Synchronizing production and delivery in flow shops with time-of-use electricity pricing

Manufacturing supply chains (SC) shift from traditional make-to-stock systems to make-to-order (MTO) systems in order to coordinate the production and distribution of complex and highly customized products. Despite the need for advanced scheduling approaches when coordinating such MTO-based SCs, there is little research focussing on practical settings that include variable processing speeds, sequence-dependent setup times (SDST) and time-of-use (TOU) electricity prices. However, these settings are important since they influence the energy consumption and the associated electricity costs have an impact on the decision-making process. Furthermore, there is also an increasing concern regarding green production in manufacturing such that the energy consumption cannot be ignored in decision making. In this study, we investigate a bi-objective energy-efficient permutation flow shop scheduling problem in MTO-based SC (EPFSPSC) with the conjoint objectives of minimising the cost of inventory, delivery, tardiness and electricity costs for production. In order to solve this problem, a genetic algorithm-based memetic algorithm is proposed and its effectiveness is demonstrated against a well-known benchmark approach. This research aims to assist production managers in making integrated production and distribution decisions, while simultaneously considering all associated costs and ensuring green manufacturing.