A hybrid genetic algorithm for a complex cost function for flowshop scheduling problem

Supply chain excellence has a real impact on business strategy. Manufacturing is an integral part of this strategy, represents one of the most exciting opportunities to create value and one of the most challenging tasks for policy makers. In this paper, we consider a performance criterion for the flowshop scheduling problem that aims to minimise a complex cost function, i.e., the sum of weighted tardiness and weighted flow-time costs. A heuristic and hybrid genetic algorithms are proposed and experimental results are provided. We address this trade-off and propose solution techniques that are easy for the shop-floor manager to implement. As scheduling function is an integral part of supply chain, the proposed solution minimises the opportunity losses and improves cost based supply chain performance. This paper addresses this interesting and challenging domain.