Managing disruption in an imperfect production–inventory system |
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| Affiliation: | 1. School of Engineering and Information Technology, University of New South Wales, Canberra, Australia;2. Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh;1. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran;2. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran;1. School of Economics and Management, Southeast University, Sipailou 2, Nanjing, 210096, China;2. Management School, University of Liverpool, Chatham Street, L6972H, Liverpool, UK;1. Department of Mathematics, Techno India University, Kolkata, India;2. Department of Mathematics, Jadavpur University, Kolkata 700032, India;1. The Logistics Institute-Asia Pacific, National University of Singapore, 21 Heng Mui Keng Terrace, #04-01 119613, Singapore;2. School of Engineering and Sciences, Tecnológico de Monterrey, E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, Mexico;3. NUS Business School, National University of Singapore, Singapore;1. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran;2. Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414, Azadi Ave., Tehran 1458889694, Iran |
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| Abstract: | In this paper, a disruption recovery model is developed for an imperfect single-stage production–inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production–inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption. |
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| Keywords: | production–inventory Disruption recovery Disruption management Imperfect production Pattern search Genetic algorithm |
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