Collaborative Flow-shop Scheduling Using Simulated Annealing and First Price Sealed Bid Auction to Minimize Total Cost and Make-span

Abstract

This work proposes a new flow-shop scheduling model that consists of several flow-shops. Every flowshop acts as an independent entity, but there is a collaboration among them. Although the relation between flow-shops and their customers is exclusive, collaboration through production sharing is possible. This circumstance is different from most studies in flow-shop scheduling problems (FSP), for example, parallel or distributed, where all jobs come from a single point and are then distributed to the production resources. In the multiple independent flow-shops, each flow-shop has its own processing time and production cost. Through collaboration, efficiency can be achieved in the make-span and total cost aspects, which becomes the objective of this work. This model is developed by combining the first price sealed bid auction and cloud theory-based simulated annealing. The first price sealed bid auction is conducted to minimize the total production cost. Meanwhile, the cloud theory-based simulated annealing is conducted to minimize the make-span. This model is then compared with the existing non-dominated sorting genetic algorithm (NSGA II) based flow-shop scheduling models. The first existing model is a parallel flow-shop, while the second one is a collaborative flow-shop. The simulation result shows that the proposed model outperforms the existing models in the total cost aspect. The proposed model creates a 13 to 29 percent lower total cost than the NSGA II-based parallel flow-shop. Meanwhile, the proposed model creates a 16 to 28 percent higher make-span than the NSGA II-based parallel flow-shop.