Baños, R., Gil, C., Paechter, B., & Ortega, J. (2007). A hybrid meta-heuristic for multi-objective optimization: MOSATS. Journal of Mathematical Modelling and Algorithms, 6(2), 213-230.
Baqir, R. (2002). Districting and government overspending. Journal of political Economy, 110(6), 1318-1354.
Benzarti, E., Sahin, E., & Dallery, Y. (2013). Operations management applied to home care services: Analysis of the districting problem. Decision Support Systems, 55(2), 587-598.
Boonjing, V., Chanvarasuth, P. & Lertwongsatien, C. (2015). An Impact of Supply Chain Management Components on Firm Performance. Paper presented at the Proceedings of the 6th International Conference on Engineering, Project and Production Management.
Bozkaya, B., Erkut, E., & Laporte, G. (2003). A tabu search heuristic and adaptive memory procedure for political districting. European journal of operational research, 144(1), 12-26.
Brooks, S. P., & Morgan, B. J. (1995). Optimization using simulated annealing. The Statistician, 44(2), 241-257.
Butsch, A., Kalcsics, J., & Laporte, G. (2014). Districting for arc routing. INFORMS Journal on Computing, 26(4), 809-824.
Camacho-Collados, M., Liberatore, F., & Angulo, J. (2015). A multi-criteria police districting problem for the efficient and effective design of patrol sector. European journal of operational research, 246(2), 674-684.
Carbonara, N., Giannoccaro, I., & Pontrandolfo, P. (2002). Supply chains within industrial districts: A theoretical framework. International Journal of Production Economics, 76(2), 159-176.
Chen, X., & Yum, T.-S. P. (2010). Patrol districting and routing with security level functions. Paper presented at the Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on.
Christopher, M. (2016). Logistics & supply chain management. Pearson UK.
Camacho-Collados, M., Liberatore, F., & Angulo, J. M. (2015). A multi-criteria police districting problem for the efficient and effective design of patrol sector. European journal of operational research, 246(2), 674-684.
Datta, D., Figueira, J., Gourtani, A., & Morton, A. (2013). Optimal administrative geographies: an algorithmic approach. Socio-Economic Planning Sciences, 47(3), 247-257.
Datta, D., & Figueira, J. R. (2011). Graph partitioning by multi-objective real-valued metaheuristics: A comparative study. Applied soft computing, 11(5), 3976-3987.
De Assis, L. S., Franca, P. M., & Usberti, F. L. (2014). A redistricting problem applied to meter reading in power distribution networks. Computers & Operations Research, 41, 65-75.
Farahani, R. Z., Fallah, S., Ruiz, R., Hosseini, S., & Asgari, N. (2018). OR models in urban service facility location: a critical review of applications and future developments. European journal of operational research, 276(1), 1-27.
Farughi, H., & Arkat, J. (2018). Healthcare Districting Optimization Using Gray Wolf Optimizer and Ant Lion Optimizer Algorithms (case study: South Khorasan Healthcare System in Iran). Journal of Optimization in Industrial Engineering, 12(1), 119-131.
Flynn, B., Pagell, M., & Fugate, B. (2018). Survey Research Design in Supply Chain Management: The Need for Evolution in Our Expectations. Journal of Supply Chain Management, 54(1), 1-15.
Fragoso, R., Rego, C., & Bushenkov, V. (2016). Clustering of territorial areas: A multi-criteria districting problem. Journal of Quantitative Economics, 14(2), 179-198.
García‐Ayala, G., González‐Velarde, J. L., Ríos‐Mercado, R. Z., & Fernández, E. (2016). A novel model for arc territory design: promoting Eulerian districts. International Transactions in Operational Research, 23(3), 433-458.
Garfinkel, R.S., & Nemhauser, G.L. (1970). Optimal political districting by implicit enumeration techniques. Management Science, 16(8), B-495-B-508.
Ghiggi, C., Puliafito, P., & Zoppoli, R. (1975). A combinatorial method for health-care districting. Paper presented at the IFIP Technical Conference on Optimization Techniques, PP.116-130.
Graham, R. L., & Hell, P. (1985). On the history of the minimum spanning tree problem. Annals of the History of Computing, 7(1), 43-57.
Hugos, M. H. (2018). Essentials of supply chain management. John Wiley & Sons.
Jiang, S., Chin, K.-S., Wang, L., Qu, G., & Tsui, K. L. (2017). Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert systems with applications, 82, 216-230.
Kalcsics, J. (2015). Districting problems Location science. Springer. (pp. 595-622).
Kim, K., Dean, D. J., Kim, H., & Chun, Y. (2016). Spatial optimization for regionalization problems with spatial interaction: a heuristic approach. International Journal of Geographical Information Science, 30(3), 451-473.
King, D. M., Jacobson, S. H., & Sewell, E. C. (2018). The geo-graph in practice: creating United States Congressional Districts from census blocks. Computational Optimization and Applications, 69(1), 25-49.
Kong, Y., Zhu, Y., & Wang, Y. (2018). A center-based modeling approach to solve the districting problem. International Journal of Geographical Information Science, 1-17. Published online: 21 May 2018.
Konur, D., & Geunes, J. (2016). Integrated districting, fleet composition, and inventory planning for a multi-retailer distribution system. Annals of Operations Research, 1-33.
Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial marketing management, 29(1), 65-83.
Lei, H., Wang, R., & Laporte, G. (2016). Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm. Computers & Operations Research, 67, 12-24.
Li, W., Church, R. L., & Goodchild, M. F. (2014). An extendable heuristic framework to solve the p-compact-regions problem for urban economic modeling. Computers, Environment and Urban Systems, 43, 1-13.
Liberatore, F., & Camacho-Collados, M. (2016). A comparison of local search methods for the multicriteria police districting problem on graph. Mathematical Problems in Engineering, 2016(3), 1-13.
Lin, H.-Y., & Kao, J.-J. (2008). Subregion districting analysis for municipal solid waste collection privatization. Journal of the Air & Waste Management Association, 58(1), 104-111.
Lin, M., Chin, K.-S., Fu, C., & Tsui, K.-L. (2017). An effective greedy method for the Meals-On-Wheels service districting problem. Computers & Industrial Engineering, 106, 1-19.
Mehregan, M., Jafarnejad, A., Mohammadi, M. (2018). Proposing a Multi-objective Model for Ground Transportation of Hazardous Materials in the Hub Network (Case Study: National Iranian Oil Products Distribution Company). Industrial Management Journal, 10(2), 201-220. (in Persian)
Minciardi, R., Puliafito, P., & Zoppoli, R. (1981). A districting procedure for social organizations. European journal of operational research, 8(1), 47-57.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
Pezzella, F., Bonanno, R., & Nicoletti, B. (1981). A system approach to the optimal health-care districting. European journal of operational research, 8(2), 139-146.
Ptak, C. A., & Schragenheim, E. (2016). ERP: tools, techniques, and applications for integrating the supply chain. Crc Press.
Razavi, M., Soukhakian, M. A. & Ziarati, K. (2011). A Meta Heuristic Algorithms Based on Ant Colony System For Solving Multi Depots Location-routing Problem with Multiple Using of Vehicle. Industrial Management, 3(6), 17-38. (in Persian)
Ríos-Mercado, R. Z., & López-Pérez, J. F. (2013). Commercial territory design planning with realignment and disjoint assignment requirements. Omega, 41(3), 525-535.
Shahin, M., Jabalameli, M. S., Jabbarzadeh, A. (2017). Multi-modal and multiproduct hierarchical hub location under uncertainty. Industrial Management, 8(4), 625-658.
Shirabe, T. (2009). Districting modeling with exact contiguity constraints. Environment and Planning B: Planning and Design, 36(6), 1053-1066.
Shirabe, T. (2012). Prescriptive modeling with map algebra for multi-zone allocation with size constraints. Computers, Environment and Urban Systems, 36(5), 456-469.
Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52, 53-64.
Tan, K. C. (2001). A framework of supply chain management literature. European Journal of Purchasing & Supply Management, 7(1), 39-48.
Tran, T.-C., Dinh, T. B., & Gascon, V. (2017). Meta-heuristics to Solve a Districting Problem of a Public Medical Clinic. Paper presented at the Proceedings of the Eighth. International Symposium on Information and Communication Technology.
Vakili, P., Hosseini-Motlagh,S.M., Gholamian, M.R, Jokar,A. (2017). A developed model and heuristic algorithm for inventory routing problem in a cold chain with pharmaceutical products. Industrial Management, 9(2), 383-407.(in Persian)
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
Wang, T., Wu, Z., & Mao, J. (2007). A new method for multi-objective tdma scheduling in wireless sensor networks using pareto-based pso and fuzzy comprehensive judgement. High Performance Computing and Communications (pp. 144-155): Springer.
Wisner, J. D., Tan, K.-C., & Leong, G. K. (2014). Principles of supply chain management: A balanced approach. Cengage Learning.