Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system
In this paper, we formulate a mixed-integer binary non-linear programming model to study a series-parallel multi-component multi-periodic inventory-redundancy allocation problem (IRAP). This IRAP is a novel redundancy allocation problem (RAP) because components (products) are purchased under an all unit discount (AUD) policy and then installed on a series-parallel system. The total budget available for purchasing the components, the storage space, the vehicle capacities, and the total weight of the system are limited. Moreover, a penalty function is used to penalize infeasible solutions, generated randomly. The overall goal is to find the optimal number of the components purchased for each subsystem so that the total costs including ordering cost, holding costs, and purchasing cost are minimized while the system reliability is maximized, simultaneously. A non-dominated sorting genetic algorithm-II (NSGA-II), a multi-objective particle swarm optimization (MOPSO), and a multi-objective harmony search (MOHS) algorithm are applied to obtain the optimal Pareto solutions. While no benchmark is available in the literature, some numerical examples are generated randomly to evaluate the results of NSGA-II on the proposed IRAP. The results are in favor of NSGA-II.
Alikar, Najmeh; Mousavi, Seyed Mohsen; Raja Ghazilla, Raja Ariffin; Tavana, Madjid; and Olugu, Ezutah Udoncy, "Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system" (2016). Business Systems and Analytics Faculty Work. 139.