Intelligent search techniques for network-based manufacturing systems: multi-objective formulation and solutions

Abstract

Effective and efficient implementation of intelligent and recently emerged networked manufacturing systems requires enterprise-level integration. The first step in this direction is to integrate the manufacturing functions such as process planning and scheduling for multi-jobs in order to generate optimal or near optimal solutions. Addressed in this paper is multi-objective optimisation in the context of a network-based manufacturing system to optimise multiple objectives, i.e. minimisation of makespan and minimisation of variation of workload, simultaneously. This paper introduces a mathematical model for calculating the above-mentioned objectives with consideration of alternative machines, as well as tools and tool approach directions. The authors propose a new modified block-based genetic algorithm (MBBGA) and modified non-dominated sorting genetic algorithm (MNSGA-II) to resolve the above-mentioned complex problem and compare the proposed algorithms’ performance and their effectiveness with the non-dominated sorting genetic algorithm (NSGA-II). An illustrative example with complex scenarios is carried out to demonstrate the feasibility of the proposed MBBGA and MNSGA-II. The experimental results presented show that the proposed algorithms perform better in comparison with NSGA-II.