One of the most significant activities in warehouse management concerns the allocation of products to the storage positions. This problem is known in the literature as the Product Allocation Problem (PAP). It mainly aims to optimize both the warehouse space utilization and the products handling costs (at least 40% of the total logistics cost). This paper addresses the \{PAP\} in a multi-layer warehouse, with compatibility constraints among the product classes. It has already been addressed from a modeling point of view in the literature and it has been formulated as a Mixed Integer Linear Programming model. However, solving the problem to optimality becomes impracticable in real-life settings. To this purpose, an Iterated Local Search-based Heuristic ( \{ILS\} ) and a Cluster-based Heuristic ( \{CH\} ) have already been proposed in the literature. This paper presents a Rollout-based heuristic whose performances are evaluated on the basis of a detailed computational phase, including also a real case study and compared with those of both the \{ILS\} and the \{CH\} , in terms of the computational times and the quality of the final solutions
Comparing heuristics for the product allocation problem in multi-level warehouses under compatibility constraints
PISACANE, ORNELLA;
2015-01-01
Abstract
One of the most significant activities in warehouse management concerns the allocation of products to the storage positions. This problem is known in the literature as the Product Allocation Problem (PAP). It mainly aims to optimize both the warehouse space utilization and the products handling costs (at least 40% of the total logistics cost). This paper addresses the \{PAP\} in a multi-layer warehouse, with compatibility constraints among the product classes. It has already been addressed from a modeling point of view in the literature and it has been formulated as a Mixed Integer Linear Programming model. However, solving the problem to optimality becomes impracticable in real-life settings. To this purpose, an Iterated Local Search-based Heuristic ( \{ILS\} ) and a Cluster-based Heuristic ( \{CH\} ) have already been proposed in the literature. This paper presents a Rollout-based heuristic whose performances are evaluated on the basis of a detailed computational phase, including also a real case study and compared with those of both the \{ILS\} and the \{CH\} , in terms of the computational times and the quality of the final solutionsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
