A scenario based approach for flexible resource loading under uncertainty

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

Scheduling vehicles in automated transportation systems : algorithms and case study

One of the major planning issues in large scale automated transportation systems is so-called empty vehicle management, the timely supply of vehicles to terminals in order to reduce cargo waiting times. Motivated by a Dutch pilot project on an underground cargo transportation system using Automated Guided Vehicles CAGV s), we developed several rules and algorithms for empty vehicle management, varying from trivial First-Come, First-Served (FCFS) via look-ahead rules to integral planning. For our application, we focus on attaining customer service levels in the presence of varying order priorities, taking into account resource capacities and the relation to other planning decisions, such as terminal management We show how the various rules are embedded in a framework for logistics control of automated transportation networks. Using simulation, the planning options are evaluated on their performance in terms of customer service levels, AGV requirements and empty travel distances. Based on our experiments, we conclude that look-ahead rules have significant advantages above FCFS. A more advanced so-called serial scheduling method outperforms the look-ahead rules if the peak demand quickly moves amongst routes in the system

Linear-programming-based heuristics for project capacity planning \ud

Many multi-project organizations are capacity driven, which means that their operations are constrained by various scarce resources. An important planning aspect in a capacity driven multi-project organization is capacity planning. By capacity planning, we mean the problem of matching demand for resources and availability of resources for the medium term. Capacity planning is a very useful method to support important tactical decisions such as due date quotation and price quotation for new projects, and to gain an insight into capacity requirements for the medium term. We present a capacity planning model in which aspects such as capacity flexibility, precedence relations between work packages, and maximum work content per period can be taken into account. For this model, we discuss several linear-programming-based heuristics. Using a large set of test instances, we compare these heuristics with some results from the literature. It turns out that some of these heuristics are very powerful for solving capacity planning problem