Agent-based modeling and simulation of pooled warehouse intelligent management

Logistics, in its most basic definition, is the efficient flow and storage of goods from their creation to their consumption. It is the part of the supply chain process that plans, implements and controls the flow of goods. Increasing production rate and product distribution velocity of warehousing facilities play a vital role in the overall supply chain process and optimizing their storage spaces capacities is one of the major challenges for logistics companies, which aim to lower cost by reducing product storage cost and damage and to improve the environmental performance of their service. Considering all the storage facilities as a single virtual space and pooling "optimally" capabilities between these storage spaces would be the right answer to solve these challenges. Through this paper, we are challenged to figure out new ways to make pooled warehouse's storage process more effective by providing an improved management of storage areas that aims to reach a maximum storage space utilization. This pooled logistics management that provides economic benefits through consolidation and accumulation operations, resembles closely to the pooled management of hosted computer servers in Cloud Computing (CC). Based on this similarity, the proposed model intends to adapt resource allocation methods and models, developed in CC environments, and their reuse for optimizing storage space allocation in a Pooled Warehouse (PW) in order to maximize the number of accepted storage requests while keeping the best quality of stored products. After an analytical study, an agent based model and simulator for an intelligent management of PW are presented, and the first simulation results are discussed. A comparison of the different allocation strategies shows that the total number of storage requests rejections differ from one scenario to another.