Job shop scheduling by pheromone approach in a dynamic environment

Abstract

Job shop scheduling problem is a NP-hard problem; therefore the objective is to create a schedule that satisfies all the constraints while taking as little overall time as possible. The paper concerns the job shop scheduling problem in cellular manufacturing systems; the schedule is created by a pheromone-based approach. The proposed approach is carried out by a Multi-agent Architecture and it is compared with a coordination approach proposed in literature used as a benchmark.

A simulation environment developed in ARENA® package was used to implement the approaches and evaluate the performance measures. The performance measures investigated are: throughput time, throughput, Work In Process, machines average utilisation and tardiness. Several scenarios are considered: from static to very dynamic conditions for internal and external exceptions of the manufacturing system. The simulation results highlighted that the performance of the proposed approach are comparable with the benchmark when the customer demand has a high fluctuation and the manufacturing system is less dynamic.

Keywords:

• dynamic scheduling
• ant colony intelligence
• pheromone
• multi-agent systems
• discrete event simulation