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Journal articles on the topic 'Rich Vehicle Routing Problems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Hartl, Richard F., Geir Hasle, and Gerrit K. Janssens. "Special issue on Rich Vehicle Routing Problems." Central European Journal of Operations Research 14, no. 2 (June 2006): 103–4. http://dx.doi.org/10.1007/s10100-006-0162-9.

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2

Lahyani, Rahma. "Unified matheuristic for solving rich vehicle routing problems." 4OR 13, no. 2 (December 11, 2014): 223–24. http://dx.doi.org/10.1007/s10288-014-0278-z.

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3

Lacomme, Philippe, Gwénaël Rault, and Marc Sevaux. "Integrated decision support system for rich vehicle routing problems." Expert Systems with Applications 178 (September 2021): 114998. http://dx.doi.org/10.1016/j.eswa.2021.114998.

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4

Lahyani, Rahma, Mahdi Khemakhem, and Frédéric Semet. "Rich vehicle routing problems: From a taxonomy to a definition." European Journal of Operational Research 241, no. 1 (February 2015): 1–14. http://dx.doi.org/10.1016/j.ejor.2014.07.048.

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5

Qi, Mingyao, Cheng Peng, and Xiaoyu Huang. "General Metaheuristic Algorithm for a Set of Rich Vehicle Routing Problems." Transportation Research Record: Journal of the Transportation Research Board 2548, no. 1 (January 2016): 97–106. http://dx.doi.org/10.3141/2548-12.

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6

Nalepa, Jakub, and Miroslaw Blocho. "Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems." International Journal of Grid and Utility Computing 9, no. 2 (2018): 179. http://dx.doi.org/10.1504/ijguc.2018.091724.

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7

Nalepa, Jakub, and Miroslaw Blocho. "Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems." International Journal of Grid and Utility Computing 9, no. 2 (2018): 179. http://dx.doi.org/10.1504/ijguc.2018.10012798.

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8

Setiawan, Fran, Nur Aini Masruroh, and Zita Iga Pramuditha. "On Modelling and Solving Heterogeneous Vehicle Routing Problem with Multi-Trips and Multi-Products." Jurnal Teknik Industri 21, no. 2 (December 16, 2019): 91–104. http://dx.doi.org/10.9744/jti.21.2.91-104.

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Vehicle routing problem (VRP) is a model to determine an optimal routing plan for a fleet of homogeneous vehicles to serve a set customer which some operational constraints are satisfied. In most practical distribution problems, customer demands are served using heterogeneous fleet of vehicles. This kind of VRP is called Heterogeneous Vehicle Routing Problem (HVRP). HVRP has evolved into a rich research area because of its practical. There were many studies of rich extensions of the standar HVRP. This research aims to enrich the extentions of HVRP which is motivated by real case in one of pharmacy distribution company in Indonesia which is delivered multi-products to its 55 customers by allowing some vehicles which has small capacity to perform multi-trips. This problem is called Heterogeneous Vehicle Routing Problem with Multi-Trips and Multi-Products (HVRPMTMP).The mixed integer linear programming is developed based on four-index vehicle flow formulation. The model can be used generally in the same context of distribution problem. HVRPMTMP is generally NP-Hard problem, so the computational time using branch and bound in LINGO 16.0 is increasing exponentially by increasing the number of customers. Genetic algorithm is proposed to solve the real case. The result of the proposed GA can reduce the total cost from Rp 352540.6,- to Rp 180555,- or 48.78% from the current company policy.

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9

Derigs, Ulrich, and Ulrich Vogel. "Experience with a framework for developing heuristics for solving rich vehicle routing problems." Journal of Heuristics 20, no. 1 (August 17, 2013): 75–106. http://dx.doi.org/10.1007/s10732-013-9232-z.

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10

Derigs, Ulrich, and Markus Pullmann. "A computational study comparing different multiple neighbourhood strategies for solving rich vehicle routing problems." IMA Journal of Management Mathematics 27, no. 1 (December 5, 2013): 3–23. http://dx.doi.org/10.1093/imaman/dpt022.

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11

İnanlı, Anıl, Başak Ünsal, and Deniz Türsel Eliiyi. "Route Optimization for the Distribution Network of a Confectionary Chain." PROMET - Traffic&Transportation 27, no. 6 (December 17, 2015): 497–503. http://dx.doi.org/10.7307/ptt.v27i6.1645.

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This study considers the distribution network of a well-known perishable food manufacturer and its franchises in Turkey. As the countrywide number of stores is increasing fast, the company is facing problems due to its central distribution of products from a single factory. The objective is to decrease the cost of transportation while maintaining a high level of customer satisfaction. Hence, the focus is on the vehicle routing problem (VRP) of this large franchise chain within each city. The problem is defined as a rich VRP with heterogeneous fleet, site-dependent and compartmentalized vehicles, and soft/hard time windows. This NP-hard problem is modelled and tried with real data on a commercial solver. A basic heuristic procedure which can be used easily by the decision makers is also employed for obtaining quick and high-quality solutions for large instances.

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12

Eck, Ronald W., and E. Darin Simpson. "Using Medical Records in Non-Motor-Vehicle Pedestrian Accident Identification and Countermeasure Development." Transportation Research Record: Journal of the Transportation Research Board 1538, no. 1 (January 1996): 54–60. http://dx.doi.org/10.1177/0361198196153800107.

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A study was conducted to determine the feasibility of using emergency room and emergency medical service records in quantifying rural non-motor-vehicle pedestrian accidents and in developing countermeasures to reduce injuries resulting from such accidents. A literature review indicated that although non-motor-vehicle pedestrian accidents are a significant injury problem in the United States, there does not appear to be an effective process for collecting data about those accidents that corresponds to the data collection methods used at present for motor-vehicle accidents. A multistep process for obtaining detailed pedestrian accident data using a combination of medical records and telephone surveys is described. Data collection was time consuming and labor intensive. Therefore, it was concluded that it is not practical for traffic engineers to use medical records on a routine basis for pedestrian accident analysis. The method outlined yields a data base rich in details about pedestrian fall accidents, making it valuable in research. Whereas the results of the telephone survey did not produce a large enough sample size to extrapolate to pedestrian accidents in general, they did point out the importance of surface condition to pedestrian safety. Two general types of surface-condition problems were identified: slippery surfaces caused by accumulation of snow and ice and surface holes or openings.

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13

Caceres-Cruz, Jose, Pol Arias, Daniel Guimarans, Daniel Riera, and Angel A. Juan. "Rich Vehicle Routing Problem." ACM Computing Surveys 47, no. 2 (January 8, 2015): 1–28. http://dx.doi.org/10.1145/2666003.

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14

Bektaş, Tolga, Gilbert Laporte, and Daniele Vigo. "Integrated vehicle routing problems." Computers & Operations Research 55 (March 2015): 126. http://dx.doi.org/10.1016/j.cor.2014.08.008.

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15

Golden, Bruce L. "Vehicle Routing Problems and Variants." American Journal of Mathematical and Management Sciences 13, no. 3-4 (February 1993): 245–48. http://dx.doi.org/10.1080/01966324.1993.10737358.

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16

Villegas, Juan G. "Vehicle routing problems with trailers." 4OR 10, no. 3 (November 12, 2011): 317–18. http://dx.doi.org/10.1007/s10288-011-0186-4.

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17

Jozefowiez, Nicolas, Frédéric Semet, and El-Ghazali Talbi. "Multi-objective vehicle routing problems." European Journal of Operational Research 189, no. 2 (September 2008): 293–309. http://dx.doi.org/10.1016/j.ejor.2007.05.055.

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18

Rekersbrink, Henning, Thomas Makuschewitz, and Bernd Scholz-Reiter. "A distributed routing concept for vehicle routing problems." Logistics Research 1, no. 1 (November 4, 2008): 45–52. http://dx.doi.org/10.1007/s12159-008-0003-4.

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19

Drexl, Michael. "Rich vehicle routing in theory and practice." Logistics Research 5, no. 1-2 (July 3, 2012): 47–63. http://dx.doi.org/10.1007/s12159-012-0080-2.

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20

Saravanan, M., and K.A.Sundararaman. "Metaheuristic Approaches for Vehicle Routing Problems." International Journal of Information Systems and Supply Chain Management 6, no. 2 (April 2013): 17–32. http://dx.doi.org/10.4018/jisscm.2013040102.

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Routing of service vehicles are the heart of many service operations. Exclusively vehicle routing problem (VRP) plays a central role in the optimization of distribution networks. The routing of service vehicles has a major impact on the quality of the service provided. In distribution of goods and services, it is time and again required to determine a combination of least cost vehicle routes through a set of geographically scattered customers, subject to side constraints. The case most commonly studied is where all vehicles are identical. Due to the complexity involved in solving the VRP, most researchers concentrate on using meta-heuristics for solving real-life problems. In this paper, heuristic methods based on Ant Colony Optimization and Simulated Annealing algorithms are developed and search strategies are investigated. Computational results are reported on randomly generated problems. These methods significantly improve in minimizing the total distances travelled by the vehicles.

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21

Chernyshev, Yu O., V. N. Kubil, and A. V. Trebukhin. "Overview of fuzzy vehicle routing problems." Advanced Engineering Research 20, no. 3 (October 5, 2020): 325–31. http://dx.doi.org/10.23947/2687-1653-2020-20-3-325-331.

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Introduction. Various algorithms for solving fuzzy vehicle routing problems are considered. The work objective was to study modern methods for the optimal solution to fuzzy, random and rough vehicle routing problems. Materials and Methods. The paper reviews fuzzy vehicle routing problems, existing methods and approaches to their solution. The most effective features of some approaches to solving fuzzy vehicle routing problems considering their specificity, are highlighted. Results. The Fuzzy Vehicle Routing Problem (FVRP) occurs whenever the routing data is vague, unclear, or ambiguous. In many cases, these fuzzy elements can better reflect reality. However, it is very difficult to use Vehicle Routing Problem (VRP) solving algorithms to solve FVRP since several fundamental properties of deterministic problems are no longer fulfilled in FVRP. Therefore, it is required to introduce new models and algorithms of fuzzy programming to solve such problems. Thus, the use of methods of the theory of fuzzy sets will provide successful simulation of the problems containing elements of uncertainty and subjectivity. Discussion and conclusions. As a result of reviewing various methods and approaches to solving vehicle routing problems, it is concluded that the development and study of new solutions come into sharp focus of researchers nowadays, but the degree of elaboration of various options varies. Methods for the optimal solution of FVRP are limited, in general, to some single fuzzy variable. There is a very limited number of papers that consider a large number of fuzzy variables.

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22

Gupta, D. K., and Nisha Gupta. "Stochastic techniques for vehicle routing problems." International Journal of Computer Mathematics 81, no. 1 (January 2004): 15–23. http://dx.doi.org/10.1080/002071603100016060612.

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23

Figliozzi, Miguel Andres, Hani S. Mahmassani, and Patrick Jaillet. "Pricing in Dynamic Vehicle Routing Problems." Transportation Science 41, no. 3 (August 2007): 302–18. http://dx.doi.org/10.1287/trsc.1070.0193.

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24

Archetti, C., and M. G. Speranza. "Vehicle routing problems with split deliveries." International Transactions in Operational Research 19, no. 1-2 (January 2012): 3–22. http://dx.doi.org/10.1111/j.1475-3995.2011.00811.x.

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25

GUPTA, ARVIND, and RAMESH KRISHNAMURTI. "PARALLEL ALGORITHMS FOR VEHICLE ROUTING PROBLEMS." Parallel Processing Letters 13, no. 04 (December 2003): 673–87. http://dx.doi.org/10.1142/s0129626403001598.

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Vehicle routing problems involve the navigation of one or more vehicles through a network of locations. Locations have associated handling times as well as time windows during which they are active. The arcs connecting locations have time costs associated with them. In this paper, we consider two different problems in single vehicle routing. The first is to find least time cost routes between all pairs of nodes in a network for navigating vehicles; we call this the all pairs routing problem. We show that there is an O( log 3 n) time parallel algorithm using a polynomial number of processors for this problem on a CREW PRAM. We next consider the problem in which a vehicle services all locations in a network. Here, locations can be passed through at any time but only serviced during their time window. The general problem is [Formula: see text] -complete under even fairly stringent restrictions but polynomial algorithms have been developed for some special cases. In particular, when the network is a line, there is no time cost in servicing a location, and all time windows are unbounded at either their lower or upper end, O(n2) algorithms have been developed. We show that under the same conditions, we can reduce this problem to the all pairs routing problem and therefore obtain an O( log 3 n) time parallel algorithm on a CREW PRAM.

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26

Dorling, Kevin, Jordan Heinrichs, Geoffrey G. Messier, and Sebastian Magierowski. "Vehicle Routing Problems for Drone Delivery." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 1 (January 2017): 70–85. http://dx.doi.org/10.1109/tsmc.2016.2582745.

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27

Cattaruzza, Diego, Nabil Absi, and Dominique Feillet. "Vehicle routing problems with multiple trips." 4OR 14, no. 3 (January 27, 2016): 223–59. http://dx.doi.org/10.1007/s10288-016-0306-2.

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28

Cattaruzza, Diego, Nabil Absi, and Dominique Feillet. "Vehicle routing problems with multiple trips." Annals of Operations Research 271, no. 1 (July 31, 2018): 127–59. http://dx.doi.org/10.1007/s10479-018-2988-7.

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29

Cattaruzza, Diego, Nabil Absi, Dominique Feillet, and Jesús González-Feliu. "Vehicle routing problems for city logistics." EURO Journal on Transportation and Logistics 6, no. 1 (January 10, 2015): 51–79. http://dx.doi.org/10.1007/s13676-014-0074-0.

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30

Díaz-Parra, Ocotlán, Jorge A. Ruiz-Vanoye, Beatriz Bernábe Loranca, Alejandro Fuentes-Penna, and Ricardo A. Barrera-Cámara. "A Survey of Transportation Problems." Journal of Applied Mathematics 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/848129.

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This paper aims at being a guide to understand the different types of transportation problems by presenting a survey of mathematical models and algorithms used to solve different types of transportation modes (ship, plane, train, bus, truck, Motorcycle, Cars, and others) by air, water, space, cables, tubes, and road. Some problems are as follows: bus scheduling problem, delivery problem, combining truck trip problem, open vehicle routing problem, helicopter routing problem, truck loading problem, truck dispatching problem, truck routing problem, truck transportation problem, vehicle routing problem and variants, convoy routing problem, railroad blocking problem (RBP), inventory routing problem (IRP), air traffic flow management problem (TFMP), cash transportation vehicle routing problem, and so forth.

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31

Gansterer, Margaretha, and Veronika Klug. "Didactic Visualization of Routing Problems." Education Sciences 10, no. 6 (June 21, 2020): 166. http://dx.doi.org/10.3390/educsci10060166.

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The Traveling Salesman and Vehicle Routing Problems are integral parts of bachelor and master programs related to Operations Research/Management Science. This is due to their relevance in both practical applications and research. For students, it is essential to gain a deep understanding of the problems’ inherent complexity. For this, several tools are available online. A systematic classification of both the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) teaching material is provided and discussed. This classification takes target groups as well as learning objectives into account. Recommended choices depending on the teaching context are provided. A special focus lies on interactive visualizations, which seem to be most useful for supporting students in their solution-oriented competencies. We observe that didactic visualization tools for vehicle routing are scarce. This is surprising since, in practice, this problem is prevalent.

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32

Gupta, D. K. "Tabu Search for Vehicle Routing Problems (VRPs)." International Journal of Computer Mathematics 79, no. 6 (January 2002): 693–701. http://dx.doi.org/10.1080/00207160211291.

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33

Zakharov, V. V., and A. N. Shchegryaev. "Stable cooperation in dynamic vehicle routing problems." Automation and Remote Control 76, no. 5 (May 2015): 935–43. http://dx.doi.org/10.1134/s0005117915050173.

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34

Francis, Peter, and Karen Smilowitz. "Modeling techniques for periodic vehicle routing problems." Transportation Research Part B: Methodological 40, no. 10 (December 2006): 872–84. http://dx.doi.org/10.1016/j.trb.2005.12.001.

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35

Bertsimas, Dimitris, Philippe Chervi, and Michael Peterson. "Computational Approaches to Stochastic Vehicle Routing Problems." Transportation Science 29, no. 4 (November 1995): 342–52. http://dx.doi.org/10.1287/trsc.29.4.342.

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36

Prins, C., N. Labadi, and M. Reghioui. "Tour splitting algorithms for vehicle routing problems." International Journal of Production Research 47, no. 2 (November 20, 2008): 507–35. http://dx.doi.org/10.1080/00207540802426599.

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37

Ulmer, Marlin W., Justin C. Goodson, Dirk C. Mattfeld, and Barrett W. Thomas. "On modeling stochastic dynamic vehicle routing problems." EURO Journal on Transportation and Logistics 9, no. 2 (June 2020): 100008. http://dx.doi.org/10.1016/j.ejtl.2020.100008.

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38

Mor, A., and M. G. Speranza. "Vehicle routing problems over time: a survey." 4OR 18, no. 2 (March 6, 2020): 129–49. http://dx.doi.org/10.1007/s10288-020-00433-2.

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39

Pisinger, David, and Stefan Ropke. "A general heuristic for vehicle routing problems." Computers & Operations Research 34, no. 8 (August 2007): 2403–35. http://dx.doi.org/10.1016/j.cor.2005.09.012.

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40

Pillac, Victor, Michel Gendreau, Christelle Guéret, and Andrés L. Medaglia. "A review of dynamic vehicle routing problems." European Journal of Operational Research 225, no. 1 (February 2013): 1–11. http://dx.doi.org/10.1016/j.ejor.2012.08.015.

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41

Bazgan, Cristina, Refael Hassin, and Jérôme Monnot. "Approximation algorithms for some vehicle routing problems." Discrete Applied Mathematics 146, no. 1 (February 2005): 27–42. http://dx.doi.org/10.1016/j.dam.2004.07.003.

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42

Kulkarni, R. V., and P. R. Bhave. "Integer programming formulations of vehicle routing problems." European Journal of Operational Research 20, no. 1 (April 1985): 58–67. http://dx.doi.org/10.1016/0377-2217(85)90284-x.

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43

Ojeda Rios, Brenner Humberto, Eduardo C. Xavier, Flávio K. Miyazawa, Pedro Amorim, Eduardo Curcio, and Maria João Santos. "Recent dynamic vehicle routing problems: A survey." Computers & Industrial Engineering 160 (October 2021): 107604. http://dx.doi.org/10.1016/j.cie.2021.107604.

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44

Pop, Petrica, and Corina Pop-Sitar. "A new efficient transformation of the generalized vehicle routing problem into the classical vehicle routing problem." Yugoslav Journal of Operations Research 21, no. 2 (2011): 187–98. http://dx.doi.org/10.2298/yjor1102187p.

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Classical combinatorial optimization problems can be generalized in a natural way by considering a related problem relative to a given partition of the nodes of the graph into node sets. In the literature one can find generalized problems such as: generalized minimum spanning tree, generalized traveling salesman problem, generalized Steiner tree problem, generalized vehicle routing problem, etc. These generalized problems typically belong to the class of NP-complete problems; they are harder than the classical ones, and nowadays are intensively studied due to their interesting properties and applications in the real world. Because of the complexity of finding the optimal or near-optimal solution in case of the generalized combinatorial optimization problems, great effort has been made, by many researchers, to develop efficient ways of their transformation into classical corresponding variants. We present in this paper an efficient way of transforming the generalized vehicle routing problem into the vehicle routing problem, and a new integer programming formulation of the problem.

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45

Pham, Quang Dung, Kim Thu Le, Hoang Thanh Nguyen, Van Dinh Pham, and Quoc Trung Bui. "A Constraint-Based Local Search for Offline and Online General Vehicle Routing." International Journal on Artificial Intelligence Tools 26, no. 02 (April 2017): 1750004. http://dx.doi.org/10.1142/s021821301750004x.

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Vehicle routing is a class of combinatorial optimization problems arising in the industry of transportation and logistics. The goal of these problems is to compute an optimal route plan for a set of vehicles for serving transport requests of customers. There are many variants of the vehicle routing problems: routing for delivering goods, routing for demand responsive transport (taxi, school bus, …). Each problem might have different constraints, objectives. In this paper, we introduce a Constraint-Based Local Search (CBLS) framework for general offline and online vehicle routing problems. We extend existing neighborhood structures in the literature by proposing new neighborhoods to facilitate the resolution of different class of vehicle routing problems in a unified platform. A novel feature of the framework is the available APIs for online vehicle routing problems where requests arrive online during the execution of the computed route plan. Experimental results on three vehicle routing problems (the min-max capacitated vehicle routing problem, the multi-vehicle covering tour problem, and the online people-andparcel share-a-ride taxis problem) show the modelling flexibility, genericity, extensibility and efficiency of the proposed framework.

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46

Editor. "Solving Rich Vehicle Routing Problem Using Three Steps Heuristic." International Journal of Artificial Intelligence 1, no. 1 (May 30, 2019): 1–19. http://dx.doi.org/10.36079/lamintang.ijai-0101.9.

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Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

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47

Editor. "Solving Rich Vehicle Routing Problem Using Three Steps Heuristic." International Journal of Artificial Intelligence 1, no. 1 (May 30, 2019): 1–19. http://dx.doi.org/10.36079/lamintang.v1i1.9.

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Abstract:

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

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48

Alcaraz, Juan J., Luis Caballero-Arnaldos, and Javier Vales-Alonso. "Rich vehicle routing problem with last-mile outsourcing decisions." Transportation Research Part E: Logistics and Transportation Review 129 (September 2019): 263–86. http://dx.doi.org/10.1016/j.tre.2019.08.004.

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49

Thibbotuwawa, Amila, Grzegorz Bocewicz, Peter Nielsen, and Zbigniew Banaszak. "Unmanned Aerial Vehicle Routing Problems: A Literature Review." Applied Sciences 10, no. 13 (June 29, 2020): 4504. http://dx.doi.org/10.3390/app10134504.

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Unmanned aerial vehicle (UAV) routing is transitioning from an emerging topic to a growing research area as the 3D flexible utilization of airspace, promogulated by UAVs, is a potential game changer in solving the urban air mobility challenge by allowing to reshape transportation and logistics in the future. This has revealed a need to classify different types of research and examine the general characteristics of the research area. This research aims to assist in identifying the main topics and emerging research streams and provides a published overview of the current state and contributions to the area of the UAV routing problem (UAVRP) and a general categorization of the vehicle routing problem (VRP) followed by a UAVRP classification with a graphical taxonomy based on the analysis of UAVRP current status. To achieve this, an analysis of the existing research contributions promulgated in this domain is conducted. This analysis is used to identify the current state of UAVRP and the gaps related to the UAVs’ flight dynamics and weather conditions, which significantly influence the fuel consumption of the UAV when modeling the UAVRP.

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50

Yoshiike, Noriko, and Yoshiyasu Takefuji. "Solving vehicle routing problems by maximum neuron model." Advanced Engineering Informatics 16, no. 2 (April 2002): 99–105. http://dx.doi.org/10.1016/s1474-0346(01)00006-4.

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