2013
DOI: 10.14232/actacyb.21.1.2013.5
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Application Oriented Variable Fixing Methods for the Multiple Depot Vehicle Scheduling Problem

Abstract: In this article, we present heuristic methods for the vehicle scheduling problem that solve it by reducing the problem size using different variable fixing approaches. These methods are constructed in a way that takes some basic driver requirements into consideration as well. We show the efficiency of the methods on real-life and random data instances too. We also give an improved way of generating random input for the vehicle scheduling problem.

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Cited by 8 publications
(12 citation statements)
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“…We tested our method on random input generated based on [9]. Both single and multiple-depot test cases were created in different sizes; namely 50, 250, 500, 1000, 1500 and 2000 trips.…”
Section: Test Resultsmentioning
confidence: 99%
“…We tested our method on random input generated based on [9]. Both single and multiple-depot test cases were created in different sizes; namely 50, 250, 500, 1000, 1500 and 2000 trips.…”
Section: Test Resultsmentioning
confidence: 99%
“…Our random input data was generated in two steps. First, random VSP inputs were created using the method in Dávid and Krész (2013). These instances had 100, 500, or 1000 trips, and used either 2 or 3 depots.…”
Section: Random Instancesmentioning
confidence: 99%
“…The resulting smaller problem is solved using the classical IP modeling approach, and solutions are obtained with a greatly reduced running time. This method is studied in our following publications: [32,34].…”
Section: Vehicle Schedulingmentioning
confidence: 99%
“…First, we give a series of variable fixing heuristics for creating vehicle schedules in a short time, then introduce an iterative algorithm that constructs schedules satisfying the basic rules of driver shifts as well. The list of our publications connected to this topic include [32,34,9].…”
Section: Chapter 1 Introductionmentioning
confidence: 99%
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