Vol 433-440 ◽  
pp. 1540-1544
Author(s):  
Mohammad Mahdi Nasiri ◽  
Farhad Kianfar
Keyword(s):  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithms ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Local Search Algorithms ◽  
Shop Scheduling ◽  
Neighborhood Structures

The effectiveness of the local search algorithms for shop scheduling problems is proved frequently. Local search algorithms like tabu search use neighborhood structures in order to obtain new solutions. This paper presents a new neighborhood for the job shop scheduling problem. In this neighborhood, few enhanced conditions are proposed to prevent cycle generation. These conditions allow that the neighborhood encompasses larger number of solutions without increasing the order of computational efforts.

Application of Ontology in Event-Driven Job-Shop Scheduling Problems

2015 ◽  
Vol 775 ◽  
pp. 458-463 ◽  
Author(s):  
Xiang Min Xu ◽  
Xi Fan Yao
Keyword(s):  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Event Driven ◽  
Flexible Flow Shop Scheduling

Aiming at the flexible flow-shop scheduling problem of cloud manufacturing, this paper introduces event driven concept and apply ontologies to Job-Shop scheduling problem FT46. The inference of ontology models allows the system to gain the dynamic information of workshop, and then rule engine is used to match event patterns to optimize the job shop scheduling problem.

Application of Hybrid Firefly Algorithm-Tabu Search Technique to Minimize the Makespan in Job Shop Scheduling problem

2016 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Manoj Govind Kharat ◽  
Siddhant Sanjeev Khadke ◽  
Rakesh D. Raut ◽  
Sachin S. Kamble ◽  
Sheetal Jaisingh Kamble ◽  
...  
Keyword(s):  
Tabu Search ◽  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Firefly Algorithm

The Job shop scheduling problem is an important concern in the manufacturing systems. In this paper, the authors have proposed a hybrid firefly algorithm-tabu search combination technique to solve the Job shop scheduling problems. In the proposed algorithm, a complete scheme of algorithm for Job shop scheduling problems is designed and tabu search algorithm is incorporated with the aim of searching for local optimum of each individual. In order to improve the quality of solutions, in each step of the hybrid algorithms, an effective heuristic is proposed. The proposed heuristic reduces the overtime costs of operations by efficient use of the operation's slack. The performance of the proposed algorithm is tested and evaluated solving well-known benchmarked problems. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution approaches. The results have proved the superiority of proposed approach to other methods such as particle swarm optimization, genetic algorithm in terms of both efficiency and success rate.

Systematische Navigation für die Reihenfolgeplanung/Systematic solution space navigation for job shop scheduling problems

2020 ◽  
Vol 110 (07-08) ◽  
pp. 563-571
Author(s):  
Edzard Weber ◽  
Eduard Schenke ◽  
Luka Dorotic ◽  
Norbert Gronau
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Solution Space ◽  
Comparative Test ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Space Navigation ◽  
Systematic Solution

Dieser Beitrag stellt einen Algorithmus für das Job-shop-Scheduling-Problem vor, welcher den Lösungsraum indexiert und eine systematische Navigation zur Lösungssuche durchführt. Durch diese problemadäquate Aufbereitung wird der Lösungsraum nach bestimmten Kriterien vorzustrukturiert. Diese Problemrepräsentation wird formal beschrieben, sodass ihre Anwendung als Grundlage für ein navigationsorientiertes Suchverfahren dienen kann. Ein vergleichender Test mit anderen Optimierungsansätzen zeigt die Effizienz dieser Lösungsraumnavigation.   This paper presents an algorithm for the job-shop scheduling problem indexing the solution space and performing systematic navigation to find good solutions. By this problem-adequate preparation of the solution space, the solution space is pre-structured according to certain criteria. This problem representation is formally described so that its application can serve as a basis for a navigation-oriented search procedure. A comparative test with other optimization approaches shows the efficiency of this solution space navigation.

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