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The focus in a KB-DSS is on the level at which the system and the user communicate Scheduling systems often use heuristics that compromise optimality for performance reasons Hofstede (1992) gives some prerequisites to which heuristics must comply if they are to be used in interactive support at the knowledge level First, the user must be able to interact during operation Second, the problem representation must consist of objects that are meaningful for the planner, and it must be possible to show the progress of the heuristic to the user Third, the operators or transitions in the heuristic must refer to actions in the real world Fourth, the control mechanism must allow the user to alter the current state during execution of the heuristic, and it must provide a way for the user to make a trade-off between the quality of the solution and the time spent in generating it.

finishes at time 2 while all other pi, 1 i s 2, , 11 finish at time 20 in the GPS system, WFQ will serve p1, 1 first In fact, the first 10 packets on session 1 all have finishing times smaller than packets belonging to any other session, which means that 10 packets on session 1 will be served back to back before packets on other sessions can be transmitted This is shown in Figure 418 c. After the burst the next packet in session 1, p1, 11 , will have a larger finishing time in the GPS system than the 10 packets at the head of other sessions queues Therefore, it will not be served until all the other 10 packets are transmitted, at which time, another 10 packets from session 1 will be served back to back.

Prietula et al (1994) describe how they applied the concept of coincident problem spaces in the scheduling system McMerl, with the following proposition: To con gure effectively a support system that can exploit the knowledge of the scheduling expert, it is important to direct the behavior of the system to function in a manner that is consistent with the key problem spaces of the scheduler; that is, the system and the scheduler should be problem solving in coincident problem spaces (p 660) They acknowledged the restricted computational abilities of human planners, and they designed a system that could augment the search of the human with search by the computer In addition, they used a mixed-initiative approach for the system because the system could not anticipate the entire set of parameters that de ne acceptable solutions (p 657).

This cycle of bursting 10 packets and going silent for 10 packets times can continue indefinitely With more sessions, the length of the period between bursting and silence can be larger Such oscillation is undesirable for flow and congestion control in data communication networks To quantify the discrepancy between the service provided by a packet discipline and the fluid GPS discipline, we consider the notion of worst-case packet fair as defined below w26x Definition 45 A service discipline s is called worst-case fair for session i if for any time , the delay of a packet arriving at is bounded above by Q is rri q Cis, that is, d is, k - a i , k q Q is a i , k ri q Cis ,.

In such an approach, both the human planner and the computer can take initiative in the proposition of schedule decisions The human planner, though, always has the last word To overcome the problem that the problem space of a human planner can change after the introduction of scheduling support, Prietula et al (1994) apply the Soar architecture This architecture allows (symbol-based) learning, so changes in the task that occur after the introduction of the scheduling support system can be depicted in the reasoning mechanism of the system Discussion The main advantage of domain-oriented techniques is that the solutions are of a high quality The main advantage of the problem-solving-oriented approaches is that the process of scheduling generation better suits human planners These two advantages are presumed to be combined in task-oriented techniques..

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