Abstract

Based on previous work in rolling stock scheduling problems (Alfieri et al. in Transp Sci 40:378–391, 2006; Cacchiani et al. in Math Progr B 124:207–231, 2010; Lin and Kwan in Electron Notes Discret Math 41:165–172, 2013; Schrijver in CWI Q 6:205–217, 1993; Ziarati et al. in Manag Sci 45:1156–1168, 1999), we generalize a local convex hull method for a class of integer multicommodity flow problems, and discuss its feasibility range in high dimensional cases. Suppose a local convex hull can be divided into an up hull, a main hull and a down hull if certain conditions are met, it is shown theoretically that the main hull can only have at most two nonzero facets. The numbers of points in the up and down hull are explored mainly on an empirical basis. The above properties of local convex hulls have led to a slightly modified QuickHull algorithm (the “2-facet QuickHull”) based on the original version proposed by Barber et al. (ACM Trans Math Softw 22:469–483, 1996). As for the feasibility in applying this method to rolling stock scheduling, our empirical experiments show that for the problem instances of ScotRail and Southern Railway, two major train operating companies in the UK, even in the most difficult real-world or artificial conditions (e.g. supposing a train can be served by any of 11 compatible types of self-powered unit), the standard QuickHull (Barber et al. in ACM Trans Math Softw 22:469–483, 1996) can easily compute the relevant convex hulls. For some even more difficult artificial instances that may fall outside the scope of rolling stock scheduling (e.g. a node in a graph can be covered by more than 11 kinds of compatible commodities), there is evidence showing that the “2-facet QuickHull” can be more advantageous over the standard QuickHull for our tested instances. When the number of commodity types is even higher (e.g. >19), or the number of points in a high dimensional space (e.g. 15 dimensions) is not small (e.g. >2000), the local convex hulls cannot be computed either by the standard or the 2-facet QuickHull methods within practical time.

Highlights

  • 1.1 A special class of multicommodity flow problemsThere are several kinds of rolling stock scheduling problems that can be modeled as a class of integer multicommodity flow problems

  • The features in Observation 1 are commonly seen in a class of integer multicommodity flow problems, typically arising in rolling stock scheduling problems as train unit or locomotive scheduling, where the nodes are train services to be covered by rolling stock, the commodities are different types of rolling stock and wkj are the number of rolling stock of type k used for train j

  • Within the realm of railway rolling stock scheduling/assignment/circulation, the method of using explicitly computed local convex hulls to strengthen linear programming (LP) relaxation first appears in Schrijver [23] for a train unit scheduling problem, where since at most two commodities are involved, the convex hull computation is done in R2+

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Summary

A special class of multicommodity flow problems

There are several kinds of rolling stock scheduling problems that can be modeled as a class of integer multicommodity flow problems. The features in Observation 1 are commonly seen in a class of integer multicommodity flow problems, typically arising in rolling stock scheduling problems as train unit or locomotive scheduling, where the nodes are train services to be covered by rolling stock, the commodities are different types of rolling stock and wkj are the number of rolling stock of type k used for train j. There are other real-world applications formulated as integer multicommodity flow problems having the features mentioned in Observation 1 and are suitable for the local convex hull method. Note that their specific formulations do not have to be exactly the same as (P). Their applications to other suitable problems can be derived by analogy

Local convex hull method
Removing incompatible commodities
A real-world example from Southern Railway
Previous studies on local convex hulls
The structure of local convex hulls
Preliminaries
Number of nonzero facets of main hull
Number of outside points
Two special conditions
Empirical experiments
Non-standalone types
A customized QuickHull algorithm to compute local convex hulls
Feasibility range of local convex hull method
Conclusions and future research
Full Text
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