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Optimal Storage Assignment in Automatic Warehousing Systems
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EngineeringStorage ManagementComputer ArchitectureStorage StructureOperations ResearchLogisticsSystems EngineeringComputer-controlled Stacker CranesCombinatorial OptimizationData ManagementQuantitative ManagementPallet AssignmentComputer EngineeringScheduling (Computing)Computer ScienceStorage AllocationScheduling ProblemCloud ComputingAutomationBusinessStorage AssignmentOptimal Storage AssignmentStorage Capacity
Automatic warehousing systems with computer‑controlled stacker cranes have proliferated, and their scheduling involves pallet assignment, storage assignment, and interleaving of requests. The study aims to scientifically schedule and design these systems, focusing on optimal storage assignment. Results compare the operating performance of three storage assignment rules: random assignment, full turnover‑based assignment, and class‑based turnover assignment. Turnover‑based storage assignment rules significantly reduce crane travel time and distance, potentially increasing throughput capacity and enabling design adjustments such as rack size, number, and crane speed to balance throughput and storage capacity.
In the past few years, increasing numbers of automatic warehousing systems using computer-controlled stacker cranes have been installed. Our research concerns the scientific scheduling and design of these systems. There are three elements to scheduling: the assignment of multiple items to the same pallet (Pallet Assignment); the assignment of pallet loads to storage locations (Storage Assignment); and rules for sequencing storage and retrieve requests (Interleaving). This paper deals with optimal storage assignment. Results are obtained which compare the operating performance of three storage assignment rules: random assignment, which is similar to the closest-open-location rule used by many currently operating systems; full turnover-based assignment: and class-based turnover assignment. It is shown that significant reductions in crane travel time (and distance) are obtainable from turnover-based rules. These improvements can, under certain circumstances, be directly translated into increased throughput capacity for existing systems, and may be used to alter the design (e.g., size and number of racks, speed of cranes, etc.) of proposed systems in order to achieve a more desirable system balance between throughput and storage capacity.