A Two-dimensional Warranty Model with Consideration of Customer and Manufacturer Objectives Solved with Non-dominated Sorting Genetic Algorithm

Document Type: Original Manuscript

Authors

1 M.Sc., Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran

2 Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran

3 Research Assistant, Systems and Industrial Engineering Department, University of Arizona, Tuscan, Arizona, USA

10.22094/joie.2018.538225

Abstract

Warranty is a powerful implement for marketing strategy that is used by manufacturersand creates satisfaction for consumers by ensuring to compensate for incorrect operation of the product. Warranty serviceresults ina cost named warranty cost for a manufacturer.This cost is a function of warranty policy, regions, and product failures pattern. Since this service coversthe cost of uncertain failure of the product, it makes some utility for customers. In this paper, we developed a novel customer utility function that is used as a customer objective to be maximized. In addition to the manufacturer objective, minimizing the warranty costisconsidered simultaneously. There are four restrictions on warranty parameters such as time, usage, unit product price and the R&D expenditure to be considered. Finally, we will propose a novel bi-objective model that maximizesthe utility function for customers and minimizesthe warranty cost for the manufacturer. This model will be solved with an evolutionary algorithmcalled Non-Dominated Sorting Genetic Algorithm (NSGA-II) and non-dominated Pareto solutionswill be gained from this method.To give a numerical instance, for a certain usage rate’s range of costumers, different warranties are provided and compared. It is believed that the computational results can help manufacturers to determine optimal solutions for the objective functions and consequentlywarranty parameters.

Graphical Abstract

A Two-dimensional Warranty Model with Consideration of Customer and Manufacturer Objectives Solved with Non-dominated Sorting Genetic Algorithm

Keywords


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