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162 DESCRIPTION OF THE HEURISTIC Before presenting the multiple-period heuristic in Section 1622, we rst explain in Section 1621 how the one-period heuristic is constructed In Section 1623 the solution procedure is illustrated for a small instance of the shunting problem We refer to Section 164 for results on a case taken from practice 1621 The One-Period Heuristic A real instance of the shunting problem is rather complex to solve Therefore we rst develop a heuristic that solves a simpli ed instance of the problem: a shunting problem where all arrivals are prior to the rst departure For example, one could think of the shunting problem during the night such that at the end of one day all arriving trains need to be parked at the marshaling yard in order of their departure the next morning.

In mathematical terms we state that the set of arrivals (A) is separated in time from the set of departures (D) A period in which the set of arrivals (A) is separated in time from the set of departures (D) is called a planning period In practice one often needs to identify multiple planning periods within the planning horizon, which is the time interval of interest A regular planning horizon for the shunting problem at the Dutch Railways is from 8 AM at some day until 8 AM the next day The one-period heuristic developed in this section solves problems that have only one planning period in the planning horizon In Section 1622 this heuristic is used as a building block in the multiple-period heuristic that deals with shunting problems that (might) have multiple planning periods in the planning horizon Figure 16.

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Figure 3.55. Effects of immobilization on ESR spectra. A protein was spin-labelled with 4-isothiocyanato-TEMPO (Figure 3.54). ESR spectra shown are for (a) free spin label, (b) spin-labelled protein in a phospholipid bilayer at 45 C and (c) spin-labelled protein in a phospholipid bilayer at 20 C. The phospholipids in the bilayer undergo a gel-liquid transition at 41 C. Note how much broader the lines are as degree of immobilization increases.

4 visualizes the one-period heuristic and its relation to the multipleperiod heuristic One clearly observes the hierarchical planning approach for each planning period: First the departures are positioned by the algorithm Blueprint, next the arrivals are matched to the departures by the algorithm Matching The decomposition in two algorithms as sketched in Figure 164 takes the departures as the base of planning Doing so makes the planning process more robust,.

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There must be a train driver to drive the train at the speci ed time. There must be a route available to drive the train from the source track to the destination track. This can be dif cult at peak times, because there must be a certain amount of time between two movements on a track. If a train must be split into units or if a train must be composed of units, one or more train shunters must be available. There are several types of train units. Only units of the same type can be connected. The con guration of incoming and outgoing trains is speci ed on unit type level, not on individual unit level. The train units are symmetrical and have their own locomotive. So they can drive in both directions. Multiple units can be put on one track. This is necessary at night because there is only a limited number of parking tracks. A logical consequence is that a train cannot be moved if there is a train parked in front of it. The time it takes to wash a train is largely independent from the size of the train. Because the track where trains can be washed is a bottleneck capacity, it is advantageous to combine units in trains before they are washed.

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there are no propagation modes in any direction for a range of frequencies, giving rise to a complete photonic bandgap [285]. 5.4.2 Applicability of Technology

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