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Titlebook: Computational Logistics; 4th International Co Dario Pacino,Stefan Vo?,Rune M?ller Jensen Conference proceedings 2013 Springer-Verlag Berlin

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31#
發(fā)表于 2025-3-26 22:29:57 | 只看該作者
32#
發(fā)表于 2025-3-27 04:24:38 | 只看該作者
0302-9743 f the 4th International Conference on Computational Logistics, ICCL 2013, held in Copenhagen, Denmark, in September 2013. The 19 papers presented in this volume were carefully reviewed and selected for inclusion in the book. They are organized in topical sections named: maritime shipping, road trans
33#
發(fā)表于 2025-3-27 06:09:41 | 只看該作者
Introduction to Example Programslve the first of the two phases, the multi-port master planning problem. Our approach combines the strength of mathematical modeling with the flexibility of a local search. We show how the new approach can solve more instances than previous mathematical models, and present an analysis of its performance.
34#
發(fā)表于 2025-3-27 12:17:59 | 只看該作者
35#
發(fā)表于 2025-3-27 15:01:36 | 只看該作者
36#
發(fā)表于 2025-3-27 19:38:50 | 只看該作者
Column Generation Based Heuristic for the Three Dimensional Vehicle Loading Problemspeed up the column generation approach. A problem specific branching technique is used to generate integer solutions in a reasonable time. Numerical experimentation is done to compare the performance of the developed approach with the available results in the literature.
37#
發(fā)表于 2025-3-27 23:01:23 | 只看該作者
38#
發(fā)表于 2025-3-28 03:00:49 | 只看該作者
TSP with Multiple Time-Windows and Selective Citiesed number of days, that minimizes a linear combination of the total traveled distance as well as the total waiting time. We present a mixed integer linear programming (MILP) model for the problem and propose a heuristic approach to solve it. Computational experiments address two real world problems that arise in different practical contexts.
39#
發(fā)表于 2025-3-28 07:49:38 | 只看該作者
40#
發(fā)表于 2025-3-28 13:39:25 | 只看該作者
From Preparedness to Recovery: A Tri-Level Programming Model for Disaster Relief Planningitate scenario exploration for decision-support. We develop an iterative dual-ascent solution approach. Computational results show that our approach is efficient, and we can also draw some insights on disaster relief planning.
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