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Journal articles on the topic 'Facility Location Optimisation Problem'

Author: Grafiati

Published: 13 September 2024

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1

Lagos, Carolina, Broderick Crawford, Enrique Cabrera, Ricardo Soto, José-Miguel Rubio, and Fernando Paredes. "Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/745921.

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Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

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Cabrera-Guerrero, Guillermo, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, and Enrique Cabrera. "Parameter Tuning for Local-Search-Based Matheuristic Methods." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1702506.

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Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.

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3

Jin, Zhongyi, Kam K. H. Ng, Chenliang Zhang, Wei Liu, Fangni Zhang, and Gangyan Xu. "A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem." Transportation Research Part E: Logistics and Transportation Review 186 (June 2024): 103538. http://dx.doi.org/10.1016/j.tre.2024.103538.

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4

Jin, Zhiyang, Yang Li, Guohua Fu, Kunhao Dai, Na Qiu, Jinyi Liu, Mao Lin, and Zhennan Qin. "Capacitated Facility Location and Allocation with Uncertain Demand for Tourism Logistics: A Multiobjective Optimisation Approach." Mathematical Problems in Engineering 2019 (June 23, 2019): 1–18. http://dx.doi.org/10.1155/2019/4158940.

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The paper develops a Multiobjective Optimisation (MOO) model for addressing Capacitated Facility Location Problem (CFLP) in tourism logistics, where two objectives are total of cost and customer service level. Nondominated Sorting Genetic Algorithm II (NSGA II) is used to solve the model. The illustrative case with imaginary data demonstrates that the model can figure out the location of the nodes of tourism logistics network and allocation of these sites, while the total of cost is reduced by up to 56.75% and customer service level is increased by an average of 105%. The distinction of this study compared to the current papers is that our model incorporates both items A and B to the subject matter of tourism logistics, where items A refer to tourism-related products and items B involve personal goods of tourists. The model established is limited with one assumption and one limitation which are associated with Vehicle Routing Problem (VRP) and the boundary of tourism logistics activity. Therefore, further research for the elimination of these limits is recommended.

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5

Ou-Yang, Chao, Taufiq Budi Hanyata, and T. M. A. Ari Samadhi. "Hybrid self-adaptive-velocity particle swarm optimisation-Cooper heuristic for the facility location allocation problem in Jakarta." International Journal of Systems Science: Operations & Logistics 3, no. 2 (April 14, 2015): 63–78. http://dx.doi.org/10.1080/23302674.2015.1029565.

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6

Lagos, Carolina, Guillermo Guerrero, Enrique Cabrera, Stefanie Niklander, Franklin Johnson, Fernando Paredes, and Jorge Vega. "A Matheuristic Approach Combining Local Search and Mathematical Programming." Scientific Programming 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/1506084.

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A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

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7

Anastasiadis, Eleftherios, Panagiotis Angeloudis, Daniel Ainalis, Qiming Ye, Pei-Yuan Hsu, Renos Karamanis, Jose Escribano Macias, and Marc Stettler. "On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study." Sustainability 13, no. 1 (December 26, 2020): 168. http://dx.doi.org/10.3390/su13010168.

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The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.

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8

Merkisz-Guranowska, Agnieszka. "A comparative study on end-of-life vehicles network design." Archives of Transport 54, no. 2 (June 30, 2020): 107–23. http://dx.doi.org/10.5604/01.3001.0014.2971.

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This paper investigates the current research in the field of the end-of-life vehicles (ELV) recycling network. The optimisation of the location of a network facilities in forward logistics in the automotive industry has received a lot of attention for many years but the reverse logistics for ELVs has been a subject of investigations since the beginning of 21st century. ELV recycling network design gained in popularity after the European Union and other countries like Japan, South Korea and recently China introduced legal obligations to organize a collecting or recycling network for used vehicles. When regulations are introduced, there is a need for a systemic solution to the problem, especially since the obligation to create a collection network is often accompanied by requirements related to its accessibility for vehicle owners or efficiency of operation. With the growing scope of legal regulations, companies or organisations responsible for the network are forced to redesign the existing recycling infrastructure in a given area so that it meets specific requirements. Initially, the most important criterion was network availability. Currently, the same importance is attached to economic, environmental and social aspects in order to meet the sustainability criteria. In this paper, forty one peer-reviewed published studies focused on network design were classified. Its main purpose is to provide an extensive review of state-of-the-art research published in the period 2000-2019. The scope of the review is limited to network design problems including facility location and flow allocation problems. Only papers that present mathematical models are considered. Studies on the ELV network design are classified based on: type of supply chain, type of network, optimisation problem, type of facilities, modelling technique, single/multi objectivity, objective function, period of time, solution approach and scope of implementation. The final part of the paper includes discussion of the methodology of the reviewed studies and some recommendations for future research area.

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9

Jolai, Fariborz, Reza Tavakkoli-Moghaddam, and Mohammad Taghipour. "A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations." International Journal of Production Research 50, no. 15 (August 2012): 4279–93. http://dx.doi.org/10.1080/00207543.2011.613863.

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10

Liu, Zhao, Fang Wang, Pengcheng Xue, and Feng Xue. "Using multi-layer nested network to optimise spatial structure of tourism development between urban and rural areas based on population mobility." Indoor and Built Environment 31, no. 4 (March 1, 2022): 1028–46. http://dx.doi.org/10.1177/1420326x211054809.

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Under the bidirectional flow pattern of urban and rural factors, the tourism industry has become an important way to promote the vitality of urban and rural areas because of rural gentrification and city migration. By constructing a ‘node-link-setting’ networked land use model, this paper maps the tourism flow and geographic space networks, proposes a multi-layer nested network theory with spatial relevance from the mobility perspective to solve practical problems, such as urban–rural dual opposition and functional segmentation. We studied the high-precision mobile smart phone signalling data combined geographical data of Yichang, analysed the dynamic change process and comprehensive development mode characteristics of tourism attractions in 92 villages and towns through the social network analysis, compared tourism flow network changes in Yichang over time to construct a tourism flow network of Yichang, analysed the level and location of tourism nodes in the network and classified the tourist destinations to reveal the spatial-temporal evolution process and change the rule of the tourism flow network. The differences were compared by constructing traffic geography and infrastructure networks. The purpose is to propose guidance countermeasures for regional village unit tourism facility construction and industrial development and to maintain the integrity of spatial correlation and structural optimisation.

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11

Redutskiy, Yury. "Pilot Study on the Application of Employee Scheduling for the Problem of Safety Instrumented System Design and Maintenance Planning for Remotely Located Oil and Gas Facilities." Engineering Management in Production and Services 10, no. 4 (December 1, 2018): 55–64. http://dx.doi.org/10.2478/emj-2018-0022.

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Abstract The technology of production, transportation, and processing of oil and gas involves various hazardous processes. To mitigate the risk that these processes pose, the technological solutions work closely with the automated control and safety systems. The design and organisation of maintenance for the automated safety instrumented systems (SIS) have a significant bearing on the overall safety of operations in this industry. Over the past few decades, many hydrocarbon resources have been discovered in unconventional environments, such as remote, offshore, and arctic locations. Transportation of engineering personnel to these remote locations and back, and thereby, the organisation of the shift work poses additional challenges for the petroleum sector. Under such circumstances, the workforce-related costs play a considerable role in the overall cost of the technological solution and thereby the decisions regarding the workforce organisation should be addressed in the framework of evaluating and choosing the appropriate safety measures. That is why the research presented in this paper aims to address the lifecycle of the technological solution integrating the problems of SIS design, maintenance planning, and employee scheduling into a single decision-making framework to optimise the set of technical and organisational safety measures inherent in the SIS. The performance and maintenance of the SIS are described with a Markov model of device failures, repairs and technological incidents occurrence. The employee scheduling part of the mathematical model utilises the set-covering formulation of maintenance crews taking particular trips. A black-box optimisation algorithm is used to find reasonable solutions to the integrated problem of engineering design and workforce planning. The decisions include the choices of the components and structures for the safety system, the facility overhaul frequencies, the maintenance personnel size, as well as the schedules of trips and shifts for the crews.

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12

Tang, Luohao, Cheng Zhu, Zaili Lin, Jianmai Shi, and Weiming Zhang. "Reliable Facility Location Problem with Facility Protection." PLOS ONE 11, no. 9 (September 1, 2016): e0161532. http://dx.doi.org/10.1371/journal.pone.0161532.

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13

Hsia, Hao-Ching, Hiroaki Ishii, and Kuang-Yih Yeh. "AMBULANCE SERVICE FACILITY LOCATION PROBLEM." Journal of the Operations Research Society of Japan 52, no. 3 (2009): 339–54. http://dx.doi.org/10.15807/jorsj.52.339.

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14

Desrochers, Martin, Patrice Marcotte, and Mihnea Stan. "The congested facility location problem." Location Science 3, no. 1 (May 1995): 9–23. http://dx.doi.org/10.1016/0966-8349(95)00004-2.

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15

Plastria, Frank. "Static competitive facility location: An overview of optimisation approaches." European Journal of Operational Research 129, no. 3 (March 2001): 461–70. http://dx.doi.org/10.1016/s0377-2217(00)00169-7.

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16

Zhang, Min, Jun Huang, and Jian‐ming Zhu. "Reliable facility location problem considering facility failure scenarios." Kybernetes 41, no. 10 (October 12, 2012): 1440–61. http://dx.doi.org/10.1108/03684921211276666.

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17

Gao, Xuehong, Chanseok Park, Xiaopeng Chen, En Xie, Guozhong Huang, and Dingli Zhang. "Globally Optimal Facility Locations for Continuous-Space Facility Location Problems." Applied Sciences 11, no. 16 (August 9, 2021): 7321. http://dx.doi.org/10.3390/app11167321.

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The continuous-space single- and multi-facility location problem has attracted much attention in previous studies. This study focuses on determining the globally optimal facility locations for two- and higher-dimensional continuous-space facility location problems when the Manhattan distance is considered. Before we propose the exact method, we start with the continuous-space single-facility location problem and obtain the global minimizer for the problem using a statistical approach. Then, an exact method is developed to determine the globally optimal solution for the two- and higher-dimensional continuous-space facility location problem, which is different from the previous clustering algorithms. Based on the newly investigated properties of the minimizer, we extend it to multi-facility problems and transfer the continuous-space facility location problem to the discrete-space location problem. To illustrate the effectiveness and efficiency of the proposed method, several instances from a benchmark are provided to compare the performances of different methods, which illustrates the superiority of the proposed exact method in the decision-making of the continuous-space facility location problems.

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18

Soltanzadeh, Amirmasoud, Vahid Baradaran, and Faezeh Soltanzadeh. "Develop an optimisation model for relief facility location in disaster." International Journal of Mathematics in Operational Research 22, no. 4 (2022): 449. http://dx.doi.org/10.1504/ijmor.2022.126043.

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19

Zhen, Lu, Qiuji Sun, Kai Wang, and Xiaotian Zhang. "Facility location and scale optimisation in closed-loop supply chain." International Journal of Production Research 57, no. 24 (March 8, 2019): 7567–85. http://dx.doi.org/10.1080/00207543.2019.1587189.

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20

Beresnev, V. L., and A. A. Melnikov. "A capacitated competitive facility location problem." Journal of Applied and Industrial Mathematics 10, no. 1 (January 2016): 61–68. http://dx.doi.org/10.1134/s1990478916010075.

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21

Álvarez-Miranda, Eduardo, Elena Fernández, and Ivana Ljubić. "The recoverable robust facility location problem." Transportation Research Part B: Methodological 79 (September 2015): 93–120. http://dx.doi.org/10.1016/j.trb.2015.06.001.

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22

Kalcsics, Jörg, Stefan Nickel, Justo Puerto, and Antonio M. Rodríguez-Chía. "The ordered capacitated facility location problem." TOP 18, no. 1 (April 19, 2009): 203–22. http://dx.doi.org/10.1007/s11750-009-0089-0.

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23

Raghavan, S., Mustafa Sahin, and F. Sibel Salman. "The capacitated mobile facility location problem." European Journal of Operational Research 277, no. 2 (September 2019): 507–20. http://dx.doi.org/10.1016/j.ejor.2019.02.055.

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24

Aboolian, Robert, Oded Berman, and Dmitry Krass. "Competitive facility location and design problem." European Journal of Operational Research 182, no. 1 (October 2007): 40–62. http://dx.doi.org/10.1016/j.ejor.2006.07.021.

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25

Bhattacharya, U., J. R. Rao, and R. N. Tiwari. "Fuzzy multi-criteria facility location problem." Fuzzy Sets and Systems 51, no. 3 (November 1992): 277–87. http://dx.doi.org/10.1016/0165-0114(92)90018-y.

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26

Arulselvan, Ashwin, Andreas Bley, and Ivana Ljubić. "The incremental connected facility location problem." Computers & Operations Research 112 (December 2019): 104763. http://dx.doi.org/10.1016/j.cor.2019.104763.

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27

Aziz, Haris, Hau Chan, Barton E. Lee, and David C. Parkes. "The capacity constrained facility location problem." Games and Economic Behavior 124 (November 2020): 478–90. http://dx.doi.org/10.1016/j.geb.2020.09.001.

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28

Abe, Fabio Henrique N., Edna A. Hoshino, and Alessandro Hill. "The Ring Tree Facility Location Problem." Electronic Notes in Discrete Mathematics 50 (December 2015): 331–36. http://dx.doi.org/10.1016/j.endm.2015.07.055.

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29

Tokgöz, Emre, and Theodore B. Trafalis. "2-Facility manifold location routing problem." Optimization Letters 11, no. 2 (December 26, 2015): 389–405. http://dx.doi.org/10.1007/s11590-015-0984-2.

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30

Chardaire, Pierre, Alain Sutter, and Marie-Christine Costa. "Solving the dynamic facility location problem." Networks 28, no. 2 (September 1996): 117–24. http://dx.doi.org/10.1002/(sici)1097-0037(199609)28:2<117::aid-net5>3.0.co;2-h.

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31

Moya-Martínez, Alejandro, Mercedes Landete, and Juan Francisco Monge. "Close-Enough Facility Location." Mathematics 9, no. 6 (March 21, 2021): 670. http://dx.doi.org/10.3390/math9060670.

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This paper introduces the concept of close-enough in the context of facility location. It is assumed that customers are willing to move from their homes to close-enough pickup locations. Given that the number of pickup locations is expanding every day, it is assumed that pickup locations can be placed everywhere. Conversely, the set of potential location for opening facilities is discrete as well as the set of customers. Opening facilities and pickup points entails an installation budget and a distribution cost to transport goods from facilities to customers and pickup locations. The (p,t)-Close-Enough Facility Location Problem is the problem of deciding where to locate p facilities among the finite set of candidates, where to locate t pickup points in the plane and how to allocate customers to facilities or to pickup points so that all the demand is satisfied and the total cost is minimized. In this paper, it is proved that the set of initial infinite number of pickup locations is finite in practice. Two mixed-integer linear programming models are proposed for the discrete problem. The models are enhanced with valid inequalities and a branch and price algorithm is designed for the most promising model. The findings of a comprehensive computational study reveal the performance of the different models and the branch and price algorithm and illustrate the value of pickup locations.

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32

Davoodi, Mansoor. "k-Balanced Center Location problem: A new multi-objective facility location problem." Computers & Operations Research 105 (May 2019): 68–84. http://dx.doi.org/10.1016/j.cor.2019.01.009.

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33

Nishizawa, Subaru, and Tsutomu Suzuki. "Dynamic Facility Location Problem Considering Age of Facility and Conversion." Journal of the City Planning Institute of Japan 50, no. 3 (October 25, 2015): 616–21. http://dx.doi.org/10.11361/journalcpij.50.616.

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34

Welch, S. B., and S. Salhi. "The obnoxious p facility network location problem with facility interaction." European Journal of Operational Research 102, no. 2 (October 1997): 302–19. http://dx.doi.org/10.1016/s0377-2217(97)00111-2.

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35

Teimoury, Ebrahim, Mohammad Modarres Yazdi, Iman Ghaleh Khondabi, and Mahdi Fathi. "Two-Facility Location Problem with Infinite Retrial Queue." International Journal of Strategic Decision Sciences 2, no. 3 (July 2011): 38–54. http://dx.doi.org/10.4018/jsds.2011070103.

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This paper analyzes a two-facility location problem under demand uncertainty. The maximum server for the ith facility is . It is assumed that primary service demand arrivals for the ith facility follow a Poisson process. Each customer chooses one of the facilities with a probability which depends on his or her distance to each facility. The service times are assumed to be exponential and there is no vacation or failure in the system. Both facilities are assumed to be substitutable which means that if a facility has no free server, the other facility is used to fulfill the demand. When there is no idle server in both facilities, each arriving primary demand goes into an orbit of unlimited size. The orbiting demands retry to get service following an exponential distribution. In this paper, the authors give a stability condition of the demand satisfying process, and then obtain the steady-state distribution by applying matrix geometric method in order to calculation of some key performance indexes. By considering the fixed cost of opening a facility and the steady state service costs, the best locations for two facilities are derived. The result is illustrated by a numerical example.

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36

OHSAWA, Yoshiaki, and Atsuo SUZUKI. "LOCATION-ALLOCATION PROBLEM OF MULTI-PERSON FACILITY." Journal of the Operations Research Society of Japan 30, no. 3 (1987): 368–95. http://dx.doi.org/10.15807/jorsj.30.368.

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37

Lynskey, Jared, Kyi Thar, Thant Oo, and Choong Hong. "Facility Location Problem Approach for Distributed Drones." Symmetry 11, no. 1 (January 20, 2019): 118. http://dx.doi.org/10.3390/sym11010118.

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Currently, industry and academia are undergoing an evolution in developing the next generation of drone applications. Including the development of autonomous drones that can carry out tasks without the assistance of a human operator. In spite of this, there are still problems left unanswered related to the placement of drone take-off, landing and charging areas. Future policies by governments and aviation agencies are inevitably going to restrict the operational area where drones can take-off and land. Hence, there is a need to develop a system to manage landing and take-off areas for drones. Additionally, we proposed this approach due to the lack of justification for the initial location of drones in current research. Therefore, to provide a foundation for future research, we give a justified reason that allows predetermined location of drones with the use of drone ports. Furthermore, we propose an algorithm to optimally place these drone ports to minimize the average distance drones must travel based on a set of potential drone port locations and tasks generated in a given area. Our approach is derived from the Facility Location problem which produces an efficient near optimal solution to place drone ports that reduces the overall drone energy consumption. Secondly, we apply various traveling salesman algorithms to determine the shortest route the drone must travel to visit all the tasks.

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38

Lee, Sang-Un. "Facility Location Problem for Blood Logistics Center." Journal of the Institute of Webcasting, Internet and Telecommunication 12, no. 2 (April 30, 2012): 135–43. http://dx.doi.org/10.7236/jiwit.2012.12.2.135.

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39

Opesemowo, B. O., and C. O. Yinka-Banjo. "Metaheuristics for solving facility location optimization problem." Journal of Computer Science and Its Application 26, no. 2 (February 11, 2020): 31. http://dx.doi.org/10.4314/jcsia.v26i2.4.

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40

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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41

Drezner, Zvi, Avram Mehrez, and George O. Wesolowsky. "The Facility Location Problem with Limited Distances." Transportation Science 25, no. 3 (August 1991): 183–87. http://dx.doi.org/10.1287/trsc.25.3.183.

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42

Tamir, Arie. "The k-centrum multi-facility location problem." Discrete Applied Mathematics 109, no. 3 (May 2001): 293–307. http://dx.doi.org/10.1016/s0166-218x(00)00253-5.

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43

Albareda-Sambola, Maria, Elena Fernández, and Francisco Saldanha-da-Gama. "The facility location problem with Bernoulli demands." Omega 39, no. 3 (June 2011): 335–45. http://dx.doi.org/10.1016/j.omega.2010.08.002.

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44

Corberán, Ángel, Mercedes Landete, Juanjo Peiró, and Francisco Saldanha-da-Gama. "The facility location problem with capacity transfers." Transportation Research Part E: Logistics and Transportation Review 138 (June 2020): 101943. http://dx.doi.org/10.1016/j.tre.2020.101943.

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45

Myung, Young-Soo, Hu-gon Kim, and Dong-wan Tcha. "A bi-objective uncapacitated facility location problem." European Journal of Operational Research 100, no. 3 (August 1997): 608–16. http://dx.doi.org/10.1016/s0377-2217(96)00168-3.

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46

Huang, Huei-Chuen, and Rongheng Li. "A k-product uncapacitated facility location problem." European Journal of Operational Research 185, no. 2 (March 2008): 552–62. http://dx.doi.org/10.1016/j.ejor.2007.01.010.

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47

Bhattacharya, U., and R. N. Tiwari. "Imprecise weights in weber facility location problem." Fuzzy Sets and Systems 62, no. 1 (February 1994): 31–38. http://dx.doi.org/10.1016/0165-0114(94)90070-1.

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48

Manthey, Bodo, and Matthijs B. Tijink. "Perturbation resilience for the facility location problem." Operations Research Letters 46, no. 2 (March 2018): 215–18. http://dx.doi.org/10.1016/j.orl.2018.01.003.

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49

Blanquero, Rafael, Emilio Carrizosa, Boglárka G.-Tóth, and Amaya Nogales-Gómez. "p-facility Huff location problem on networks." European Journal of Operational Research 255, no. 1 (November 2016): 34–42. http://dx.doi.org/10.1016/j.ejor.2016.04.039.

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Litvinchev, Igor, and Edith Lucero Ozuna. "Lagrangian Heuristic for the Facility Location Problem." IFAC Proceedings Volumes 46, no. 24 (September 2013): 107–13. http://dx.doi.org/10.3182/20130911-3-br-3021.00022.

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