Abstract
The aim of the present paper is to introduce an innovative algorithmic approach, the Distance Related Artificial Bee Colony Algorithm (DRABC), as a variant of the original Artificial Bee Colony (ABC) algorithm. The aforementioned approach has been employed in the solution of the team orienteering problem (TOP). TOP fits into the category of vehicle routing problems with Profits, and such, each node is associated with a score value. The objective of the TOP is the formation of feasible routes with respect to a total travel time limit that corresponds to the total score value maximization. Summarizing the proposed approach, the algorithm applies the original equations of the ABC, on accordingly encoded solution vectors, namely on vectors that present the Euclidean distance between consecutive nodes in a route. This process is combined with a decoding method, to express the solution vector as an ordered sequence of nodes. This encoding/decoding method is referred to as “Distance Related” procedure. The proposed approach achieves most of the best known solutions of the benchmark instances found in the literature, and the performance of the DRABC algorithm is compared to others regarding the solution of the TOP.


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Alzaqebah M, Abdullah S, Jawarneh S (2016) Modified artificial bee colony for the vehicle routing problems with time windows. SpringerPlus 5(1):1298
Archetti C, Hertz A, Speranza M (2007) Metaheuristics for the team orienteering problem. J Heuristics 13:49–76
Archetti C, Speranza MG, Vigo D (2014) Vehicle routing problems with profits. In: Toth P, Vigo D (eds) Vehicle routing: problems, methods, and applications. MOS-SIAM series on optimization. SIAM, Philadelphia, pp 273–298
Bouly H, Dang DC, Moukrim A (2010) A memetic algorithm for the team orienteering problem. 4OR 8(1):49–70
Brajevic I (2011) Artificial bee colony algorithm for the capacitated vehicle routing problem. In: Proceedings of the European computing conference, pp 239–244
Butt S, Cavalier T (1994) A heuristic for the multiple tour maximum collection problem. Comput Oper Res 21:101–111
Cao E, Lai M, Yang H (2014) Open vehicle routing problem with demand uncertainty and its robust strategies. Expert Syst Appl 41(7):3569–3575
Chao IM, Golden BL, Wasil EA (1996a) The team orienteering problem. Eur J Oper Res 88(3):464–474
Chao IM, Golden BL, Wasil EA (1996b) A fast and effective heuristic for the orienteering problem. Eur J Oper Res 88(3):475–489
Cura T (2014) An artificial bee colony algorithm approach for the team orienteering problem with time windows. Comput Ind Eng 74:270–290
Dang DC, Guibadj RN, Moukrim A (2011) A PSO-based memetic algorithm for the team orienteering problem. In: European conference on the applications of evolutionary computation. Springer, Berlin, pp 471–480
Dang DC, Guibadj RN, Moukrim A (2013) An effective PSO-inspired algorithm for the team orienteering problem. Eur J Oper Res 229(2):332–344
Ferreira J, Quintas A, Oliveira JA (2014) Solving the team orienteering problem: developing a solution tool using a genetic algorithm approach. In: Kromer P, Koppen M, Schaefer G (eds) Soft computing in industrial applications. Advances in intelligent systems and computing, vol 223. Springer, Berlin, pp 365–375
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. J Heuristics 20(3):291–328
Golden BL, Levy L, Vohra R (1987) The orienteering problem. Naval Res Logist 34(3):307–318
Gomez A, Salhi S (2014) Solving capacitated vehicle routing problem by artificial bee colony algorithm. In: IEEE symposium on computational intelligence in production and logistics systems (CIPLS). IEEE, pp 48–52
Gunawan A, Lau HC, Vansteenwegen P (2016) Orienteering problem: a survey of recent variants, solution approaches and applications. Eur J Oper Res 255(2):315–332
Iqbal S, Kaykobad M, Rahman MS (2015) Solving the multi-objective vehicle routing problem with soft time windows with the help of bees. Swarm Evolut Comput 24:50–64
Ji P, Wu Y (2011) An improved artificial bee colony algorithm for the capacitated vehicle routing problem with time-dependent travel times. In: Tenth international symposium on operations research and its applications, pp 75–82
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty. Computer Engineering Department 200
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39:459–471
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8:687–697
Ke L, Archetti C, Feng Z (2008) Ants can solve the team orienteering problem. Comput Ind Eng 54:648–665
Ke L, Zhai L, Li J, Chan FT (2016) Pareto mimic algorithm: an approach to the team orienteering problem. Omega 61:155–166
Kim BI, Li H, Johnson AL (2013) An augmented large neighbourhood search method for solving the team orienteering problem. Expert Syst Appl 40(8):3065–3072
Lin SW (2013) Solving the team orienteering problem using effective multi-start simulated annealing. Appl Soft Comput 13(2):1064–1073
Mao S, Zheng M, Zhao X, Xie W, Wang Z (2016) The uncertain time dependent vehicle routing problem with soft time windows. In: IEEE International conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 38–45
Marinakis Y, Marinaki M, Migdalas A (2017) Particle swarm optimization for the vehicle routing problem: a survey and a comparative analysis. In: Martí Rafael, Panos Pardalos, Resende Mauricio (eds) Handbook of heuristics. Springer, Berlin, pp 1–34
Muthuswamy S, Lam SS (2011) Discrete particle swarm optimization for the team orienteering problem. Memet Comput 3(4):287–303
Nahum OE, Hadas Y, Spiegel U (2014) Multi-objective vehicle routing problems with time windows: a vector evaluated artificial bee colony approach. Int J Comput Inf Technol 3(1):41–47
Rosenkrantz DJ, Stearns RE, Lewis PM II (1977) An analysis of several heuristics for the travelling salesman problem. SIAM J Comput 6(3):563–581
Seidgar H, Kiani M, Fazlollahtabar H (2016) Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem. Prod Manuf Res 4(1):133–151
Shi YJ, Meng FW, Shen GJ (2012) A modified artificial bee colony algorithm for vehicle routing problems with time windows. Inf Technol J 11(10):1490
Souffriau W, Vansteenwegen P, Berghe GV, Van Oudheusden D (2010) A path relinking approach for the team orienteering problem. Comput Oper Res 37(11):1853–1859
Szeto WY, Ho SC (2011) An artificial bee colony algorithm for the capacitated vehicle routing problem. Eur J Oper Res 215(1):126–135
Tang H, Miller-Hooks E (2005) A tabu search heuristic for the team orienteering problem. Comput Oper Res 32:1379–1407
Tuntitippawan N, Asawarungsaengkul K (2016) An artificial bee colony algorithm with local search for vehicle routing problem with backhauls and time windows. KKU Eng J 43:404–408
Vansteenwegen P, Souffriau W, Van Berghe G, Van den Oudheusden D (2009) A guided local search metaheuristic for the team orienteering problem. Eur J Oper Res 196(1):118–127
Vansteenwegen P, Souffriau P, Vanden Berghe G, Van Oudheusden D (2009) Metaheuristics for tourist trip planning. In: Geiger M, Habenicht W, Sevaux M, Sörensen K (eds) Metaheuristics in the service industry. Lecture notes in economics and mathematical systems, vol 624. Springer, Berlin, pp 15–31
Vansteenwegen P, Souffriau W, Van Oudheusden D (2011) The orienteering problem: a survey. Eur J Oper Res 209(1):1–10
Wu B, Lin JG, Dong M (2013a) Artificial bee colony algorithm for three-dimensional loading capacitated vehicle routing problem, in Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, pp 815–825
Wu B, Cai H, Cui Z (2013b) Artificial bee colony algorithm for two-dimensional loading capacitated vehicle routing problem. In: International conference on management science and engineering (ICMSE). IEEE, pp 406–412
Yao B, Hu P, Zhang M, Wang S (2013) Artificial bee colony algorithm with scanning strategy for the periodic vehicle routing problem. Simulation 89(6):762–770
Yin PY, Chuang YL (2016) Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking. Appl Math Model 40(21):9302–9315
Yu S, Tai C, Liu Y, Gao L (2016) An improved artificial bee colony algorithm for vehicle routing problem with time windows: a real case in Dalian. Adv Mech Eng 8(8):1–9
Zettam M, Elbenani B (2016) A novel randomized heuristic for the team orienteering problem. In: 3rd International conferenceon logistics operations management (GOL). IEEE
Zhang SZ, Lee CKM (2015) An improved artificial bee colony algorithm for the capacitated vehicle routing problem. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 2124–2128
Zhang S, Lee CKM, Choy KL, Ho W, Ip WH (2014) Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transp Res D Transp Environ 31:85–99
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Trachanatzi, D., Rigakis, M., Marinaki, M. et al. Distance related: a procedure for applying directly Artificial Bee Colony algorithm in routing problems. Soft Comput 24, 9071–9089 (2020). https://doi.org/10.1007/s00500-019-04438-w
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DOI: https://doi.org/10.1007/s00500-019-04438-w