No abstract

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach for resource loading under uncertainty that minimises the expected costs. The approach uses an MILP to find a plan that has minimum expected costs over all relevant scenarios. We propose an exact and a heuristic solution approach to solve this MILP. A disadvantage of this approach is that the MILP may become too large to solve in reasonable time. We therefore propose another approach that uses an MILP with a sample of all scenarios. We use the same exact and heuristic methods to solve this MILP.Computational experiments show that, especially for instances with much slack, solutions obtained with deterministic techniques for a expected scenario can be improved with respect to their expected costs. We also show that for large instances the heuristic outperforms the exact approach given a computation time as a stopping criterion.

Trade-off between inventory and repair capacity i n spare part networks A Sleptchenko*,', MC van der Heijden2 and A van Harten2

In many industries production facilities are used which process products in a batch-wise manner. Guided by research in the aircraft industry, where the process of hardening synthetic aircraft parts was studied, we propose a new control strategy for these types of systems. Given the availability of information on a few near future arrivals the strategy decides on when to schedule a job in order to minimize logistical costs. The fact that different cost structures can be incorporated, makes it a valuable tool for use in practical situations in business. The potential of the new strategy is demonstrated by an extensive series of simulation experiments, in which its response for various system configurations was tested in comparison with existing heuristics.

We consider a multi-server queuing model with two priority classes that consist of multiple customer types. The customers belonging to one priority class customers are lost if they cannot be served immediately upon arrival. Each customer type has its own Poisson arrival and exponential service rate. We derive an exact method to calculate the steady state probabilities for both preemptive and nonpreemptive priority disciplines. Based on these probabilities, we can derive exact expressions for a wide range of relevant performance characteristics for each customer type, such as the moments of the number of customers in the queue and in the system, the expected postponement time and the blocking probability. We illustrate our method with some numerical examples.