An Artificial Bee Colony Approach for Multi-objective Job Shop Scheduling

Abstract

Scheduling is the proper allocation of resources over a period for performing a set of tasks with the objective of optimizing one or more performance measures. The actual assignment of starting and completion times of operations on jobs, if the manufacturing order is to be completed on time is known as Production scheduling. The Job Shop Scheduling Problem (JSSP) is one of the most difficult scheduling problems. Since JSSP is NP-complete, that is, the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. This is an important practical problem in the field of production management and combinatorial optimization. Inspired by the decision-making capability of bee swarms in the nature, this paper proposes an efficient scheduling method based on Artificial Bee Colony (ABC) for solving the JSSP. Most of the researchers in production scheduling are concerned with the optimization of a single criterion. However, the performance of a schedule often involves more than one aspect and, therefore requires a multi-objective treatment. Minimization of makespan and total tardiness are the two performance measures considered in this paper. The Artificial Bee Colony algorithm was coded in MATLAB 2009. A parameter analysis was done to fix the control parameters of Artificial Bee Colony algorithm. The performance of the algorithm was analyzed on the benchmark problems provided by E. Taillard.