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Abstract: Facility location allocation (FLA) is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.

考虑区域约束的车辆检测站选址模糊费用—利润均衡模型

汽车检测站是综合运用现代检测技术实现汽车运行状态检测及诊断的场所或服务机构。合理规划及管理汽车检测站是保障汽车安全运行的客观需要，也是方便用户需求、促进区域经济协调发展的必然要求。作为检测站规划的第一步，检测站选址涉及诸多约束，是一个复杂的决策问题。 考虑到汽车检测站选址即网点布局的不确定性，为更切合地描述实际情况，引入检测车辆数量为模糊变量的汽车检测站模糊选址问题，以充分反映专家评估的偏见。另外，考虑到自然环境限制或政策限定等因素，构建了保证投资商获得一定利润、且检测用户总运输费用最低的均衡模型。 建立反映选址实际情况的模糊费用—利润均衡模型，提出应用融合模糊模拟和遗传算法的混合智能算法进行求解分析。 求解结果表明所提方法不仅很好地描述了专家评估的偏见，且和传统的确定性求解方法结果基本一致，说明所构建的模型有效。
费用—利润均衡；可信性理论；模糊模拟；模糊规划；遗传算法

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