A heuristic genetic algorithm for product portfolio planning
A heuristic genetic algorithm for product portfolio planning
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DOI：
10.1016/j.cor.2005.05.033
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
R. Jiao;Y. Zhang;Yi Wang
中科院分区：
文献类型：
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作者：
R. Jiao;Y. Zhang;Yi Wang
产品组合规划已被公认为是跨行业所有公司面临的关键决策。它旨在选择接近最佳的产品组合和属性水平，以提供给目标市场。它构成了一个组合优化问题，被认为是NP-难的性质。传统的基于枚举的优化技术变得令人望而却步，因为可能的组合的数量可能是巨大的。遗传算法在解决组合优化问题中表现出优异的性能。为了更有效地解决产品组合规划问题，提出了一种启发式遗传算法。一个通用的编码方案被引入到同步产品组合生成和选择相干。适应度函数的建立是基于一个共享的盈余措施，利用客户和工程的关注。提出了一个非均衡指标来描述产品组合方案的精英性。
Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.