A multi-objective tabu search algorithm for product portfolio selection: A case study in the automotive industry
作者： Arianna Alfieri , Claudio Castiglione , Erica Pastore
DOI: 10.1016/J.CIE.2020.106382
关键词: Algorithm 、 Knapsack problem 、 Product (category theory) 、 Selection (genetic algorithm) 、 Tabu search 、 Set (abstract data type) 、 Computer science 、 Final product 、 Automotive industry 、 Portfolio
摘要: Abstract In the automotive industry, high customizability of final products leads to need for managing large portfolios variants same product, given different combination optional components that characterizes each variant. Due number and variability product demand brought about by such customizability, planning how many units per variant should be produced in a period is critical task. This especially true medium term, when few available are left. this paper, proactive approach can help find best set variants, which capacity allocated, proposed. The problem finding rephrased as portfolio selection modeled multiple-objective multi-dimensional knapsack. A tabu search algorithm has been developed provide solution problem. proposed tested real case study from industry; results show its effectiveness terms providing good trade-off among manager choose.
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