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一个新的生产规划和基于类的存储位置分配的框架:多标准分类方法
Mehmet Akif Yerlikaya1, Feyzan Arıkan2
1Department of Industrial Engineering, Bitlis Eren University, Bitlis, 13000, Turkey.
Heliyon
|September 23, 2024
概括
本研究提出了一个新的混合整数非线性编程 (MINLP) 模型,用于存储位置分配问题 (SLAP). 它使用类似于理想解决方案的订单偏好技术 (TOPSIS) 来优化仓库库存和降低成本.
科学领域:
- 运营研究 运营研究
- 物流管理物流管理
- 工业工程 工业工程 工业工程
背景情况:
- 有效的仓库管理对于优化库存和降低运营成本至关重要.
- 存储位置分配问题 (SLAP) 是仓库运营中的一个关键挑战.
- 现有的方法往往缺乏对各种库存属性的全面考虑.
研究的目的:
- 为存储位置分配问题 (SLAP) 开发一种新的混合整数非线性编程 (MINLP) 模型.
- 在仓库管理策略中整合多标准决策,特别是通过与理想解决方案相似的订单偏好技术 (TOPSIS).
- 通过根据产品特性和需求战略性地分配存储位置来提高仓库效率.
主要方法:
- 为SLAP制定一种新的混合整数非线性编程 (MINLP) 模型.
- 通过与理想解决方案相似的订单偏好技术 (TOPSIS) 的应用,根据物理特征和易腐蚀性来评估和分类库存.
- 整合TOPSIS结果作为输入到存储位置分配的数学模型中.
- 为可适应的仓库管理场景开发多功能决策支持系统.
主要成果:
- 拟议的MINLP模型通过战略性地分配存储位置来有效地解决SLAP问题.
- 整合TOPSIS允许对存储地点进行更全面的评估,考虑空间,需求和物理方面.
- 决策支持系统为基于单个或多个标准的决策提供了实用工具,包括立方体每顺序指数 (COI).
结论:
- 通过TOPSIS增强的新型MINLP模型显著提高了仓库管理效率和成本效益.
- 该方法为复杂的存储位置分配挑战提供了实用和可适应的解决方案.
- 这项研究为物流和供应链优化领域提供了有价值的方法.
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