Available online at www.sciencedirect.com European Journal of Operational Research 187 (2008) 1402–1421 www.elsevier.com/locate/ejor A multicriteria facility location model for municipal solid waste management in North Greece Erhan Erkut a,*, Avraam Karagiannidis b, George Perkoulidis b, Stevanus A. Tjandra c a Faculty of Business Administration, Bilkent University, Ankara, Turkey b Laboratory of Heat Transfer and Environmental Engineering, Department of Mechanical Engineering, Box 483, Aristotle University, GR-54124 Thessaloniki, Greece c School of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6 Available online 17 November 2006 Abstract Up to 2002, Hellenic Solid Waste Management (SWM) policy specified that each of the country’s 54 prefectural gov- ernments plan its own SWM system. After 2002, this authority was shifted to the country’s 13 regions entirely. In this paper, we compare and contrast regional and prefectural SWM planning in Central Macedonia. To design the prefectural plan, we assume that each prefecture must be self-sufficient, and we locate waste facilities in each prefecture. In contrast, in the regional plan, we assume cooperation between prefectures and locate waste facilities to serve the entire region. We pres- ent a new multicriteria mixed-integer linear programming model to solve the location–allocation problem for municipal SWM at the regional level. We apply the lexicographic minimax approach to obtain a ‘‘fair’’ nondominated solution, a solution with all normalized objectives as equal to one another as possible. A solution to the model consists of locations and technologies for transfer stations, material recovery facilities, incinerators and sanitary landfills, as well as the waste flow between these locations. Ó 2006 Elsevier B.V. All rights reserved. Keywords: Environment; Location; Municipal solid waste; Multiple criteria analysis 1. Introduction as product packaging, grass clippings, furniture, clothing, bottles, food scraps, newspapers, appli- Municipal solid waste (MSW), commonly known ances, paint and batteries. If not dealt with prop- as trash or garbage, consists of everyday items such erly, waste can create serious environmental and health problems. In this paper, we focus on MSW management in the region of Central Macedonia in Greece. We develop a mixed-integer linear pro- * Corresponding author. Tel.: +1 780 492 3068; fax: +1 780 492 gramming model with multiple objectives to solve 3325. E-mail addresses: [email protected] (E. Erkut), makis@ the location–allocation problem, including the tech- aix.meng.auth.gr (A. Karagiannidis), [email protected] nology selection of transfer stations, material recov- (G. Perkoulidis), [email protected] (S.A. Tjandra). ery facilities (MRFs), incinerators, and sanitary 0377-2217/$ - see front matter Ó 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2006.09.021 E. Erkut et al. / European Journal of Operational Research 187 (2008) 1402–1421 1403 landfills. Our model is more comprehensive and regional MSW management planning in Central complex than the models in the literature, as we Macedonia. Finally, we conclude the paper with a consider different waste processing technologies in discussion of possible further research in Section 7. MRFs and incinerators. We take economic and environmental criteria into account. 2. Municipal solid waste management in Greece Our research does not involve real decision mak- ers (DMs) from prefectures of the region. We As a country’s population increases and its stan- assume that all objectives are equally important, dard of living improves, its amount of waste pro- and we aim for a ‘‘fair’’ solution, i.e., a solution with duction increases and its landfill space becomes all normalized objective function values as close to scarce. The MSW production in Greece increased one another as possible. A popular fairness-oriented from 3900 thousand tons (kt) in 1997 to 4559 kt in approach in the literature is the lexicographic mini- 2001 (see Fig. 1). The Hellenic MSW consists max (see e.g. [17,19]). Lexicographic minimax solu- mainly of organics (47%) and paper (20%), as illus- tions are also called lexicographic max-ordering [9], trated in Fig. 2. lexicographic centers in location problems [20], and MSW management in Greece is different than nucleolar solutions in game theory [18]. A variant that of most European Union (EU) countries. The of this approach, lexicographic maximin, is used quantity of waste in Greece continues to be some- for telecommunication network design (see e.g. what lower than in other European countries, [21,23]). Following Ogryczak et al. [21], we discuss reflecting a less intense consumption pattern. The the conversion of the original lexicographic mini- composition of waste in Greece is also different, max problem to a lexicographic minimization prob- being high in biodegradable materials and low in lem. This enables us to use the standard sequential packaging materials. These positive characteristics algorithm for lexicographic minimization. To the are balanced by certain negative features in the best of our knowledge, this paper presents the first waste management sector. The high number of open attempt to apply the fairness concept of multiobjec- dump sites (reduced from over 5500 in 1990 to 1260 tive optimization to solid waste management. in 2004) constitutes the most negative element, and In Greece each region consists of a number of the percentage of useful material recovered is low. prefectures. Legislation in Greece specified that each prefecture of a region should plan its own solid waste management system. These plans were finan- 5000 cially supported by the region. Recently the plan- ning authority shifted to the regions. We apply 4500 our model to compare and contrast regional and 4559 4447 prefectural solid waste management planning. In 4264 4000 the prefectural planning, we look for the optimal 4082 location and allocation decision of waste facilities 3900 per prefecture taking into account multiple criteria. MSW produced in kt 3500 In the regional planning, we unite all prefectures in 1996 1998 2000 2002 the Central Macedonia region and look for the opti- Fig. 1. Trends in MSW generation in Greece 1997–2001 [6]. mal location and allocation decision of waste facili- ties that cover the needs of all seven prefectures in this region. The paper is organized as follows. Section 2 con- tains an introduction to the municipal solid waste problem in Greece. Section 3 discusses the relevant literature. In Section 4, we provide the formulation of the model (the notation is in the Appendix). In Section 5, we provide a short review of multiobjec- tive mathematical programming and lexicographic minimax approach. We also discuss the fair solution in this section. Section 6 contains our empirical results; namely an analysis of prefectural versus Fig. 2. Waste fractions of MSW in 2001 in Greece [6]. 1404 E. Erkut et al. / European Journal of Operational Research 187 (2008) 1402–1421 Greece, within the framework of the EU, is plan- system of the Central Macedonia region situated in ning its solid waste policy with the goals of protect- North Greece (Fig. 3). This region has the largest area ing human health and preserving the natural and the second largest population among all regions. environment. Greece has developed a comprehen- It comprises 14.2% of the country’s total area sive legislative system that harmonizes with the (18,779 km2) and consists of seven prefectures: European legal framework. Various measures have Pieria, Imathia, Pella, Kilkis, Thessaloniki, Chalki- been put in place for the integrated solid waste man- diki, and Serres, as shown in Fig. 3. The areas and agement (ISWM) policy. This policy promotes populations of these prefectures are shown in Table 1. waste reduction programs, recycling, and energy In the region of Central Macedonia, 713 kt of recovery rather than landfills for waste disposal. waste was generated in 2001, from which only The recycle-at-the-source programme, as part of 10.5% ended up in sanitary landfills, whereas the ISWM policy, has the following quantitative 39.6% and 49.9% ended up in uncontrolled and goals by the end of 2005: utilising 50–65% of the semi-controlled landfills, respectively. weight of packaging waste; recycling 25–45% of The trend in total waste generation is shown in the weight of packaging waste; and recycling 15% Fig. 4. According to Greek legislation, until 2020, of the packaging material [25]. In order to reach 50% (107 kt) of the estimated 214.5 kt of packaging these goals, facilities for material recovery are con- material and 78.9% (450 kt) of the 570 kt of organ- structed in various municipalities. In the future, ics should be diverted from landfills to material Hellenic national legislation will continue to comply recovery facilities. with EU regulations and directives on waste man- The ISWM system is structured into four phases agement. Greece and the EU aim for a significant of collection, transportation, processing,anddisposal decrease in the amount of waste generated, through [3,13]. MSW collection initially involves picking up new waste prevention initiatives, better use of refuse at the sources via collection vehicles. Between resources, and a shift to more sustainable consump- the waste collection and the waste disposal stages, tion patterns. Specific EU targets include reducing some processing operations such as separation, the quantity of waste disposed by 20% by 2010 and by 50% by 2050, with special emphasis on haz- Table 1 ardous waste. The distribution of population and size of seven prefectures in the Greece consists of 13 administrative regions, region of Central Macedonia 2 which are further subdivided into 54 prefectures. Prefecture Area (km ) (% of the Population (% of the We focus our discussion on the MSW management area of Greece) population of Greece) Pieria 1516 (1.1%) 129,846 (1.2%) Imathia 1701 (1.3%) 144,172 (1.3%) Pella 2506 (1.9%) 144,340 (1.3%) Kilkis 2519 (1.9%) 89,611 (0.8%) Thessaloniki 3683 (2.8%) 1,046,851 (9.5%) Chalkidiki 2886 (2.2%) 92,117 (0.8%) Serres 3968 (3.0%) 200,916 (1.8%) 1200 966 901 875 1000 713 582 800 600 400 200 0 1995 2001 2010 2013 2020 Fig.