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

Abstract

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.

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

“…A hybrid algorithm was proposed to solve the lot sizing and IPPS problem in the case of plastic molding industry. The proposed approach was compared with the simulated annealing for test its effectiveness [48].…”

“…lem in the case of plastic molding industry. The proposed approach was compared w the simulated annealing for test its effectiveness [48].…”

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

“…They considered a one-day demand phase and a one-day lead time. Meta-heuristics were used by Klement et al [49] for the lot-sizing and scheduling problem. Di Nardo et al [50] proposed a stock dynamic sizing optimization, where the safety stock is considered to fill up the demand variability, where the lead time is constant.…”

Chance-constrained programming with robustness for lot-sizing and scheduling problems under complex uncertainty

“…Production lot sizing and scheduling are the most important tasks performed in a manufacturing system because making the optimal lot sizing and scheduling can effectively save production costs, improve production efficiency and enhance machine utilization rate (Boonmee and Sethanan, 2016; Ramezanian et al , 2013). The term “lot sizing” reflects the quantity of the products manufactured with limited production capacity at given midterm planning period (Klement et al , 2021). The objectives are to schedule match operations of products to relative machines and to arrange processing sequences on a machine in short terms (Fandel and Stammen-Hegene, 2006; Sangroudi and Ranjbar-Bourani, 2019).…”