Smart Production by Integrating Product-Mix Planning and Revenue Management for Semiconductor Manufacturing

Marzieh Khakifirooz (), Jei-Zheng Wu () and Mahdi Fathi ()
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Marzieh Khakifirooz: Tecnológico de Monterrey
Jei-Zheng Wu: Soochow University
Mahdi Fathi: Mississippi State University

A chapter in Optimization in Large Scale Problems, 2019, pp 129-164 from Springer

Abstract: Abstract Semiconductor manufacturing is a capital-intensive industry, in which matching the demand and capacity is the most important and challenging decision due to the long lead time for capacity expansion and shortening product life cycles of various demands. Most of the previous works focused on capacity investment strategy or product-mix planning based on single evaluation criteria such as total cost or total profit. However, a different combination of product-mix will contribute to a different combination of key financial indicators such as revenue, profit, gross margin. This study aims to model the multi-objective product-mix planning and revenue management for the manufacturing systems with unrelated parallel machines. Indeed, the present problem is a multi-objective nonlinear integer programming problem. Thus, this study developed a multi-objective genetic algorithm for revenue management (MORMGA) with an efficient algorithm to generate the initial solutions and a Pareto ranking selection mechanism using elitist strategy to find the effective Pareto frontier. A number of standard multi-objective metrics including distance metrics, spacing metrics, maximum spread metrics, rate metrics, and coverage metrics are employed to compare the performance of the proposed MORMGA with mathematical models and experts’ experiences. The proposed model can help a company to formulate a competitive strategy to achieve the first-priority objective without sacrificing other benefits. A case study in real settings was conducted in a leading semiconductor company in Taiwan for validation. The results showed that MORMGA outperformed the efficient multi-objective genetic algorithm, i.e., NSGA-II, as well as expert knowledge of the case corporation in both revenue and gross margin. An evaluation scheme was demonstrated by comparing the effectiveness of manufacturing flexibility from the multi-objective perspective.

Keywords: Multiple objectives; Genetic algorithm; Pareto ranking; Semiconductor manufacturing; Revenue management; Manufacturing flexibility (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-030-28565-4_16

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