Lot-Sizing and Scheduling for the Plastic Injection Molding Industry-A Hybrid Optimization Approach

The management of industrial systems is done through different levels, ranging from strategic (designing the system), to tactical (planning the activities and assigning the resources) and operational (scheduling the activities). In this paper, we focus on the latter level by considering a real-world scheduling problem from a plastic injection company, where the production process combines parallel machines and a set of resources. We present a scheduling algorithm that combines a metaheuristic and a list algorithm. Two metaheuristics are tested and compared when used in the proposed scheduling approach: the stochastic descent and the simulated annealing. The method's performances are analyzed through an experimental study and the obtained results show that its outcomes outperform those of the scheduling policy conducted in a case-study company. Moreover, besides being able to solve large real-world problems in a reasonable amount of time, the proposed approach has a structure that makes it flexible and easily adaptable to several different planning and scheduling problems. Indeed, since it is composed by a reusable generic part, the metaheuristic, it is only required to develop a list algorithm adapted to the objective function and constraints of the new problem to be solved.

Automated summary

In this paper, a scheduling algorithm that combines a metaheuristic and a list algorithm is proposed and tested. Experimental results show that this algorithm outperforms the scheduling policy conducted in a case-study company. The method is not only able to solve large real-world problems efficiently, but also has a flexible structure that can be easily adapted to various planning and scheduling problems.