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Titlebook: Variable Neighborhood Search; 6th International Co Angelo Sifaleras,Said Salhi,Jack Brimberg Conference proceedings 2019 Springer Nature Sw

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樓主: fumble
51#
發(fā)表于 2025-3-30 11:35:42 | 只看該作者
52#
發(fā)表于 2025-3-30 15:06:46 | 只看該作者
Basic VNS Algorithms for Solving the Pollution Location Inventory Routing Problem,d environmental impacts. A Mixed Integer Programming (MIP) formulation is employed and experimental results on ten randomly generated small-sized instances using CPLEX are reported. Furthermore, it is shown that, CPLEX could not compute any feasible solution on another set of ten randomly generated
53#
發(fā)表于 2025-3-30 17:35:40 | 只看該作者
54#
發(fā)表于 2025-3-30 23:22:44 | 只看該作者
New VNS Variants for the Online Order Batching Problem,d most extended version of the problem consists in minimizing the total time needed to collect a group of orders. However, this version has been evolved with many other variants, where the restrictions and/or the objective function might change. In this paper, we deal with the Online Order Batching
55#
發(fā)表于 2025-3-31 03:24:11 | 只看該作者
56#
發(fā)表于 2025-3-31 05:12:48 | 只看該作者
57#
發(fā)表于 2025-3-31 10:21:02 | 只看該作者
58#
發(fā)表于 2025-3-31 16:57:32 | 只看該作者
59#
發(fā)表于 2025-3-31 18:05:29 | 只看該作者
A Variable Neighborhood Search with Integer Programming for the Zero-One Multiple-Choice Knapsack Pod search (VNS) with an integer programing (IP). Our approach, based on a local search technique with an adaptive perturbation mechanism to assign the classes to knapsack, and then if the assignment is identified to be promising by comparing its result to the upper bound, we applied the IP to select
60#
發(fā)表于 2025-4-1 01:30:42 | 只看該作者
A VNS-Based Algorithm with Adaptive Local Search for Solving the Multi-Depot Vehicle Routing Probleo serve all customers, such that the maximum number of vehicle per depot, the vehicle capacity and the maximum time for each route are respected. The objective is to minimize the total cost of transportation. This paper presents an algorithm, named VNSALS, based on the Variable Neighborhood Search (
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