Simultaneous solution of location and routing problem in critical times with the help of mathematical modeling
محورهای موضوعی : Operation Research
1 - دانشگاه آزاد اردبیل
کلید واژه: meta heuristic, location-routing problem, heuristic, heterogeneous vehicle ,
چکیده مقاله :
This study processes a basic pre-disaster transportation problem at the management level, where a decision maker decides on different intersection locations considering different possible vehicle routes. Modeling and practical solution methods in the operation of transportation systems ensure that the damage caused by the disaster is minimized. A decision management model is proposed for location selection and distribution operations in relief strategy with conventional fuel consumption estimation. In the mentioned problem, the amount of demand of each node depends on the extent and size of the possible disaster. The possibility of each arch/road being closed or open and heterogeneous vehicle fleets in terms of vehicle size are also observed. This process is expressed as probabilistic multi-objective mixed integer linear programming whose objectives are to minimize the total system cost (i.e. fixed vehicle cost, fuel cost and fixed opening cost) and the total travel time to the destination. According to the studies carried out in this particular field, the proposed decision support model is unique in terms of the features that are simultaneously considered. The application of this process deals with a case study and subsequent numerical analysis of a possible earthquake in Tehran. Throughout the paper, it has been proven that the proposed model has the potential to assist managers in preparing for a natural disaster. A solution approach based on the clustering method is also proposed to solve the larger problems of the problem. The effective application of this heuristic method is demonstrated by presenting it to real-scale problems.
چکیده انگلیسی:
This study processes a basic pre-disaster transportation problem at the management level, where a decision maker decides on different intersection locations considering different possible vehicle routes. Modeling and practical solution methods in the operation of transportation systems ensure that the damage caused by the disaster is minimized. A decision management model is proposed for location selection and distribution operations in relief strategy with conventional fuel consumption estimation. In the mentioned problem, the amount of demand of each node depends on the extent and size of the possible disaster. The possibility of each arch/road being closed or open and heterogeneous vehicle fleets in terms of vehicle size are also observed. This process is expressed as probabilistic multi-objective mixed integer linear programming whose objectives are to minimize the total system cost (i.e. fixed vehicle cost, fuel cost and fixed opening cost) and the total travel time to the destination. According to the studies carried out in this particular field, the proposed decision support model is unique in terms of the features that are simultaneously considered. The application of this process deals with a case study and subsequent numerical analysis of a possible earthquake in Tehran. Throughout the paper, it has been proven that the proposed model has the potential to assist managers in preparing for a natural disaster. A solution approach based on the clustering method is also proposed to solve the larger problems of the problem. The effective application of this heuristic method is demonstrated by presenting it to real-scale problems.
منابع و مأخذ:
Adarang, H., Bozorgi-Amiri, A., Khalili-Damghani, K., Tavakkoli-Moghaddam, R., 2020. A robust bi-objective
location-routing model for providing emergency medical services. Journal of Humanitarian Logistics and Supply Chain Management, 10, 3, 285–319.
Aghighi, A., Goli, A.,Malmir, B., Tirkolaee, E.B., 2021. The stochastic location-routing-inventory problem of perishable products with reneging and balking. Journal of Ambient Intelligence and Humanized Computing, 14, 6497–6516.
Ahmadi, M., Seifi, A., Tootooni, B., 2015. A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163.
Alamatsaz, K., Ahmadi, A., Mirzapour Al-E-Hashem, S.M.J., 2021. A multiobjective model for the green capacitated location-routing problem considering drivers’ satisfaction and time window with uncertain demand. Environmental Science and Pollution Research, 29, 4, 5052–5071.
Albareda-Sambola, M., Fernandez, E., Laporte, G., 2007. Heuristic and lower bound for a stochastic location-routing problem. European Journal of Operational Research, 179, 3, 940–955.
Araghi, M.E.T., Tavakkoli-Moghaddam, R., Jolai, F., Molana, S.M.H., 2021. A green multi-facilities open locationrouting problem with planar facility locations and uncertain customer. Journal of Cleaner Production, 282, 124343.
Azad,N., Davoudpour,H., 2013. Designing a stochastic distribution network model under risk. The International Journal of Advanced Manufacturing Technology, 64, 1–4, 23–40.
Barth, M., Younglove, T., Scora, G., 2005. Development of a heavy-duty diesel modal emissions and fuel consumption model. California Partners for Advanced Transportation Technology, UC Berkeley. Available at https:// escholarship.org/uc/item/67f0v3zf (accessed March 2021).
Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R., Seyedaliakbar, S.M., 2020a. A location-routing model for assessment of the injured people and relief distribution under uncertainty. International Journal of Engineering, 33, 7, 1274–1284.
Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R., Seyedaliakbar, S.M., 2020b. Multi-objective optimization of multivehicle relief logistics considering satisfaction levels under uncertainty. International Journal of Engineering, 33, 5, 814–824.
Bilir, C., 2023. Effect of demand uncertainty on omnichannel distribution network design strategies. International Transactions in Operational Research. https://doi.org/10.1111/itor.13310
Beiki, H., Seyedhosseini, S.M., Mihardjo, L.W., Seyedaliakbar, S.M., 2021. Multiobjective location-routing problem of relief commodities with reliability. Environmental Science and Pollution Research.
Boonmee, C., Arimura, M., Asada, T., 2017. Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485–498.
Bozorgi-Amiri, A., Khorsi,M., 2016. A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. The International Journal of Advanced Manufacturing Technology, 85, 5, 1633–1648.
Bruni, M.E., Khodaparasti, S., 2022. A variable neighborhood descent matheuristic for the drone routing problem with beehives sharing. Sustainability, 14, 16, 9978.
Burkhardt, M., Nitsch, F.J., Spinler, S., van Wassenhove, L., 2023. The effect of acute stress on humanitarian supplies management. Production and Operations Management, 32, 2546–2559.
Caunhye,A.M., Zhang,Y., Li, M.,Nie,X., 2016.Alocation-routingmodel for prepositioning and distributing emergency supplies. Transportation Research Part E Logistics and Transportation Review, 90, 161–176.
Chan, Y., Carter, W.B., Burnes, M.D., 2001. A multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands. Computers & Operations Research, 28, 8, 803–826.
Chang, K., Zhou, H., Chen, G., Chen, H., 2017. Multiobjective location routing problem considering uncertain data
after disasters. Discrete Dynamics in Nature and Society, 2017, 1703608.
Chang, K.H., Chen, T.L., Yang, F.H., Chang, T.Y., 2023. Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident. European Journal of Operational Research, 309, 3, 1237–1262.
Cotes, N., Cantillo, V., 2019. Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89–100.
Curtis, F.E., Liu, S., Robinson, D.P., 2023. Fair machine learning through constrained stochastic optimization and an _-constraint method. Optimization Letters.
Daroudi, S., Kazemipoor, H., Najafi, E., Fallah, M., 2021. The minimum latency in location routing fuzzy inventory
problem for perishable multi-product materials. Applied Soft Computing, 110, 107543.
De Maio, A., Musmanno, R., Vocaturo, F., 2022. Unbiased decision making in location-routing problems with uncertain customer demands. Soft Computing.
De Veluz, M.R.D., Redi, A.A.N.P., Maaliw, R.R., Persada, S.F., Prasetyo, Y.T., Young, M.N., 2023. Scenario-based multi-objective location-routing model for pre-disaster planning: a Philippine case study. Sustainability, 15(6), 4882.
Delfani, F., Kazemi, A., Seyedhosseini, S.M., Niaki, S.T.A., 2020. A green hazardous waste location-routing problem considering the risks associated with transportation and population, International Journal of Engineering, 33, 11, 2272–2284.
Delfani, F., Kazemi, A., Seyedhosseini, S.M., Niaki, S.T.A., 2021. A novel robust possibilistic programming approach for the hazardous waste location-routing problem considering the risks of transportation and population. International Journal of Systems Science: Operations & Logistics, 8, 4, 383–395.
Deng, X., 2021. Location-routing problem of emergency facilities under uncertain demand by branch-price and cut. Journal of Mathematics, 2021, 9152989. DEZIM., 2020. Kartal olası deprem kayıp tahminleri kitapcı˘gı. Turkiye: ˙Istanbul.
Drexl, M., Schneider, M., 2015. A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241, 2, 283–308.
Erdo˘gan, S., Miller-Hooks, E., 2012. A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.
Fathollahi, A., Derakhshandeh, S. Y., Ghiasian, A., Masoum, M.A., 2022. Optimal siting and sizing of wireless EV charging infrastructures considering traffic network and power distribution system. IEEE Access, 10, 117105–117117.
Geismar, H.N., Huang, Y., Pillai, S.D., Sriskandarajah, C., Youn, S., 2020. Location-routing with conflicting objectives: coordinating eBeam phytosanitary treatment and distribution of Mexican import commodities. Production and Operations Management, 29, 6, 1506–1531.
Ghaderi, A., Burdett, R.L., 2019. An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transportation Research Part E: Logistics and Transportation Review, 127, 49–65.
Ghaffari-Nasab, N., Ahari, S.G., Ghazanfari, M., 2013a. A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy demands. Scientia Iranica, 20, 3, 919–930.
Ghaffari-Nasab, N., Jabalameli, M.S., Aryanezhad, M.B., Makui, A., 2013b. Modeling and solving the bi-objective capacitated location-routing problem with probabilistic travel times. The International Journal of Advanced Manufacturing Technology, 67, 9-12, 2007–2019.
Ghahremani-Nahr, J., Nozari, H., Rahmaty, M., Zeraati Foukolaei, P., Sherejsharifi, A., 2023. Development of a novel fuzzy hierarchical location-routing optimization model considering reliability. Logistics, 7, 3, 64.
Ghasemi, P., Goodarzian, F., Munuzuri, J., Abraham, A., 2022. A cooperative game theory approach for locationrouting- inventory decisions in humanitarian relief chain incorporating stochastic planning. Applied Mathematical Modelling, 104, 750–781.
Gholamian, M. R., Heydari, M. (2017). An inventory model with METRIC approach in location-routing-inventory
problem. Advances in Production Engineering & Management, 12(2), 115–126. https://doi.org/10.14743/apem2017. 2.244
Govindan, K.,Mina, H., Esmaeili, A., Gholami-Zanjani, S.M., 2020. An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production, 242, 118317.
Govindan, K., Nosrati-Abarghooee, S., Nasiri, M.M., Jolai, F., 2022. Green reverse logistics network design for medical waste management: a circular economy transition through case approach. Journal of Environmental Management, 322, 115888.
Haimes, Y., 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1, 3, 296–297.
Harati, S., Roghanian, E., Hafezalkotob, A., Shojaie, A.A., 2021. A robust two-stage stochastic location-routinginventory model for perishable items. Tehnicki vjesnik/Technical Gazette, 28, 6, 1989–1995.
Hashemi, L., Mahmoodi, A., Jasemi, M., Millar, R.C., Laliberte, J., 2022. Designing a locating-routing three-echelon supply chain network under uncertainty. International Journal of Intelligent Computing and Cybernetics, 15, 4, 562– 588.
Hassan-Pour, H.A., Mosadegh-Khah, M., Tavakkoli-Moghaddam, R., 2009. Solving a multi-objective multi-depot
stochastic location-routing problem by a hybrid simulated annealing algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223, 8, 1045–1054.
Hassan-Pour, S.T., Ke, G.Y., Tulett, D.M., 2021. A time-dependent location-routing problem of hazardous material transportation with edge unavailability and time window. Journal of Cleaner Production, 322, 128951.
Hauder, V.A., Karder, J., Beham, A., Wagner, S., Affenzeller, M., 2018. A general solution approach for the location routing problem. In Moreno-Diaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory– EUROCAST2017: 16th International Conference, February 19-24, Las Palmas de Gran Canaria, Spain, Revised Selected Papers, Part I 16. Springer International Publishing, Cham, pp. 257–265.
Huang, S.H., 2015. Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands. Computers & Industrial Engineering, 87, 104–113.
Javid, A.A., Azad, N., 2010. Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E- Logistics and Transportation Review, 46(5), 582–597.
Karimi, H., Setak, M., 2018. A bi-objective incomplete hub location-routing problem with flow shipment scheduling. Applied Mathematical Modelling, 57, 406–431.
Kazanc, H.C., Soysal, M., Cimen, M., 2021. Modeling heterogeneous fleet vehicle allocation problem with emissions considerations. The Open Transportation Journal, 15, 1, 93–107.
Kawase, R., Iryo, T., 2023. Optimal stochastic inventory-distribution strategy for damaged multi-echelon humanitarian logistics network. European Journal of Operational Research, 309, 2, 616–633.
Khorsi, M., Chaharsooghi, S.K., Kashan, A.H., Bozorgi-Amiri, A., 2021. Pareto-based grouping meta-heuristic algorithm for humanitarian relief logistics with multistate network reliability. OR Spectrum, 43, 327–365.
Khoshgehbari, F., Mirzapour Al-e Hashem, S.M.J., 2023. Ambulance location routing problem considering all sources of uncertainty: progressive estimating algorithm. Computers & Operations Research, 160, 106400.
Kılcı, F., 2012. A decision support system for shelter site selection with GIS integration: case for Turkey. PhD thesis, Bilkent University, Ankara.
Kılcı, F., Kara, B.Y., Bozkaya, B., 2015. Locating temporary shelter areas after an earthquake: a case for Turkey. European Journal of Operational Research, 243, 1, 323–332.
Kimms, A., Maiwald, M., 2018. Bi-objective safe and resilient urban evacuation planning. European Journal of Operational Research, 269, 3, 1122–1136.
Koc, C., Bekta，s, T., Jabali, O., Laporte, G., 2016. The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B: Methodological, 84, 81–102.
Kordi, G., Hasanzadeh-Moghimi, P., Paydar, M.M., Asadi-Gangraj, E., 2022. A multi-objective location-routing model for dental waste considering environmental factors. Annals of Operations Research, 328, 755–792.
Li, L., Lo, H.K., Huang,W., Xiao, F., 2021. Mixed bus fleet location-routing-scheduling under range uncertainty. Transportation Research Part B: Methodological, 146, 155–179.
Long, S., Zhang, D., Liang, Y., Li, S., Chen, W., 2021. Robust optimization of the multi-objective multi-period locationrouting problem for epidemic logistics system with uncertain demand. IEEE Access, 9, 151912–151930.
Mansoori, S., Bozorgi-Amiri,A., Pishvaee, M.S., 2020.Arobustmulti-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications, 32, 7, 2183–2203.
Marinakis, Y., 2015. An improved particle swarm optimization algorithm for the capacitated location routing problem and for the location routing problem with stochastic demands. Applied Soft Computing Journal, 37, 680 701.
Marinakis, Y., Marinaki, M., Migdalas, A., 2016. A hybrid clonal selection algorithm for the location routing problem with stochastic demands. Annals of Mathematics and Artificial Intelligence, 76(1-2), 121–142.
Martinez-Reyes, A., Quintero-Araujo, C.L., Solano-Charris, E.L., 2021. Supplying personal protective equipment to intensive care units during the COVID-19 outbreak in Colombia. A simheuristic approach based on the locationrouting problem. Sustainability, 13, 14, 7822.
Mehrjerdi, Y.Z., Nadizadeh, A., 2013. Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229, 1, 75–84.
Meidute-Kavaliauskiene, I., Yıldırım, F., Ghorbani, S., Cˇ incˇikaite˙, R., 2022. The design of a multi-period and multiechelon perishable goods supply network under uncertainty. Sustainability, 14, 4, 2472.
Minato, N., Morimoto, R., 2012. Collaborative management of regional air transport during natural disasters: case of the 2011 East Japan earthquake and tsunami. Research in Transportation Business & Management, 4, 13–21.
Mirzaei-Khafri, S., Bashiri, M., Soltani, R., 2020. A robust optimization model for a location-arc routing problem. International Journal of Industrial Engineering-Theory Applications and Practice, 27, 2, 288–307.
Mohammadi,M., Razmi, J., Tavakkoli-Moghaddam, R., 2013. Multi-objective invasive weed optimization for stochastic green hub location routing problem with simultaneous pick-ups and deliveries. Economic Computation & Economic Cybernetics Studies & Research, 47, 3, 247–266.
Moshref-Javadi, M., Lee, S., 2016. The latency location-routing problem. European Journal of Operational Research, 255, 2, 604–619.
Mousavi, S.M., Vahdani, B., 2017. A robust approach to multiple vehicle location-routing problems with time windows for optimization of cross-docking under uncertainty. Journal of Intelligent and Fuzzy Systems, 32, 1, 49–62.
Nadizadeh, A., Nasab, H.H., 2014. Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. European Journal of Operational Research, 238, 2, 458–470.
Nagy, G., Salhi, S., 2007. Location-routing: issues, models and methods. European Journal of Operational Research, 177, 2, 649–672.
Nakhjirkan, S.,Mokhatab Rafiei, F., 2017. An integratedmulti-echelon supply chain network design considering stochastic demand: A genetic algorithm based solution. Promet—Traffic & Transportation, 29, 4, 391–400.
Natarajan, K.V., Swaminathan, J.M., 2017. Multi-treatment inventory allocation in humanitarian health settings under funding constraints. Production and Operations Management, 26, 6, 1015–1034.
Nasrollahi, M., Razmi, J., Ghodsi, R., 2018. A computational method for measuring transport related carbon emissions in a healthcare supply network under mixed uncertainty: an empirical study. Promet—Traffic & Transportation, 30, 6, 693–708.
Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V.R., Javanmard, A.S., 2014. Solving a new bi-objective
location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering, 76, 204–221.
Nikzamir, M., Baradaran, V., 2020. A healthcare logistic network considering stochastic emission of contamination: biobjective model and solution algorithm. Transportation Research Part E: Logistics and Transportation Review, 142, 102060.
Oruc, B.E., Kara, B., 2018. Post-disaster assessment routing problem. Transportation Research Part B: Methodologıcal, 116, 76–102. https://doi.org/10.1016/j.trb.2018.08.002
Panadero, J., Juan, A.A., Ghorbani, E., Faulin, J., Pages-Bernaus, A., 2023. Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method. International Transactions in Operational Research, 31, 5, 3036–3060.
Perdana, T., Chaerani, D., Achmad, A.L.H., Hermiatin, F.R., 2020. Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty. Heliyon, 6, 10, e05128.
Perdana, T., Chaerani, D., Hermiatin, F.R., Achmad, A.L.H., Fridayana, A., 2022. Improving the capacity of local food network through local food hubs’ development. Open Agriculture, 7, 1, 311–322.
Pollution-routing problem. Available at http://www.apollo.management.soton.ac.uk/prplib.htm (accessedMarch 2021).
Pourmohammadi, P., Tavakkoli-Moghaddam, R., Rahimi, Y., Triki, C., 2021. Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm. Annals of Operations Research, 324, 1099–1128.
Prodhon, C., Prins, C., 2014. A survey of recent research on location-routing problems. European Journal of Operational Research, 238, 1, 1–17.
Rabbani, M., Aghabegloo, M., Farrokhi-Asl, H., 2017. Solving a bi-objective mathematical programming model for bloodmobiles location routing problem. International Journal of Industrial Engineering Computations, 8, 1, 19–32.
Rabbani, M., Heidari, R., Yazdanparast, R., 2019. A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. European Journal of Operational Research, 272, 3, 945– 961.
Rabiei, P., Arias-Aranda, D., Stantchev, V., 2023. Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA. Expert Systems with Applications, 226, 120142.
Raeisi, D., Jafarzadeh Ghoushchi, S., 2022. A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions. Applied Intelligence, 52, 12, 13435–13455.
Rafie-Majd, Z., Pasandideh, S.H. R., Naderi, B., 2018. Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers and Chemical Engineering, 109, 9–22.
Roosta, S., Mirnajafizadeh, S., Harandi, H., 2023. Development of a robust multi-objective model for green capacitated location-routing under crisis conditions. Journal of Project Management, 8, 1, 1–24.
Sabouhi, F., Bozorgi-Amiri, A., Vaez, P., 2021. Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50, 9, 2632–2650.
Saeidi-Mobarakeh, Z., Tavakkoli-Moghaddam, R., Navabakhsh, M., Amoozad-Khalili, H., 2020. A bi-level and robust optimization-based framework for a hazardous waste management problem: a real-world application. Journal of Cleaner Production, 252, 119830.
Schiffer, M.,Walther, G., 2018. Strategic planning of electric logistics fleet networks: a robust location-routing approach. Omega, 80, 31–42.
Shen, L., Tao, F., Shi, Y., Qin, R., 2019. Optimization of location-routing problem in emergency logistics considering carbon emissions. International Journal of Environmental Research and Public Health, 16, 16, 2982.
Shiripour, S., Mahdavi-amiri, N., Mahdavi, I., 2015. A nonlinear model for a capacitated random transportation network. Journal of Industrial and Production Engineering, 32, 8, 500–515.
Shiripour, S., Mahdavi-Amiri, N., Mahdavi, I., 2017. A transportation network model with intelligent probabilistic travel times and two hybrid algorithms. Transportation Letters, 9, 2, 90–122.
Shiripour, S., Mahdavi, I., Mahdavi-Amiri, N., 2016. A nonlinear model for a capacitated stochastic transportation network usingmultiple linear regression. International Journal of Management Science and EngineeringManagement, 11, 4, 252–261.
Snoeck, A., Winkenbach, M., 2020. The value of physical distribution flexibility in serving dense and uncertain urban markets. Transportation Research Part A: Policy and Practice, 136, 151–177.
Soysal, M., Bloemhof-Ruwaard, J. M., Bekta，s, T., 2015. The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. International Journal of Production Economics, 164, 366–378.
Sutrisno, H., Yang, C.L., 2023. A two-echelon location routing problem with mobile satellites for last-mile delivery: mathematical formulation and clustering-based heuristic method. Annals of Operations Research, 323(1–2), 203– 228.
Tavana, M., Abtahi, A.R., Di Caprio, D., Hashemi, R., Yousefi-Zenouz, R., 2018. An integrated location-inventoryrouting humanitarian supply chain network with pre-and post-disastermanagement considerations. Socio-Economic Planning Sciences, 64, 21–37.
Tirkolaee, E.B., Abbasian, P.,Weber,G.W., 2021. Sustainable fuzzymulti-trip location-routing problem formedical waste management during the COVID-19 outbreak. Science of the Total Environment, 756, 143607.
Tonissen, D.D., Arts, J.J., Shen, Z.J., 2019. Maintenance location routing for rolling stock under line and fleet planning uncertainty. Transportation Science, 53, 5, 1252–1270.
Tonissen, D.D., Arts, J.J., 2020. The stochastic maintenance location routing allocation problem for rolling stock. International Journal of Production Economics, 230, 107826.
Tonissen, D.D., Arts, J.J., Shen, Z.J.M., 2021. A column-and-constraint generation algorithm for two-stage stochastic programming problems. Top, 29, 3, 781–798.
Tordecilla, R.D., Montoya-Torres, J.R., Quintero-Araujo, C.L., Panadero, J., Juan, A.A., 2023. The location routing
problem with facility sizing decisions. International Transactions in Operational Research, 30, 2, 915–945.
Torfi, F., Farahani, R.Z., Mahdavi, I., 2016. Fuzzy MCDM for weight of object’s phrase in location routing problem Applied Mathematical Modelling, 40, 1, 526–541.
Unal, G., 2011. Acil Lojistik Yardım Operasyonu Deprem Lojisti˘gi Karar Destek Sistemi: Alyo-Dlkds (Olası ˙Istanbul Depremi Uygulaması). PhD thesis, Turkish Military Academy Defense Sciences Institutes. UNICEF., 2023.
Vahdani, B., Veysmoradi, D., Mousavi, S.M., Amiri, M., 2022. Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty. Socio-Economic Planning Sciences, 80, 101158.
Vakili, R., Akbarpour Shirazi, M., Gitinavard, H., 2021. Multi-echelon green open-location-routing problem: a robustbased stochastic optimization approach. Scientia Iranica, 28, 2, 985–1000.
van Wassenhove, L.N., Pedraza Martinez, A.J., 2012. Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in Operational Research, 19, 1–2, 307–322.
Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., Mousavi, S.M., 2018. Multi-objective open location-routing model for relief distribution networks with split delivery andmulti-mode transportation under uncertainty. Scientia Iranica, 25, 6, 3635–3653.
Vural, D., Dell, R.F., Kose, E., 2021. Locating unmanned aircraft systems for multiple missions under different weather conditions. Operational Research, 21, 1, 725–744.
Wang, D., Yang, K., Yang, L., Li, S., 2022. Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity. International Transactions in Operational Research, 31, 3, 1736–1761.
Wang, C., Peng, Z., Xu,W., 2023. Robust bi-level optimization for maritime emergencymaterials distribution in uncertain decision-making environments. Mathematics, 11, 19, 4140.
Wu, J., He, J., Lin, Y., Bie, Y., 2023. Co-evolutionary location-routing model of medical isolation areas for major public health emergencies considering the uncertainty of future traffic environment. Transportation Research Record, 2677, 3, 1408–1423.
Xiong, X., Zhao, F., Wang, Y., Wang, Y., 2019. Research on the model and algorithm for multimodal distribution of emergency supplies after earthquake in the perspective of fairness. Mathematical Problems in Engineering, 2019, 1–12.
Xu, J., Wang, Z., Zhang, M., Tu, Y., 2016. A new model for a 72-h post-earthquake emergency logistics location routing problem under a random fuzzy environment. Transportation Letters, 8, 5, 270–285.
Yang, L., Zhang, C.,Wan, P., Li, H., 2022. Optimization of location-routing for the waste household appliances recycling logistics under the uncertain condition. Tehniˇcki Vjesnik, 29, 1, 127–138.
Yavari, M., Enjavi, H., Geraeli, M., 2020. Demand management to cope with routes disruptions in location-inventoryrouting problem for perishable products. Research in Transportation Business & Management, 37, 100552.
Zandkarimkhani, S., Mina, H., Biuki,M., Govindan, K., 2020. A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Annals of Operations Research, 295, 1, 425–452.
Zarandi,M.H.F., Hemmati, A., Davari, S., Turksen, I.B., 2013. Capacitated location-routing problem with time windows under uncertainty. Knowledge-Based Systems, 37, 480-489.
Zarandi, M.H.F., Hemmati, A., Davari, S., Turksen, I.B., 2014. A simulated annealing algorithm for routing problems with fuzzy constrains. Journal of Intelligent & Fuzzy Systems, 26, 6, 2649–2660.
Zargary, S., Samouei, P., 2022. Production-routing-inventory in post-disaster conditions: a multi-objective mathematical model and two algorithms. Process Integration and Optimization for Sustainability, 6, 4, 1163 1183.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., Mohammadi, M., 2016. Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182–214.
Zhang, Y., Qi, M., Lin, W.H., Miao, L., 2015. A metaheuristic approach to the reliable location routing problem under disruptions. Transportation Research Part E-Logistics and Transportation Review, 83, 90–110.
Zhang, B., Li, H., Li, S., Peng, J., 2018. Sustainable multi-depot emergency facilities location-routing problem with uncertain information. Applied Mathematics and Computation, 333, 506–520.
Zhang, S., Chen, M., Zhang,W., 2019. A novel location-routing problem in electric vehicle transportation with stochastic demands. Journal of Cleaner Production, 221, 567–581.
Zhang, H., Liu, F., Ma, L., Zhang, Z., 2020. A hybrid heuristic based on a particle swarm algorithm to solve the capacitated location-routing problem with fuzzy demands. IEEE Access, 8, 153671–153691.
Zhang, L., Lu, J., Yang, Z., 2021. Dynamic optimization of emergency resource scheduling in a large-scale maritime oil spill accident. Computers and Industrial Engineering, 152, 107028.
Zhang, P., Liu, Y., Yang, G., Zhang, G., 2022. A multi-objective distributionally robust model for sustainable last mile relief network design problem. Annals of Operations Research, 309, 689–730.
Zhang, Y., Chen, Z., 2023. Social sustainability-oriented multi-period medical supply adjustment in response to disasters. IEEE Access, 11, 91485–91499. Zhao, J., Wu, B., Ke, G.Y., 2021. A bi-objective robust optimization approach for the management of infectious wastes with demand uncertainty during a pandemic. Journal of Cleaner Production, 314, 127922.
Adarang, H., Bozorgi-Amiri, A., Khalili-Damghani, K., Tavakkoli-Moghaddam, R., 2020. A robust bi-objective
location-routing model for providing emergency medical services. Journal of Humanitarian Logistics and Supply Chain Management, 10, 3, 285–319.
Aghighi, A., Goli, A.,Malmir, B., Tirkolaee, E.B., 2021. The stochastic location-routing-inventory problem of perishable products with reneging and balking. Journal of Ambient Intelligence and Humanized Computing, 14, 6497–6516.
Ahmadi, M., Seifi, A., Tootooni, B., 2015. A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163.
Alamatsaz, K., Ahmadi, A., Mirzapour Al-E-Hashem, S.M.J., 2021. A multiobjective model for the green capacitated location-routing problem considering drivers’ satisfaction and time window with uncertain demand. Environmental Science and Pollution Research, 29, 4, 5052–5071.
Albareda-Sambola, M., Fernandez, E., Laporte, G., 2007. Heuristic and lower bound for a stochastic location-routing problem. European Journal of Operational Research, 179, 3, 940–955.
Araghi, M.E.T., Tavakkoli-Moghaddam, R., Jolai, F., Molana, S.M.H., 2021. A green multi-facilities open locationrouting problem with planar facility locations and uncertain customer. Journal of Cleaner Production, 282, 124343.
Azad,N., Davoudpour,H., 2013. Designing a stochastic distribution network model under risk. The International Journal of Advanced Manufacturing Technology, 64, 1–4, 23–40.
Barth, M., Younglove, T., Scora, G., 2005. Development of a heavy-duty diesel modal emissions and fuel consumption model. California Partners for Advanced Transportation Technology, UC Berkeley. Available at https:// escholarship.org/uc/item/67f0v3zf (accessed March 2021).
Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R., Seyedaliakbar, S.M., 2020a. A location-routing model for assessment of the injured people and relief distribution under uncertainty. International Journal of Engineering, 33, 7, 1274–1284.
Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R., Seyedaliakbar, S.M., 2020b. Multi-objective optimization of multivehicle relief logistics considering satisfaction levels under uncertainty. International Journal of Engineering, 33, 5, 814–824.
Bilir, C., 2023. Effect of demand uncertainty on omnichannel distribution network design strategies. International Transactions in Operational Research. https://doi.org/10.1111/itor.13310
Beiki, H., Seyedhosseini, S.M., Mihardjo, L.W., Seyedaliakbar, S.M., 2021. Multiobjective location-routing problem of relief commodities with reliability. Environmental Science and Pollution Research.
Boonmee, C., Arimura, M., Asada, T., 2017. Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485–498.
Bozorgi-Amiri, A., Khorsi,M., 2016. A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. The International Journal of Advanced Manufacturing Technology, 85, 5, 1633–1648.
Bruni, M.E., Khodaparasti, S., 2022. A variable neighborhood descent matheuristic for the drone routing problem with beehives sharing. Sustainability, 14, 16, 9978.
Burkhardt, M., Nitsch, F.J., Spinler, S., van Wassenhove, L., 2023. The effect of acute stress on humanitarian supplies management. Production and Operations Management, 32, 2546–2559.
Caunhye,A.M., Zhang,Y., Li, M.,Nie,X., 2016.Alocation-routingmodel for prepositioning and distributing emergency supplies. Transportation Research Part E Logistics and Transportation Review, 90, 161–176.
Chan, Y., Carter, W.B., Burnes, M.D., 2001. A multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands. Computers & Operations Research, 28, 8, 803–826.
Chang, K., Zhou, H., Chen, G., Chen, H., 2017. Multiobjective location routing problem considering uncertain data
after disasters. Discrete Dynamics in Nature and Society, 2017, 1703608.
Chang, K.H., Chen, T.L., Yang, F.H., Chang, T.Y., 2023. Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident. European Journal of Operational Research, 309, 3, 1237–1262.
Cotes, N., Cantillo, V., 2019. Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89–100.
Curtis, F.E., Liu, S., Robinson, D.P., 2023. Fair machine learning through constrained stochastic optimization and an _-constraint method. Optimization Letters.
Daroudi, S., Kazemipoor, H., Najafi, E., Fallah, M., 2021. The minimum latency in location routing fuzzy inventory
problem for perishable multi-product materials. Applied Soft Computing, 110, 107543.
De Maio, A., Musmanno, R., Vocaturo, F., 2022. Unbiased decision making in location-routing problems with uncertain customer demands. Soft Computing.
De Veluz, M.R.D., Redi, A.A.N.P., Maaliw, R.R., Persada, S.F., Prasetyo, Y.T., Young, M.N., 2023. Scenario-based multi-objective location-routing model for pre-disaster planning: a Philippine case study. Sustainability, 15(6), 4882.
Delfani, F., Kazemi, A., Seyedhosseini, S.M., Niaki, S.T.A., 2020. A green hazardous waste location-routing problem considering the risks associated with transportation and population, International Journal of Engineering, 33, 11, 2272–2284.
Delfani, F., Kazemi, A., Seyedhosseini, S.M., Niaki, S.T.A., 2021. A novel robust possibilistic programming approach for the hazardous waste location-routing problem considering the risks of transportation and population. International Journal of Systems Science: Operations & Logistics, 8, 4, 383–395.
Deng, X., 2021. Location-routing problem of emergency facilities under uncertain demand by branch-price and cut. Journal of Mathematics, 2021, 9152989. DEZIM., 2020. Kartal olası deprem kayıp tahminleri kitapcı˘gı. Turkiye: ˙Istanbul.
Drexl, M., Schneider, M., 2015. A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241, 2, 283–308.
Erdo˘gan, S., Miller-Hooks, E., 2012. A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.
Fathollahi, A., Derakhshandeh, S. Y., Ghiasian, A., Masoum, M.A., 2022. Optimal siting and sizing of wireless EV charging infrastructures considering traffic network and power distribution system. IEEE Access, 10, 117105–117117.
Geismar, H.N., Huang, Y., Pillai, S.D., Sriskandarajah, C., Youn, S., 2020. Location-routing with conflicting objectives: coordinating eBeam phytosanitary treatment and distribution of Mexican import commodities. Production and Operations Management, 29, 6, 1506–1531.
Ghaderi, A., Burdett, R.L., 2019. An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transportation Research Part E: Logistics and Transportation Review, 127, 49–65.
Ghaffari-Nasab, N., Ahari, S.G., Ghazanfari, M., 2013a. A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy demands. Scientia Iranica, 20, 3, 919–930.
Ghaffari-Nasab, N., Jabalameli, M.S., Aryanezhad, M.B., Makui, A., 2013b. Modeling and solving the bi-objective capacitated location-routing problem with probabilistic travel times. The International Journal of Advanced Manufacturing Technology, 67, 9-12, 2007–2019.
Ghahremani-Nahr, J., Nozari, H., Rahmaty, M., Zeraati Foukolaei, P., Sherejsharifi, A., 2023. Development of a novel fuzzy hierarchical location-routing optimization model considering reliability. Logistics, 7, 3, 64.
Ghasemi, P., Goodarzian, F., Munuzuri, J., Abraham, A., 2022. A cooperative game theory approach for locationrouting- inventory decisions in humanitarian relief chain incorporating stochastic planning. Applied Mathematical Modelling, 104, 750–781.
Gholamian, M. R., Heydari, M. (2017). An inventory model with METRIC approach in location-routing-inventory
problem. Advances in Production Engineering & Management, 12(2), 115–126. https://doi.org/10.14743/apem2017. 2.244
Govindan, K.,Mina, H., Esmaeili, A., Gholami-Zanjani, S.M., 2020. An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production, 242, 118317.
Govindan, K., Nosrati-Abarghooee, S., Nasiri, M.M., Jolai, F., 2022. Green reverse logistics network design for medical waste management: a circular economy transition through case approach. Journal of Environmental Management, 322, 115888.
Haimes, Y., 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1, 3, 296–297.
Harati, S., Roghanian, E., Hafezalkotob, A., Shojaie, A.A., 2021. A robust two-stage stochastic location-routinginventory model for perishable items. Tehnicki vjesnik/Technical Gazette, 28, 6, 1989–1995.
Hashemi, L., Mahmoodi, A., Jasemi, M., Millar, R.C., Laliberte, J., 2022. Designing a locating-routing three-echelon supply chain network under uncertainty. International Journal of Intelligent Computing and Cybernetics, 15, 4, 562– 588.
Hassan-Pour, H.A., Mosadegh-Khah, M., Tavakkoli-Moghaddam, R., 2009. Solving a multi-objective multi-depot
stochastic location-routing problem by a hybrid simulated annealing algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223, 8, 1045–1054.
Hassan-Pour, S.T., Ke, G.Y., Tulett, D.M., 2021. A time-dependent location-routing problem of hazardous material transportation with edge unavailability and time window. Journal of Cleaner Production, 322, 128951.
Hauder, V.A., Karder, J., Beham, A., Wagner, S., Affenzeller, M., 2018. A general solution approach for the location routing problem. In Moreno-Diaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory– EUROCAST2017: 16th International Conference, February 19-24, Las Palmas de Gran Canaria, Spain, Revised Selected Papers, Part I 16. Springer International Publishing, Cham, pp. 257–265.
Huang, S.H., 2015. Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands. Computers & Industrial Engineering, 87, 104–113.
Javid, A.A., Azad, N., 2010. Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E- Logistics and Transportation Review, 46(5), 582–597.
Karimi, H., Setak, M., 2018. A bi-objective incomplete hub location-routing problem with flow shipment scheduling. Applied Mathematical Modelling, 57, 406–431.
Kazanc, H.C., Soysal, M., Cimen, M., 2021. Modeling heterogeneous fleet vehicle allocation problem with emissions considerations. The Open Transportation Journal, 15, 1, 93–107.
Kawase, R., Iryo, T., 2023. Optimal stochastic inventory-distribution strategy for damaged multi-echelon humanitarian logistics network. European Journal of Operational Research, 309, 2, 616–633.
Khorsi, M., Chaharsooghi, S.K., Kashan, A.H., Bozorgi-Amiri, A., 2021. Pareto-based grouping meta-heuristic algorithm for humanitarian relief logistics with multistate network reliability. OR Spectrum, 43, 327–365.
Khoshgehbari, F., Mirzapour Al-e Hashem, S.M.J., 2023. Ambulance location routing problem considering all sources of uncertainty: progressive estimating algorithm. Computers & Operations Research, 160, 106400.
Kılcı, F., 2012. A decision support system for shelter site selection with GIS integration: case for Turkey. PhD thesis, Bilkent University, Ankara.
Kılcı, F., Kara, B.Y., Bozkaya, B., 2015. Locating temporary shelter areas after an earthquake: a case for Turkey. European Journal of Operational Research, 243, 1, 323–332.
Kimms, A., Maiwald, M., 2018. Bi-objective safe and resilient urban evacuation planning. European Journal of Operational Research, 269, 3, 1122–1136.
Koc, C., Bekta，s, T., Jabali, O., Laporte, G., 2016. The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B: Methodological, 84, 81–102.
Kordi, G., Hasanzadeh-Moghimi, P., Paydar, M.M., Asadi-Gangraj, E., 2022. A multi-objective location-routing model for dental waste considering environmental factors. Annals of Operations Research, 328, 755–792.
Li, L., Lo, H.K., Huang,W., Xiao, F., 2021. Mixed bus fleet location-routing-scheduling under range uncertainty. Transportation Research Part B: Methodological, 146, 155–179.
Long, S., Zhang, D., Liang, Y., Li, S., Chen, W., 2021. Robust optimization of the multi-objective multi-period locationrouting problem for epidemic logistics system with uncertain demand. IEEE Access, 9, 151912–151930.
Mansoori, S., Bozorgi-Amiri,A., Pishvaee, M.S., 2020.Arobustmulti-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications, 32, 7, 2183–2203.
Marinakis, Y., 2015. An improved particle swarm optimization algorithm for the capacitated location routing problem and for the location routing problem with stochastic demands. Applied Soft Computing Journal, 37, 680 701.
Marinakis, Y., Marinaki, M., Migdalas, A., 2016. A hybrid clonal selection algorithm for the location routing problem with stochastic demands. Annals of Mathematics and Artificial Intelligence, 76(1-2), 121–142.
Martinez-Reyes, A., Quintero-Araujo, C.L., Solano-Charris, E.L., 2021. Supplying personal protective equipment to intensive care units during the COVID-19 outbreak in Colombia. A simheuristic approach based on the locationrouting problem. Sustainability, 13, 14, 7822.
Mehrjerdi, Y.Z., Nadizadeh, A., 2013. Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229, 1, 75–84.
Meidute-Kavaliauskiene, I., Yıldırım, F., Ghorbani, S., Cˇ incˇikaite˙, R., 2022. The design of a multi-period and multiechelon perishable goods supply network under uncertainty. Sustainability, 14, 4, 2472.
Minato, N., Morimoto, R., 2012. Collaborative management of regional air transport during natural disasters: case of the 2011 East Japan earthquake and tsunami. Research in Transportation Business & Management, 4, 13–21.
Mirzaei-Khafri, S., Bashiri, M., Soltani, R., 2020. A robust optimization model for a location-arc routing problem. International Journal of Industrial Engineering-Theory Applications and Practice, 27, 2, 288–307.
Mohammadi,M., Razmi, J., Tavakkoli-Moghaddam, R., 2013. Multi-objective invasive weed optimization for stochastic green hub location routing problem with simultaneous pick-ups and deliveries. Economic Computation & Economic Cybernetics Studies & Research, 47, 3, 247–266.
Moshref-Javadi, M., Lee, S., 2016. The latency location-routing problem. European Journal of Operational Research, 255, 2, 604–619.
Mousavi, S.M., Vahdani, B., 2017. A robust approach to multiple vehicle location-routing problems with time windows for optimization of cross-docking under uncertainty. Journal of Intelligent and Fuzzy Systems, 32, 1, 49–62.
Nadizadeh, A., Nasab, H.H., 2014. Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. European Journal of Operational Research, 238, 2, 458–470.
Nagy, G., Salhi, S., 2007. Location-routing: issues, models and methods. European Journal of Operational Research, 177, 2, 649–672.
Nakhjirkan, S.,Mokhatab Rafiei, F., 2017. An integratedmulti-echelon supply chain network design considering stochastic demand: A genetic algorithm based solution. Promet—Traffic & Transportation, 29, 4, 391–400.
Natarajan, K.V., Swaminathan, J.M., 2017. Multi-treatment inventory allocation in humanitarian health settings under funding constraints. Production and Operations Management, 26, 6, 1015–1034.
Nasrollahi, M., Razmi, J., Ghodsi, R., 2018. A computational method for measuring transport related carbon emissions in a healthcare supply network under mixed uncertainty: an empirical study. Promet—Traffic & Transportation, 30, 6, 693–708.
Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V.R., Javanmard, A.S., 2014. Solving a new bi-objective
location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering, 76, 204–221.
Nikzamir, M., Baradaran, V., 2020. A healthcare logistic network considering stochastic emission of contamination: biobjective model and solution algorithm. Transportation Research Part E: Logistics and Transportation Review, 142, 102060.
Oruc, B.E., Kara, B., 2018. Post-disaster assessment routing problem. Transportation Research Part B: Methodologıcal, 116, 76–102. https://doi.org/10.1016/j.trb.2018.08.002
Panadero, J., Juan, A.A., Ghorbani, E., Faulin, J., Pages-Bernaus, A., 2023. Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method. International Transactions in Operational Research, 31, 5, 3036–3060.
Perdana, T., Chaerani, D., Achmad, A.L.H., Hermiatin, F.R., 2020. Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty. Heliyon, 6, 10, e05128.
Perdana, T., Chaerani, D., Hermiatin, F.R., Achmad, A.L.H., Fridayana, A., 2022. Improving the capacity of local food network through local food hubs’ development. Open Agriculture, 7, 1, 311–322.
Pollution-routing problem. Available at http://www.apollo.management.soton.ac.uk/prplib.htm (accessedMarch 2021).
Pourmohammadi, P., Tavakkoli-Moghaddam, R., Rahimi, Y., Triki, C., 2021. Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm. Annals of Operations Research, 324, 1099–1128.
Prodhon, C., Prins, C., 2014. A survey of recent research on location-routing problems. European Journal of Operational Research, 238, 1, 1–17.
Rabbani, M., Aghabegloo, M., Farrokhi-Asl, H., 2017. Solving a bi-objective mathematical programming model for bloodmobiles location routing problem. International Journal of Industrial Engineering Computations, 8, 1, 19–32.
Rabbani, M., Heidari, R., Yazdanparast, R., 2019. A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. European Journal of Operational Research, 272, 3, 945– 961.
Rabiei, P., Arias-Aranda, D., Stantchev, V., 2023. Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA. Expert Systems with Applications, 226, 120142.
Raeisi, D., Jafarzadeh Ghoushchi, S., 2022. A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions. Applied Intelligence, 52, 12, 13435–13455.
Rafie-Majd, Z., Pasandideh, S.H. R., Naderi, B., 2018. Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers and Chemical Engineering, 109, 9–22.
Roosta, S., Mirnajafizadeh, S., Harandi, H., 2023. Development of a robust multi-objective model for green capacitated location-routing under crisis conditions. Journal of Project Management, 8, 1, 1–24.
Sabouhi, F., Bozorgi-Amiri, A., Vaez, P., 2021. Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50, 9, 2632–2650.
Saeidi-Mobarakeh, Z., Tavakkoli-Moghaddam, R., Navabakhsh, M., Amoozad-Khalili, H., 2020. A bi-level and robust optimization-based framework for a hazardous waste management problem: a real-world application. Journal of Cleaner Production, 252, 119830.
Schiffer, M.,Walther, G., 2018. Strategic planning of electric logistics fleet networks: a robust location-routing approach. Omega, 80, 31–42.
Shen, L., Tao, F., Shi, Y., Qin, R., 2019. Optimization of location-routing problem in emergency logistics considering carbon emissions. International Journal of Environmental Research and Public Health, 16, 16, 2982.
Shiripour, S., Mahdavi-amiri, N., Mahdavi, I., 2015. A nonlinear model for a capacitated random transportation network. Journal of Industrial and Production Engineering, 32, 8, 500–515.
Shiripour, S., Mahdavi-Amiri, N., Mahdavi, I., 2017. A transportation network model with intelligent probabilistic travel times and two hybrid algorithms. Transportation Letters, 9, 2, 90–122.
Shiripour, S., Mahdavi, I., Mahdavi-Amiri, N., 2016. A nonlinear model for a capacitated stochastic transportation network usingmultiple linear regression. International Journal of Management Science and EngineeringManagement, 11, 4, 252–261.
Snoeck, A., Winkenbach, M., 2020. The value of physical distribution flexibility in serving dense and uncertain urban markets. Transportation Research Part A: Policy and Practice, 136, 151–177.
Soysal, M., Bloemhof-Ruwaard, J. M., Bekta，s, T., 2015. The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. International Journal of Production Economics, 164, 366–378.
Sutrisno, H., Yang, C.L., 2023. A two-echelon location routing problem with mobile satellites for last-mile delivery: mathematical formulation and clustering-based heuristic method. Annals of Operations Research, 323(1–2), 203– 228.
Tavana, M., Abtahi, A.R., Di Caprio, D., Hashemi, R., Yousefi-Zenouz, R., 2018. An integrated location-inventoryrouting humanitarian supply chain network with pre-and post-disastermanagement considerations. Socio-Economic Planning Sciences, 64, 21–37.
Tirkolaee, E.B., Abbasian, P.,Weber,G.W., 2021. Sustainable fuzzymulti-trip location-routing problem formedical waste management during the COVID-19 outbreak. Science of the Total Environment, 756, 143607.
Tonissen, D.D., Arts, J.J., Shen, Z.J., 2019. Maintenance location routing for rolling stock under line and fleet planning uncertainty. Transportation Science, 53, 5, 1252–1270.
Tonissen, D.D., Arts, J.J., 2020. The stochastic maintenance location routing allocation problem for rolling stock. International Journal of Production Economics, 230, 107826.
Tonissen, D.D., Arts, J.J., Shen, Z.J.M., 2021. A column-and-constraint generation algorithm for two-stage stochastic programming problems. Top, 29, 3, 781–798.
Tordecilla, R.D., Montoya-Torres, J.R., Quintero-Araujo, C.L., Panadero, J., Juan, A.A., 2023. The location routing
problem with facility sizing decisions. International Transactions in Operational Research, 30, 2, 915–945.
Torfi, F., Farahani, R.Z., Mahdavi, I., 2016. Fuzzy MCDM for weight of object’s phrase in location routing problem Applied Mathematical Modelling, 40, 1, 526–541.
Unal, G., 2011. Acil Lojistik Yardım Operasyonu Deprem Lojisti˘gi Karar Destek Sistemi: Alyo-Dlkds (Olası ˙Istanbul Depremi Uygulaması). PhD thesis, Turkish Military Academy Defense Sciences Institutes. UNICEF., 2023.
Vahdani, B., Veysmoradi, D., Mousavi, S.M., Amiri, M., 2022. Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty. Socio-Economic Planning Sciences, 80, 101158.
Vakili, R., Akbarpour Shirazi, M., Gitinavard, H., 2021. Multi-echelon green open-location-routing problem: a robustbased stochastic optimization approach. Scientia Iranica, 28, 2, 985–1000.
van Wassenhove, L.N., Pedraza Martinez, A.J., 2012. Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in Operational Research, 19, 1–2, 307–322.
Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., Mousavi, S.M., 2018. Multi-objective open location-routing model for relief distribution networks with split delivery andmulti-mode transportation under uncertainty. Scientia Iranica, 25, 6, 3635–3653.
Vural, D., Dell, R.F., Kose, E., 2021. Locating unmanned aircraft systems for multiple missions under different weather conditions. Operational Research, 21, 1, 725–744.
Wang, D., Yang, K., Yang, L., Li, S., 2022. Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity. International Transactions in Operational Research, 31, 3, 1736–1761.
Wang, C., Peng, Z., Xu,W., 2023. Robust bi-level optimization for maritime emergencymaterials distribution in uncertain decision-making environments. Mathematics, 11, 19, 4140.
Wu, J., He, J., Lin, Y., Bie, Y., 2023. Co-evolutionary location-routing model of medical isolation areas for major public health emergencies considering the uncertainty of future traffic environment. Transportation Research Record, 2677, 3, 1408–1423.
Xiong, X., Zhao, F., Wang, Y., Wang, Y., 2019. Research on the model and algorithm for multimodal distribution of emergency supplies after earthquake in the perspective of fairness. Mathematical Problems in Engineering, 2019, 1–12.
Xu, J., Wang, Z., Zhang, M., Tu, Y., 2016. A new model for a 72-h post-earthquake emergency logistics location routing problem under a random fuzzy environment. Transportation Letters, 8, 5, 270–285.
Yang, L., Zhang, C.,Wan, P., Li, H., 2022. Optimization of location-routing for the waste household appliances recycling logistics under the uncertain condition. Tehniˇcki Vjesnik, 29, 1, 127–138.
Yavari, M., Enjavi, H., Geraeli, M., 2020. Demand management to cope with routes disruptions in location-inventoryrouting problem for perishable products. Research in Transportation Business & Management, 37, 100552.
Zandkarimkhani, S., Mina, H., Biuki,M., Govindan, K., 2020. A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Annals of Operations Research, 295, 1, 425–452.
Zarandi,M.H.F., Hemmati, A., Davari, S., Turksen, I.B., 2013. Capacitated location-routing problem with time windows under uncertainty. Knowledge-Based Systems, 37, 480-489.
Zarandi, M.H.F., Hemmati, A., Davari, S., Turksen, I.B., 2014. A simulated annealing algorithm for routing problems with fuzzy constrains. Journal of Intelligent & Fuzzy Systems, 26, 6, 2649–2660.
Zargary, S., Samouei, P., 2022. Production-routing-inventory in post-disaster conditions: a multi-objective mathematical model and two algorithms. Process Integration and Optimization for Sustainability, 6, 4, 1163 1183.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., Mohammadi, M., 2016. Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182–214.
Zhang, Y., Qi, M., Lin, W.H., Miao, L., 2015. A metaheuristic approach to the reliable location routing problem under disruptions. Transportation Research Part E-Logistics and Transportation Review, 83, 90–110.
Zhang, B., Li, H., Li, S., Peng, J., 2018. Sustainable multi-depot emergency facilities location-routing problem with uncertain information. Applied Mathematics and Computation, 333, 506–520.
Zhang, S., Chen, M., Zhang,W., 2019. A novel location-routing problem in electric vehicle transportation with stochastic demands. Journal of Cleaner Production, 221, 567–581.
Zhang, H., Liu, F., Ma, L., Zhang, Z., 2020. A hybrid heuristic based on a particle swarm algorithm to solve the capacitated location-routing problem with fuzzy demands. IEEE Access, 8, 153671–153691.
Zhang, L., Lu, J., Yang, Z., 2021. Dynamic optimization of emergency resource scheduling in a large-scale maritime oil spill accident. Computers and Industrial Engineering, 152, 107028.
Zhang, P., Liu, Y., Yang, G., Zhang, G., 2022. A multi-objective distributionally robust model for sustainable last mile relief network design problem. Annals of Operations Research, 309, 689–730.
Zhang, Y., Chen, Z., 2023. Social sustainability-oriented multi-period medical supply adjustment in response to disasters. IEEE Access, 11, 91485–91499. Zhao, J., Wu, B., Ke, G.Y., 2021. A bi-objective robust optimization approach for the management of infectious wastes with demand uncertainty during a pandemic. Journal of Cleaner Production, 314, 127922.
