Abstract - In order to achieve sustainability the design of the recovery network has to fulfill the different objectives from the economic, social, and environmental perspectives. In this paper a lexicographic integer linear goal programming (ILGP) model is developed to design a multi- commodity recovery network for products of modular structure. The model results in locating facilities in a multi- echelon network and assigning the products to the different end of life (EOL) options. Keywords – Goal Programming, recovery network, reverse logistics, sustainability I. INTRODUCTION Classically, the economic aspect was the foremost concern of the industry. Recently emerging goals perceive the concept of sustainability; these were urged to by the limited natural resources, the scarcity of landfills, and the increased pollution rates. In the business community the term “the triple bottom line” was coined to explain the importance of achieving sustainability. It implies that industry has to expand the traditional economic focus to include environmental and social dimensions, in order to create a more “sustainable” business [1]. The design of recovery network has been subject to recent research. Due to the differences in the characteristics of flow between the forward and reverse channels, the design of reverse logistics network attracted much research effort. Multiple case studies in different fields such as automotive industry, electric appliances industry, paper recycling, and cellular phones, as in [2], have been conducted to the end of designing an efficient reverse network capable of dealing efficiently with the reverse flow of products. This paper presents a lexicographic integer linear goal programming (ILGP) model to support the design of a recovery network meant for assembly industry. Products of modular structure are collected at the end of their life at collection centers. They are inspected and end users are refunded. Returned products are then transported to disassembly centers. Upon disassembly a set of parts and assemblies are obtained. The latter may be further disassembled to obtain its constituting parts. Assemblies and parts flowing through the network are assigned to four different End of Life (EOL) options, direct reuse, remanufacturing, recycling, and disposal. Assemblies and parts may be directly reused as spare part. Some assemblies are remanufactured and then reused. Assemblies and parts technically infeasible for reuse or remanufacturing are recycled or disposed of. The target is to determine the location and capacities of the three different types of facilities: collection centers, disassembly centers, and remanufacturing facilities, as well as to allocate the flow to spare part dealers, recyclers, and disposal sites so as to have an economic viable and environmentally efficient network. This is a vital issue in developing countries where the concepts of sustainable development and environmental awareness are still incipient. Due to limited capital resources and lack of environmental legislation there is still little interest in product recovery. Developing profitable recovery networks may be a prominent step towards establishing sustainable industrial practices. The remainder of this text is structured as follows. In the next section previous research of GP applied to the recovery network design problem is reviewed. In section three a mathematical model is formulated. In section four a numerical instance is introduced and solved. Finally, conclusions are drawn and possible avenues of future research are suggested. II. REVIEW OF LITERATURE The recovery network design problem has drawn the attention of researchers and practitioners in the last two decades. In order to successfully exploit the opportunities of recovering value from used products, companies need to design a logistics structure that facilitates the arising reverse flow of goods in an optimal way. To this end, decisions need to be taken on where to locate the various processes of the reverse supply chain and how to design the corresponding transportation links [3].Extensive reviews of this strategic decision may be found in the literature. The reader is referred to [4] and [5] for further reviews. Different approaches in dealing with the problem may be identified. Originally a single objective, either cost minimization or profit maximization, was considered. Yet, in view of technological change, limited natural resources, and the concern about environmental issues, other non-economic objectives became essential to be considered. Hence, the multi-criteria approach became a necessity. Goal programming (GP), a powerful and effective methodology for the modeling, solution, and analysis of problems having multiple and conflicting goals and objectives, has often been cited as being the “workhorse” Sustainable Recovery Network Design Noha Galal 1 , Nermine Harraz 2 , Nashat Fors 2 , Hamdy Elwany 2 1 Department of Industrial Engineering, Alexandria Higher Institute of Engineering and Technology, Alexandria, Egypt 2 Industrial Engineering, Department of Production Engineering, Alexandria University, Alexandria, Egypt (nharraz@gmail.com.eg) 978-1-4244-4870-8/09/$26.00 ©2009 IEEE 563