How to cite this paper
Ren, X., Meng, L., Liu, Z & Xiujuan, X. (2024). Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm.International Journal of Industrial Engineering Computations , 15(1), 69-88.
Refrences
Ai, C., He, S., & Fan, X. (2023). Parameter estimation of fractional-order chaotic power system based on lens imaging learning strategy state transition algorithm. IEEE Access, 11, 13724-13737.
Barreto, S., Ferreira, C., Paixao, J., & Santos, B. S. (2007). Using clustering analysis in a capacitated location-routing problem. European journal of operational research, 179(3), 968-977.
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, 1-10.
Caunhye, A. M., Zhang, Y., Li, M., & Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation research part E: logistics and transportation review, 90, 161-176.
Cui, Q., Liu, P., Du, H., Wang, H., & Ma, X. (2023). Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots. Frontiers in Neurorobotics, 17, 1196683.
Dukkanci, O., Kara, B. Y., & Bektaş, T. (2019). The green location-routing problem. Computers & Operations Research, 105, 187-202.
Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2019). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, 283, 199-224.
Feng, J. R., Gai, W. M., Li, J. Y., & Xu, M. (2020). Location selection of emergency supplies repositories for emergency logistics management: A variable weighted algorithm. Journal of Loss Prevention in the Process Industries, 63, 1040
Hassanpour, S. T., Ke, G. Y., Zhao, J., & Tulett, D. M. (2023). Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows. Computers & Industrial Engineering, 177, 109066.
Lai, K., Wen, L., Lei, J., Chen, G., Xiao, P., & Maaref, A. (2018). Secure transmission with interleaver for uplink sparse code multiple access system. IEEE Wireless Communications Letters, 8(2), 336-339.
Lamos Díaz, H., Aguilar Imitola, K., Barreto Robles, M. A., Niño Niño, P. N., & Martínez Quezada, D. O. (2018). A memetic algorithm for location-routing problem with time windows for the attention of seismic disasters a case study from Bucaramanga, Colombia. INGE CUC, 14(1), 75-86.
Leng, L., Zhang, C., Zhao, Y., Wang, W., Zhang, J., & Li, G. (2020). Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production, 273, 122801.
Li, B., & Wang, H. (2022). Multi-objective sparrow search algorithm: A novel algorithm for solving complex multi-objective optimisation problems. Expert Systems with Applications, 210, 118414.
Lin, C. K. Y., & 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), 1833-1849.
Luo, Q., Yin, S., Zhou, G., Meng, W., Zhao, Y., & Zhou, Y. (2023). Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems. Structural and Multidisciplinary Optimization, 66(5), 114.
Mara, S. T. W., Kuo, R. J., & Asih, A. M. S. (2021). Location‐routing problem: a classification of recent research. International Transactions in Operational Research, 28(6), 2941-2983.
Maranzana, F. E. (1964). On the location of supply points to minimize transport costs. Journal of the Operational Research Society, 15(3), 261-270.
McVernon, J., & Liberman, J. (2023). WHO keeps covid-19 a public health emergency of international concern. bmj, 380.
Meng, X., Chang, J., Wang, X., & Wang, Y. (2019). Multi-objective hydropower station operation using an improved cuckoo search algorithm. Energy, 168, 425-439.
Nedjati, A., Izbirak, G., & 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.
Nguyen, T. T., & Vo, D. N. (2017). Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling. Swarm and evolutionary computation, 37, 73-89.
Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of operations research, 129, 217-245.
Peng, H., Zeng, Z., Deng, C., & Wu, Z. (2021). Multi-strategy serial cuckoo search algorithm for global optimization. Knowledge-Based Systems, 214, 106729.
Peng, Z., Wang, C., Xu, W., & Zhang, J. (2022). Research on location-routing problem of maritime emergency materials distribution based on bi-level programming. Mathematics, 10(8), 1243.
Perl, J., & Daskin, M. S. (1984). A unified warehouse location-routing methodology. Journal of Business Logistics, 5(1), 92-111.
Ponboon, S., Qureshi, A. G., & Taniguchi, E. (2016). Branch-and-price algorithm for the location-routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review, 86, 1-19.
Qin, L., Xu, W., Zhao, X., & Ma, Y. (2020). Typhoon track change–based emergency shelter location–allocation model: a case study of Wenchang in Hainan province, China. Injury prevention, 26(3), 196-203.
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.
Sankararao, B., & Yoo, C. K. (2011). Development of a robust multiobjective simulated annealing algorithm for solving multiobjective optimization problems. Industrial & engineering chemistry research, 50(11), 6728-6742.
Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96-115.
Schmidt, C. E., Silva, A. C., Darvish, M., & Coelho, L. C. (2019). The time-dependent location-routing problem. Transportation Research Part E: Logistics and Transportation Review, 128(C), 293-315.
Shaw, P. (1998, October). Using constraint programming and local search methods to solve vehicle routing problems. In International conference on principles and practice of constraint programming (pp. 417-431). Berlin, Heidelberg: Springer
Sheikholeslami, F., & Navimipour, N. J. (2017). Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Swarm and Evolutionary Computation, 35, 53-64.
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.
Shen, X., Chang, Z., Xie, X., & Niu, S. (2022). Task offloading strategy of vehicular networks based on improved bald eagle search optimization algorithm. Applied Sciences, 12(18), 9308.
Sheu, J. B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation research part B: methodological, 67, 284-305.
Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30, 8
Wang, C., Liu, Y., & Yang, G. (2023). Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy. Socio-Economic Planning Sciences, 87, 101563.
Wang, Q., & Nie, X. (2023). A location-inventory-routing model for distributing emergency supplies. Transportation Research Part E: Logistics and Transportation Review, 175, 103156.
Wang, S. L., & Sun, B. Q. (2023). Model of multi-period emergency material allocation for large-scale sudden natural disasters in humanitarian logistics: Efficiency, effectiveness and equity. International Journal of Disaster Risk Reduction.
Wang, X. P., Ma, C., & R, J. H. (2013). Optimal dispatching of emergency supplies considering public psychological risk perception. Systems Engineering Theory and Practice, 33(2013), 1735-1743.
Watson-Gandy, C. D. T., & Dohrn, P. J. (1973). Depot location with van salesmen—a practical approach. Omega, 1(3), 321-329.
Webb, M. H. J. (1968). Cost functions in the location of depots for multiple-delivery journeys. Journal of the Operational Research Society, 19, 311-320.
Wu, X., Guo, J., Wu, X., & Guo, J. (2021). Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics. Economic Impacts and Emergency Management of Disasters in China, 221-258.
Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.
Yang, X. S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616-1624.
Yu, J., Guo, J., Zhang, X., Zhou, C., Xie, T., & Han, X. (2022). A Novel Tent-Levy Fireworks Algorithm for the UAV Task Allocation Problem Under Uncertain Environment. IEEE Access, 10, 102373-102385.
Yue, C., Suganthan, P. N., Liang, J., Qu, B., Yu, K., Zhu, Y., & Yan, L. (2021). Differential evolution using improved crowding distance for multimodal multiobjective optimization. Swarm and Evolutionary Computation, 62, 100849.
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, Z., Ding, S., & Sun, Y. (2020a). A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410, 185-201.
Zhang, Z., Hong, W. C., & Li, J. (2020b). Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access, 8, 14642-14658.
Zhao, W., Zhang, Z., Mirjalili, S., Wang, L., Khodadadi, N., & Mirjalili, S. M. (2022). An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems. Computer Methods in Applied Mechanics and Engineering, 398, 115223.
Zhou, Y., Liu, C., & Xu, Q. (2023). Time-Dependent Green Location-Routing Problem under Carbon Cap-and-Trade Policy. Transportation Research Record, 2677(5), 1135-1150.
Zhu, C. F., Zhang, Z. K., & Wang, Q. R. (2019). Path choice of emergency logistics based on cumulative prospect theory. Journal of advanced transportation, 2019(PT.2), 1-11.
Barreto, S., Ferreira, C., Paixao, J., & Santos, B. S. (2007). Using clustering analysis in a capacitated location-routing problem. European journal of operational research, 179(3), 968-977.
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, 1-10.
Caunhye, A. M., Zhang, Y., Li, M., & Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation research part E: logistics and transportation review, 90, 161-176.
Cui, Q., Liu, P., Du, H., Wang, H., & Ma, X. (2023). Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots. Frontiers in Neurorobotics, 17, 1196683.
Dukkanci, O., Kara, B. Y., & Bektaş, T. (2019). The green location-routing problem. Computers & Operations Research, 105, 187-202.
Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2019). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, 283, 199-224.
Feng, J. R., Gai, W. M., Li, J. Y., & Xu, M. (2020). Location selection of emergency supplies repositories for emergency logistics management: A variable weighted algorithm. Journal of Loss Prevention in the Process Industries, 63, 1040
Hassanpour, S. T., Ke, G. Y., Zhao, J., & Tulett, D. M. (2023). Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows. Computers & Industrial Engineering, 177, 109066.
Lai, K., Wen, L., Lei, J., Chen, G., Xiao, P., & Maaref, A. (2018). Secure transmission with interleaver for uplink sparse code multiple access system. IEEE Wireless Communications Letters, 8(2), 336-339.
Lamos Díaz, H., Aguilar Imitola, K., Barreto Robles, M. A., Niño Niño, P. N., & Martínez Quezada, D. O. (2018). A memetic algorithm for location-routing problem with time windows for the attention of seismic disasters a case study from Bucaramanga, Colombia. INGE CUC, 14(1), 75-86.
Leng, L., Zhang, C., Zhao, Y., Wang, W., Zhang, J., & Li, G. (2020). Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production, 273, 122801.
Li, B., & Wang, H. (2022). Multi-objective sparrow search algorithm: A novel algorithm for solving complex multi-objective optimisation problems. Expert Systems with Applications, 210, 118414.
Lin, C. K. Y., & 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), 1833-1849.
Luo, Q., Yin, S., Zhou, G., Meng, W., Zhao, Y., & Zhou, Y. (2023). Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems. Structural and Multidisciplinary Optimization, 66(5), 114.
Mara, S. T. W., Kuo, R. J., & Asih, A. M. S. (2021). Location‐routing problem: a classification of recent research. International Transactions in Operational Research, 28(6), 2941-2983.
Maranzana, F. E. (1964). On the location of supply points to minimize transport costs. Journal of the Operational Research Society, 15(3), 261-270.
McVernon, J., & Liberman, J. (2023). WHO keeps covid-19 a public health emergency of international concern. bmj, 380.
Meng, X., Chang, J., Wang, X., & Wang, Y. (2019). Multi-objective hydropower station operation using an improved cuckoo search algorithm. Energy, 168, 425-439.
Nedjati, A., Izbirak, G., & 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.
Nguyen, T. T., & Vo, D. N. (2017). Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling. Swarm and evolutionary computation, 37, 73-89.
Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of operations research, 129, 217-245.
Peng, H., Zeng, Z., Deng, C., & Wu, Z. (2021). Multi-strategy serial cuckoo search algorithm for global optimization. Knowledge-Based Systems, 214, 106729.
Peng, Z., Wang, C., Xu, W., & Zhang, J. (2022). Research on location-routing problem of maritime emergency materials distribution based on bi-level programming. Mathematics, 10(8), 1243.
Perl, J., & Daskin, M. S. (1984). A unified warehouse location-routing methodology. Journal of Business Logistics, 5(1), 92-111.
Ponboon, S., Qureshi, A. G., & Taniguchi, E. (2016). Branch-and-price algorithm for the location-routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review, 86, 1-19.
Qin, L., Xu, W., Zhao, X., & Ma, Y. (2020). Typhoon track change–based emergency shelter location–allocation model: a case study of Wenchang in Hainan province, China. Injury prevention, 26(3), 196-203.
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.
Sankararao, B., & Yoo, C. K. (2011). Development of a robust multiobjective simulated annealing algorithm for solving multiobjective optimization problems. Industrial & engineering chemistry research, 50(11), 6728-6742.
Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96-115.
Schmidt, C. E., Silva, A. C., Darvish, M., & Coelho, L. C. (2019). The time-dependent location-routing problem. Transportation Research Part E: Logistics and Transportation Review, 128(C), 293-315.
Shaw, P. (1998, October). Using constraint programming and local search methods to solve vehicle routing problems. In International conference on principles and practice of constraint programming (pp. 417-431). Berlin, Heidelberg: Springer
Sheikholeslami, F., & Navimipour, N. J. (2017). Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Swarm and Evolutionary Computation, 35, 53-64.
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.
Shen, X., Chang, Z., Xie, X., & Niu, S. (2022). Task offloading strategy of vehicular networks based on improved bald eagle search optimization algorithm. Applied Sciences, 12(18), 9308.
Sheu, J. B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation research part B: methodological, 67, 284-305.
Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30, 8
Wang, C., Liu, Y., & Yang, G. (2023). Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy. Socio-Economic Planning Sciences, 87, 101563.
Wang, Q., & Nie, X. (2023). A location-inventory-routing model for distributing emergency supplies. Transportation Research Part E: Logistics and Transportation Review, 175, 103156.
Wang, S. L., & Sun, B. Q. (2023). Model of multi-period emergency material allocation for large-scale sudden natural disasters in humanitarian logistics: Efficiency, effectiveness and equity. International Journal of Disaster Risk Reduction.
Wang, X. P., Ma, C., & R, J. H. (2013). Optimal dispatching of emergency supplies considering public psychological risk perception. Systems Engineering Theory and Practice, 33(2013), 1735-1743.
Watson-Gandy, C. D. T., & Dohrn, P. J. (1973). Depot location with van salesmen—a practical approach. Omega, 1(3), 321-329.
Webb, M. H. J. (1968). Cost functions in the location of depots for multiple-delivery journeys. Journal of the Operational Research Society, 19, 311-320.
Wu, X., Guo, J., Wu, X., & Guo, J. (2021). Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics. Economic Impacts and Emergency Management of Disasters in China, 221-258.
Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.
Yang, X. S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616-1624.
Yu, J., Guo, J., Zhang, X., Zhou, C., Xie, T., & Han, X. (2022). A Novel Tent-Levy Fireworks Algorithm for the UAV Task Allocation Problem Under Uncertain Environment. IEEE Access, 10, 102373-102385.
Yue, C., Suganthan, P. N., Liang, J., Qu, B., Yu, K., Zhu, Y., & Yan, L. (2021). Differential evolution using improved crowding distance for multimodal multiobjective optimization. Swarm and Evolutionary Computation, 62, 100849.
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, Z., Ding, S., & Sun, Y. (2020a). A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410, 185-201.
Zhang, Z., Hong, W. C., & Li, J. (2020b). Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access, 8, 14642-14658.
Zhao, W., Zhang, Z., Mirjalili, S., Wang, L., Khodadadi, N., & Mirjalili, S. M. (2022). An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems. Computer Methods in Applied Mechanics and Engineering, 398, 115223.
Zhou, Y., Liu, C., & Xu, Q. (2023). Time-Dependent Green Location-Routing Problem under Carbon Cap-and-Trade Policy. Transportation Research Record, 2677(5), 1135-1150.
Zhu, C. F., Zhang, Z. K., & Wang, Q. R. (2019). Path choice of emergency logistics based on cumulative prospect theory. Journal of advanced transportation, 2019(PT.2), 1-11.