Planning and Control of Automated Material Handling Systems: The Merge Module

Abstract

AbstractWe address the field of internal logistics, embodied in Automated Material Handling Systems (AMHSs), which are complex installations employed in sectors such as Baggage Handling, Physical Distribution, and Parcel & Postal. We work on designing an integral planning and real-time control architecture, and a set of generic algorithms for AMHSs. Planning and control of these systems need to be robust, and to yield close-to-optimal system performance. Currently, planning and control of AMHSs is highly customized and project specific. This has important drawbacks for at least two reasons. From a customer point of view, the environment and user requirements of systems may vary over time, yielding the need for adaptation of the planning and control procedures. From a systems’ deliverer point of view, an overall planning and control architecture that optimally exploits synergies between the different market sectors, and at the same time is flexible with respect to changing business parameters and objectives is highly valuable. An integral planning and control architecture should clearly describe the hierarchical framework of decisions to be taken at various levels, as well as the required information for decisions at each level, e.g., from overall workload planning to local traffic control. In this research, we identify synergies among the different sectors, and exploit these synergies to decompose AMHSs into functional modules that represent generic building blocks. Thereafter, we develop generic algorithms that achieve (near) optimal performance of the modules. As an example, we present a functional module from the Parcel & Postal sector. In this module, we study merge configurations of conveyor systems, and develop a generic priority-based real-time scheduling algorithm.KeywordsControl ArchitectureSpace UtilizationIntegral PlanningSpace AllocationWorkload BalanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.