區位途程問題（LRP）目前是物流研究中的一個新興領域，其結合了兩個眾所周知的配銷規劃工作：設施區位問題（FLP）和車輛途程問題（VRP）。配銷規劃網絡的主要目標是獲得最小的系統成本，這類似於公司的經濟目標。然而，為了實現地球永續經營的未來，現在公司必須更重視環境保護的問題，例如：員工工作量的產出要與公司物流所造成的汙染排放達到平衡。挑戰點出現於永續問題可能與公司的經濟目標相衝突。因此，本研究努力尋求最佳解決方案。
本研究的文獻探討發現必須在有限工作量下的LRP才可以解決永續經營的問題。因此，本研究考量提出一個多目標函數的LRP來解決這個問題。該模型考慮了三個目標函數：（1）最小化設施、車輛和物流配送相關的總成本，（2）平衡物流配送的工作量及（3）最小化物流配送所產生的二氧化碳排放。該模型的主要目的是計算物流中心的最佳位置、最佳化車輛數量及滿足所有這三個目標函數的配送路線。
為了解決該問題，本研究為多目標途程問題提出一種創新的啟發式演算法，即多目標正弦餘弦演算法（MOSCA）。該演算法採用簡單的離散化方法進行修改，以處理離散空間的尋優問題。然後，採用幾個測試實例以評估MOSCA的性能，並應用於解決印尼Daerah Istimewa Yogyakarta市的實際個案研究。將結果與三種傳統啟發式演算法進行比較，包括非優勢排列遺傳演算法（NSGA-II）、多目標粒子群優化算法（MOPSO）和柏拉圖保存式演化策略演算法（PAES）。實驗結果顯示， MOSCA的性能在五個比較指標上優於其他演算法。

The location-routing problem (LRP) is an emerging area in logistics research which combines two well-known distribution planning tasks: facility location problem (FLP) and vehicle routing problem (VRP). The main goal of planning a distribution network is to obtain a minimum systemwide cost, which resembles companies’ economical objective. However, in order to achieve a more sustainable future, nowadays companies have to start to give attention to the sustainability issue, such as providing workload balance for their employees and producing less emission from their operational activities. The challenge arises because sustainability issue is potentially conflicting with companies’ economical objective. Therefore, an effort has to be made to find the optimal solution.
Our literature review finds only limited works in LRP which have already addressed sustainability issue comprehensively. Thus, this study intends to present a multi-objective sustainable LRP to address this issue. The proposed model considers three objective functions: (1) to minimize the total cost associated with facility, vehicle, and distribution, (2) to balance the workload in distribution activities, and (3) to minimize CO2 emission from transportation activities. The main purposes of this model are to obtain the optimal location of distribution centers, a number of vehicles established, and delivery routes which satisfy all of these three objectives.
In order to solve the model, this study implements a novel metaheuristic for multi-objective routing problem, namely the multi-objective sine-cosine algorithm (MOSCA). The algorithm is modified with a simple discretization technique to deal with discrete search space. Then, the performance of the proposed algorithm is evaluated with several test instances and applied to solve a real world case study in Daerah Istimewa Yogyakarta, Indonesia. The results are compared to three classical metaheuristics, namely non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and Pareto archived evolution strategy (PAES). The experimental results indicate that, based on five comparison metrics, MOSCA performs better than the other algorithms.

Alresheedi, S.S., Lu, S., Elaziz, M.A. and Ewees, A.A., 2019. Improved Multiobjective Salp Swarm Optimization for Virtual Machine Placement in Cloud Computing. Human-centric Computing and Information Sciences, 9(1), p.15.
Alumur, S. and Kara, B.Y., 2007, A New Model for the Hazardous Waste Location-Routing Problem, Computers & Operations Research, 34(5), 1406-1423.
Asgari, N., Rajabi, M., Jamshidi, M., Khatami, M. and Farahani, R.Z., 2017. A Memetic Algorithm for a Multi-Objective Obnoxious Waste Location-Routing Problem: A Case Study. Annals of Operations Research, 250(2), pp.279-308.
Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal Power Flow Solution in Power Systems Using a Novel Sine-Cosine Algorithm. International Journal of Electrical Power & Energy Systems, 99, 331-343.
Averbakh, I. and Berman, O., 1994. Routing and Location-Routing P-Delivery Men Problems on A Path. Transportation Science, 28(2), pp.162-166.
Balakhrisnan, A., Ward, J. and Wong, R., 1987, Integrated Facility Location and Vehicle Routing Models: Recent Work and Future Prospects, American Journal of Mathematical and Management Sciences, 7, 35-61.
Bektaş, T. and Laporte, G., 2011. The Pollution-Routing Problem. Transportation Research Part B: Methodological, 45(8), pp.1232-1250.
Berman, O., Jaillet, P. and Simchi-Levi, D., 1995, Location-Routing Problems with Uncertainty. In Drezner, Z., Facility Location: A Survey of Applications and Methods, pp. 427-452. New York: Springer.
Berry, A. and Vamplew, P., 2005, The Combative Accretion Model–Multiobjective Optimisation without Explicit Pareto Ranking. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 77-91). Springer, Berlin, Heidelberg.
Boyer, O., Hong, T.S., Pedram, A., Yussuf, R.B.M. and Zulkifli, N., 2013, A Mathematical Model for the Industrial Hazardous Waste Location-Routing Problem, Journal of Applied Mathematics, Vol. 2013.
Caballero, R., Gonzáles, M., Guerrero, F.M., Molina, J. and Paralera, C., 2007, Solving a Mutiobjective Location Routing Problem with a Metaheuristic Based on Tabu Search. Application to a Real Case in Andalusia, European Journal of Operational Research, 177(3), 1751-1763.
Chang, K., Zhou, H., Chen, G. and Chen, H., 2017. Multiobjective Location Routing Problem Considering Uncertain Data After Disasters. Discrete Dynamics in Nature and Society, Vol. 2017.
Chen, C., Qiu, R. and Hu, X., 2018. The Location-Routing Problem with Full Truckloads in Low-Carbon Supply Chain Network Designing. Mathematical Problems in Engineering, Vol. 2018.
Coello, C.C. and Lechuga, M.S., 2002. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.
Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C., 2009. Introduction to algorithms. MIT press.
Deb K.., 2014 Multi-Objective Optimization. In: Burke E., Kendall G. (eds) Search Methodologies. Springer, Boston, MA
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.A.M.T., 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp.182-197.
Dukkanci, O., Kara, B.Y. and Bektaş, T., 2019. The Green Location-Routing Problem. Computers & Operations Research, 105, 187-202.
Drexl, M. and Schneider, M., 2015, A Survey of Variants and Extensions of The Location-Routing Problem, European Journal of Operational Research, 241(2), pp. 283-308.
Elalouf, A., Tsadikovich, D. and Levner, E., 2018. A Location-Routing Problem within Blood Sample Collection Chains. In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 488-491). IEEE.
Farrokhi-Asl, H., Tavakkoli-Moghaddam, R., Asgarian, B. and Sangari, E., 2017. Metaheuristics for A Bi-objective Location-Routing-Problem in Waste Collection Management. Journal of Industrial and Production Engineering, 34(4), pp.239-252.
Feretti, I., Zanoni, S., Zavanella, L. and Diana, A., 2007, Greening the Alumunium Supply Chain, International Journal of Production Economics, 108(2007), pp. 236-256.
FitchRatings, 2016., Fitch: Indonesian Mini Markets to Outpace Growth of Larger Stores, https://www.fitchratings.com/site/pr/1003361, (online accessed: 13 May 2019).
Forouzanfar, F., Tavakkoli-Moghaddam, R., Bashiri, M., Baboli, A. and Molana, S.H., 2018. New Mathematical Modeling for A Location–Routing–Inventory Problem in A Multi-Period Closed-Loop Supply Chain in a Car Industry. Journal of Industrial Engineering International, 14(3), pp.537-553.
Ghezavati, V.R. and Beigi, M., 2016. Solving a Bi-objective Mathematical Model for Location-Routing Problem with Time Windows in Multi-Echelon Reverse Logistics using Metaheuristic Procedure. Journal of Industrial Engineering International, 12(4), pp.469-483.
Golmohammadi, A.M, Bonab, S.A., and Parishani, A., 2016, A Multi-Objective Location Routing Problem using Imperialist Competitive Algorithm, International Journal of Industrial Engineering Computations, 7 (3), pp. 481-488.
Govindan, K., Jafarian, A., Khodaverdi, R. and Devika, K., 2014. Two-Echelon Multiple-Vehicle Location–Routing Problem with Time Windows for Optimization of Sustainable Supply Chain Network of Perishable Food. International Journal of Production Economics, 152, 9-28.
Hadian, H., Golmohammadi, A., Hemmati, A. and Mashkani, O., 2019, A Multi-Depot Location Routing Problem to Reduce the Differences Between the Vehicles’ Traveled Distances: A Comparative Study of Heuristics, Uncertain Supply Chain Management, 7 (1), pp.17-32.
Hassan-Pour, H.A., Mosadegh-Khah, M. and 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), pp.1045-1054.
Hekimoğlu, B., 2019. Sine-Cosine Algorithm-based Optimization for Automatic Voltage Regulator System. Transactions of the Institute of Measurement and Control, 41(6), pp.1761-1771.
Holland, J. H., 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
Hu, H., Li, X., Zhang, Y., Shang, C. and Zhang, S., 2019. Multi-Objective Location-Routing Model for Hazardous Material Logistics with Traffic Restriction Constraint in Inter-City Roads. Computers & Industrial Engineering, 128, 861-876.
Hua-li, S., Xun-qing, W. and Yao-feng, X., 2012. A Bi-level Programming Model for a Multi-Facility Location-Routing Problem in Urban Emergency System. In Engineering Education and Management (pp. 75-80). Springer, Berlin, Heidelberg.
Jacobs, T.L. and Warmerdam, J.M., 1994, Simultaneous Routing and Siting for Hazardous-Waste Operations, Journal of Urban Planning and Development, 120(3), 115-131.
Khalili-Damghani, K., Abtahi, A.R. and Ghasemi, A., 2015. A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach. Journal of Mathematical Modelling and Algorithms in Operations Research, 14(3), pp.287-312.
Knowles, J. and Corne, D., 1999. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In Congress on Evolutionary Computation (CEC99) (Vol. 1, pp. 98-105).
Knowles, J. and Corne, D.W., 2000. Approximating the Nondominated Front using the Pareto Archived Evolution Strategy. Evolutionary computation, 8(2), pp.149-172.
Laporte, G., 1988, Location-Routing Problems. In Golden, B. and Assad, A., Vehicle Routing: Method and Studies, pp. 163-198. Amsterdam: North Holland.
Laporte, G., 1989, A Survey of Algorithms for Location-Routing Problems, Investigación Operativa, 1, 93-123.
Leng, L., Zhao, Y., Wang, Z., Zhang, J., Wang, W. and Zhang, C., 2019. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability, 11(6), p.1596.
Lerhlaly, S., Lebbar, M., Allaoui, H., Afifi, S. and Ouazar, D., 2017. An Inventory Location Routing Model with Environmental Considerations. In MATEC Web of Conferences (Vol. 105, p. 00002). EDP Sciences.
Li, M. and Yao, X., 2019, Quality Evaluation of Solution Sets in Multiobjective Optimisation: A Survey, ACM Computing Surveys.
Lin, C., Choy, K.L., Ho, G.T., Chung, S.H. and Lam, H.Y., 2014. Survey of Green Vehicle Routing Problem: Past and Future Trends. Expert Systems with Applications, 41(4), pp.1118-1138.
Lin, C.K.Y. and Kwok, R.C.W., 2006, Multi-Objective Metaheuristics for a Location-Routing Problem with Multiple Use of Vehicles on Real Data and Simulated Data, European Journal of Operational Research, 175 (3), pp.1833-1849.
Liu, J. and Kachitvichyanukul, V., 2015. A Pareto-Based Particle Swarm Optimization Algorithm for Multi-Objective Location Routing Problem. International Journal of Industrial Engineering, 22(3).
Lopes, R.B., Ferreira, C., Santos, B.S. and Barreto, S., 2013. A Taxonomical Analysis, Current Methods and Objectives on Location‐Routing Problems. International Transactions in Operational Research, 20(6), pp.795-822.
Manavizadeh, N. and Moayedi, I., 2018. Solving a Multi-Objective Location-Routing Problem for Hazardous Waste Management, Industrial. Journal of Industrial and Systems Engineering, 11(1), pp.270-286.
Marinakis, Y., Iordanidou, G.R. and Marinaki, M., 2013. Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands. Applied Soft Computing, 13(4), pp.1693-1704.
Martínez-Salazar, I.A., Molina, J., Ángel-Bello, F., Gómez, T. and Caballero, R., 2014a, Solving a Bi-Objective Transportation Location Routing Problem by Metaheuristic Algorithms, European Journal of Operational Research, 234 (1), pp.25-36.
Martínez-Salazar, I.A., Molina, J., Caballero, R. and Ángel—Bello, F., 2014b, Memetic Algorithms for Solving a Bi-objective Transportation Location Routing Problem, in IIE Annual Conference. Proceedings, pp. 3654. Institute of Industrial and Systems Engineers (IISE).
Min, H., Jayaraman, V. and Srivastava, R., 1998, Combined Location-Routing Problems: A Synthesis and Future Research Directions, European Journal of Operational Research, 108, 1-15.
Minister of Natural Resources Canada., 2014, Learn the Facts: Fuel Consumption and CO2, https://www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/oee/pdf/
transportation/fuel-efficient-technologies/autosmart_factsheet_6_e.pdf (online accessed: 5 April 2019)
Mirjalili, S., 2016. SCA: A Sine Cosine Algorithm for Solving Optimization Problems. Knowledge-Based Systems, 96, 120-133.
Mohammadpour, H. and Alinezhad, A., 2015. A Mathematical Programming Model for Location-Routing Problem and Solve It Using an Efficient Meta-heuristic Method. J. Appl. Environ. Biol. Sci, 5(12S), pp.579-594.
Moore, J. and Chapman, R., 1999. Application of Particle Swarm to Multi-Objective Optimization: Dept. Comput. Sci. Software Eng., Auburn Univ.
Morrow, P.C. and Crum, M.R., 2004, Antecedents of Fatigue, Close Calls, and Crashes among Commercial Motor-vehicle Drivers, Journal of Safety Research, 35, 59-69.
Mousavi, S.M., Sadeghi, J., Niaki, S.T.A. and Tavana, M., 2016. A Bi-objective Inventory Optimization Model under Inflation and Discount Using Tuned Pareto-based Algorithms: NSGA-II, NRGA, and MOPSO. Applied Soft Computing, 43, pp.57-72.
Nagy, G. and Salhi, S., 2007, Location-Routing: Issues, Models, and Methods, European Journal of Operational Research, 177, 649-672.
Nedjati, A., Izbirak, G. and Arkat, J., 2017. Bi-objective Covering Tour Location Routing Problem with Replenishment at Intermediate Depots: Formulation and Meta-heuristics. Computers & Industrial Engineering, 110, 191-206.
Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V.R. and Javanmard, S.H., 2014. Solving a New Bi-objective Location-Routing-Inventory Problem in A Distribution Network by Meta-heuristics. Computers & Industrial Engineering, 76, 204-221.
Nema, A.K. and Gupta, S., 1999, Optimization of Regional Hazardous Waste Management Systems: An Improved Formulation, Waste Management, 19(7), pp. 441-451.
Nema, A.K. and Gupta, S., 2003, Multiobjective Risk Analysis and Optimization of Regional Hazardous Waste Management System, Practice Periodical of Hazardous, Toxic, and Radioactive Waste Management, 7(2), pp. 69-77.
Perl, J., 1983. A unified warehouse location-routing analysis.
Prodhon, C. and Prins, C., 2014, A Survey of Recent Research on Location-Routing Problem, European Journal of Operational Research, 238 (1), pp. 1-17
PWC., 2015, Make It Your Business: Engaging with the Sustainable Development Goals, https://www.pwc.com/gx/en/sustainability/SDG/SDG%20Research
_FINAL.pdf, (online accessed: 25 April 2019).
Rabbani, M., Davoudkhani, M. and Farrokhi-Asl, H., 2017a. A New Multi-Objective Green Location Routing Problem with Heterogonous Fleet of Vehicles and Fuel Constraint, International Journal of Strategic Decision Sciences, 8(3), 99–119.
Rabbani, M., Farrokhi-Asl, H. and Asgarian, B., 2017b. Solving a Bi-objective Location Routing Problem by a NSGA-II Combined with Clustering Approach: Application in Waste Collection Problem. Journal of Industrial Engineering International, 13(1), pp.13-27.
Rabbani, M., Heidari, R., Farrokhi-Asl, H. and Rahimi, N., 2018a. Using Metaheuristic Algorithms to Solve a Multi-Objective Industrial Hazardous Waste Location-Routing Problem Considering Incompatible Waste Types. Journal of Cleaner Production, 170, 227-241.
Rabbani, M., Heidari, R. and 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), pp.945-961.
Rabbani, M., Navazi, F., Farrokhi-Asl, H. and Balali, M.H., 2018b, A Sustainable Transportation-Location-Routing Problem with Soft Time Windows for Distribution Systems. Uncertain Supply Chain Management, 6, 229-254.
Rath, S. and Gutjahr, W.J., 2014. A Math-heuristic for the Warehouse Location–Routing Problem in Disaster Relief. Computers & Operations Research, 42, pp.25-39.
Raut, U. and Mishra, S., 2018. Power Distribution Network Reconfiguration Using an Improved Sine-Cosine Algorithm-Based. Soft Computing for Problem Solving: SocProS 2017, Vol. 1, p.1.
Rayat, F., Musavi, M. and Bozorgi-Amiri, A., 2017. Bi-objective Reliable Location-Inventory-Routing Problem with Partial Backordering under Disruption Risks: A Modified AMOSA Approach. Applied Soft Computing, 59, pp.622-643.
Revelle, C., Cohon, J. and Shobrys, D., 1991, Simultaneous Siting and Routing in the Disposal of Hazardous Wastes, Transportation Science, 25(2), 138-145.
Rizk-Allah, R.M., 2018a. Hybridizing Sine Cosine Algorithm with Multi-Orthogonal Search Strategy for Engineering Design Problems. Journal of Computational Design and Engineering, 5(2), pp.249-273.
Rizk-Allah, R.M., 2018b. An Improved Sine-Cosine Algorithm Based on Orthogonal Parallel Information for Global Optimization. Soft Computing, pp.1-27.
Rizk-Allah, R.M. and El-Sehiemy, R.A., 2018, A Novel Sine Cosine Approach for Single and Multiobjective Emission/Economic Load Dispatch Problem. In 2018 International Conference on Innovative Trends in Computer Engineering (ITCE) (pp. 271-277). IEEE.
Salhi, S. and Rand, G.K., 1989, The Effect of Ignoring Routes when Locating Depots, European Journal of Operational Research, 33(4), pp. 894-909.
Schneider, M. and Drexl, M., 2017, A Survey of the Standard Location-Routing Problem, Annals of Operations Research, 259 (1-2), pp. 389-414.
Sindhu, R., Ngadiran, R., Yacob, Y.M., Zahri, N.A.H. and Hariharan, M., 2017. Sine-Cosine Algorithm for Feature Selection with Elitism Strategy and New Updating Mechanism. Neural Computing and Applications, 28(10), pp.2947-2958.
Singh, N., Chiclana, F. and Magnot, J.P., 2019. A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-linear Functions. Engineering with Computers, pp.1-28.
Sivaramkumar, V., Thansekhar, M.R., Saravanan, R. and Amali, S.M.J., 2018, Demonstrating the Importance of Using Total Time Balance Instead of Route Balance on a Multi-Objective Vehicle Routing Problem with Time Windows, The International Journal of Advanced Manufacturing Technology, 98 (5-8), pp.1287-1306.
Snapcart, 2017., Rise of Minimarkets: Indonesia’s Purchase Shift from Super and Hypermarkets to Minimarkets, https://snapcart.global/rise-minimarkets-indonesia/, (online accessed: 12 May 2019).
Srinivas, N. and Deb, K., 1994. Muiltiobjective Optimization using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3), pp.221-248.
Stowers, C.L. and Palekar, U.S. 1993, Location Models with Routing Considerations for a Single Obnoxious Facility, Transportation Science, 27(4), 350-362.
Tang, J., Ji, S. and Jiang, L., 2016. The Design of a Sustainable Location-Routing-Inventory Model Considering Consumer Environmental Behavior. Sustainability, 8(3), p.211.
Tavakkoli-Moghaddam, R., Makui, A. and Mazloomi, Z., 2010. A New Integrated Mathematical Model for a Bi-objective Multi-Depot Location-Routing Problem Solved by a Multi-Objective Scatter Search Algorithm. Journal of Manufacturing Systems, 29(2-3), pp.111-119.
Tavana, M., Abtahi, A.R., Di Caprio, D., Hashemi, R. and Yousefi-Zenouz, R., 2018. An Integrated Location-Inventory-Routing Humanitarian Supply Chain Network with Pre-and Post-Disaster Management Considerations. Socio-Economic Planning Sciences, 64, pp.21-37.
Tawhid, M.A. and Savsani, V., 2017. Multi-Objective Sine-Cosine Algorithm (MO-SCA) for Multi-Objective Engineering Design Problems. Neural Computing and Applications, pp.1-15.
Tawhid, M.A. and Savsani, P., 2018. Discrete Sine-Cosine Algorithm (DSCA) with Local Search for Solving Traveling Salesman Problem. Arabian Journal for Science and Engineering, pp.1-11.
Toro, E.M., Franco, J.F., Echeverri, M.G. and Guimarães, F.G., 2017. A Multi-Objective Model for the Green Capacitated Location-Routing Problem Considering Environmental Impact. Computers & Industrial Engineering, 110, pp.114-125.
Toro, E., Franco, J., Echeverri, M., Guimarães, F. and Rendón, R., 2017b. Green Open Location-Routing Problem Considering Economic and Environmental Costs. International Journal of Industrial Engineering Computations, 8(2), pp.203-216.
Tripathi, P.K., Bandyopadhyay, S. and Pal, S.K., 2007. Multi-objective Particle Swarm Optimization with Time Variant Inertia and Acceleration Coefficients. Information Sciences, 177(22), pp.5033-5049.
Tuzun, D. and Burke, L.I., 1999, A Two-phase Tabu Search Approach to the Location Routing Problem, European Journal of Operational Research, 116 (1), pp.87-99.
Ubeda, S., Arcelus, F.J. and Faulin, J., 2011. Green Logistics at Eroski: A Case Study. International Journal of Production Economics, 131(1), pp.44-51.
Vahdani, B., Veysmoradi, D., Shekari, N. and Mousavi, S.M., 2018a. Multi-Objective, Multi-Period Location-Routing Model to Distribute Relief After Earthquake by Considering Emergency Roadway Repair. Neural Computing and Applications, 30(3), pp.835-854.
Vahdani, B., Veysmoradi, D., Noori, F. and Mansour, F., 2018b. Two-Stage Multi-Objective Location-Routing-Inventory Model for Humanitarian Logistics Network Design Under Uncertainty. International Journal of Disaster Risk Reduction, 27, pp.290-306.
Yu, V.F., Lin, S.W., Lee, W. and Ting, C.J., 2010. A Simulated Annealing Heuristic for the Capacitated Location Routing Problem. Computers & Industrial Engineering, 58(2), pp.288-299.
Wang, H., Du, L. and Ma, S., 2014. Multi-Objective Open Location-Routing Model with Split Delivery for Optimized Relief Distribution in Post-Earthquake. Transportation Research Part E: Logistics and Transportation Review, 69, 160-179.
Wang, S., Tao, F. and Shi, Y., 2018a. Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint. International Journal of Environmental Research and Public Health, 15(1), p.86.
Wang, X. and Li, X., 2017. Carbon Reduction in the Location Routing Problem with Heterogeneous Fleet, Simultaneous Pickup-Delivery and Time Windows. Procedia Computer Science, 112, 1131-1140.
Wang, Y., Assogba, K., Liu, Y., Ma, X., Xu, M. and Wang, Y., 2018b, Two-Echelon Location-Routing Optimization with Time Windows Based on Customer Clustering, Expert Systems with Applications, 104, 244-260.
Wichapa, N. and Khokhajaikiat, P., 2018. Solving a Multi-Objective Location Routing Problem for Infectious Waste Disposal using Hybrid Goal Programming and Hybrid Genetic Algorithm. International Journal of Industrial Engineering Computations, 9(1), pp.75-98.
World Economic Forum, 2009, Supply Chain Decarbonization: The Role of Logistics and Transport in Reducing Supply Chain Carbon Emissions, World Economic Forum, Geneva.
Yang, X., Bostel, N., Dejax, P. and Paquet, M., 2016, Green Hub-location Routing Problem for LTL Transport: Model and Solution. In Société française de recherche opérationnelle et d'aide à la décision (ROADEF).
Zhalecian, M., Tavakkoli-Moghaddam, R., Zahiri, B. and Mohammadi, M., 2016, Sustainable Design of a Closed-Loop Location-Routing-Inventory Supply Chain Network Under Mixed Uncertainty, Transportation Research Part E, 89, 182-214
Zhang, B., Li, H., Li, S. and Peng, J., 2018. Sustainable multi-depot emergency facilities location-routing problem with uncertain information. Applied Mathematics and Computation, 333, pp.506-520.
Zhao, J., Huang, L., Lee, D.H. and Peng, Q., 2016. Improved Approaches to the Network Design Problem in Regional Hazardous Waste Management Dystems. Transportation Research Part E: Logistics and Transportation Review, 88, 52-75.
Zhao, J. and Ke, G.Y., 2017. Incorporating Inventory Risks in Location-Routing Models for Explosive Waste Management, International Journal of Production Economics, 193,123-136.
Zhao, J. and Verter, V., 2015. A Bi-objective Model for the Used Oil Location-Routing Problem. Computers & Operations Research, 62, 157-168.
Zhao, J. and Zhao, J., 2010, Model and Algorithm for Hazardous Waste Location-Routing Problem. In International Conference of Logistics Engineering and Management (ICLEM), 2010American Society of Civil Engineers.
Zitzler, E., Deb, K. and Thiele, L., 2000. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2), pp.173-195.
Zografos, K.G. and Samara, S., 1989, Combined Location-Routing Model for Hazardous Waste Transportation and Disposal, Transportation Research Record, 1245, 52-59.