Integrated network design models for crossdocking and warehousing strategies with tactical considerations

Nayoung Cho, Purdue University

Abstract

In this thesis, we identify the critical operational characteristics for various distribution strategies, and then develop integrated distribution network design models by taking into account those operational factors. The first part of this thesis presents a crossdocking network design problem for a two–stage and single commodity supply chain. While crossdocking is an attractive distribution strategy in terms of the substantial reduction of both the inventories at distribution facilities and the flow time of products within a network, the lead time experienced by a customer could be higher in a crossdocking system than in warehousing systems. In crossdocking, customers have to wait additional time for inbound transportation from the plants to the crossdocking center, whereas in warehousing, they receive the products from nearby warehouses which maintain stock in anticipation of the customers’ orders. Load balancing at a crossdocking center is another critical operational consideration. If incoming loads are not sufficient, then the retailers face stock–outs and if the incoming loads are excessive, then overstocked crossdocking centers force managers to acquire third–party storage services at premium rates. Since the effectiveness of a crossdocking strategy primarily depends on the successful management of the lead time and load balancing which are a function of inventory decisions as well as the network topology, both the lead time and inventory decisions should be considered at the strategic network design stage. We present a capacitated crossdocking network design model that minimizes the total logistics cost while controlling the lead times, inventory levels, and load–balancing. The proposed heuristics that are based on Lagrangian relaxation show good performance in terms of solution quality as well as computational requirements. The second part of this thesis is devoted to warehouse capacity acquisition–location models. While location models for distribution facilities have been widely studied, the capacity of warehouses is mostly ignored or assumed to be given. In addition, the capacity of a warehouse should be measured by its physical size, such as the available floor space or the maximum amount of inventory to be stored at any given time, and not the throughput, such as the average number of products going through the warehouse per time period. In this sense, the inventory management decisions should be considered at the strategic network design stage. Furthermore, the capacity cost under our consideration exhibits the economies and diseconomies of scale as well. As the size of a warehouse increases, the unit increment of the capacity cost decreases at first, and after the size of a warehouse exceeds a certain point, the unit increment of the capacity cost increases. The objective of this research is to develop integrated models for designing optimal or near optimal capacitated warehouse–distribution networks by determining the number, the location, the capacities, as well as the inventory policy of warehouses simultaneously. Heuristics based on Lagrangian relaxation are proposed. The third part of this thesis compares crossdocking and warehousing distribution strategies in a quantitative form. The ultimate goal of our research is providing decision support tools for companies to aid them in determining the appropriate distribution strategy under their own business environment. We analyze and compare the efficiency of each distribution strategy in a quantitative form. As a first step towards deriving comparable models for various distribution strategies, we propose analytical integrated network design models under deterministic demand as well as simulation models under stochastic demand. First, we introduce analytical models, which approximate the supply chain network design for both crossdocking and warehousing systems. Specifically, we consider a two–stage, single product, and single plant supply chain. We determine the number of distribution centers to minimize the sum of the location cost for distribution centers (i.e., crossdocking center or warehouse) and the total logistics cost across the supply chain. Each distribution center serves a set of retailers each of which faces deterministic demand. Next, we present simulation models to compare these two strategies when the retailers face stochastic demand. We determine the number of distribution centers and inventory policies at the distribution centers and the retailers. Numerical results showing the performance of these two strategies and the relationship between the level of uncertainty in demand and the total cost of each distribution strategy are presented. Finally, we offer some managerial insights regarding the type of distribution strategies and the associated operational policies that may be appropriate for a given business environment. (Abstract shortened by UMI.)

Degree

Ph.D.

Advisors

Ozsen, Purdue University.

Subject Area

Management|Industrial engineering

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