In a nutshell, lot-sizing (or broadly, production planning) aims to determine optimal quantities of production, inventory, backlogs, etc. under key constraints such as satisfying customer orders on time. With its significant importance in practice (not only the classical production and inventory control, but also appplicable to integrated systems and logistics), as well as inherent theoretical complexities and its close relation to various other problems such as fixed charge networks, lot-sizing has been studied for a number of decades. Despite the significant focus on deterministic variants of the problem in the literature, there has been increasingly more work on numerous variants under uncertainty. In this light, we will discuss two specific examples of lot-sizing under uncertainty in this talk. In the first part, we will look into a variant with the option of remanufacturing, including the case with multiple components. We will discuss various formulations as well as the impact of imposing uncertainty on customer returns, which will be modelled using uncertainty sets. We propose robust formulations and decomposition approaches to tackle this problem, and will discuss various insights and results. In the second part, we will introduce a novel way of modeling uncertainty on demand, where the uncertainty is not, as it most often does, related to the demand quantity, but rather to the demand "timing" (or more specifically, when demand occurs fully in a period, but we do not know when). Dynamic programs will be discussed for the general case as well as for several special cases, and we will discuss further directions of interest including complexity and more complex problem settings such as multiple items. In both parts of the talk, we will also look into related applications in other areas such as transportation and logistics. First part joint work with Oyku Naz Attila, Agostinho Agra and Ashwin Arulselvan; second part joint work with Melek Rodoplu and Stéphane Dauzère-Pérès.

Biography: Kerem Akartunali is Professor at the Department of Management Science, Strathclyde Business School, where he leads the Optimisation & Analytics Research Group. Before joining Strathclyde in 2010, he worked as a postdoctoral researcher at University of Melbourne, in collaboration with CTI, an Australian software company, on the development of methodology and software for airline planning and scheduling problems. He gained his PhD in 2007 from University of Wisconsin-Madison. He has research expertise in integer, network and robust optimisation and their applications, including production planning, transportation scheduling/planning, and health and energy applications. He worked with many companies and organisations, including AGS Airports, Capita, Cordia, NHS, Preactor, Scottish Power Renewables, SSE, and The Drone Office, from short-term consultancy to long-term research partnerships. His research has been funded by various organisations, such as Innovate UK, EPSRC, Horizon 2020, US Air Force Office of Scientific Research, and various industry partners including Capita, SSE, SPR and Technip.