Production Scheduling with Stock- and Staff-Related Restrictions

• Published in: Computational Logistics Vol. 13004; pp. 142 - 162
• Main Authors: Sartori, Carlo S., Gandra, Vinícius, Çalık, Hatice, Smet, Pieter
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Integer programming; Metaheuristic; Production scheduling; Stock levels
• ISBN: 3030876713; 9783030876715
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-87672-2_10

Effective production scheduling allows manufacturing companies to be flexible and well-adjusted to varying customer demand. In practice, production scheduling decisions are subject to several complex constraints which emerge from staff working hours and skills, delivery schedules, stock capacities, machine maintenance and machine setup. This paper introduces a novel production scheduling problem based on the real-world case of a manufacturing company in Belgium. Given a set of customer requests which may only be delivered together on one of the provided potential shipment days, the problem is to select a subset of these requests and schedule the production of the required item quantities subject to the aforementioned restrictions. All decisions must be taken for a time horizon of several days, leading to a complex problem where there may not be enough resources to serve all requests. We provide an integer programming formulation of this novel problem which is capable of solving small-scale instances to proven optimality. In order to efficiently solve large-scale instances, we develop a metaheuristic algorithm. A computational study with instances generated from real-world data indicates that the metaheuristic can quickly produce high-quality solutions, even for cases comprising several days, requests and limited stock capacities. We also conduct a sensitivity analysis concerning characteristics of the schedules and instances, the results of which can be exploited to increase production capacity and revenue.