An Ant Colony Optimization Algorithm for Flexible Job Shop Scheduling Problem

Abstract

Scheduling involves the allocation of resources over a period of time to perform a collection of tasks (Baker, 1974). It is a decision-making process that plays an important role in most manufacturing and service industries (Pinedo, 2005). Scheduling in the context of manufacturing systems refers to the determination of the sequence in which jobs are to be processed over the production stages, followed by the determination of the start-time and finish-time of processing of jobs (Conway et al., 1967). An effective schedule enables the industry to utilize its resources effectively and attain the strategic objectives as reflected in its production plan. The most common manufacturing system worldwide is the job shop. Job shops are associated with the production of small volumes/large variety products and operate in a make-to-order environment (Groover, 2003). Hoitomt et al. (1993) mentions that approximately 50 to 75 % of all manufactured components fall into this category of low volume/high variety and due to the market trends this percentage is likely to increase. Even though flexible manufacturing systems are today’s keywords that frequently appear in many research agendas, scheduling of job shops still receive ample attention from both researchers and practitioners due to the reason that job shop scheduling problems exist in many forms in most of the advanced manufacturing systems (Kutanoglu & Sabuncuoglu, 1999). Besides, analysis of job shop scheduling problems provides important insights into the solution of the scheduling problems encountered in more realistic and complicated systems (Pinedo 2005). In this context, this chapter focuses on scheduling job shops which is an important task for manufacturing industry in terms of improving machine utilization or reducing lead time.