Multi-criteria logistics distribution network design using SAS/OR William Ho * , Ali Emrouznejad Operations and Information Management Group, Aston Business School, Aston University, Birmingham B4 7ET, United Kingdom article info Keywords: SAS SAS/OR Multiple criteria decision making Analytic hierarchy process Goal programming Logistics distribution network abstract This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualitative factors. In the integrated approach, AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, a GP model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. To facilitate the use of integrated multiple criteria decision making approach by SAS users, an ORMCDM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear programming models based on the selected GP model. An example is given to illustrate how one could use the code to design the logistics distribution network. Ó 2008 Elsevier Ltd. All rights reserved. 1. Introduction The logistics distribution problem is to allocate a number of points of consumption to a number of points of supply, including suppliers, manufacturers, warehouses, distribution centers, and customers. The connection of these various logistics stakeholders by a mean of transportation facilities is regarded as the logistics distribution network. Logistics distribution network design is one of the major decision problems arising in contemporary supply chain management. There are mainly two inadequacies in the tra- ditional approaches for the problem. First, a single criterion was fo- cused only. The objective was either to minimize the total logistics cost (Hwang, 2005; Su, 1998; Wasner & Zäpfel, 2004) or total deliv- ery time (Su, 1999). Second, only quantifiable data were considered in the optimization techniques. Some qualitative factors, which are mainly customer oriented, were not considered. Multiple criteria decision making (MCDM) techniques have been used in recent years. One of the most prevalent techniques is ana- lytic hierarchy process (AHP) (Ho, 2008). Some researchers (Korpela & Lehmusvaara, 1999; Korpela, Lehmusvaara, & Tuominen, 2001a, Korpela, Kyläheiko, Lehmusvaara, & Tuominen, 2001b, 2002) ap- plied the combined AHP-mixed integer linear programming (MILP) model approach for the network design, whereas another group of researchers (Chan & Chung, 2004a, 2004b, 2005; Chan, Chung, & Wadhwa, 2004, 2005, 2006) applied the combined AHP-genetic algorithm (GA) approach to solve the problem. For the combined AHP-MILP approach, the selection of distribution network was sim- ply based on the customer satisfaction priorities instead of mini- mizing the total logistics cost or maximizing the total profit. Therefore, it is believed that the selected distribution network may not be cost effective. For the combined AHP-GA approach, the evaluation criteria used in AHP are all quantitative such as total cost, total delivery day, effectiveness of capacity utilization for warehouses, and so on. Some qualitative factors such as flexibility of capacity and value-added services were neglected. These factors are crucial in the integrated logistics system because they affect the customer satisfaction directly. To overcome the drawbacks, this paper develops a systematic and prominent MCDM technique, combining AHP and goal pro- gramming (GP), to design an optimal logistics distribution network. The combined AHP-GP approach considers both quantitative and qualitative factors and also aims at maximizing the benefits of deliverer and customers. SAS is recognized as one of the lead packages for statistical anal- ysis and as a powerful tool for data base systems in many organi- zations, both in public and private sectors. SAS users come from every major industry (banking to pharmaceuticals, manufacturing to telecommunications, and so on). All with the same basic needs to make better strategic decisions and to gain a competitive edge (Emrouznejad, 2005). There are many applications in SAS that the users recognized as powerful tools in organizational management. For example, the SAS/OR system has numerous optimization procedures which han- dle the standard problems such as linear programming (LP) and nonlinear programming (NLP) with all types of constraints. In 0957-4174/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2008.09.012 * Corresponding author. Tel.: +44 0121 2043342. E-mail address: w.ho@aston.ac.uk (W. Ho). Expert Systems with Applications 36 (2009) 7288–7298 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa