scholarly journals Facility Location Decisions with Random Disruptions and Imperfect Estimation

2013 ◽  
Vol 15 (2) ◽  
pp. 239-249 ◽  
Author(s):  
Michael K. Lim ◽  
Achal Bassamboo ◽  
Sunil Chopra ◽  
Mark S. Daskin
Author(s):  
David Kik ◽  
Matthias Gerhard Wichmann ◽  
Thomas Stefan Spengler

AbstractLocation choice is a crucial planning task with major influence on a company’s future orientation and competitiveness. It is quite complex, since multiple location factors are usually of decision-relevance, incomparable, and sometimes conflictual. Further, ongoing urbanization is associated with locational dynamics posing major challenges for the regional location management of companies and municipalities. For example, respecting urban space as location factor, a scarcity growing over time leads to different assessment and requirements on a company’s behalf. For both companies and municipalities, there is a need for location development which implies an active change of location factor characteristics. Accordingly, considering locational dynamics is vital, as they may be decisive in the location decision-making. Although certain dynamics are considered within conventional Facility Location Problem (FLP) approaches, a systematic consideration of active location development is missing so far. Consequently, they may propagate long-term unfavorable location decisions, as major potentials associated with company-driven and municipal development measures are neglected. Therefore, this paper introduces a comprehensive decision support framework for the Regional Facility Location and Development planning Problem (RFLDP). It provides an operationalization of development measures, and thus anticipates dynamic adaptations to the environment. An established multi-criteria approach is extended to this new application. A complementary guideline ensures its meaningful applicability by practitioners. Based on a real-life case study, the decision support framework’s strength for practical application is demonstrated. Here, major advantages over conventional FLP approaches are highlighted. It is shown that the proposed methodology results in alternative location decisions which are structurally superior.


2017 ◽  
Vol 25 (6) ◽  
pp. 991-1005 ◽  
Author(s):  
Dragan Simić ◽  
Vladimir Ilin ◽  
Vasa Svirčević ◽  
Svetlana Simić

Abstract Facility location decisions are critical in strategic planning for a wide range of operational and logistical decisions. Facility location problem with focus on logistics distribution centre (LDC) in Balkan Peninsula (BP) is discussed in this article. Methodological hybrid genetic algorithm, Analytical Hierarchy Process, and fuzzy c-means method is proposed here and it is shown how such a model can be of assistance in analysing a multi criteria decision-making problem. This research represents continuation of three existing studies. The experimental results in our research could be well compared with other official results of the feasibility study of the LDC located in BP.


2018 ◽  
Vol 21 (6) ◽  
pp. 520-533 ◽  
Author(s):  
Amir Hossein Nobil ◽  
Sajjad Jalali ◽  
Seyed Taghi Akhavan Niaki

Author(s):  
Chun Cheng ◽  
Yossiri Adulyasak ◽  
Louis-Martin Rousseau

Facility networks can be disrupted by, for example, power outages, poor weather conditions, or natural disasters, and the probabilities of these events may be difficult to estimate. This could lead to costly recourse decisions because customers cannot be served by the planned facilities. In this paper, we study a fixed-charge location problem (FLP) that considers disruption risks. We adopt a two-stage robust optimization method, by which facility location decisions are made here and now and recourse decisions to reassign customers are made after the uncertainty information on the facility availability has been revealed. We implement a column-and-constraint generation (C&CG) algorithm to solve the robust models exactly. Instead of relying on dualization or reformulation techniques to deal with the subproblem, as is common in the literature, we use a linear programming–based enumeration method that allows us to take into account a discrete uncertainty set of facility failures. This also gives the flexibility to tackle cases when the dualization technique cannot be applied to the subproblem. We further develop an approximation scheme for instances of a realistic size. Numerical experiments show that the proposed C&CG algorithm outperforms existing methods for both the robust FLP and the robust p-median problem.


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