DISPLAN: A multiproduct plant/warehouse location model with nonlinear inventory costs
DISPLAN: A multiproduct plant/warehouse location model with nonlinear inventory costs
复制标题
DISPLAN：具有非线性库存成本的多产品工厂/仓库位置模型
DOI：
10.1016/0272-6963(84)90008-1
复制
发表时间：
1984
影响因子：
7.8
通讯作者：
R. Ballou
中科院分区：
文献类型：
--
作者：
R. Ballou
DISPLAN是一个大型计算机模型，用于实物供应和分销网络的战略规划。该计划可能涉及确定工厂、仓库、库存、码头等设施的数量、大小和位置。虽然存在一些数学模型来处理这个问题，DISTRIBUTION的一个独特的功能是它的非线性库存costs.The适当的网络设计的经济分析，要求所有相关的成本进行平衡，以实现最低成本配置的设施。这些成本包括设施进出运输成本、仓库储存和处理成本、订单处理成本、设施固定成本和库存持有成本。在这些成本中，准确描述库存管理成本至关重要，因为它们是决定网络中设施数量的主要因素。由于许多求解方法的局限性，库存携带成本被忽略或被视为网络中设施数量的线性函数。然而，如果一个公司的库存政策是基于经济订货量的概念，那么网络中的设施数量和总库存之间的关系是非线性的，通常被称为库存合并效应。通过线性近似不准确地表示这种影响可能会导致网络中设施的数量不正确。DISTRIBUTION方法是一种启发式过程，它使用线性规划的三维运输算法以迭代方式收敛于设施容量和客户服务约束下的最小成本网络配置。仓库固定成本和库存成本在每次迭代后重新计算，一旦仓库吞吐量已经建立。这些单位成本可以添加到线性规划算法的单位运输，处理和订单处理成本中。修改后的问题已解决。由于库存成本曲线是非线性的，这个过程可能会在总成本曲线上的一个局部最优点处终止。当连续迭代之间的总成本变化小于给定百分比时终止过程的停止规则不是绝对的。可以计算额外的迭代，以帮助确保实现全局最优。最后，对于固定成本占总成本的比例很高的情况，在计算过程中包括重新考虑例程。主启发式可能会在网络中留下太多的仓库，这个例程会探索减少数量的仓库。这确保了一个巨大的成本节约机会不会被忽视。DISTRIBUTION方法已被应用于各种行业的许多网络配置问题。典型的是零售业、制造业和备件分销业。该方法显示出适度的计算机运行时间与问题大小呈线性关系。
DISPLAN is a large‐scale computer model used for the strategic planning of physical supply and distribution networks. This planning may involve the determination of the number, size, and location of plants, warehouses, inventories, terminals, and like facilities. Although a number of mathematical models exist to treat this problem, a unique feature of DISPLAN is its handling of nonlinear inventory costs.The proper economic analysis of network designs requires that all relevant costs be balanced to achieve the minimum cost configuration of facilities. These costs include facility inbound and outbound transportation costs, warehouse storage and handling costs, order processing costs, facility fixed costs, and inventory carrying costs. Among these costs, it is vital that inventory carrying costs be accurately described since they are a major factor determining the number of facilities in a network. Due to the limitations of many solution methods, inventory carrying costs are neglected or they are treated as a linear function of the number of facilities in the network. However, if a company's inventory policy is based on the concept of the economic order quantity, then the relationship between the number of facilities and the total inventory in the network is nonlinear, commonly referred to as the inventory consolidation effect. Inaccurate representation of this effect through linear approximation can lead to an incorrect number of facilities in the network.The methodology of DISPLAN is a heuristic procedure that uses the 3‐dimensional transportation algorithm of linear programming in an iterative fashion to converge on the minimum cost network configuration subject to facility capacity and customer service constraints. Warehouse fixed costs and inventory costs are recomputed on a per‐unit basis after each iteration, once the warehouse throughput has been established. These per‐unit costs can be added to the per‐unit transportation, handling, and order processing costs of the linear programming algorithm. The revised problem is resolved. The process is repeated until the best number, location, and size of warehouses are determined.Because of the nonlinear inventory cost curve, the procedure may terminate at a local optimum on the total cost curve. The stopping rule that terminates the procedure when the change in total costs between successive iterations is less than a given percentage is not absolute. Additional iterations may be computed to help assure that the global optimum is realized.Finally, a reconsideration routine is included in the computational procedure for those cases where fixed costs are a high proportion of total costs. The main heuristic may leave too many warehouses in the network and this routine explores a reduced number of warehouses. This assures that a substantial cost‐saving opportunity is not overlooked.The DISPLAN methodology has been applied to many network configuration problems in a variety of industries. Typical of these are retailing, manufacturing, and spare parts distribution. The method has shown modest computer running times that are linear with problem size.