Individualized requirement-driven multi-task scheduling in cloud manufacturing using an extended multifactorial evolutionary algorithm

Abstract

Cloud manufacturing is an emerging manufacturing paradigm, which enables the simultaneous processing of multiple manufacturing tasks based on customer requirements through centralized management and planning of manufacturing services provided by distributed enterprises. How to optimally schedule the multiple manufacturing tasks is an important problem in cloud manufacturing. As cloud manufacturing is a demand-driven manufacturing mode and the requirement of each customer is highly individualized, a new individualized requirement-driven cloud manufacturing multi-task scheduling (IRCMMS) model is proposed in this study. It aims to benefit not only individual customers but also the whole system. To solve the proposed model, an extended multifactorial evolutionary algorithm is designed to obtain the approximate optimal Pareto solution set, which offers more alternatives for the cloud manufacturing system. Experimental results based on different simulation instances confirm the feasibility and effectiveness of the IRCMMS model as well as the efficiency of the algorithm in solving the IRCMMS model.