An interactive methodology to explore optimization scenarios of a self-reconfigurable factory

High value manufacturing (HVM) is a key sector; however, its products have a long development time. Adaptation in factories according to production requirements has been proposed to increase HVM's efficiency. In self-reconfigurable factories there is rapid relocation of production resources to perform different tasks. Due to this flexibility, the allocation and scheduling of jobs to resources is a key challenge that needs to be solved with tractable methods. A prior problem resides in the vast number of ways to formulate the allocation and scheduling problem (ASP). The ASP formulation might consider variables such as the number of resources, their locations and capacities; and their number increases by adding variables or options, e.g. a problem with 50 binary variables has order of ~1015. Due to this complexity, here is proposed a novel methodology to compare abstract formulations (optimization scenarios). Its novelty resides in the systematic and hierarchical generation of partial scenarios in sequential stages with partial groups of variables; then experts select partial scenarios at each stage to become the bases for more complex scenarios at successive stages. A case study is presented and addressed with the methodology, the results show a reduction of more than 90% generated scenarios.