A disruption recovery model for a single stage production-inventory system

• Published in: European journal of operational research Vol. 222; no. 3; pp. 464 - 473
• Main Authors: Hishamuddin, H., Sarker, R.A., Essam, D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2012; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; Disruption; Economic production quantity; Exact sciences and technology; Genetic algorithms; Heuristic; Inventory control; Inventory control, production control. Distribution; Mathematical programming; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Production management; Production- inventory system; Studies; Production- inventory system; Economic production quantity; Disruption; Production system; Costs; Program optimization; Evolutionary algorithm; Linear programming; Scheduling; Recovery time; Real time; Modeling; Constrained optimization; Production management; Small series production; Shortage; Heuristic method; Inventory control
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2012.05.033

► Develop a disruption recovery model for a production and inventory system. ► The model is suitable for the system experiences a sudden disruption. ► Proposed an efficient heuristic to solve the developed model. ► The heuristic solves the model with little computational time. ► The method will be a very useful tool to find optimal recovery plan in real time. This paper presents a newly developed disruption recovery model for a single stage production and inventory system, where the production is disrupted for a given period of time during the production up time. The model is categorized as a constrained non-linear optimization program which we have solved using an efficient heuristic developed in this paper. The model was also solved using an evolutionary algorithm and a comparison of the results from both methods was performed. The heuristic was able to accurately solve the model with significantly less time compared to the evolutionary algorithm. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as the extent of the disruption. The proposed model offers a potentially useful tool to help manufacturers decide on the optimal recovery plan in real time whenever the production system experiences a sudden disruption.