Modelling and Solving Industrial Production Tasks as Planning-Scheduling Tasks

Abstract

Industrial production planning or manufacturing concerns the selection of activities that can produce a desired product and scheduling them on resources that perform these activities. To deal with such problems techniques in the fields of Automated Planning and Scheduling might be leveraged, which are usually pursued separately even though they are (very) complementary. In manufacturing, the activities represent elementary steps in the production and each activity requires a specific input in order to produce a desired output. From that perspective, activities resemble actions in planning as they can capture such a requirement. Selecting proper activities including their (partial) ordering can be understood as a planning task while allocating the activities to the resources can be understood as a scheduling task. This paper formalises the concept of “combined” planning and scheduling tasks by defining planning-scheduling tasks that are suitable for problems concerning industrial production or manufacturing. In particular, we define two types of activities – production and maintenance activities – where the former describes elementary production tasks while the latter modifies attributes of the resources (e.g. changing the configuration of reconfigurable machines). We introduce an extension of Planning Domain Definition Language (PDDL), a well-known language for describing planning tasks, to support modelling of planning-scheduling tasks. To tackle planning-scheduling tasks we propose two compilation schemes, one into temporal planning (in PDDL 2.1) and one into classical planning. We evaluated our approaches in three use cases of industrial production planning — Reconfigurable Machines, Woodworking, and Tube Factory domains. The results showed that solving planning-scheduling tasks by compiling them into planning tasks in order to use off-the-shelf planning engines is suitable as it scales reasonably well with the size of the actual tasks (although the resulting solutions are suboptimal).