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Patrick Siarry - Optimization and Machine Learning

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Название:
Optimization and Machine Learning
Автор:
Patrick Siarry
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unrecognised / на английском языке
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неизвестен
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Optimization and Machine Learning: краткое содержание, описание и аннотация

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Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.<br /><br /><i>Optimization and Machine Learning</i> presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.

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1.3.4. Computational analysis

The set of instances used for the 3L-CVRP is available at http://www.or.deis.unibo.it/research.htmland was introduced by Gendreau et al . (2006). There are in total 27 3L-CVRP instances available on the web which provide an interesting test bed for the comparison of different heuristic and metaheuristic solutions. The vehicle characteristics are W = 25, H = 30 and L = 60, respectively. The demand of each customer is between 1 and 3. The capacity of vehicles is 0.75.

Table 1.2. Comparative study of the 3L-CVRP

Author	Problem	Routing problem	Loading problem
		Solution methods	Solution methods
Gendreau et al . (2006) Apile et al . (2007) Tarantilis et al . (2009) Fuellerer et al . (2010) Ren et al . (2011) Massen et al . (2012) Bortfeldt (2012) Wisniewski et al . (2011) Zhu et al . (2012) Miao et al . (2012) Ruan et al . (2013) Bortfeldt and Homberger-2013 Tao and Wang (2015) Junqueira etal. (2013) Hokima et al . (2016) Vega et al . (2020) Moura (2008) Moura and Oliveira (2009) Zhang et al . (2017) Moura et al . (2019) Vega et al . (2019) Ceschia etal. (2013) Pace etal. (2015) Pollaris et al . (2017) Bortfeldt et al . (2015) Koch et al . (2018) Reil et al . (2018) Koch et al . (2020) Bartok and Imreh (2011) Mannel and Bortfeldt (2016) Yi and Bortfeldt (2016) Li et al . (2018) Bortfeldt and Yi (2020)	3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRP 3L-CVRPTW 3L-CVRPTW 3L-CVRPTW 3L-CVRPTW 3L-CVRPTW 3L-HCVRP 3L-HFCVRPTW CVRP with pallet loading and axle weight constraints 3L-VRPB 3L-VRPBTW 3L-VRPBTW 3L-VRPMB 3L-VRPPD 3L-VRPD 3L-SDVRP 3L-SDVRP 3L-SDVRP	TS SA LS-GLS ACO Branch-and- bound black box algorithm TS TS TS GA HBMO P1R2 TS integer linear programming model Branch-and-Cut GRASP-CWS GA LS, GRASP TS-ABC MILP NLMIP LNS ILS and SA ILS LNS/VNS LNS TS TS LS LNS TS Data-driven 3-layer GA-LS	TS SA LS-GLS ACO Branch-and-bound black box algorithm TRSA bottom-left Deepest-Bottom-Left-Fill heuristic and the Maximum Touching Area TS six heuristics P1R2 least waste algorithm Branch-and-Cut GRASP-CWS GA LS, GRASP TS-ABC MILP NLMIP LNS ILS and SA ILS TSH LNS TS TS LS TSH TS Data-driven 3-layer GA-LS

1.4. Perspectives on future research

In this review, the last decade of publications related to the combination VRP with 2/3D loading problems with additional variants and constraints has been surveyed. A comparative study of the existing optimization methods such as exact, heuristic and metaheuristic is described. These promising research areas give the opportunity for solving real-world problems in transportation. Given the importance of this problem, it still remains a challenge. However, future research could extend on the 2L-CVRP with multi-objective optimization. In addition, the 2L-VRP has been studied on the static case in which all information is known at the time of the planning routes. However, in most real-life applications, new customer requests can happen over time and thus trouble the optimal routing schedule that was originally invented. Therefore, the problem can be studied in the dynamic case.

1.5. References

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Araujo, L.J., Ozcan, E., Atkin, J.A., Baumers, M. (2019). Analysis of irregular three-dimensional packing problems in additive manufacturing: A new taxonomy and dataset. International Journal of Production Research , 57(18), 5920–5934.

Attanasio A., Fuduli A., Ghiani, G., Triki, C. (2007). Integrated shipment dispatching and packing problems: A case study. Journal of Mathematical Modelling and Algorithms , 6(1), 77–85.

Bartok, T. and Imreh, C. (2011). Pickup and delivery vehicle routing with multidimensional loading constraints. Acta Cybernetica , 20, 17–33.

Bortfeldt, A. (2012). A hybrid algorithm for the capacitated vehicle routing problem with three-dimensional loading constraints. Computers & Operations Research , 39(9), 2248–2257.

Bortfeldt, A. and Homberger, J. (2013). Packing first, routing second: A heuristic for the vehicle routing and loading problem. Computers & Operations Research , 40(3), 873–885.

Bortfeldt, A. and Yi, J. (2020). The split delivery vehicle routing problem with three-dimensional loading constraints. European Journal of Operational Research , 282(2), 545–558.

Bortfeldt, A., Hahn, T., Mannel, D., Monch, L. (2015). Hybrid algorithms for the vehicle routing problem with clustered backhauls and 3D loading constraints. European Journal of Operational Research , 243, 82–96.

Ceschia, S., Schaerf, A., Stutzle, T. (2013). Local search techniques for a routing-packing problem. Computers & Industrial Engineering , 66(4), 1138–1149.

Christofides, N. and Beasley, J. (1984). The period routing problem. Networks , 14, 237–256.

Cordeau, J.F., Gendreau, M., Laporte, G., Potvin, J.Y., Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society , 53(5), 512–522.

Cote, J.F., Gendreau, M., Potvin, J.Y. (2013). The Vehicle Routing Problem with Stochastic Two-Dimensional Items . CIRRELT, Quebec.

Cote, J.F., Gendreau, M., Potvin, J.Y. (2020). The vehicle routing problem with stochastic two-dimensional items. Transportation Science , 54(2), 453–469.

Dominguez, O., Guimarans, D., Juan, A.A., de la Nuez, I. (2016). A biased-randomised large neighbourhood search for the two-dimensional vehicle routing problem with backhauls. European Journal of Operational Research , 255(2), 442–462.

Duhamel, C., Lacomme, P., Quilliot, A., Toussaint, H. (2011). A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem. Computers & Operations Research , 38, 617–640.

Fekete, S.P. and Schepers, J. (2004). A general framework for bounds for higher-dimensional orthogonal packing problems. Mathematical Methods of Operations Research , 60(2), 311–329.

Fisher, M., Jakumar, R., van Wassenhove, L. (1981). A generalized assignment heuristic for vehicle routing. Networks , 11, 109–124.

Fuellerer, G., Doerner, K., Hartl, R., Iori, M. (2009). Ant colony optimization for the two-dimensional loading vehicle routing problem. Computers & Operations Research , 36, 655–673.

Fuellerer, G., Doerner, K.F., Hartl, R.F., Iori, M. (2010). Metaheuristics for vehicle routing problems with three-dimensional loading constraints. European Journal of Operational Research , 201(3), 751–759.

Gendreau, M., Iori, M., Laporte, G., Martello, S. (2006). A Tabu search algorithm for a routing and container loading problem. Transportation Science , 40(3), 342–350.

Gendreau, M., Iori, M., Laporte, G., Martello, S. (2008). A Tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints. Networks , 51, 4–18.

Guimarans, D., Dominguez, O., Panadero, J., Juan, A.A. (2018). A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times. Simulation Modelling Practice and Theory , 89, 1–14.