An evolutionary approach for the offsetting inventory cycle problem

In inventory management, a fundamental issue is the rational use of required space. Among the numerous techniques adopted, an important role is played by the determination of the replenishment cycle offsetting which minimizes the warehouse space within a considered time horizon. The NP-completeness of the Offsetting Inventory Cycle Problem (OICP) has led the researchers towards the development and the comparison of specific heuristics. We propose and implement a genetic algorithm for the OICP, whose effectiveness is validated by comparing its solutions with those found by a mixed integer programming model. The algorithm, tested on realistic instances, shows a high reduction of the maximum space and a more regular warehouse saturation with negligible increase of the total cost. This paper, unlike other papers currently available in literature, provides instances data and results necessary for reproducibility, aiming to become a benchmark for future comparisons with other OICP algorithms. *Corresponding author. Chiara Franciosi, Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II,132, Fisciano, SA 84084, Italy E-mail: [email protected] Reviewing editor: Duc Pham, University of Birmingham, UK Additional information is available at the end of the article ABOUT THE AUTHORS This work originates from the collaboration of the Department of Industrial Engineering and the Department of Mathematics of University of Salerno, Italy. Chiara Franciosi is a PhD student in Industrial Engineering at University of Salerno; her current research interests include Inventory Management, Industrial Maintenance Management, and Sustainable Manufacturing. Francesco Carrabs is assistant professor at the Department of Mathematics of University of Salerno; his research interests include combinatorial optimization, heuristics, metaheuristics, hybrid heuristics for mixed integer linear programming problems, vehicle routing problems. Raffaele Cerulli is Associate Professor at the Department of Mathematics of University of Salerno; his research interests include combinatorial optimization problems, mainly on mathematical models and algorithm design for covering problems on graphs and wireless sensor networks problems. Salvatore Miranda is Associate Professor at the Department of Industrial Engineering of University of Salerno; his research interests include Operations Management, Maintenance, Human Reliability Analysis, Simulation, Logistics and Inventory Management. PUBLIC INTEREST STATEMENT One of the main issues in industrial companies concerns a proper inventory management and a suitable use of the warehouse space. Consequently, several techniques have been proposed to manage, in an efficient and effective way, the inventory and the limited warehouse space. The evolutionary approach proposed in this paper can be useful to manage the orders of a large amount of items/products with the aim of using in a better way the whole warehouse space, allowing larger order quantities while satisfying the market demand. In particular, the developed algorithm allows us to select the order quantities, which minimize ordering, holding and purchasing costs, and sets the optimal time of the first replenishment of each item in order to minimize the volume peak of the warehouse. Unlike other previous studies, this paper also provides data of instances and results of the algorithm applications to make possible future comparisons with other approaches. Received: 01 June 2017 Accepted: 11 August 2017 First Published: 24 August 2017 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.