A Hybrid Jump Search and Tabu Search Metaheuristic for the Unmanned Aerial Vehicle (UAV) Routing Problem

In this research, we provide a new meta-heuristic, a jump search I tabu search hybrid, for addressing the vehicle routing problem with real-life constraints. A tour construction heuristic creates candidate solutions or jump points for the problem. A tabu search algorithm uses these jump points as starting points for a guided local search. We provide statistical analysis on the performance of our algorithm and compare it to other published algorithms. Our algorithm provides solutions within 10% of the best known solutions to benchmark problems and does so in a fraction of the time required by competing algorithms. The timeliness of the solution is vitally import to the unmanned aerial vehicle (UAV) routing problem. UAVs provide the lion\u27s share of reconnaissance support for the US military. This reconnaissance mission requires the UAVs to visit hundreds of target areas in a rapidly changing combat environment. Air vehicle operators (AVOs) must prepare a viable mission plan for the UAVs while contending with such real-life constraints as time windows, target priorities, multiple depots, heterogeneous vehicle fleet, and pop-up threats. Our algorithm provides the AVOs with the tools to perform their mission quickly and efficiently

A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

[EN] This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the vehicles are not required to return to the depot after completing a service. In this new problem, the demands of customers are fulfilled by a heterogeneous fixed fleet of vehicles having various capacities, fixed costs and variable costs. This problem is an important variant of the open vehicle routing problem (OVRP) and can cover more practical situations in transportation and logistics. Since this problem belongs to NP-hard Problems, An approach based on column generation (CG) is applied to solve the HFFOVRP. A tight integer programming model is presented and the linear programming relaxation of which is solved by the CG technique. Since there have been no existing benchmarks, this study generated 19 test problems and the results of the proposed CG algorithm is compared to the results of exact algorithm. Computational experience confirms that the proposed algorithm can provide better solutions within a comparatively shorter period of time.Yousefikhoshbakht, M.; Dolatnejad, A. (2017). A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem. International Journal of Production Management and Engineering. 5(2):55-71. doi:10.4995/ijpme.2017.5916SWORD557152Aleman, R. E., & Hill, R. R. (2010). A tabu search with vocabulary building approach for the vehicle routing problem with split demands. International Journal of Metaheuristics, 1(1), 55. doi:10.1504/ijmheur.2010.033123Anbuudayasankar, S. P., Ganesh, K., Lenny Koh, S. C., & Ducq, Y. (2012). Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications, 39(3), 2296-2305. doi:10.1016/j.eswa.2011.08.009Brandão, J. (2009). A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. European Journal of Operational Research, 195(3), 716-728. doi:10.1016/j.ejor.2007.05.059Çatay, B. (2010). A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Expert Systems with Applications, 37(10), 6809-6817. doi:10.1016/j.eswa.2010.03.045Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91. doi:10.1287/mnsc.6.1.80Gendreau, 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. doi:10.1016/j.trc.2006.03.002Gendreau, M., Laporte, G., Musaraganyi, C., & Taillard, É. D. (1999). A tabu search heuristic for the heterogeneous fleet vehicle routing problem. Computers & Operations Research, 26(12), 1153-1173. doi:10.1016/s0305-0548(98)00100-2Lei, H., Laporte, G., & Guo, B. (2011). The capacitated vehicle routing problem with stochastic demands and time windows. Computers & Operations Research, 38(12), 1775-1783. doi:10.1016/j.cor.2011.02.007Li, X., Leung, S. C. H., & Tian, P. (2012). A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365-374. doi:10.1016/j.eswa.2011.07.025Li, X., Tian, P., & Aneja, Y. P. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 46(6), 1111-1127. doi:10.1016/j.tre.2010.02.004Penna, P. H. V., Subramanian, A., & Ochi, L. S. (2011). An Iterated Local Search heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2), 201-232. doi:10.1007/s10732-011-9186-ySaadati Eskandari, Z., YousefiKhoshbakht, M. (2012). Solving the Vehicle Routing Problem by an Effective Reactive Bone Route Algorithm, Transportation Research Journal, 1(2), 51-69.Subramanian, A., Drummond, L. M. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Computers & Operations Research, 37(11), 1899-1911. doi:10.1016/j.cor.2009.10.011Syslo, M., Deo, N., Kowalik, J. (1983). Discrete Optimization Algorithms with Pascal Programs, Prentice Hall.Taillard, E. D. (1999). A heuristic column generation method for the heterogeneous fleet VRP, RAIRO Operations Research, 33, 1-14. https://doi.org/10.1051/ro:1999101Tarantilis, C. D., & Kiranoudis, C. T. (2007). A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research, 179(3), 806-822. doi:10.1016/j.ejor.2005.03.059Wang, H.-F., & Chen, Y.-Y. (2012). A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering, 62(1), 84-95. doi:10.1016/j.cie.2011.08.018Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9), 2565-2575. doi:10.1080/00207543.2013.855337Yousefikhoshbakht, M., & Khorram, E. (2012). Solving the vehicle routing problem by a hybrid meta-heuristic algorithm. Journal of Industrial Engineering International, 8(1). doi:10.1186/2251-712x-8-1

Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem

In this paper, we develop an extended guided tabu search (EGTS) and a new heuristic packing algorithm for the two-dimensional loading vehicle routing problem (2L-CVRP). The 2L-CVRP is a combination of two wellknown NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. We propose a meta-heuristic methodology EGTS which incorporates theories of tabu search and extended guided local search (EGLS). It has been proved that tabu search is a very good approach for the CVRP, and the guiding mechanism of the EGLS can help tabu search to escape effectively from local optimum. Furthermore, we have modified a collection of packing heuristics by adding a new packing heuristic to solve the loading constraints in 2L-CVRP, in order to improve the cost function significantly. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances

Vehicle routing problem with stochastic travel times including soft time windows and service costs

This paper studies a vehicle routing problem with soft time windows and stochastic travel times. A model is developed that considers both transportation costs (total distance traveled, number of vehicles used and drivers’ total expected overtime) and service costs (early and late arrivals). We propose a Tabu Search method to solve our model. An initialization algorithm is developed to construct feasible routes by taking into account the travel time stochasticity. Solutions provided by the Tabu Search algorithm are further improved by a post-optimization method. We conduct our computational experiments for well-known problem instances. Results show that our Tabu Search method performs well by obtaining very good final solutions in a reasonable amount of time

A Simulated Annealing/Tabu Search Algorithm for the Vehicle Routing Problem

The Vehicle Routing Problem is an NP-complete problem that has been studied extensively since it was introduced in 1958 by G. B. Dantzig and J. H. Ramser. This thesis creates three algorithms that endeavor to find an optimal solution for each problem tested. Two of the algorithms (Simulated Annealing and Tabu Search) have been used previously to solve this problem. These two solution methods are revisited to discover whether a new approach to creating routes will produce the best-known optimal values every time. New routes are created by forming route neighborhoods and then selecting cities from these neighborhoods for insertion. The third algorithm is an original algorithm which combines Simulated Annealing and Tabu Search. The algorithms presented do not produce the best-known optimal values, but are competitive with previously published algorithms

Using the Vehicle Routing Problem to Reduce Field Completion Times with Multiple Machines

The Vehicle Routing Problem (VRP) is a powerful tool used to express many logistics problems, yet unlike other vehicle routing challenges, agricultural field work consists of machine paths that completely cover a field. In this work, the allocation and ordering of field paths among a number of available machines has been transformed into a VRP that enables optimization of completion time for the entire field. A basic heuristic algorithm (a modified form of the common Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed for optimization. Both techniques were evaluated through computer simulations in two fields: a hypothetical basic rectangular field and a more complex, real-world field. Field completion times and effective field capacity were calculated for cases when 1, 2, 3, 5, and 10 vehicles were used simultaneously. Although the Tabu Search method required more than two hours to produce its solution on an Intel i7 processor compared to less than one second for the method based on Clarke-Wright, Tabu Search provided better solutions that resulted in reduced field completion times and increased effective field capacity. The benefit provided by Tabu Search was larger in the more complex field and as the number of vehicles increased. With ten vehicles in the real-world field, the benefit provided by Tabu Search over the modified Clarke-Wright resulted in reduced completion time of 32%, but even with only three vehicles a 15% reduction was obtained. While ten vehicles may only be applicable with future autonomous machines, simultaneous usage of three machines is not uncommon in current production. As producers consider using multiple machines to improve field completion times and effective field capacity, optimization of the vehicle routing will play an important role in ensuring those improvements are fully realized

A genetic algorithm for the vehicle routing problem with time windows

The objective of the vehicle routing problem (VRP) is to deliver a set of customers with known demands on minimum-cost vehicle routes originating and terminating at the same depot. A vehicle routing problem with time windows (VRPTW) requires the delivery be made within a speci¯c time frame given by the customers. Prins (2004) recently proposed a simple and e®ective genetic algorithm (GA) for VRP. In terms of average solution cost, it outperforms most published tabu search results. We implement this hybrid GA to handle VRPTW. Both the implementation and computational results will be discussed

Aplikasi Algoritma Tabu Search dan Safety Stock Pada Penentuan Rute Distribusi Air Mineral di Daerah Istimewa Yogyakarta

Pendistribusian produk berperan penting dalam dunia industri.  Salah satu usaha yang dapat dilakukan perusahaan untuk mengoptimalkan pendistribusian produk adalah meminimalkan biaya tranportasi melalui penentuan rute optimal kendaraan yang disebut dengan VRP (Vehicle Routing Problem). Tujuan dari VRP adalah menentukan rute optimal yaitu rute dengan jarak minimum untuk mendistribusikan produk kepada konsumen. Salah satu variasi VRP adalah Capacitated Vehicle Routing Problem (CVRP), yaitu VRP dengan kendala kapasitas kendaraan. Kasus CVRP tersebut dapat diselesaikan dengan menggunakan Algoritma Tabu Search. Cara kerja Algoritma Tabu Search dimulai dengan penentuan initial solution menggunakan Nearest Neighbor, evaluasi move menggunakan  Exchange, 2-Opt, Relocated, dan Cross Exchange, update Tabu List, kemudian apabila kriteria pemberhentian terpenuhi  maka proses Algoritma Tabu Search berhenti jika tidak, maka kembali pada evaluasi move. Proses perhitungan Algoritma Tabu Search dilakukan secara manual pada PT IAP. Setiap perusahaan distributor atau pun jasa selalu mengadakan persediaan, salah satunya adalah Safety Stock. Perhitungan sederhana Safety Stock dapat membantu menyelesaikan persediaan pengaman yang harus dipersiapkan perusahaan untuk mengurangi tingkat kerugian. Berdasarkan proses perhitungan manual diperoleh solusi pendekatan optimal yaitu rute dengan total jarak terpendek sebesar 138,834 km dan nilai untuk Safety Stock adalah ± 9 karton. [Distribution of the product play an important role in the industry field. The effort done by the companies to optimize the distribution is minimize transportation fee by deciding the shortest route of the vehicle, known as Vehicle Routing Problem (VRP). The purpose of VRP is to determine the optimal route of the route with a minimum distance to distribute product to the consumer. One of the varieties of VRP is Capacitated Vehicle Routing Problem (CVRP), which is VRP with vehicle capacity problems. CVRP case can be solved by using Tabu Search Algorithm. How it works Tabu Search Algorithm starts with the determination of the initial solution using the Nearest Neighbor, evaluating the move using Exchange, 2-Opt, Relocated, and Cross Exchange, updates Tabu List, then when the criteria for termination are met then the Tabu Search algorithm stop if not, then go back to the evaluation of the move. Tabu Search Algorithm calculation process is done manually PT IAP.  Every distributor or service company always hold inventory, one of them is Safety Stock. The simple calculation of Safety Stock can help solve the safety availability that should be prepared by the companies and reduce the level of losses. Based on the manual calculation process obtained optimal solution approach that is route with the shortest route to the optimal total distance of 138,834 km and the value of safety stock is ± 9 cartons.

A tabu search algorithm for the periodic vehicle routing problem

Orientador: Vinicius Amaral ArmentanoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho aborda o problema de roteamento periódico de veículos, que consiste em designar uma combinação de dias de visitas a cada cliente, e definir as rotas de veículos em cada dia de um horizonte de planejamento, de forma a minimizar o custo ou a duração total das rotas. Um algoritmo de busca tabu é proposto para a resolução do problema. A história da busca tabu, usada para guiar o processo de busca, é representada através de memórias de curto e longo prazo. A eficiência das estratégias sugeridas para diversificação e intensificação, associadas à memória de logo prazo, são verificadas experimentalmente. O desempenho do algoritmo de busca tabu é testado computacionalmente em problemas da literatura. Um procedimento de busca tabu proposto na literatura é implementado e comparado com o algoritmo aqui propostoAbstract: This work addresses the periodic vehicle routing problem that consists of assigning a combination of visiting days to each client, and defining the routes every day of a planning horizon, in such a way as to minimize the cost or duration of the routes. A tabu search algorithm is proposed for solving this problem. The history of the tabu search, used to guide the search process, is represented by short and long term memories. The efficacy of the suggested strategies for diversification and intensification, associated to the long term memory, is verified experimentally. The performance of the tabu search algorithm is tested computationally in instances from the literature. A tabu search procedure suggested in the literature is implemented and its performance is tested against the tabu search algorithm developed in this workMestradoAutomaçãoMestre em Engenharia Elétric