Solving Multi Objective Job Shop Scheduling Problems Using Artificial Immune System Shifting Bottleneck Approach

Abstract

Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered in this work. The Artificial Immune System Shifting Bottleneck Approach is used for finding optimal makespan, mean flow time, mean tardiness values of two benchmark problems. In this Artificial Immune System Shifting Bottleneck Approach (AISSB), initial sequences are generated with Artificial Immune System Algorithm (AIS) and Shifting Bottleneck Algorithm (SB) is used for finding final solutions. The results show that the AISSB Approach is effective algorithm that gives better results than literature results. The proposed AISSB Approach is an efficient problem-solving technique for multi objective job shop scheduling problem.