Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm

Abstract

Abstract With the development of economic globalization and sustainable manufacturing, sustainable scheduling of distributed manufacturing has attracted increasing concern. However, distributed manufacturing with the non-identical factory is rarely researched. Meanwhile, scheduling can affect the sustainability of manufacturing. Thus, this paper is the first attempt to investigate a sustainable distributed permutation flow-shop scheduling problem with a non-identical factory (DPFSP-NF). We formulate a novel mathematical model of this DPFSP-NF with objectives of minimizing makespan, negative social impact (NSI), and total energy consumption (TEC). A knowledge-based multi-objective memetic optimization algorithm (KMMOA) is presented to address this DPFSP-NF. First, a new energy conservation strategy is designed and embedded in the model to reduce TEC criterion. Second, a cooperative initialization mechanism is presented to yield initial solutions with good diversity and convergence. Third, several properties of DPFSP-NF are investigated and utilized to develop the knowledge-based local search operator. The impact of parameter configuration on KMMOA is studied by the Taguchi method. Finally, we compare KMMOA to its variants and other well-known multi-objective optimization algorithms by performing a number of experiments. Experiment results demonstrate the effectiveness of each improvement component of the KMMOA, and verify that KMMOA is an effective approach to deal with the DPFSP-NF.