Optimization of Energy-Aware Flexible Job Shop Scheduling Problem Using VNS-Based GA Approach

Abstract

In today’s world, the manufacturing systems are growing day by day and capable to produce the products on time as per the customers’ requirement. However, the energy consumption by these manufacturing systems has been ignored, and a higher amount of energy is consuming to increase the production rate. Therefore, it is must to consider the criteria of energy consumption along with other traditional objectives of performance measures. Thus, in the present work, energy consumption has been considered with other measures to solve the flexible job shop scheduling (JSS) problem. It is a non-polynomial (NP) hard problem, and this problem belongs to the class of combinatorial optimization, so it is difficult to solve with a simple and exact mathematical formulation. Thus, this article presents the modified genetic algorithm (GA)-based methodology to deal with flexible JSS problem. The GA has been modified in order to increase local search using variable neighbourhood search (VNS)-based mutation operator in order to avoid premature convergence of regular GA. The proposed approach considers multiple objectives in order to produce an optimized solution for flexible JSS problem such as makespan, processing cost as well as the energy consumption. In present work, an alternative (flexible) manufacturing process has been considered to extend the JSS problem. A suitable chromosome has been designed to code the schedule (solution) for JSS problem having additional processing flexibility. A case study (of 6 jobs and 15 machines) has been presented in order to assess the effectiveness of projected modified GA method. Results reveal that the proposed VNS-based approach in GA is effective enough to reduce makespan, processing cost as well as energy consumption performance measures.