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Journal articles on the topic 'Shelter location under uncertainty'

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Published: 15 June 2024

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1

Bayram, Vedat, and Hande Yaman. "Shelter Location and Evacuation Route Assignment Under Uncertainty: A Benders Decomposition Approach." Transportation Science 52, no. 2 (March 2018): 416–36. http://dx.doi.org/10.1287/trsc.2017.0762.

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2

Xiang, Xiaoshu, Ye Tian, Jianhua Xiao, and Xingyi Zhang. "A Clustering-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Shelter Location Problem Under Uncertainty of Road Networks." IEEE Transactions on Industrial Informatics 16, no. 12 (December 2020): 7544–55. http://dx.doi.org/10.1109/tii.2019.2962137.

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3

Celik, Erkan. "Analyzing the Shelter Site Selection Criteria for Disaster Preparedness Using Best–Worst Method under Interval Type-2 Fuzzy Sets." Sustainability 16, no. 5 (March 4, 2024): 2127. http://dx.doi.org/10.3390/su16052127.

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Shelters are vital for affected people after a disaster because of the accommodation, safety, and security. In this paper, we analyze the shelter site selection criteria for disaster preparedness applying the best–worst method under interval type-2 fuzzy sets. The proposed approach utilizes the advantages of fewer pairwise comparisons with the best–worst method and better reflection of uncertainty with interval type-2 fuzzy sets. For this reason, the criteria are determined based on a literature review and the opinion of nine disaster experts. The experts have worked as disaster officers in a variety of locations, including Sivrice (Elazığ), Pazarcık, and Elbistan (Kahramanmaras) and Syrian refugee camps such as Nizip container city. In this step, 6 main criteria and 25 sub-criteria are evaluated using the proposed approach. According to the nine experts’ opinions, the most important main criterion is determined as proximity. Distribution center capacity, adequate distribution logistics personnel, available electricity, distance to settlement, and landslides and flooding are also determined as the five most important sub-criteria. For disaster preparedness, responsible organizations and managers should consider these important criteria for temporary shelter site selection.
4

Trivedi, Ashish, and Amol Singh. "A multi-objective approach for locating temporary shelters under damage uncertainty." International Journal of Operational Research 38, no. 1 (2020): 31. http://dx.doi.org/10.1504/ijor.2020.10027955.

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5

Trivedi, Ashish, and Amol Singh. "A multi-objective approach for locating temporary shelters under damage uncertainty." International Journal of Operational Research 38, no. 1 (2020): 31. http://dx.doi.org/10.1504/ijor.2020.106359.

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6

Galemba, Rebecca, Katie Dingeman, Kaelyn DeVries, and Yvette Servin. "Paradoxes of Protection: Compassionate Repression at the Mexico–Guatemala Border." Journal on Migration and Human Security 7, no. 3 (July 29, 2019): 62–78. http://dx.doi.org/10.1177/2331502419862239.

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Executive Summary Anti-immigrant rhetoric and constricting avenues for asylum in the United States, amid continuing high rates of poverty, environmental crisis, and violence in Guatemala, Honduras, and El Salvador, have led many migrants from these countries to remain in Mexico. Yet despite opportunities for humanitarian relief in Mexico, since the early 2000s the Mexican government, under growing pressure from the United States, has pursued enforcement-first initiatives to stem northward migration from Central America. In July 2014, Mexico introduced the Southern Border Program (SBP) with support from the United States. The SBP dramatically expanded Mexico’s immigration enforcement efforts, especially in its southern border states, leading to rising deportations. Far from reducing migration or migrant smuggling, these policies have trapped migrants for longer in Mexico, made them increasingly susceptible to crimes by a wide range of state and nonstate actors, and exacerbated risk along the entire migrant trail. In recognition of rising crimes against migrants and heeding calls from civil society to protect migrant rights, Mexico’s 2011 revision to its Migration Law expanded legal avenues for granting humanitarian protection to migrants who are victims of crimes in Mexico, including the provision of a one-year humanitarian visa so that migrants can collaborate with the prosecutor’s office in the investigation of crimes committed against them. The new humanitarian visa laws were a significant achievement and represent a victory by civil society keen on protecting migrants as they travel through Mexico. The wider atmosphere of impunity, however, alongside the Mexican government’s prioritization of detaining and deporting migrants, facilitates abuses, obscures transparent accounting of crimes, and limits access to justice. In practice, the laws are not achieving their intended outcomes. They also fail to recognize how Mexico’s securitized migration policies subject migrants to risk throughout their journeys, including at border checkpoints between Guatemala and Honduras, along critical transit corridors in Guatemala, and on the Guatemalan side of Mexico’s southern border. In this article, we examine a novel set of data from migrant shelters — 16 qualitative interviews with migrants and nine with staff and advocates in the Mexico–Guatemala border region, as well as 118 complaints of abuses committed along migrants’ journeys — informally filed by migrants at a shelter on the Guatemalan side of the border, and an additional eight complaints filed at a shelter on the Mexican side of the border. We document and analyze the nature, location, and perpetrators of these alleged abuses, using a framework of “compassionate repression” (Fassin 2012) to examine the obstacles that migrants encounter in denouncing abuses and seeking protection. We contend that while humanitarian visas can provide necessary protection for abuses committed in Mexico, they are limited by their temporary nature, by being nested within a migration system that prioritizes migrant removal, and because they recognize only crimes that occur in Mexico. The paradox between humanitarian concerns and repressive migration governance in a context of high impunity shapes institutional and practical obstacles to reporting crimes, receiving visas, and accessing justice. In this context, a variety of actors recognize that they can exploit and profit from migrants’ lack of mobility, legal vulnerability, and uncertain access to protection, leading to a commodification of access to humanitarian protection along the route.
7

Ozbay, Eren, Özlem Çavuş, and Bahar Y. Kara. "Shelter site location under multi-hazard scenarios." Computers & Operations Research 106 (June 2019): 102–18. http://dx.doi.org/10.1016/j.cor.2019.02.008.

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8

Bayram, Vedat, and Hande Yaman. "A stochastic programming approach for Shelter location and evacuation planning." RAIRO - Operations Research 52, no. 3 (July 2018): 779–805. http://dx.doi.org/10.1051/ro/2017046.

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Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.
9

Alumur, Sibel A., Stefan Nickel, and Francisco Saldanha-da-Gama. "Hub location under uncertainty." Transportation Research Part B: Methodological 46, no. 4 (May 2012): 529–43. http://dx.doi.org/10.1016/j.trb.2011.11.006.

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10

Li, Anna C. Y., Linda Nozick, Ningxiong Xu, and Rachel Davidson. "Shelter location and transportation planning under hurricane conditions." Transportation Research Part E: Logistics and Transportation Review 48, no. 4 (July 2012): 715–29. http://dx.doi.org/10.1016/j.tre.2011.12.004.

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11

Tan, Lin-Ti, and Song-Ken Hsu. "Production Location under Technology Uncertainty." Environment and Planning A: Economy and Space 32, no. 3 (March 2000): 445–54. http://dx.doi.org/10.1068/a31195.

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In this paper we examine the implications of technology uncertainty for production location in a general setting, in which the location and production decisions are determined ex ante and a general specification of technology uncertainty is adopted. In addition, to examine the endpoint optimality issue, we also provide comparative static results which are novel in the literature. Moreover, the exhaustion of information contained in the second-order optimal conditions in deriving the conditions for endpoint optimality differentiates our analysis from previous works.
12

Christou, Charalambos, and Nikolaos Vettas. "Location choices under quality uncertainty." Mathematical Social Sciences 50, no. 3 (November 2005): 268–78. http://dx.doi.org/10.1016/j.mathsocsci.2005.05.002.

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13

Ma, Yunjia, Wei Xu, Lianjie Qin, and Xiujuan Zhao. "Site Selection Models in Natural Disaster Shelters: A Review." Sustainability 11, no. 2 (January 14, 2019): 399. http://dx.doi.org/10.3390/su11020399.

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Constructing natural disaster shelters is important for disaster emergency management, and site selection models provide a feasible technique and method. This paper presents site selection models for natural disaster shelters. A synthesis of the types, objectives, constraints, methods of solutions, targeted disasters and applications of different site selection models for natural disaster shelters is investigated. Shelter location models can be classified as single-objective models, multiobjective models and hierarchical models, according to the objective and hierarchy type. Minimizing the evacuation distance or time, shelter construction cost or number, and the total risk are the general objectives of the models. Intelligent optimization algorithms are widely used to solve the models, instead of the Geographic Information System (GIS) method, due to the complexity of the problem. The results indicate that the following should be the main focuses of future works: How to set a model that can be applied for determining the shelter locations of multiple disasters; how to consider the uncertainty in the models; how to improve the existing algorithms or models to solve large-scale location-allocation problems; and how to develop a new resource-saving model that is consistent with the concept of sustainable development, as advocated by shelter planners and policy makers, which can be applied in real situations. This study allows those undertaking shelter location research to situate their work within the context of shelter planning.
14

Song, Shuyang, Hong Zhou, and Wenyan Song. "Sustainable shelter-site selection under uncertainty: A rough QUALIFLEX method." Computers & Industrial Engineering 128 (February 2019): 371–86. http://dx.doi.org/10.1016/j.cie.2018.12.053.

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15

Snyder, Lawrence V. "Facility location under uncertainty: a review." IIE Transactions 38, no. 7 (June 2006): 547–64. http://dx.doi.org/10.1080/07408170500216480.

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16

Mohammadi, M., S. A. Torabi, and R. Tavakkoli-Moghaddam. "Sustainable hub location under mixed uncertainty." Transportation Research Part E: Logistics and Transportation Review 62 (February 2014): 89–115. http://dx.doi.org/10.1016/j.tre.2013.12.005.

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17

Hosseini, Meysam, and S. A. MirHassani. "Refueling-station location problem under uncertainty." Transportation Research Part E: Logistics and Transportation Review 84 (December 2015): 101–16. http://dx.doi.org/10.1016/j.tre.2015.10.009.

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18

Mémin, Etienne. "Fluid flow dynamics under location uncertainty." Geophysical & Astrophysical Fluid Dynamics 108, no. 2 (March 4, 2014): 119–46. http://dx.doi.org/10.1080/03091929.2013.836190.

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19

Bhadury, Joyendu. "COMPETITIVE LOCATION UNDER UNCERTAINTY OF COSTS." Journal of Regional Science 36, no. 4 (November 1996): 527–54. http://dx.doi.org/10.1111/j.1467-9787.1996.tb01117.x.

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20

Hsu, Song-ken, and Lin-ti Tan. "Agroindustry location under output price uncertainty." Annals of Regional Science 33, no. 3 (August 19, 1999): 289–303. http://dx.doi.org/10.1007/s001680050106.

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21

Muppirisetty, L. Srikar, Tommy Svensson, and Henk Wymeersch. "Spatial Wireless Channel Prediction under Location Uncertainty." IEEE Transactions on Wireless Communications 15, no. 2 (February 2016): 1031–44. http://dx.doi.org/10.1109/twc.2015.2481879.

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22

Higaki, Masahiro, Tooru Mabuchi, and Naonori Nishida. "The Facility Location Problem under Demand Uncertainty." IEEJ Transactions on Electronics, Information and Systems 110, no. 10 (1990): 670–78. http://dx.doi.org/10.1541/ieejeiss1987.110.10_670.

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23

Ulu, Erva, James Mccann, and Levent Burak Kara. "Lightweight structure design under force location uncertainty." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1–13. http://dx.doi.org/10.1145/3072959.3073626.

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24

Bonein, Aurélie, and Stéphane Turolla. "Sequential location under one-sided demand uncertainty." Research in Economics 63, no. 3 (September 2009): 145–59. http://dx.doi.org/10.1016/j.rie.2009.07.003.

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25

Habibi, M. K. Khakim, Hamid Allaoui, and Gilles Goncalves. "Collaborative hub location problem under cost uncertainty." Computers & Industrial Engineering 124 (October 2018): 393–410. http://dx.doi.org/10.1016/j.cie.2018.07.028.

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26

Gülpınar, Nalan, Dessislava Pachamanova, and Ethem Çanakoğlu. "Robust strategies for facility location under uncertainty." European Journal of Operational Research 225, no. 1 (February 2013): 21–35. http://dx.doi.org/10.1016/j.ejor.2012.08.004.

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27

Wang, Cheng, Zhuo Hu, Ming Xie, and Yuxiang Bian. "Sustainable facility location‐allocation problem under uncertainty." Concurrency and Computation: Practice and Experience 31, no. 9 (April 29, 2018): e4521. http://dx.doi.org/10.1002/cpe.4521.

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28

Tanti, Lili, Syahril Efendi, Maya Silvi Lydia, and Herman Mawengkang. "Model Dynamic Facility Location in Post-Disaster Areas in Uncertainty." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 1 (November 16, 2022): 105–16. http://dx.doi.org/10.30812/matrik.v22i1.2095.

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Indonesia has many disaster-prone areas, natural disasters that occur in Indonesia in 2021 are 5,402 disasters. For disaster management in post-disaster areas, logistical planning is needed in the distribution of logistical assistance, it is estimated that the logistics costs of disaster assistance reach approximately 80% of the total costs in disaster management so that logistical assistance is an expensive activity of disaster relief. However, so far the process of distributing logistical assistance to disaster posts has not been evenly distributed. One of the causes of the unequal distribution is the inappropriate selection of distribution post locations. The facility location model is dynamic and has the objective function of minimizing the distance between emergency posts and refugee posts in terms of distribution of disaster relief goods in one cluster group. For grouping unsupervised learning data using a machine learning clustering algorithm, k-means. Model validation has been carried out using max run and max optimization 1000 times with results reaching 90%. This proves that the emergency facility location model can be used to determine the location of the emergency center, where the determination of the location of the emergency center has the closest distance to the request point/post shelter for disaster victims
29

Wu, Jiang, and Jingfeng Li. "Dynamic Coal Logistics Facility Location under Demand Uncertainty." Open Journal of Social Sciences 02, no. 09 (2014): 33–39. http://dx.doi.org/10.4236/jss.2014.29006.

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30

Braham, Hajer, Sana Ben Jemaa, Gersende Fort, Eric Moulines, and Berna Sayrac. "Spatial Prediction Under Location Uncertainty in Cellular Networks." IEEE Transactions on Wireless Communications 15, no. 11 (November 2016): 7633–43. http://dx.doi.org/10.1109/twc.2016.2605676.

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31

Tafakkori, Keivan, Ali Bozorgi-Amiri, and Abolghasem Yousefi-Babadi. "Sustainable generalized refueling station location problem under uncertainty." Sustainable Cities and Society 63 (December 2020): 102497. http://dx.doi.org/10.1016/j.scs.2020.102497.

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32

Johnson, Michael P. "Tenant-based subsidized housing location planning under uncertainty." Socio-Economic Planning Sciences 35, no. 3 (September 2001): 149–73. http://dx.doi.org/10.1016/s0038-0121(01)00003-9.

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33

Meraklı, Merve, and Hande Yaman. "Robust intermodal hub location under polyhedral demand uncertainty." Transportation Research Part B: Methodological 86 (April 2016): 66–85. http://dx.doi.org/10.1016/j.trb.2016.01.010.

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34

Mathur, V. K. "Location theory of the firm under price uncertainty." Regional Science and Urban Economics 15, no. 4 (November 1985): 597–98. http://dx.doi.org/10.1016/0166-0462(85)90045-6.

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35

Meagher, Kieron J., and Klaus G. Zauner. "Product differentiation and location decisions under demand uncertainty." Journal of Economic Theory 117, no. 2 (August 2004): 201–16. http://dx.doi.org/10.1016/j.jet.2003.12.006.

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36

Drezner, Tammy. "Location of retail facilities under conditions of uncertainty." Annals of Operations Research 167, no. 1 (September 18, 2007): 107–20. http://dx.doi.org/10.1007/s10479-007-0253-6.

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37

Qi, Mingyao, Ying Yang, and Chun Cheng. "Location and inventory pre-positioning problem under uncertainty." Transportation Research Part E: Logistics and Transportation Review 177 (September 2023): 103236. http://dx.doi.org/10.1016/j.tre.2023.103236.

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38

Cheng, Chun, Yossiri Adulyasak, and Louis-Martin Rousseau. "Robust Facility Location Under Disruptions." INFORMS Journal on Optimization 3, no. 3 (July 2021): 298–314. http://dx.doi.org/10.1287/ijoo.2021.0054.

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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.
39

Pankova, Tatyana I. "Dynamics of the agroecological state of typical chernozem under forest belts depending on the location in the relief (Kursk region, Russia)." SOCIALNO-ECOLOGICHESKIE TECHNOLOGII 10, no. 1 (2020): 40–62. http://dx.doi.org/10.31862/2500-2961-2020-10-1-40-62.

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Features of fertility dynamics indicators of typical chernozem determining agroecological state soil under these ecosystems are established under differentage forest shelter belts located on various slope exposures on the territory of the experimental field of All-Russia Research Institute of Arable Farming and Soil Erosion Control (Kursk Region, Medvensky District). The study period was 16 years, the age of the forest shelter belts is 20 and 36 years. It is found out that longterm growth of woody plants on typical chernozem results in an increase in its fertility. Moreover, there is the variation of soil properties differs in intensity in forest shelter belts that differ by location in the relief. In all forest shelter belts, regardless of the location in the relief, there is an improvement in the agrophysical state of the soil, an increase in the content of humus, mobile humus substances, mobile potassium, and phosphorus. However, the intensity of changes varies depending on the exposure of the forest shelter belt on the slope. So, on the southern slope there was a maximum increase in the content of humus, mobile phosphorus, exchange bases, and water-flow aggregates. In the soil of the watershed plateau, there was a significant increase in the content of humus, the degree of humification, mobile humus substances, but their qualitative composition and content of agronomically valuable aggregates have not changed. On the northern slope, there was the maximum increase in the criterion of water resistance of soil. With increasing age of forest belts, the aboveground herbaceous phytomass increases with an increase in its share of green biomass and the amount of accumulated litter decreases. The highest phytomass productivity was in the offshore forest belt of the southern slope. Studies have shown that long-term growth of forest shelter belts improves the agroecological state of the soil, regardless of their position in the terrain, which contributes to an increase in the ecological potential of the agricultural landscape. A correct understanding of the direction of changes in the agroecological state of the soil will optimize the structure and functioning of the agricultural landscape.
40

Gu, Jeongmin, and Gyu M. Lee. "Medical Relief Shelter Location Considering the Severity of Patients Under Limited Relief Budget." Journal of the Korean Institute of Industrial Engineers 43, no. 5 (October 31, 2017): 372–79. http://dx.doi.org/10.7232/jkiie.2017.43.5.372.

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41

Gu, Jeongmin, Yanjie Zhou, Amrit Das, Ilkyeong Moon, and Gyu M. Lee. "Medical relief shelter location problem with patient severity under a limited relief budget." Computers & Industrial Engineering 125 (November 2018): 720–28. http://dx.doi.org/10.1016/j.cie.2018.03.027.

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42

Cheng, Chun, Yossiri Adulyasak, and Louis-Martin Rousseau. "Robust facility location under demand uncertainty and facility disruptions." Omega 103 (September 2021): 102429. http://dx.doi.org/10.1016/j.omega.2021.102429.

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43

Nematian, Javad. "Novel Reliable Uncapacitated P-Hub Location Problems Under Uncertainty." International Journal of Fuzzy System Applications 7, no. 4 (October 2018): 115–55. http://dx.doi.org/10.4018/ijfsa.2018100106.

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Hubs are facilities to collect, arrange and distribute commodities in telecommunication networks, cargo delivery systems, etc. In this article, it will study two popular hub location problems (p-hub center and p-hub maximal covering problems) under uncertainty. First, novel reliable uncapacitated p-hub location problems are introduced based on considering the failure probability of hubs, in which the parameters are random fuzzy variables, but the decision variables are real variables. Then, the proposed hub location problems under uncertainty are solved by new methods using random fuzzy chance-constrained programming based on the idea of possibility theory. These methods can satisfy optimistic and pessimistic decision makers under uncertain framework. Finally, some benchmark problems are solved as numerical examples to clarify the described methods and show their efficiency.
44

Li, Lu, Hong K. Lo, Wei Huang, and Feng Xiao. "Mixed bus fleet location-routing-scheduling under range uncertainty." Transportation Research Part B: Methodological 146 (April 2021): 155–79. http://dx.doi.org/10.1016/j.trb.2021.02.005.

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45

Tan, Lin‐Ti, and Song‐Ken Hsu. "The Location Invariance Theorem in Weberian Space Under Uncertainty." Journal of Regional Science 41, no. 1 (February 2001): 97–116. http://dx.doi.org/10.1111/0022-4146.00209.

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46

Lin, Chin-Tsai, and Cheng-Ru Wu. "Optimizing location under exchange rate uncertainty: applying real options." Journal of Information and Optimization Sciences 23, no. 3 (September 2002): 459–73. http://dx.doi.org/10.1080/02522667.2002.10699540.

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47

Fazel Zarandi, Mohammad Hossein, Ahmad Hemmati, Soheil Davari, and I. Burhan Turksen. "Capacitated location-routing problem with time windows under uncertainty." Knowledge-Based Systems 37 (January 2013): 480–89. http://dx.doi.org/10.1016/j.knosys.2012.09.007.

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48

Tan, Lin-Ti. "Agroindustry location under uncertainty: The effects of business taxes." Papers in Regional Science 82, no. 1 (January 1, 2003): 123–35. http://dx.doi.org/10.1007/s101100200114.

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49

De Rosa, Vincenzo, Evi Hartmann, Marina Gebhard, and Jens Wollenweber. "Robust capacitated facility location model for acquisitions under uncertainty." Computers & Industrial Engineering 72 (June 2014): 206–16. http://dx.doi.org/10.1016/j.cie.2014.03.009.

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50

Louveaux, François, and Jacques-François Thisse. "Production and location on a network under demand uncertainty." Operations Research Letters 4, no. 4 (December 1985): 145–49. http://dx.doi.org/10.1016/0167-6377(85)90020-3.

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