One of the major issues in Warehouse Management is to optimally assign the product classes to the storage locations (slots, for short) on the basic principle that the most required items have to be allocated closer to the I/O doors (Products Allocation Problem-PAP). The aim of this paper is to study a special version of PAP considering a multi-layers warehouse with compatibility constraints among the classes (two aspects that, at the best of our knowledge, have not been addressed in scientific literature yet). First, we modelled the problem (as already described in Guerriero et al (2012)) with the aim of minimizing the total logistics costs (due to the handling operations and the products decentralization in the warehouse) satisfying specific operational constraints (for example, compatibility and capacity constraints). However, since on large-scale instances the complexity of the model (in terms of number of decision variables and constraints) becomes computationally intractable by optimization solvers, we also design, implement and test a cluster-based heuristic approach for overcoming this limitation. Finally, we compare the results from two points of views: the solutions quality and the computational overhead.
A CLUSTER-BASED HEURISTIC FOR ALLOCATING PRODUCTS IN MULTILEVELS WAREHOUSES
PISACANE, ORNELLA;
2012-01-01
Abstract
One of the major issues in Warehouse Management is to optimally assign the product classes to the storage locations (slots, for short) on the basic principle that the most required items have to be allocated closer to the I/O doors (Products Allocation Problem-PAP). The aim of this paper is to study a special version of PAP considering a multi-layers warehouse with compatibility constraints among the classes (two aspects that, at the best of our knowledge, have not been addressed in scientific literature yet). First, we modelled the problem (as already described in Guerriero et al (2012)) with the aim of minimizing the total logistics costs (due to the handling operations and the products decentralization in the warehouse) satisfying specific operational constraints (for example, compatibility and capacity constraints). However, since on large-scale instances the complexity of the model (in terms of number of decision variables and constraints) becomes computationally intractable by optimization solvers, we also design, implement and test a cluster-based heuristic approach for overcoming this limitation. Finally, we compare the results from two points of views: the solutions quality and the computational overhead.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
