Order acceptance decisions in Engineer-To-Order (ETO) environments are often based on incomplete or uncertain information about the order specifications and the status of the production system. To quote reliable due dates and manage the production system adequately, resource loading techniques that account for uncertainty are essential. They are useful as support tools for order acceptance and thus profitable ETO production. In this paper we propose two multi-objective optimization models for Robust Resource Loading (RRL). The first model is a multi-objective MILP model with implicitly modeled precedence relations wich we solve using a branch-and-price approach. In the second approach we use a resource loading formulation with explicitly modeled precedence relations. The models generate robust plans by including robustness in the objective function. We introduce two indicators to measure robustness: resource plan robustness and activity plan robustness. Resource plan robustness measures robustness from a resource managers viewpoint. Activity plan robustness measures robustness from a customers viewpoint. Computational experiments with the models show that accounting for robustness in the objective function improves the characteristics of a plan significantly with respect to dealing with uncertainty. Furthermore, the model with explicit precedence constraints outperforms the implicit approach.
|Publisher||Beta Research School for Operations Management and Logistics, University of Twente|
- resource loading
- planning under uncertainty
- Multi-objective optimization