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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.10 No.1 pp.7-14

A Shaking Optimization Algorithm for Solving Job Shop Scheduling Problem

Ehab A. Abdelhafiez, 1Fahd A. Alturki
Mechanical and Industrial Engineering Department Faculty of Engineering, Majmaah University, Majmaah, Saudi Arabia
1Electrical Engineering Department Faculty of Engineering, King Saud University, Riyadh, Saudi Arabia
Received, January 17, 2011; Revised, February 14, 2011; Accepted, February 21, 2011


In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled “Shaking Optimization Algorithm” is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.