Location-Routing for Distribution Centers
Read this article. One objective of this paper is to determine distribution center locations. Compare and contrast the two cases presented.
Literature review
This study integrates multimodal transportation
and LRP. Both of them are apparently independent fields of research and
will be discussed separately. Also a few papers jointly discussing the
two concepts are also cited. Different researchers used combination of
problems to define their model. Shen et al. presented location
and inventory problems together. Azadeh et al. considered vehicle
routing and inventory decisions simultaneously. Beginning of LRP
development can be traced back to 70 and 80s when Laporte and Nobert presented a single-depot model which was subsequently used by
other researchers. Loperte et al. considered multi-depot LRP to
further develop the problem. Thereafter, various authors has introduced
different approaches to the problem which can be classified based on
problem structure (single-echelon or multi-echelon), type of data
(deterministic, probabilistic, or fuzzy), number of product types
(single-product or multi-product), number and capacity of facilities
(single-facility or multi-facility, capacitated or incapacitated), type
and capacity of vehicles (homogenous or heterogeneous, capacitated or
incapacitated), time window and problem type (soft or hard), number of
objective functions (single-objective or multi-objective), and solving
methods (exact, heuristic, meta-heuristic, and combinational). In this
regard, some researchers present review
papers.
Wu et al. designed a three echelon LRP. They also
considered tight time windows and time deadlines to create services for
high-speed trains. Albareda-Sambola et al. proposed a
multi-period LRP model and a stable model against time. An approximation
based on replacing vehicle routes by spanning trees is proposed, and
its capability for providing good quality solutions is assessed in a
series of computational experiments Karaoglan et al. used goods
pick and delivery planning in LRP. The authors proposed two
polynomial-size mixed integer linear programming formulations for the
problem and a family of valid inequalities to strengthen their
formulation. Rodriguez-Martin and Salzar-Gonzalez considered
locations of and routing through hub facilities. Proposed model decide
on the location of hubs, the allocation of nodes to hubs, and the
routing among the nodes allocated to the same hubs, with the aim of
minimizing the total transportation cost. Ahmadi-Javid and Seddighi set probabilistic facility capacity and disruption risk in the
problem. The goal is to determine the location, allocation and routing
decisions that minimize the annual cost of location, routing and
disruption. Tavakkoli-Moghaddam and Raziei took demands as fuzzy
numbers. They present a bi-objective location-routing-inventory problem
with heterogeneous fleets in a two-echelon distribution network and
fuzzy demands. Ghezavati and Beigi proposed a bi-objective
mathematical model for location-routing problem in a multi-echelon
reverse logistic network. Their proposed network consists of hybrid
collection/inspection centers, recovery centers and disposal centers.
They considered total cost minimization and minimizing the maximum time
of completion of the collecting return products as objective functions.
Hiassat et al. also considered location-routing-inventory problem
for perishable products distribution. Samanlioglu developed a
LRP for dangerous materials handling. In this paper, a new
multi-objective location-routing model is developed, and implemented in
the Marmara region of Turkey. Shahabi et al. added inventory
management to three levels LRP. They also assumed that demand across the
retailers is to be correlated as Najjartabar et al. which
considered correlated demands in location-inventory problem in a three
level supply chain. Aghighi and Malmir used location-routing
inventory problem on perishable product distribution system design. In
this paper, authors considered stochastic demands and travel times and
solved problem in two phases. And Fazel Zarandi et al. considered
time window, fuzzy demand, and fuzzy travel times at the same time.
Moreover these papers, Govindan et al. proposed LRP in a
sustainable network. Sustainable networks have a growing trend in supply
chain problems. Afshar-Bakeshloo et al. developed a model, named
Satisfactory-Green Vehicle Routing Problem. It consists of routing a
heterogeneous fleet of vehicles to serve a set of customers within
predefined time windows. Also, Najjartabar-Bisheh et al. analyzed
the role of third-party companies in a sustainable supply chain design.
As
LRPs, multimodal transportation papers can be classified based on their
planning time horizon (strategic, tactical, or operational). More
recently, Crainic and Steadie Seifi et al. classified the
researches on multimodal transportation in two review papers and can be
referred for more studies.
Following studies are joint papers for
both LRP and multimodal transportation. Li et al. introduced
location of terminals problem on a multimodal transportation network.
The proposed model simultaneously considers choices of travelers on
route, parking location and mode between auto and transit. Chiadamrong
and Kawtummachai designed a methodology to support decision
making on sugar industry. This aim of this paper is to suggest the best
inventory position and transportation route in the distribution system
considering different transportation options. Tiwari et al. considered route selection on a multimodal transportation network with
several objectives: minimization of travel time and travel cost, later
schedules and delivery times of every service provider in each pair of
location, and lastly variable cost must be included in every location.
Alumur et al. considered hub location problem and hub network
design and assumed the hub and non-hub nodes are linked via multimodal
transportation. Also, Moccia et al. considered hub facilities on
multimodal network, but they proposed and solved a routing problem. Xie
et al. considered mode changing facility location and multimodal
route selection problem on a multimodal network for dangerous materials
transportation. They considered different origin/destination pairs and
selected the best multimodal route with determining mode changing
points. Hajibabai and Ouyang presented a location and routing
problem for biofuel transformation facilities on a multimodal network.
In this study, first, some locations were selected for biofuel
transformation facilities which were to be located between supplier and
customers; then, multimodal routes were determined connecting suppliers
to the facilities and then the facilities to customers. However, they
presumed the routes to be of multimodal nature, neglecting to consider
different modes and mode changing issues. Tuzkaya et al. presented distribution facilities location problem in a multimodal
transportation network. In their approach, in the first phase, using
analytic network process (ANP), a decision was made on the best
transportation mode and the best potential sites to establish
facilities; in the second phase, distribution facilities location
problem was solved. Finally, Ayar and Yamaded time windows to
multimodal routing problems.
Even though location-routing
problem has experienced various developments during recent past, yet few
papers are reported, wherein multimodal transportation is accounted for
in such problems. To the best of our knowledge, no paper in the
literature considered products delivery tours from DCs to customers
while determining routs from supplier to DCs on multimodal network at
the same time. Using multimodal logistics is an indispensable option for
world class organizations, so the present paper is an attempt to
fill-in this gap in the research field of location-routing.