Recent Advances in Computational Optimization: Results of by Stefka Fidanova PDF

By Stefka Fidanova

ISBN-10: 331912630X

ISBN-13: 9783319126302

ISBN-10: 3319126318

ISBN-13: 9783319126319

Our way of life is unthinkable with out optimization. we attempt to lessen our attempt and to maximise the completed revenue. Many actual international and business difficulties coming up in engineering, economics, medication and different domain names should be formulated as optimization tasks.

This quantity is a entire number of prolonged contributions from the Workshop on Computational Optimization 2013. It offers fresh advances in computational optimization. the amount contains very important actual lifestyles difficulties like parameter settings for controlling methods in bioreactor, source restricted venture scheduling, difficulties coming up in shipping companies, blunders correcting codes, optimum procedure functionality and effort intake and so forth. It exhibits the best way to increase algorithms for them in accordance with new metaheuristic equipment like evolutionary computation, ant colony optimization, constrain programming and others.

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For the static DARP, [4] is an important study on the subject. In this work, they use the Tabu search metaheuristic to solve the problem. Starting from an initial solution, the resolution process moves from a solution to another in a neighborhood. The operator which executes these moves will take a demand already assigned to vehicle k and insert it in another vehicle k . The Tabu list saves each application of the operator in order to avoid cycling. The insertion parameters of the demand (for the origin and the destination) are chosen in order to minimize the total distance.

More specifically, the technique does not change unlike the state of each route. The first node is not a depot node anymore but a dynamic node related to the vehicle’s location. The entire constraint propagation process is applied on these new routes. A simulation will be necessary to evaluate the anticipation of the future demands including in the dynamic context. 8 Computational Experiments In this section, we study the behavior of our Insertability measure used in the resolution of Dial-a-Ride instances.

Transp. Sci. 17, 351–357 (1983) 11. : Variable neighborhood search for the dial-a-ride problem. Comput. Oper. Res. 37, 1129–1138 (2010) 12. : A new extension of local search applied to the dial-a-ride problem. Eur. J. Oper. Res. 83, 83–104 (1995) 13. : A heuristic algorithm for the multi-vehicle manyto-many advance request dial-a-ride problem. Transp. Res. B 20B, 243–257 (1986) 14. : A heuristic algorithm for the a dial-a-ride problem with time windows, multiple capacities, and multiple objectives.

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Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization WCO 2013 by Stefka Fidanova


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