Proceedings of the 2019 Winter Simulation Conference N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, eds. AN INVENTORY-ROUTING PROBLEM WITH STOCHASTIC DEMAND AND STOCK-OUT: A SOLUTION AND RISK ANALYSIS USING SIMHEURISTICS Bhakti Stephan Onggo CORMSIS – Southampton Business School University of Southampton University Road Southampton, SO17 1BJ, UK Angel A. Juan Javier Panadero Universitat Oberta de Catalunya Euncet Business School Av. Carl Friedrich Gauss 5 Castelldefels, 08860, SPAIN Canan G. Corlu Metropolitan College Boston University 1010 Commonwealth Avenue Boston, MA, 02215, USA Alba Agustin Dept. of Statistics, Informatics, and Mathematics Public University of Navarre Campus de Arrosadia Pamplona, 31006, SPAIN ABSTRACT Supply chain operations have become more complex. Hence, in order to optimise supply chain operations, we often need to simplify the optimisation problem in such a way that it can be solved efficiently using either exact methods or metaheuristics. One common simplification is to assume all model inputs are deterministic. However, for some management decisions, considering the uncertainty in model inputs (e.g., demands, travel times, processing times) is essential. Otherwise, the results may be misleading and might lead to an incorrect decision. This paper considers an example of a complex supply chain operation that can be viewed as an Inventory-Routing Problem with stochastic demands. We demonstrate how a simheuristic framework can be employed to solve the problem. Further, we illustrate the risks of not considering input uncertainty. The results show that simheuristics can produce a good result, and ignoring the uncertainty in the model input may lead to sub-optimal results. 1 INTRODUCTION Supply chain operations have become more complex. Due to factors such as increasing competition and regulations, there has been an increasing need to cover more stages in a supply chain when making a management decision. As more supply chain stages are considered when making decisions, the complexity of the problem increases. One such example is when we want to optimise the combined operations covering both inventory and vehicle routing operations. This problem is widely known as the Inventory-Routing Problem (IRP). IRP is an optimization problem that aims at minimizing the total cost associated with the inventory and vehicle routing operations of a supply chain, thus providing a holistic perspective to the enhancement of the supply chain performance. To enable us to solve a complex IRP, we often need to simplify the problem so that it can be solved efficiently using exact methods or metaheuristics. One common simplification is to assume all inputs to be deterministic. Hence, it is not surprising that most of the early works on IRP assume that the model inputs (e.g., customers’ demands, travel costs, etc.) are 1977 978-1-7281-3283-9/19/$31.00 ©2019 IEEE