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Location Decision in Distribution Centers
Ninlawan Choomrit
Department of Industrial Engineering, Srinakharinwirot University
ninlawan@swu.ac.th
Abstract
Location decision in distribution Centers is often one of the most critical
fundamentals in an organization’s success. This paper gathers some recent applications
related to DC location decision for general approaches in logistic environment and in
supply chain environment. The sight in location variables and methodology also
illustrates and guides to future prospects.
Keywords: Distribution Center, Location Decision, Supply Chain Management
1. Introduction
In designing a production–distribution system, locating distribution centers (DCs)
is one of important concern. The distribution center system consists of one or more
production plants, a group of distribution centers, and retail outlets and customers (see
figure 1). Two questions related to DC location decision are “How many DCs should
there be?” and “Where should they be located?” which refer to the numbers and locations
of distribution centers under different circumstances.
Figure 1 A basic distribution center system
In general, the main goal in site selection is to optimize customer service while
minimizing costs associated with transportation, labor, real estate and taxes. For the most
important selection criteria, Atkinson (2002) concluded that “Transportation
consideration” are the most important factor with different aspects: the proximity to
customers (being able to make quick and dependable JIT deliveries), the expense, with
transportation costs being a large part of the supply chain budget, the shortage of drivers
who are interested in long-haul driving, and the proximity to transportation routes
(interstates, rail service, waterways, ocean ports). Other criteria are in concern: “Labor
costs and availability”, “Real estate costs”, “Taxes”, “Incentives (from governments and
economic development group)”, and “Utilities”. Applications of location problems are
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abundant and are found in both manufacturing and service. Some applications provide
graphical solutions showing numbers and locations of distribution centers as see in Figure
2.
Figure 2 Graphical solution of distribution centers
(From Nozick and Turnquist 2001, p.439)
2. Some recent approaches of DC location decision
2.1 General approaches in logistic environment
In automotive manufacturers, locating distribution centers is one of important
focus in their logistic system. Nozick and Turnquist (2001) proposed a trade–off between
costs and customer service coverage. Only inventory costs and transportation costs are
considered in cost reduction meanwhile demand coverage also is another concern in
customer fulfillment. To minimize costs and to maximize customer coverage are multiple
objectives in making decision of DC locations. Integration of both objectives is appeared
by using the fixed–charge facility location model with given a weight related to the
objective of customer demand. Applying this methodology in an automotive
manufacturer, number of distribution centers as a trade–off solution with minimum costs
and maximum a percentage of demand coverage is derived.
Nozick and Turnquist (2001) presented another approach of the location
optimization in different matter. Inventory stocking policy in each product turns on to be
joined with DC locations for making decision. What products should have safety stock at
the DCs and the plant, and those should be stocked only at the plant are chosen for the
stocking policy. A fix–charge facility location model is used to decide in DC locations;
also an inventory allocation model fulfills answers for stocking policy. Integration in both
models leads to obtain the number and location of DCs, and what products to stock at
each level.
Making decision in DC locations under fuzzy environment is in concern when the
values for the qualitative criteria in situation are often imprecisely defined, also the
desired value and importance weight of criteria are usually described in linguistic terms,
for example, “low”, “medium”, “high”. Fuzzy set theory was developed on the principle
that the key elements in human thinking are not numbers, but linguistic terms. Chen
(2001) interested in a fuzzy decision–making method under multiple criteria
consideration for location selection by integrating linguistic assessments and weights.
Five criteria to select the most suitable location are investment cost, expansion possibility,
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availability of acquirement material, human resource, and closeness to demand market.
Results reveal in both the ranking order of all alternatives and the degree of preference of
each pair of alternatives.
Chan et al. (2001) proposed another method for a multiple-depot, multiple–
vehicle, location–routing problem with stochastically processed demands. The
requirement for raw materials as stochastically processed demands refers to agile logistics
which raw materials are stored at supply depots and surplus inventory is strictly
disallowed at the plants. Formulation in a stochastic location–routing problem (SLRP)
with stochastic demands is generated by a queuing network. At first, the lower bound for
heuristic–validation purposes is served with formulating multiple–depot–location and
multiple–vehicle–routing problem. Then a stochastic formulation, called the stochastic
multiple–vehicle–routing multiple–facility location problem is created for stochastic
demands. A medical–evacuation of U.S. Air Force is used as a time–sensitive case study
to allocate and route aircrafts to cover most patients. In addition, this study is to test the
adequacy of both the delivery system and hospitals in the emergency responsiveness.
Solving the multi-depots location–routing problem (MDLRP) in logistic
environments is proposed by Wu et al. (2002) in order to find the optimal solution for DC
locations, allocation of customers to each service area, and transportation plans
connecting customers. The multiple objective is to minimize total costs of fixed depot–
establishing cost, delivery cost, and dispatching cost for the vehicles assigned. Location–
allocation problem (LAP) to select DC sites and a plan for allocating customers to each
chosen DC site is used to drive input to the Vehicle Routing problem (VRP) for
generating a starting feasible set of routes. Simulated annealing (SA) is used as the basis
for developing search methods for both the LAP and VRP. Comparison results derived
from the proposed method and another two methods is done for a better solution with
shorter CPU time.
Hwang (2002) concerned in both required service levels (in the number of
warehouse or distribution center, W/D) and vehicle routing schedule in order to reach
high logistic performance. Stochastic set–covering problem is used to determine the
minimum number of W/D centers among a discrete set of possible location sites so that
the probability of each customer to be covered it not less than a critical value (required
service level) and 0–1 programming method is a solver of this problem. Meanwhile, an
integrated vehicle routing problem (VRP)–solver based on improved genetic algorithm
(GA) is developed to minimize the total logistic cost for the service of the set of
customers without being tardy or exceeding the capacity and available travel time of the
vehicles and GUI–type (Graphical User Interface) programming is developed for
computational purpose.
Ridlehoover (2004) presented the facility location problems with the utilization of
Monte Carlo simulation and risk analysis to determine the best “economic–risk” location
through the expected annual worth. Starting with a P-median model, the weighted
distance from the depot to all customer sites is minimized which candidate locations are
selected. For financial analysis, Monte Carlo Simulation is used to determine the costs
and benefits of each site which consisting of initial investment, annual operating costs,
annual benefits (transportation savings), and interest rates (capital costs). For risk
analysis, labor, transportation, and real estate appreciation are key geographic factors in
assessing the risk in each site. The use of the Certainty Equivalence Method is to compare
in company’s risk profile.
Avittathur et al. (2005) developed the model to locate distribution centers with
minimizing central sales tax (CST) in India. The CST is a tariff that is applicable to inter–
state trade and is paid by a manufacturer when trading goods to his retailer in different
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state. To analyze DC locations, fixed cost, transportation cost, product variety, service
level and demand distribution across the demand point are involved with the CST rate.
Using the approximate mixed integer programming (AMIP) instead of the complex non–
linear mixed integer programming (NLMIP) provides a near-optimal solution for a
manufacturer’s distribution networks.
2.2 Approaches in supply chain environment
With consideration in the Chinese PC domestic market, an integrated supply–
chain based spatial intersection model is developed for the oversea facility network
design problem. This model also leads to the supply chain management (both
manufacturing and distribution centers), and the inbound and outbound logistics–related
factors; for example, material source accessibility, transportation and inventory cost,
potential benefits, inter–province distribution restrictions, and long–term regional market
conditions to alleviate the decision bias for locating the facilities. Sheu (2003) proposed
this model to determine facility location with two aspects: higher rate of return on
investment (reflected by the financial measures in model), and the demand–over–supply
operational status (urged to expand local facilities for satisfying the deficiency of PC
supply volumes).
In the PLOT (Production, Logistics, Outbound, Transportation) design system, a
class of distribution network design problems is illustrated by multiple product families, a
central manufacturing plant site, multiple distribution center and cross–docking sites, and
retail outlets (customer zones). Jayaraman and Ross (2003) developed the PLOT system
into two models: P1 and P2. “Model P1” is a strategic model which offers the strategic
choice of deciding which warehouses and cross–docks need to be opened and functional
in any given time period. The objectives of this model are to minimize fixed costs to open
warehouses and cross–docks, costs to transport products from warehouses to cross–docks,
and costs to supply products from cross–docks to satisfy customer demand. “Model P2”
is an operational model which deals with the optimal flow of product families from
warehouse through cross-docks to satisfy the customer demand. Minimizing
transportation costs incurred by shipping product families to warehouses, transshipment
costs to ship product families from warehouses to cross–docks, and distribution costs to
ship product families from cross–docks to customer zones are model p2’s objectives.
Simulated Annealing (SA) procedure is adopted to combine the strategic model and
operational planning scheme into a single large problem. The results determine the best
set of warehouses and cross–docks to operate while minimizing fixed costs, transportation
costs from warehouses to cross–docks, and costs to supply products based on customer
demands.
Oum and Park (2004) identified the important factors for regional distribution
centers’ location decision in the Northeast Asian (NEA) of multinational companies
(MNCs). A structured questionnaire–based survey of MNCs’ distribution managers, and
personal interviews with MNCs senior executives and governments officials are used for
data collection and analysis. The important factors for MNCs distribution centers relate to
the market and service in issues of “market size and growth potential”, “geographic
location and market accessibility”, “transport facilities”, “political stability”, “skilled
labor and labor peace”, “flexible government”, and “logistics service providers”. Market
size is a major driving force for MNCs location selection. Findings from doing interviews
with senior executives/officials of 29 institutions (MNCs and government agencies), the
way to be a major logistics hub in the NEA region starts by making target in strategically
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important industries, for which the size of the domestic market is large enough to provide
economic incentives for foreign MNCs to locate a distribution center.
For outbound supply chain network of an automobile company, location and size
are concerned for vehicle distribution centers (VDCs) in transporting vehicles from plants
to dealers. Eskigun et al. (2005) interested in the lead–time reduction which widely
mentions in supply chain management. Transportation costs, fixed costs for VDCs and
lead–time costs are minimized as a multiple objective in a proposed capacitated network
design model (NDMC). For short computational time in a large-scale integer linear
programming of NDMC model, Lagrangian heuristic is used to obtain near–optimal
solutions. The results show the average delivery lead–time gains and the percentage of
truck usage which refers to transportation costs and the number of open VDCs.
The US–based companies are encouraged to do their business in Mexico under the
North American Free Trade Agreement (NAFTA). Decision support for the strategic
design of an assembly system is needed before making investment. Wilhelm et al. (2005)
contributed the mixed integer–programming model to deal with the complexities of the
international design problem and to integrate relevant enterprise-wide decisions in the
US–Mexico business environment under NAFTA. The objective is to maximize total
after–tax profit under constraints of eight categories: revenues and cost, income taxes,
international considerations (transfer prices, the safe harbor rule, local–content rules, and
transportation–charge allocation), logical constraints for system design, logical
constraints for material flows, flow scaling, design of the assembly system and its supply
chain, and prescribing material flows through the supply chain. The model application in
an enterprise of laptop computer shows the number of trade–offs, for example,
centralization versus decentralization, make versus buy, outsourcing versus in–house
assembly, flexible versus dedicated technologies, and economies of scale versus
economies of process. In addition, this model can evaluate a variety of factors such as
limitations on transfer prices, facility locations, tariff impacts, exchange rate impacts, tax
impacts, dollar valuation, local–content rules, and the costs of transportation and
distribution.
3. Conclusion and future prospect
Most applications use the multiple objective functions, basically to minimize
costs, in DC location decision with different solution methods up to situations. Look in
supply chain management (SCM) is widely seen in recent applications. Atkinson (2002)
concluded that the growth in popularity of strategically–located DCs is by reason of lack
of inventory control (due to better inventory management via SCM, visible pipeline
activities, e–commerce and internet), customer delivery demands (within a specific time),
methods of distribution (require different types of handling and technology), and volume
of import/export business (due to evolution of off-shore manufacturing/sourcing).
In addition, some aspects are proposed for location decisions such as “Top 10
most powerful factors in location decisions”: Reasonable cost for property, Roadway
access for trucks, Nearness to customers, Cost of labor, Low taxes, Tax exemptions, Tax
credits, Low union profile, Ample room for expansion, Favorable attitude of
community/residents to industry (from Transportation & Distribution magazine).
“Population site location”: in rural areas (somewhat near larger urban areas for quick
access to transportation routes and sufficient labor at reasonable rates), near airports and
hubs (as a result of the increased demand for JIT delivery, on–line retailing, and rising
customer expectations), at ports (focusing on lower-cost, less–skilled manufacturing
around the world, port-related facilities), in urban areas (having a trend toward DCs
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away from urban areas to more rural areas), near manufacturing sites (becoming a
popular alternative due to getting more direct shipments).
Geographic information systems (GIS), especially when coupled with location
models and efficient algorithms, have the ability to vividly display the results of the user
input and, most importantly, the changes that result from input modifications. This
display tool, coupled with techniques designed to deal with fuzzy input from multiple
decision makers may, after all, result in many more location models being applied by
practitioners (Revelle and Eiselt, 2005). Also, qualitative factors or non–tangible features
are directed to location models for more effective results, for example, reliability, quality,
security, labor skill, attitude, reputation.
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