物流運輸是人類從有商業活動開始便有的物流活動，商業活動愈發達，則愈需要更有效率的運輸方式以達到貨暢其流的目標，有效的降低物流營運成本和費用。尤其是電子商務時代，消費者快速到貨的期望讓這個問題更形重要。
物流問題的規劃主要落在旅行推銷員(TSP)或是車輛路徑規劃(VRP)兩個方向，過去也有大量的相關研究文獻，提出許多不同的演算法。但是在過去TSP或VRP的研究中，大多尋求路徑或時間的最短，對於實務上非常重要的運載的均衡問題卻較少著墨，這會造成原有的演算法在實務應用上受到許多限制。因此本研究針對加入運載均衡的目標後的問題，藉由改良傳統螞蟻演算法為基礎，發展出地域多螞蟻演算法。本演算法加入了1.多群螞蟻、2.互競爭食與3.地域觀念等三個特性，協助螞蟻在尋找最短路徑的同時，同時達成路徑平衡負載這樣的多目標問題。
本研究設計了一個以地域多螞蟻演算法進行運輸路徑規劃的新方法，並與隨機地圖、mTSP、CVRP等多項國際測試例題進行比較，結果發現所提出的新演算法有明顯的優勢，且當節點數N越大時，地域多螞蟻演算法帶來的優勢越大。然後再透過實做一個物流派遣支援系統的雛型，收集實際的個案資料進行驗證，經過各項指標的數值統計後發現，在提昇路線均衡上及減少超時工作上都能產生明顯較佳的效果，為個案公司帶來明顯效益，顯示所提出的方法有新的貢獻。

Logistics is an important issue for business activities. We need more efficient logistics to reduce the time of goods transportation. This becomes more important in the electronic commerce age, when the consumers demand quick delivery of their orders.
Traditionally, merchandise delivery falls in the travelling salesman problem (TSP) or the vehicle routing problem (VRP). Numerous papers have been published in these two areas. However, most previous research did not take route balance into their models, which restrict their practical applicability. In reality, route balance is an important concern. Thus, in this study, we propose a revised ACO (ant colony Optimization) called MACOT (Multiple Ant Colony Optimization with Territorialism) that adds three features to include Multi Colony, Competition and Territorialism into ACO to find the shortest path under the condition of balancing the route simultaneously.
To examine the performance of MACOT, we compared it with some datasets such as the random map, mTSP, CVRP from priors’ research. Our result shows that MACOT had an advantage over the existing methods, especially when the number of nodes growing.
We developed a prototype logistics support system and evaluate it with data provided by a case company. Four indicators were used assess the performance of the system. The proposed system performed well in this real-world data test. This indicates the contribution of MACOT.

第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究流程 2
第二章 文獻探討 5
第一節 物流產業與資訊化物流 5
第二節 決策支援系統 8
第三節 車輛途程問題 12
第四節 均衡路徑問題與其研究 18
第五節 動態問題與其研究 30
第三章 研究方法 40
第一節 設計科學研究法 40
第二節 地域多螞蟻演算法模式發展 45
第三節 系統架構 53
第四章 研究結果 56
第一節 地域螞蟻演算法結果與分析 56
第二節 個案效益分析 69
第三節 系統有效性評估 85
第五章 結論與建議 88
第一節 結論 88
第二節 建議 89

中文部份
行政院主計處. (2013). 102年運輸及倉儲業產值調查報告. 台北.
梁定澎. (2006). 決策支援系統與企業智慧. 台灣: 智勝出版.

英文部份
Anbuudayasankar, S. P., Ganesh, K., Koh, S. C. L., & Mohandas, K. (2009). Clustering-based heuristic for the workload balancing problem in enterprise logistics. International Journal of Value Chain Management, 3(3), 302-315. doi: 10.1504/IJVCM.2009.028605
Anbuudayasankar, S. P., Ganesh, K., & Mohapatra, S. (2014). Survey of Methodologies for TSP and VRP Models for Practical Routing Problems in Logistics (pp. 11-42): Springer International Publishing.
Augerat, P., Belenguer, J. M., Benavent, E., Corberán, A., & Naddef, D. (1998). Separating capacity constraints in the CVRP using tabu search. European Journal of Operational Research, 106(2–3), 546-557. doi: http://dx.doi.org/10.1016/S0377-2217(97)00290-7
Baños, R., Ortega, J., Gil, C., Fernández, A., & de Toro, F. (2013). A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Systems with Applications, 40(5), 1696-1707. doi: http://dx.doi.org/10.1016/j.eswa.2012.09.012
Bektas, T. (2006). The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega, 34(3), 209-219. doi: http://dx.doi.org/10.1016/j.omega.2004.10.004
Borenstein, Y., Shah, N., Tsang, E., Dorne, R., Alsheddy, A., & Voudouris, C. (2010). On the partitioning of dynamic workforce scheduling problems. Journal of Scheduling, 13(4), 411-425.
Breedam, A. V. (2001). Comparing descent heuristics and metaheuristics for the vehicle routing problem. Computers & Operations Research, 28(4), 289-315. doi: http://dx.doi.org/10.1016/S0305-0548(99)00101-X
Brown, E. C., Ragsdale, C. T., & Carter, A. E. (2007). A GROUPING GENETIC ALGORITHM FOR THE MULTIPLE TRAVELING SALESPERSON PROBLEM. International Journal of Information Technology & Decision Making, 6(2), 333-347.
Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., & Juan, A. A. (2014). Rich Vehicle Routing Problem. ACM Computing Surveys, 47(2), 1-28. doi: 10.1145/2666003
Carter, A. E., & Ragsdale, C. T. (2006). A new approach to solving the multiple traveling salesperson problem using genetic algorithms. European Journal of Operational Research, 175(1), 246-257.
Chandran, N., Narendran, T. T., & Ganesh, K. (2006). A clustering approach to solve the multiple travelling salesmen problem. International Journal of Industrial and Systems Engineering, 1(3), 372-387.
Cheung, B. K.-S., Choy, K., Li, C.-L., Shi, W., & Tang, J. (2008). Dynamic routing model and solution methods for fleet management with mobile technologies. International Journal of Production Economics, 113(2), 694-705.
Christofides, N. (1979). Combinatorial optimization. Chichester; New York: Wiley.
Clarke, G. u., & Wright, J. W. (1964). Scheduling of vehicles from a central depot to a number of delivery points. Operations research, 12(4), 568-581.
Cordeau, J.-F., Laporte, G., Savelsbergh, M. W., & Vigo, D. (2006). Vehicle routing. Transportation, handbooks in operations research and management science, 14, 367-428.
Créput, J.-C., Hajjam, A., Koukam, A., & Kuhn, O. (2012). Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem. Journal of Combinatorial Optimization, 24(4), 437-458.
Daugherty, P. J., Myers, M. B., & Richey, R. G. (2002). INFORMATION SUPPORT FOR REVERSE LOGISTICS: THE INFLUENCE OF RELATIONSHIP COMMITMENT. Journal of Business Logistics, 23(1), 85-106. doi: 10.1002/j.2158-1592.2002.tb00017.x
De-gang, J., & Dong-mei, H. (2012). A research based on K-means clustering and Artificial Fish-Swarm Algorithm for the Vehicle Routing Optimization. Paper presented at the Natural Computation (ICNC), 2012 Eighth International Conference on.
Dial, R. B. (1995). Autonomous dial-a-ride transit introductory overview. Transportation Research Part C: Emerging Technologies, 3(5), 261-275.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), 28-39.
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. Evolutionary Computation, IEEE Transactions on, 1(1), 53-66.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1), 29-41.
Drucker, P. (1962). The Economy's Dark Continent: Fortune.
Ferrucci, F., & Bock, S. (2014). Real-time control of express pickup and delivery processes in a dynamic environment. Transportation Research Part B: Methodological, 63(0), 1-14. doi: http://dx.doi.org/10.1016/j.trb.2014.02.001
Fisher, M. L., & Jaikumar, R. (1981). A generalized assignment heuristic for vehicle routing. Networks, 11(2), 109-124. doi: 10.1002/net.3230110205
Ganesh, K., & Narendran, T. (2007). CLOVES: a cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up. European Journal of Operational Research, 178(3), 699-717.
Gendreau, M., Guertin, F., Potvin, J.-Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157-174.
Gendreau, M., Guertin, F., Potvin, J.-Y., & Taillard, E. (1999). Parallel tabu search for real-time vehicle routing and dispatching. Transportation Science, 33(4), 381-390.
Ghannadpour, S. F., Noori, S., Tavakkoli-Moghaddam, R., & Ghoseiri, K. (2014). A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application. Applied Soft Computing, 14, Part C(0), 504-527. doi: http://dx.doi.org/10.1016/j.asoc.2013.08.015
Ghoseiri, K., & Ghannadpour, S. F. (2010). Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing, 10(4), 1096-1107.
Groër, C., Golden, B., & Wasil, E. (2010). A library of local search heuristics for the vehicle routing problem. Mathematical Programming Computation, 2(2), 79-101. doi: 10.1007/s12532-010-0013-5
Guimarans, D., Herrero, R., Riera, D., Juan, A. A., & Ramos, J. J. (2011). Combining probabilistic algorithms, constraint programming and lagrangian relaxation to solve the vehicle routing problem. Annals of Mathematics and Artificial Intelligence, 62(3-4), 299-315.
Hadiguna, R. A., Kamil, I., Delati, A., & Reed, R. (2014). Implementing a web-based decision support system for disaster logistics: A case study of an evacuation location assessment for Indonesia. International Journal of Disaster Risk Reduction, 9(0), 38-47. doi: http://dx.doi.org/10.1016/j.ijdrr.2014.02.004
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design (Vol. 1): Pws Boston.
Hassold, S., & Ceder, A. (2014). Public transport vehicle scheduling featuring multiple vehicle types. Transportation Research: Part B, 67, 129-143. doi: 10.1016/j.trb.2014.04.009
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). DESIGN SCIENCE IN INFORMATION SYSTEMS RESEARCH. MIS quarterly, 28(1), 75-105.
Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88-105.
Hopfield, J. J., & Tank, D. W. (1985). “Neural” computation of decisions in optimization problems. Biological cybernetics, 52(3), 141-152.
Hu, Z.-H., & Sheng, Z.-H. (2014). A decision support system for public logistics information service management and optimization. Decision Support Systems, 59(0), 219-229. doi: http://dx.doi.org/10.1016/j.dss.2013.12.001
Iivari, J. (2007). A paradigmatic analysis of information systems as a design science. Scandinavian Journal of Information Systems, 19(2), 5.
Jaillet, P., & Wagner, M. R. (2008). Generalized online routing: New competitive ratios, resource augmentation, and asymptotic analyses. Operations research, 56(3), 745-757.
Jozefowiez, N., Semet, F., & Talbi, E.-G. (2002). Parallel and hybrid models for multi-objective optimization: Application to the vehicle routing problem Parallel Problem Solving from Nature—PPSN VII (pp. 271-280): Springer.
Jozefowiez, N., Semet, F., & Talbi, E.-G. (2006). Enhancements of NSGA II and Its Application to the Vehicle Routing Problem with Route Balancing. In E.-G. Talbi, P. Liardet, P. Collet, E. Lutton & M. Schoenauer (Eds.), Artificial Evolution (Vol. 3871, pp. 131-142): Springer Berlin Heidelberg.
Jozefowiez, N., Semet, F., & Talbi, E.-G. (2009). An evolutionary algorithm for the vehicle routing problem with route balancing. European Journal of Operational Research, 195(3), 761-769.
Jozefowiez, N., Semet, F., & Talbi, E. G. (2007). Target aiming Pareto search and its application to the vehicle routing problem with route balancing. Journal of Heuristics, 13(5), 455-469. doi: DOI 10.1007/s10732-007-9022-6
Kaiwartya, O., Kumar, S., Lobiyal, D., Tiwari, P. K., Abdullah, A. H., & Hassan, A. N. (2015). Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization. Journal of Sensors, 2015.
Keen, P. G. W., & Morton, M. S. S. (1978). Decision Support Systems: An Organizational Perspective Addison-Wesley.
Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70(0), 239-254. doi: http://dx.doi.org/10.1016/j.trb.2014.09.008
Kok, A. L., Meyer, C. M., Kopfer, H., & Schutten, J. M. J. (2010). A dynamic programming heuristic for the vehicle routing problem with time windows and European Community social legislation. Transportation Science, 44(4), 442-454.
Korf, R. E. (1985). Depth-first iterative-deepening: An optimal admissible tree search. Artificial intelligence, 27(1), 97-109.
Laporte, G. (2007). What you should know about the vehicle routing problem. Naval Research Logistics (NRL), 54(8), 811-819.
Laporte, G. (2009). Fifty Years of Vehicle Routing. Transportation Science, 43(4), 408-416. doi: doi:10.1287/trsc.1090.0301
Laporte, G., Gendreau, M., Potvin, J. Y., & Semet, F. (2000). Classical and modern heuristics for the vehicle routing problem. International Transactions in Operational Research, 7(4-5), 285-300. doi: 10.1111/j.1475-3995.2000.tb00200.x
Layeb, A., & Chikhi, S. (2014). Two novel sweep-based heuristics for the vehicle routing problem. International Journal of Computer Applications in Technology, 49(3-4), 263-269.
Lee, T.-R., & Ueng, J.-H. (1999). A study of vehicle routing problems with load-balancing. International Journal of Physical Distribution & Logistics Management, 29(10), 646-657.
Lewis, J. R. (1995). IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. International Journal of Human‐Computer Interaction, 7(1), 57-78.
Lewis, J. R. (2002). Psychometric Evaluation of the PSSUQ Using Data from Five Years of Usability Studies. International Journal of Human-computer Interaction, 14(3-4), 463-488. doi: 10.1207/S15327590IJHC143&4_11
Logistics Performance Index Global Rankings 2014. (2015). from http://lpi.worldbank.org/international/global
Markus, M. L. (2001). Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success. Journal of Management Information Systems, 18(1), 57-94.
Maurya, K., Singh, M., & Jain, N. (2012). Real Time Vehicle Tracking System using GSM and GPS Technology-An Anti-theft Tracking System. International Journal of Electronics and Computer Science Engineering (IJECSE, ISSN: 2277-1956), 1(03), 1103-1107.
Mendoza, J. E., Medaglia, A. L., & Velasco, N. (2009). An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 46(3), 730-742. doi: http://dx.doi.org/10.1016/j.dss.2008.11.019
Mes, M., Van Der Heijden, M., & Van Harten, A. (2007). Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems. European Journal of Operational Research, 181(1), 59-75.
Modares, A., Somhom, S., & Enkawa, T. (1999). A self-organizing neural network approach for multiple traveling salesman and vehicle routing problems. International Transactions in Operational Research, 6(6), 591-606.
Mohan, B. C., & Baskaran, R. (2012). A survey: Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 39(4), 4618-4627.
Morton, S. (1971). Management Decision Systems; Computer-based support for decision making: Cambridge, MA: Division of Research, Harvard University.
Ngai, E. W. T., Leung, T. K. P., Wong, Y. H., Lee, M. C. M., Chai, P. Y. F., & Choi, Y. S. (2012). Design and development of a context-aware decision support system for real-time accident handling in logistics. Decision Support Systems, 52(4), 816-827. doi: http://dx.doi.org/10.1016/j.dss.2011.11.016
Nunamaker, D., Butterweck, D., & Provost, M. (1990). Fatigue fractures in thoroughbred racehorses: relationships with age, peak bone strain, and training. Journal of Orthopaedic Research, 8(4), 604-611.
Nunamaker Jr, J., Dennis, A. R., Valacich, J. S., & Vogel, D. R. (1991). Information technology for negotiating groups: generating options for mutual gain. Management Science, 37(10), 1325-1346.
Oyola, J., & Løkketangen, A. (2014). GRASP-ASP: An algorithm for the CVRP with route balancing. Journal of Heuristics, 20(4), 361-382. doi: 10.1007/s10732-014-9251-4
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45-77.
Pichpibul, T., & Kawtummachai, R. (2012). An improved Clarke and Wright savings algorithm for the capacitated vehicle routing problem. ScienceAsia, 38(3), 307-318.
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1-11.
Poon, T. C., Choy, K. L., Chow, H. K. H., Lau, H. C. W., Chan, F. T. S., & Ho, K. C. (2009). A RFID case-based logistics resource management system for managing order-picking operations in warehouses. Expert Systems with Applications, 36(4), 8277-8301. doi: http://dx.doi.org/10.1016/j.eswa.2008.10.011
Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research, 31(12), 1985-2002.
Prins, C., Lacomme, P., & Prodhon, C. (2014). Order-first split-second methods for vehicle routing problems: A review. Transportation Research Part C: Emerging Technologies, 40(0), 179-200. doi: http://dx.doi.org/10.1016/j.trc.2014.01.011
Qi, M., Lin, W.-H., Li, N., & Miao, L. (2012). A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows. Transportation Research Part E: Logistics and Transportation Review, 48(1), 248-257. doi: http://dx.doi.org/10.1016/j.tre.2011.07.001
Rai, A., Pavlou, P. A., Im, G., & Du, S. (2012). INTERFIRM IT CAPABILITY PROFILES AND COMMUNICATIONS FOR COCREATING RELATIONAL VALUE: EVIDENCE FROM THE LOGISTICS INDUSTRY. MIS quarterly, 36(1), 233-A235.
Ribeiro, R., & Ramalhinho-Lourenço, H. (2001). A multi-objective model for a multi-period distribution management problem. Paper presented at the Proceeding of the First International Conference on Integrated Logistics, Singapore.
Ruhan, H., Weibin, X., Jiaxia, S., & Bingqiao, Z. (2009, 21-22 Nov. 2009). Balanced K-Means Algorithm for Partitioning Areas in Large-Scale Vehicle Routing Problem. Paper presented at the Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on.
Salhi, S., Imran, A., & Wassan, N. A. (2014). The multi-depot vehicle routing problem with heterogeneous vehicle fleet: Formulation and a variable neighborhood search implementation. Computers & Operations Research, 52, 315-325. doi: 10.1016/j.cor.2013.05.011
Santos, L., Coutinho-Rodrigues, J., & Antunes, C. H. (2011). A web spatial decision support system for vehicle routing using Google Maps. Decision Support Systems, 51(1), 1-9. doi: http://dx.doi.org/10.1016/j.dss.2010.11.008
Schneider, M., Stenger, A., Schwahn, F., & Vigo, D. (2014). Territory-Based Vehicle Routing in the Presence of Time-Window Constraints. Transportation Science, 141223041352002. doi: 10.1287/trsc.2014.0539
Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 33(5), 560-572.
Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations research, 35(2), 254-265.
Soonpracha, K., Mungwattana, A., Janssens, G. K., & Manisri, T. (2014). Heterogeneous VRP review and conceptual framework. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists.
Spliet, R., & Gabor, A. F. (2014). The Time Window Assignment Vehicle Routing Problem. Transportation Science. doi: 10.1287/trsc.2013.0510
Stützle, T., & Hoos, H. H. (2000). MAX–MIN ant system. Future generation computer systems, 16(8), 889-914.
Talbi, E.-G. (2009). Metaheuristics: from design to implementation (Vol. 74): John Wiley & Sons.
Taş, D., Gendreau, M., Dellaert, N., van Woensel, T., & de Kok, A. G. (2014). Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach. European Journal of Operational Research, 236(3), 789-799. doi: http://dx.doi.org/10.1016/j.ejor.2013.05.024
Thierauf, R. J. (1982). Decision support systems for effective planning and control : a case study approach / Robert J. Thierauf. Englewood Cliffs, N.J: Prentice-Hall.
Tu, W., Fang, Z., Li, Q., Shaw, S.-L., & Chen, B. (2014). A bi-level Voronoi diagram-based metaheuristic for a large-scale multi-depot vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 61(0), 84-97. doi: http://dx.doi.org/10.1016/j.tre.2013.11.003
Vallivaara, I. (2008). A team ant colony optimization algorithm for the multiple travelling salesmen problem with minmax objective. Paper presented at the Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control.
Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G., & Shapiro, A. (2003). The sample average approximation method applied to stochastic routing problems: a computational study. Computational Optimization and Applications, 24(2-3), 289-333.
Walls, J. G., Widmeyer, G. R., & El Sawy, O. A. (1992). Building an information system design theory for vigilant EIS. Information systems research, 3(1), 36-59.
Wang, Y., Ho, O. K. W., Huang, G. Q., & Li, D. (2008). Study on vehicle management in logistics based on RFID, GPS and GIS. International Journal of Internet Manufacturing and Services, 1(3), 294-304. doi: 10.1504/IJIMS.2008.0212
Waters, D., & Rinsler, S. (2014). Global Logistics: New Directions in Supply Chain Management: Kogan Page.
Yan, S., Lin, J.-R., & Lai, C.-W. (2013). The planning and real-time adjustment of courier routing and scheduling under stochastic travel times and demands. Transportation Research Part E: Logistics and Transportation Review, 53, 34-48.
Yoshiike, N., & Takefuji, Y. (1999). Vehicle routing problem using clustering algorithm by maximum neural networks. Paper presented at the Intelligent Processing and Manufacturing of Materials, 1999. IPMM'99. Proceedings of the Second International Conference on.
Yuan, S., Skinner, B., Huang, S., & Liu, D. (2013). A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. European Journal of Operational Research, 228(1), 72-82.
Zeng, K., Peng, G., Cai, Z., Huang, Z., & Yang, X. (2012). A Hybrid Natural Computing Approach for the VRP Problem Based on PSO, GA and Quantum Computation. In S.-S. Yeo, Y. Pan, Y. S. Lee & H. B. Chang (Eds.), Computer Science and its Applications (Vol. 203, pp. 23-28): Springer Netherlands.
Zhang, S., Lee, C. K. M., Choy, K. L., Ho, W., & Ip, W. H. (2014). Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transportation Research Part D: Transport and Environment, 31(0), 85-99. doi: http://dx.doi.org/10.1016/j.trd.2014.05.015
Zou, Z., Chen, Q., Uysal, I., & Zheng, L. (2014). Radio frequency identification enabled wireless sensing for intelligent food logistics. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372(2017).