Discrete-time, economic lot scheduling problem on multiple, non-identical production lines

This paper deals with a general discrete time dynamic demand model to solve real time resource allocation and lot-sizing problems in a multimachine environment. In particular, the problem of apportioning item production to distinct manufacturing lines with different costs (production, setup and inventory) and capabilities is considered. Three models with different cost definitions are introduced, and a set of algorithms able to handle all the problems are developed. The computational results show that the best of the developed approaches is able to handle problems with up to 10000 binary variables outperforming general-purpose solvers and other randomized approaches. The gap between lower and upper bound procedures is within 1.0% after about 500 seconds of CPU time on a 2.66 Ghz Intel Core2 PC.

► We model 3 different versions of the Economic Lot Scheduling Problem.
► Different heuristics are presented and tested against state of the art solvers.
► We tackle models with up to 10 K binary variables.
► Integration of an RBS and a Matheuristic procedure provides the best results.
► We obtain a gap on the optimal solution within 1% in reasonable CPU time.