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3.3.2.4 PECAN Policies. The PECAN policy language by and large conforms to the description given in 2 earlier. A PECAN policy rule is composed of events, conditions, and actions. A policy rule has zero or more events, an optional condition, and one or more actions. When the events of a rule occur, the conditions are evaluated; if they evaluate to true, the actions are executed. The general format of a policy rule is as follows: When (events) if expression action Events, conditions, and actions are described in more detail below. 3.3.2.4.1

Huang, A technique for an array to generate circular polarization with linearly polarized elements, IEEE Trans Antennas Propagat 34, 1113 1124, Sept 1986 29 W H Hsu and K L Wong, A dual capacitively fed broadband patch antenna with reduced cross-polarization radiation, Microwave Opt Technol Lett 26, 169 171, Aug 5, 2000 30 K L Wong and W S Chen, Compact microstrip antenna with dual-frequency operation, Electron Lett 33, 646 647, April 10, 1997 31 K L Wong and Y C Chen, Resonant frequency of a slot-coupled cylindrical-rectangular microstrip structure, Microwave Opt Technol Lett 7, 566 570, Aug 20, 1994..

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A naive implementation of a request queue will incur race conditions or 'busy waiting' when multiple threads insert and remove requests. For example, multiple concurrent producer and consumer threads can corrupt the queue's internal state if it is not synchronized properly. Similarly, these threads will 'busy wait' when the queue is empty or full, which wastes CPU cycles unnecessarily.

than others, especially if the problem size is limited and model conditions can be speci ed with reasonable certainty For detailed planning and scheduling, algorithms have been of limited value The four reasons Buxey (1989, p 29) gives for this lack of value are: (1) in exible models that are not able to cope with changing environments, (2) higher-level priorities that are not correctly taken into account, (3) algorithms that are not able to cope with uncertainty regarding the actual state of the system, and (4) objectives of the algorithms that are only soft constraints and that for the system as a whole are only of limited value These conclusions seem also valid in the related eld of transportation planning and railway scheduling.

Watson (2000) investigated various railway planners in the United Kingdom, and he states that the reasons for not using optimization models in railway scheduling are (1) a lack of integration with software that ts the needs of the users, (2) a lack of cooperation between software developers and model builders, (3) a lack of extendibility of the models to real-life circumstances, and nally (4) no international applicability of the models We suggest that the more fundamental reason for not using these models can be found in the design approach applied Traditionally, model builders use a problemoriented approach, focusing on the conceptualization of the planning problem as viewed by the experts This approach ignores information on the strategies that are used by the planner in solving the problem, but focuses directly on the total problem that has to be solved by the planner.

The OOM loop approach described in Section A.2 also had its issues: 1. It was hard to determine where to set heap failure limits i.e. the maximum value of k .

If the planner is not explicitly considered as an object of study in the design of the support system, two inherent dangers have to be faced First, the planner may not accept the solutions generated by the support system, because he has no insight into the way these solutions are constructed Second, the quality of the solutions may deteriorate over time due to the fact that the optimization model is not a perfect representation of the planning problem faced by the planner Constraints that were neglected in the optimization model can at some moment in time become important for the planner to include in his solution approach The life cycle of an optimization model is therefore often shorter than assumed by the original designer, as already noticed by Ackoff (1957, p 27) This chapter proposes and applies a planner-oriented focus in designing OR algorithms for a railway planning support system.