Review of ACO algorithm on network and scheduling problem
Keywords:
ant colony optimization, network problem, scheduling problem, metaheuristicAbstract
The ant colony optimization algorithm is based on the behaviour of real ants. This algorithm was introduced in the 1990s with the aim of finding solutions to problems which simulates the decision-making processes through the use of ants artificial. This paper provides an overview of some of the previous studies and research progress on the traditional and specialized applications of the ACO algorithm towards scheduling and network problems, such as oil pipelines, water distribution system, and natural gas pipelines.
References
Maniezzo, V. (1996). Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on SYSTEMS, Man and Cybernetics-Part B, 26(1), 1–13. https://doi.org/https://doi.org/10.1109/3477.484436.
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. https://doi.org/10.1016/j.ejor.2012.08.015.
Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed Optimization by Ants Colonies. Proceedings of ECAL - European Conference on Artificial Life, Paris, France, (or D), 12.
Vitekar, K. N. (2013). Review of Solving Software Project Scheduling Problem with Ant Colony Optimization, 2(4), 1177–1182..
Yu-Hsin Chen, G. (2013). A New Data Structure of Solution Representation in Hybrid Ant Colony Optimization for Large Dynamic Facility Layout Problems. International Journal of Production Economics, 142(2), 362–371. https://doi.org/10.1016/j.ijpe.2012.12.012
Xu, S., Liu, Y., & Chen, M. (2017). Optimisation of Partial Collaborative Transportation Scheduling in Supply Chain Management With 3PL Using ACO. Expert Systems with Applications, 71, 173–191. https://doi.org/10.1016/j.eswa.2016.11.016.
Groba, C., Sartal, A., & Vázquez, X. H. (2015). Solving the Dynamic Traveling Salesman Problem Using a Genetic Algorithm With Trajectory Prediction: An Application to Fish Aggregating Devices. Computers and Operations Research, 56, 22–32. https://doi.org/10.1016/j.cor.2014.10.012.
Tsuji, Y., Kuroda, M., Kitagawa, Y., & Imoto, Y. (2012). Ant Colony Optimization Approach for Solving Rolling Stock Planning for Passenger Trains. IEEE/SICE International Symposium on System Integration (SII), 716–721. https://doi.org/10.1109/SII.2012.6427319.
Hole, K. R., Meshram, R. A., & Deshmukh, P. P. (2015). Review : Applications of Ant Colony Optimization, 4(6), 12740–12744.
Suresh, L. P., Dash, S. S., & Panigrahi, B. K. (2015). Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, 325, 275–284. https://doi.org/10.1007/978-81-322-2135-7.
Rao, IAnitha and Hegde, K., Rao, A., Hegde, K., Rao, IAnitha and Hegde, K., Rao, A., & Hegde, S. K. (2015). Literature Survey On Travelling Salesman Problem Using Genetic Algorithms. International Journal of Advanced Research in Eduation Technology (IJARET), 2(1), 4.
Salama, K. M., & Freitas, A. A. (2013). Learning Bayesian Network Classifiers Using Ant Colony Optimization. Swarm Intelligence, 7(2–3), 229–254.
Wang, X., Li, X., & Leung, V. C. M. (2015). Artificial Intelligencebased Techniques for Emerging Heterogeneous Network: State of the arts, opportunities, and challenges. IEEE Access, 3, 1379–1391. https://doi.org/10.1109/ACCESS.2015.2467174.
Ardjmand, E., Young, W. A., Weckman, G. R., Bajgiran, O. S., Aminipour, B., & Park, N. (2016). Applying Genetic Algorithm to a New bi-Objective Stochastic Model for Transportation, Location, and Allocation of Hazardous Materials. Expert Systems with Applications, 51, 49–58. https://doi.org/10.1016/j.eswa.2015.12.036.
Aimoerfu, Shi, M., Li, C., Wang, D., & Hairihan. (2017). Implementation of the Protein Sequence Model Based on Ant Colony Optimization Algorithm. Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, 661–665. https://doi.org/10.1109/ICIS.2017.7960075.
Liu, Y., Chen, W.-N., Hu, X., & Zhang, J. (2015). An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN. Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO ’15, 41–48. https://doi.org/10.1145/2739480.2754671.
Colorni, A., Dorigo, M., & Maniezzo, V. (1992). An investigation of Some Properties of an “Ant Algorithm.” Ppsn 92, (Ppsn 92), 509–520. Retrieved from http://staff.washington.edu/paymana/swarm/colorni92-ppsn.pdf.
Moon, Y. J., Yu, H. C., Gil, J. M., & Lim, J. B. (2017). A Slave Ants Based Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing Environments. Human-Centric Computing and Information Sciences, 7(1), 28. https://doi.org/10.1186/s13673-017-0109-2.
Garcia, M. A. P., Montiel, O., Castillo, O., Sepúlveda, R., & Melin, P. (2009). Path Planning for Autonomous Mobile Robot Navigation With Ant Colony Optimization and Fuzzy Cost Function Evaluation. Applied Soft Computing Journal, 9(3), 1102–1110. https://doi.org/10.1016/j.asoc.2009.02.014.
Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to The Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66. https://doi.org/10.1109/4235.585892.
Stũtzle, T., & Dorigo, M. (2002). A Short Convergence Proof for a Class of Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 6(4), 358–365. https://doi.org/10.1109/TEVC.2002.802444.
Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. Encyclopedia of Machine Learning. Retrieved from http://link.springer.com/content/pdf/10.1007/978-0-387-30164- 8_22.pdf.
Zhang, H., Liang, Y., Liao, Q., Wu, M., & Yan, X. (2017). A Hybrid Computational Approach for Detailed Scheduling of Products in a Pipeline With Multiple Pump Stations. Energy, 119, 612–628. https://doi.org/10.1016/j.energy.2016.11.027.
Zhigang, D., Yongtu, L., Qiang, G., Qiao, X., Haoran, Z., & Guoxi, H. (2016). An Automatic Detailed Scheduling Method of Refined Products Pipeline. IEEE International Conference on Control and Automation, ICCA, 2016–July, 816–823. https://doi.org/10.1109/ICCA.2016.7505379.
Chu, F., & Chen, S. (2012). Optimal Design of Pipeline Based on the Shortest Path. Physics Procedia, 33, 216–220. https://doi.org/10.1016/j.phpro.2012.05.054.
Pharris, T. C., & Kolpa, R. L. (2008). Overview of the Design, Construction, And Operation of Interstate Liquid Petroleum Pipelines., 1–93. https://doi.org/10.2172/925387.
Cafaro, D. C., & Cerdá, J. (2010). Operational Scheduling of Refined Products Pipeline Networks With Simultaneous Batch Injections. Computers and Chemical Engineering, 34(10), 1687–1704. https://doi.org/10.1016/j.compchemeng.2010.03.005.
Sasikumar, M., Ravi Prakash, P., Patil, S. M., & Ramani, S. (1997). PIPES: A Heuristic Search Model for Pipeline Schedule Generation. Knowledge-Based Systems, 10(3), 169–175. https://doi.org/10.1016/S0950-7051(97)00026-9.
Hane, C. A., & Ratliff, H. D. (1995). Sequencing Inputs to MultiCommodity Pipelines. Annals of Operations Research, 57(1), 73–101. https://doi.org/10.1007/BF02099692.
Maruyama Mori, F., Lueders, R., Valeria Ramos de Arruda, L., Yamamoto, L., Vicente Bonacin, M. r., Luis Polli, H., … Fernando de Jesus Bernardo, L. (2007). Simulating the Operational Scheduling of a Realworld Pipeline Network. Computer Aided Chemical Engineering, 24, 691–696. https://doi.org/10.1016/S1570-7946(07)80138-6.
Magatão, L., Arruda, L. V. R., & Neves-Jr, F. (2011). A Combined CLP-MILP Approach for Scheduling Commodities in a Pipeline. Journal of Scheduling, 14(1), 57–87. https://doi.org/10.1007/s10951-010-0186-9.
Rejowski, R., & Pinto, J. M. (2003). Scheduling of a Multiproduct Pipeline System. Computers and Chemical Engineering, 27(8–9), 1229–1246. https://doi.org/10.1016/S0098-1354(03)00049-8.
Zyngier, D., & Kelly, J. D. (2009). Optimization and Logistics Challenges in the Enterprise (30). https://doi.org/10.1007/978-0- 387-88617-6.
Wang, Y., & Lu, J. (2015). Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm, 467–480. https://doi.org/10.3390/info6030467.
Razavi, S., & Jalali-farahani, F. (2010). Journal of Petroleum Science and Engineering Optimization and parameters estimation in
petroleum engineering problems using ant colony algorithm. Journal of Petroleum Science and Engineering, 74(3–4), 147–153. https://doi.org/10.1016/j.petrol.2010.08.009.
Rothfarb, M. G. (1970). Characteristic Length and Temperature Dependence of Surface Enhanced Raman Scattering of Nanoporous
Gold. Journal of Physical Chemistry C, 113(25), 10956–10961. https://doi.org/10.1021/jp903137n.
Arya, A. K., & Honwad, S. (2017). Multiobjective Optimization of a Gas Pipeline Network: An Ant Colony Approach. Journal of Petroleum Exploration and Production Technology, (123456789). https://doi.org/10.1007/s13202-017-0410-7.
Mikolajková, M., Saxén, H., & Pettersson, F. (2018). Mixed Integer Linear Programming Optimization of Gas Supply to a Local Market. Industrial and Engineering Chemistry Research, 57(17), 5951–5965. https://doi.org/10.1021/acs.iecr.7b04197.
Cheboubaa, A., Yalaouib, F., Amodeob, L., Smatia, A., & Tairia, A. (2006). New Method to Minimize Fuel Consumption of Gas Pipeline Using Ant Colony Optimization Algorithms Rij, 0–5
Allan, J. D. (2009). Influence of Land Use and Landscape Setting on the Ecological Status of Rivers. Limnetica, 23(3–4), 187–198.
https://doi.org/10.1146/annurev.ecolsys.35.120202.110122.
Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y., & Tan, C. L. (2003). Ant Colony Optimization for Design of Water Distribution Systems. Journal of Water Resources Planning and Management, 129(3), 200–209. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(200).
Tong, L., Han, G., & Qiao, J. (2011). Design of Water Distribution Network Via Ant Colony Optimization. Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011, (PART 1), 366–370. https://doi.org/10.1109/ICICIP.2011.6008266.
Alperovits, E., & Shamir, U. (1977). Design of Optimal Water Distribution Systems. Water Resources Research, 13(6), 885–900. https://doi.org/10.1029/WR013i006p00885.
“Ant Colony Optimization for the Design of Water Distribution Systems,” pp. 1–10, 2004.
Abdelhafidh, M. (2018). Linear WSN Lifetime Maximization for Pipeline Monitoring using Hybrid K-means ACO Clustering Algorithm, 178–180.
Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization Applied to the Design of Water Supply Systems. Computers and Mathematics with Applications, 56(3), 769–776. https://doi.org/10.1016/j.camwa.2008.02.006.
Yang, L., & Stacey, D. A. (2011). Solving the Travelling Salesman Problem Using a Genetic Algorithm. Cities, 2(1), 307–316. Retrieved from www.ijacsa.thesai.org.
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive Feedback as a Search Strategy. Technical Report 91-016, (September 2015).
Retrieved from http://ukpmc.ac.uk/abstract/CIT/45098.
Eldem, H., & Ülker, E. (2017). The Application of Ant Colony Optimization in the Solution of 3D Traveling Salesman Problem on a Sphere. Engineering Science and Technology, an International Journal, 20(4), 1242–1248. https://doi.org/10.1016/j.jestch.2017.08.005.
Bontoux, B., & Feillet, D. (2008). Ant Colony Optimization for the Traveling Purchaser Problem. 35, 628–637. https://doi.org/10.1016/j.cor.2006.03.023.
Pasti, R., & Nunes de Castro, L. (2006). A Neuro-Immune Network for Solving the Traveling Salesman Problem. The 2006 IEEE International Joint Conference on Neural Network Proceedings, 3760–3766. https://doi.org/10.1109/IJCNN.2006.247394.
Cheng, C. B., & Mao, C. P. (2007). A Modified Ant Colony System for Solving the Travelling Salesman Problem With Time Windows.
Mathematical and Computer Modelling, 46(9–10), 1225–1235. https://doi.org/10.1016/j.mcm.2006.11.035.
Dong, G., Guo, W. W., & Tickle, K. (2012). Solving the Traveling Salesman Problem Using Cooperative Genetic Ant Systems. Expert Systems with Applications, 39(5), 5006–5011.https://doi.org/10.1016/j.eswa.2011.10.012.
Gündüz, M., Kiran, M. S., & Özceylan, E. (2015). A Hierarchic Approach Based on Swarm Intelligence to Solve the Traveling Salesman Problem. Turkish Journal of Electrical Engineering and Computer Sciences, 23(1), 103–117. https://doi.org/10.3906/elk-1210-147.
Flood, M. M. (1977). The traveling-salesman problem. Mathematics in Science and Engineering, 130(C), 69–75. https://doi.org/10.1016/S0076-5392(08)61182-0.
Gong, W., & Fu, Z. (2010). ABC-ACO for Perishable Food Vehicle Routing Problem With Time Windows. 2010 International Conference on Computational and Information Sciences, 1261–1264. https://doi.org/10.1109/ICCIS.2010.311.
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80–91. https://doi.org/10.1287/mnsc.6.1.80.
Abderrahman, A., Karim, E. L. B., Hilali, E. L., & Ahmed, A. (2017). a Hybrid Algorithm for Vehicle Routing, 95(1), 0–8.
Yu, B., Yang, Z. Z., & Xie, J. X. (2011). A Parallel Improved Ant Colony Optimization for Multi-Depot Vehicle Routing Problem. Journal of the Operational Research Society, 62(1), 183–188. https://doi.org/10.1057/jors.2009.161.
Taillard, E., Badeau, P., Gendreau, M., Geurtin, F., & Potvin, J. Y. (1997). A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transportation Science, 31(November 2016), 170–186.
Braekers, K., Caris, A., & Janssens, G. K. (2011). A Deterministic Annealing Algorithm for a Bi-Objective Full Truckload Vehicle Routing Problem in Drayage Operations. Procedia - Social and Behavioral Sciences, 20, 344–353. https://doi.org/10.1016/j.sbspro.2011.08.040.
Bellabdaoui, A., & Bouyahyaoui, K. E. L. (2015). A New Approach to Solving the Full Truckload Vehicle Routing Problem Using Genetic Algorithm. 39(2012), 26–27.
Zhang, L. Z., Chen, S. Y., & Cui, Y. Y. (2013). Genetic Algorithm Optimization in Vehicle Routing Problem. Applied Mechanics and Materials, 361–363, 2249–2254. https://doi.org/10.4028/www.scientific.net/AMM.361-363.2249.
Arunapuram, S., Mathur, K., & Solow, D. (2003). Vehicle Routing and Scheduling with Full Truckloads. Transportation Science, 37(2), 170–182. https://doi.org/10.1287/trsc.37.2.170.15248.
Liu, R., Jiang, Z., Liu, X., & Chen, F. (2010). Task Selection and Routing Problems in Collaborative Truckload Transportation. Transportation Research Part E: Logistics and Transportation Review. 46(6), 1071–1085. https://doi.org/10.1016/j.tre.2010.05.003.
Guo-hua, S.U.N., 2012. Modeling and Algorithm for Open Vehicle Routing Problem With Full-Truckloads and Time Windows [J]. Systems Engineering-Theory & Practice , 8 , p.022.
Yangzhou Chen, Jiang Luo, Wei Li, E. Z., & Shi, J. (2014). CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems. 3743–3751.
El Bouyahyiouy, K., & Bellabdaoui, A. (2017). An Ant Colony Optimization Algorithm for Solving the Full Truckload Vehicle Routing Problem With Profit. 2017 International Colloquium on Logistics and Supply Chain Management: Competitiveness and Innovation in Automobile and Aeronautics Industries, LOGISTIQUA 2017, 142–147. https://doi.org/10.1109/LOGISTIQUA.2017.7962888.
Shapiro, J. F. (1993). Mathematical Programming Models and Methods for Production Planning and Scheduling. Handbooks in Operations Research and Management Science, 4(C), 371–443. https://doi.org/10.1016/S0927-0507(05)80188-4.
Duncan, W. P. (2011). Methods for Reducing Changeover Times Through Scheduling. ProQuest Dissertations and Theses, 184.
Jovanovic, J. R., Milanovic, D. D., & Djukic, R. D. (2014). Manufacturing Cycle Time Analysis and Scheduling to Optimize Its Duration. Strojniski Vestnik/Journal of Mechanical Engineering, 60(7–8), 512–524. https://doi.org/10.5545/sv-jme.2013.1523.
Dessouky, M. M., & Wilson, J. R. (1991). Minimizing Production Costs for a Robotic Assembly System. Engineering Costs and Production Economics, 21(1), 81–92. https://doi.org/10.1016/0167-188X(91)90021-S.
Evolutionary Computation for Modeling and Optimization. (2006), 51(6), 2008. https://doi.org/10.1007/0-387-31909-3.
Jiang, W. (2017). Optimization of Refinery Production Scheduling Based on Ant Colony Algorithm, 62, 1393–1398. https://doi.org/10.3303/CET1762233.
Ho, N. B., Tay, J. C., & Lai, E. M. K. (2007). An Effective Architecture for Learning and Evolving Flexible Job-Shop Schedules. European Journal of Operational Research, 179(2), 316–333. https://doi.org/10.1016/j.ejor.2006.04.007.
Gao, J., Sun, L., & Gen, M. (2008). A Hybrid Genetic and Variable Neighborhood Descent Algorithm for Flexible Job Shop Scheduling
Problems. Computers and Operations Research, 35(9), 2892–2907. https://doi.org/10.1016/j.cor.2007.01.001.
Chen, J. C., Chen, K. H., Wu, J. J., & Chen, C. W. (2008). A Study of the Flexible Job Shop Scheduling Problem With Parallel Machines And Reentrant Process. International Journal of Advanced Manufacturing Technology, 39(3–4), 344–354. https://doi.org/10.1007/00170-007-1227-1.
Zhou, G., Wang, L., Xu, Y., & Wang, S. (2011). An Effective Artificial Bee Colony Algorithm for Multi-Objective Flexible JobShop Scheduling Problem. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6839 LNAI, 1–8. https://doi.org/10.1007/978-3-642-25944-9_1.
Choi, H., Lim, J., Yu, H., & Lee, E. (2016). Task Classification Based Energy-aware Consolidation in Clouds. Scientific Programming, 2016. https://doi.org/10.1155/2016/6208358.
Motavaselalhagh, F., Safi Esfahani, F., & Arabnia, H. R. (2015). Knowledge-Based Adaptable Scheduler for Saas Providers in Cloud
Computing. Human-Centric Computing and Information Sciences, 5(1). https://doi.org/10.1186/s13673-015-0031-4.
Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333–346. https://doi.org/10.1109/TEVC.2002.802450.
Islam, D. M. Z., Fabian Meier, J., Aditjandra, P. T., Zunder, T. H., & Pace, G. (2013). Logistics and Supply Chain Management. Research in Transportation Economics, 41(1), 3–16. https://doi.org/10.1016/j.retrec.2012.10.006.
Schyns, M. (2015). An Ant Colony System for Responsive Dynamic Vehicle Routing. European Journal of Operations Research, 245, 704–718.
Mavrovouniotis, M., & Yang, S. (2015). Ant Algorithms With Immigrants Schemes for the Dynamic Vehicle Routing Problem. Information Sciences, 294, 456–477. https://doi.org/10.1016/j.ins.2014.10.002.
Kuo, R. J., Wibowo, B. S., & Zulvia, F. E. (2016). Application of a Fuzzy Ant Colony System to Solve the Dynamic Vehicle Routing Problem With Uncertain Service Time. Applied Mathematical Modelling, 40(23–24), 9990–10001. https://doi.org/10.1016/j.apm. 016.06.025.
Ardjmand, E., Weckman, G., Park, N., Taherkhani, P., & Singh, M. (2015). Applying Genetic Algorithm to a New Location and Routing Model of Hazardous Materials. International Journal of Production Research, 53(3), 916–928. https://doi.org/0.1080/00207543.2014.942010.
Escobar, J. W., Linfati, R., Baldoquin, M. G., & Toth, P. (2014). A Granular Variable Tabu Neighborhood Search for the Capacitated Location-Routing Problem. Transportation Research Part B: Methodological, 67, 344–356. https://doi.org/10.1016/j.trb.2014.05.014.
Kalayci, C. B., & Kaya, C. (2016). An Ant Colony System Empowered Variable Neighborhood Search Algorithm for the Vehicle Routing Problem With Simultaneous Pickup and Delivery. Expert Systems with Applications, 66, 163–175.https://doi.org/10.1016/j.eswa.2016.09.017.
Zhou, L., Wang, X., Ni, L., & Lin, Y. (2016). Location-Routing Problem With Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases. Sustainability (Switzerland), 8(8). https://doi.org/10.3390/su8080828.
Schweiger, K., & Sahamie, R. (2013). A Hybrid Tabu Search Approach for the Design of a Paper Recycling Network. Transportation Research Part E: Logistics and Transportation Review, 50(1), 98–119. https://doi.org/10.1016/j.tre.2012.10.006. [91] Lin, C., Choy, K. L., Ho, G. T. S., & Ng, T. W. (2014). A Genetic Algorithm-Based Optimization Model for Supporting Green Transportation Operations. Expert Systems with Applications, 41(7), 3284–3296. https://doi.org/10.1016/j.eswa.2013.11.032.
Dao, S. D., Abhary, K., & Marian, R. (2014). Optimisation of Partner Selection and Collaborative Transportation Scheduling in Virtual Enterprises Using GA. Expert Systems with Applications, 41(15), 6701–6717. https://doi.org/10.1016/j.eswa.2014.04.030.
Goerigk, M., Deghdak, K., & Heßler, P. (2014). A Comprehensive Evacuation Planning Model and Genetic Solution Algorithm. Transportation Research Part E: Logistics and Transportation Review, 71, 82–97. https://doi.org/10.1016/j.tre.2014.08.007.
Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2016). A Comparison of Three Idling Options in Long-Haul Truck Scheduling. Transportation Research Part B: Methodological, 93, 631–647. https://doi.org/10.1016/j.trb.2016.08.006.
Lai, D. S. W., Caliskan Demirag, O., & Leung, J. M. Y. (2016). A Tabu Search Heuristic for the Heterogeneous Vehicle Routing Problem on A Multigraph. Transportation Research Part E: Logistics and Transportation Review, 86, 32–52. https://doi.org/10.1016/j.tre.2015.12.001.
Paquette, J., Cordeau, J. F., Laporte, G., & Pascoal, M. M. B. (2013). Combining Multicriteria Analysis and Tabu Search for Dial-A-Ride Problems. Transportation Research Part B: Methodological, 52, 1–16. https://doi.org/10.1016/j.trb.2013.02.007.
Samà, M., Pellegrini, P., D’Ariano, A., Rodriguez, J., & Pacciarelli, D. (2016). Ant Colony Optimization for the Real-Time Train Routing Selection Problem. Transportation Research Part B: Methodological, 85, 89–108. https://doi.org/10.1016/j.trb.2016.01.005.
Verbas, Ö., S. Mahmassani, H., & F. Hyland, M. (2016). GapBased Transit Assignment Algorithm With Vehicle Capacity Constraints: Simulation-Based Implementation and Large-Scale Application. Transportation Research Part B: Methodological, 93, 1–16. https://doi.org/10.1016/j.trb.2016.07.002.
Xue, Z., Zhang, C., Lin, W. H., Miao, L., & Yang, P. (2014). A Tabu Search Heuristic for the Local Container Drayage Problem Under a New Operation Mode. Transportation Research Part E: Logistics and Transportation Review, 62, 136–150. https://doi.org/10.1016/j.tre.2013.12.007.
Urban, L. A. (1973). Gas Path Analysis Applied to Turbine Engine Condition Monitoring. Journal of Aircraft, 10(7), 400–406. https://doi.org/10.2514/3.60240.
Aydogmus, Z., & Aydogmus, O. (2015). A Comparison of Artificial Neural Network and Extended Kalman Filter Based Sensorless Speed Estimation. Measurement: Journal of the International Measurement Confederation, 63, 152–158. https://doi.org/10.1016/j.measurement.2014.12.010.
Yang, R., Gabbouj, M., & Neuvo, Y. (1995). Fast Algorithms for Analyzing and Designing Weighted Median Filters. Signal Processing, 41(2), 135–152. https://doi.org/10.1016/0165-1684(94)00096-I.
Charmouti, B., Junoh, A. K., Muhamad, W. Z. A. W., Mansor, M.N., Hasan, M. Z., & Mashor, M. Y. (2017). Extended Median Filter for Salt and Pepper Noise. International Journal of Applied Engineering Research, 12(22), 12914–12918.
Gotmare, A., Bhattacharjee, S. S., Patidar, R., & George, N. V. (2017). Swarm and Evolutionary Computing Algorithms for System Identification and Filter Design: A Comprehensive Review. Swarm and Evolutionary Computation, 32, 68–84. https://doi.org/10.1016/j.swevo.2016.06.007.
Raikar, C., & Ganguli, R. (2017). Denoising Signals Used in Gas Turbine Diagnostics with Ant Colony Optimized Weighted Recursive Median Filters. INAE Letters, 2(3), 133–143. https://doi.org/10.1007/s41403-017-0023-y.
Al-Hinai, N., & Elmekkawy, T. Y. (2011). Robust and Stable Flexible Job Shop Scheduling With Random Machine Breakdowns Using a Hybrid Genetic Algorithm. International Journal of Production Economics, 132(2), 279–281.https://doi.org/10.1016/j.ijpe.2011.04.020.
Nasiri, M. M., & Kianfar, F. (2011). A GA/TS Algorithm for the Stage Shop Scheduling Problem. Computers and Industrial Engineering, 61(1), 161–170. https://doi.org/10.1016/j.cie.2011.03.006.
Werner, F. (2011). Genetic algorithms for shop scheduling problems: A survey. Preprint, 21(11), 1–66. Retrieved from http://www.math.uni-magdeburg.de/~werner/preprints/p11-31.pdf.
Lusby, R. M., Larsen, J., Ehrgott, M., & Ryan, D. (2011). Railway Track Allocation: Models and Methods. OR Spectrum, 33(4), 843–883. https://doi.org/10.1007/s00291-009-0189-0.
Sama, M., D’Ariano, A., Pacciarelli, D., Pellegrini, P., & Rodriguez, J. (2017). Ant Colony Optimization for Train Routing Selection: Operational Vs Tactical Application. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings, 297–302. https://doi.org/10.1109/MTITS.2017.8005684.
Gholami, O., & Sotskov, Y. N. (2012). Train Routing and Timetabling Via a Genetic Algorithm. IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 14). IFAC. https://doi.org/10.3182/20120523-3-RO-2023.00294.
Tormos, P., Lova, A., Barber, F., Ingolotti, L., Abril, M., & Salido, M. A. (2008). A Genetic Algorithm for Railway Scheduling Problems. Studies in Computational Intelligence, 128(2008), 255–276. https://doi.org/10.1007/978-3-540-78985-7_10.
May, M. (2009). 011-0661 A Simulation-Based Genetic Algorithm for the HSR Timetabling Problem Vincent F. Yu. European Journal
Of Operational Research, 1–6.
Wegele, S., & Schnieder, E. (2004). Dispatching of Train Operations Using Genetic Algorithms. Advances in Transport, 15, 775–784.
Liu, S. Q., & Kozan, E. (2011). Scheduling Trains with Priorities: A No-Wait Blocking Parallel-Machine Job-Shop Scheduling Model. Transportation Science, 45(2), 175–198. https://doi.org/10.1287/trsc.1100.0332.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
