The Declarative Framework Approach to Decision Support for Constrained Search Problems - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 165

carried out for different aspects of a company's supply chain. According to (Shapiro, 2001),
there are four dimensions to integration.
 Functional integration : It relates to the different functions performed by the

organization, for example, purchasing, location, manufacturing, warehousing, and
transportation.

 Spatial integration : This type of integration is done over a target group of supply chain

entities - vendors, manufacturing facilities, warehouses, and markets.

 Hierarchical integration : It refers to integration of the overlapping decisions in the

strategic, tactical, and operational planning horizons. For example, it is important to
strategically locate the manufacturing facilities with respect to the vendors to help in
optimizing the supply policies.

 Enterprise integration : It emphasizes the importance of strategic and tactical planning

decisions like, integration of supply chain management with demand management and
financial management to maximize the revenues and also to increase long-term returns
on investments.

2.2 Decision support in SCM
Several issues need to be considered while building a decision support system (DSS) for
managing the supply chain. A manufacturer will find offers to sell the materials they need
as well as offers to purchase goods they produce. All this information will supplement the
data already in the firm's ERP (Enterprise Resource Planning) system, which includes
inventory, customer orders, and the state of the manufacturing schedule, distribution etc..
Taken together, these data can enable much better decisions as to what products to make,
what raw materials to purchase, what orders to accept, how to distribute etc.. This paper
will focus on logistic where decision support or optimization is very reasonable. A logistic
network should maximize profits and provide a least total cost system, while also achieving
desired customer service level. The models of outbound logistics are often mixed-integer
programming (MIP) formulation that seek to achieve the following:
 Inventory optimization and reduction at the warehouse (the factory warehouse) or/and
distribution center;
 Load optimization for transportation from the factory warehouse to the delivery
locations (i.e., DCs, bins, plants, and direct-delivery (DD) customers, Distribution
Centers(DC)),while allowing direct (plant-to-store) and interplant shipments;
 Flow optimization throughout the entire outbound supply chain.
A number of quantitative models use mixed-integer programming (MIP) to solve the supply
chain optimization problems. One of the first attempts was done by Geoffrion and Graves
[4], where a MIP model (Frühwirth & Slim, 2003) (Vanderbei, 2008) (Schrijver, 1998)
(Cornelis et al., 2006) was formulated for the multicommodity location problem. This
seminal research involved the determination of distribution center (DC) locations, their
capacities, customer zones and transportation flow patterns for all commodities. A solution
to the location portion of the problem was presented, based on Bender's Decomposition
(BD). The transportation portion of the problem is decoupled into a separate classical
transportation problem for each commodity. Their approach shows a high degree of
effectiveness and advantage of using BD over branch-and-bound. The technique has been
applied on a real problem to test its performance. However, the computational requirements
and technical resources required for its implementation make it a difficult choice in classical
MIP tools.