An ant colony optimization-based algorithm for minimizing the makespan of a job shop problem

Abstract

Combinatorial optimization is a branch of optimization in applied mathematics and computer science, related to operations research, algorithm theory and computational complexity theory that sit at the intersection of several fields, including artificial intelligence, mathematics and software engineering. The quest for a solution to NP-hard problem has brought many researchers into developing approximation method, an algorithm which attempts to find solutions to hard optimization problems but gives no guarantee that the solution is the best possible solution. In this research Ant Colony Optimization (ACO) heuristics algorithm is proposed to solve Job Shop Scheduling Problem (JSP). An appropriate ACO algorithm based on the job shop problem is developed and implemented on a case study of JSP with the aims of improving the performance of the algorithm in term of computational effort and time. It is about minimizing the total completion time, such as the makespan of a selected n-jobs and m-machines problem. Results from the case study have shown that the proposed ACO algorithm has a competitive advantage over the best given solution.