A bi-objective multi-period series-parallel inventory-redundancy allocation problem with time value of money and inflation considerations. (February 2017)

• Record Type: Journal Article
• Authors: Alikar, Najmeh; Mousavi, Seyed Mohsen; Ghazilla, Raja Ariffin Raja; Tavana, Madjid; Olugu, Ezutah Udoncy
• Is Part Of: Computers & industrial engineering. Volume 104(2017)
• Page Start: 51
• Page End: 67
• Publication Date: 2017-02
• Subjects: Multi-objective inventory system -- Redundancy allocation problem -- Inflation -- Time value of money -- CE-NRGA -- NSGA-II -- MOPSO; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.12.006
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We formulate a multi-component multi-period series-parallel inventory redundancy allocation problem. We consider time value of money and inflation rate in optimizing the problem. We find the optimal order quantity for each subsystem by optimizing inventory costs and reliability, concurrently. CE-NRGA, NSGA-II, and MOPSO are used to solve the SPIRAP. Numerical examples demonstrate the applicability of the algorithms. Abstract: A large number of existing research studies on reliability redundancy allocation problems do not take into consideration the time value of the money and the inflations costs associated with the component inventories. In this study, we formulate a multi-component multi-period series-parallel inventory redundancy allocation problem (SPIRAP) as a mixed-integer nonlinear mathematical model where: (a) the costs are calculated by considering the time value of money and inflation rates; and (b) the total warehouse capacity to store the components, the total budget to purchase the components and the truck capacity are subject to constraints. The primary goal in this study is to find the optimal order quantity of the components for each subsystem in each period such that the total inventory costs are minimized and the system reliability is maximized, concurrently. A controlled elitism non-dominated ranked genetic algorithm (CE-NRGA), a NSGA-II, and a multi-objective particle swarm optimization (MOPSO) are presented to solve the proposed SPIRAP. A series of numerical examples are used to demonstrate the applicability and exhibit the efficacy of the procedures and algorithms. The results reveal that the CE-NRGA outperforms both NSGA-II and MOPSO.