Title of the Report: Joint Capacity Allocation and Job Assignment under Uncertainty

Time: July 2 (Tuesday), 10:00-11:30 AM

Venue: Room B309, School of Economics and Management

Speaker: Gar Goei Loke, Associate Professor, Durham University, UK

Host: Professor Chun Cheng

Abstract:

In this paper, we consider the multi-period joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes, and assigning of jobs of I different demand origins to these J nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We furthermore consider three features: (i) supply is replenishable after random time, (ii) demand is random; and (iii) demand can wait and need not be fully fulfilled immediately. Such problems emerge in many service management settings such as ride-sharing fleet re-positioning, and patient management in healthcare. We introduce a distributive decision rule, which decides on the proportion of jobs to be served by each of the supply nodes. We borrow ideas from the pipeline queues framework Bandi and Loke (2018), which cannot be directly applied to our setting, and hence requires the development of new reformulation techniques. Our model has a convex reformulation and can be solved by a sequence of linear programs, in practice. We test our model against state-of-the-art models that focus solely on the capacity allocation or job assignment decisions, in the setting of nurse scheduling and patient overflow respectively. Our model performs strongly against the benchmarks, recording 1-15% reductions in costs, and shorter computation times. Our model opens the door to consider new problems in platform operations and online services where the planner is able to influence the supply of services or resources partially.

Speaker Biography:

Gar Goei Loke is an associate professor with the Department of Management and Marketing, Durham Business School, Durham University, since November 2023. Prior to joining Durham Business School, he was an assistant professor in the School of Management, Erasmus University (2021-2023), and a visiting assistant professor in the NUS Business School, National University of Singapore (2019-2021). He obtained his PhD from National University of Singapore, and did his Bachelor's and Master's in the University of Cambridge, UK. His research focuses around decision-making under uncertainty, and developing models, frameworks, methods and algorithms that help decision-makers go from data to decisions. In his primary stream of research, he has applied techniques in robust optimization to the solution of optimization problems in queueing networks. More recently, he is studying what divides existing paradigms in machine learning and optimization and is developing and proposing new ways to integrate them harmoniously. His research is primarily applied to business areas such as service operations management, supply chain management, healthcare operations management, and energy and water. His research has been published in journals such as Operations Research and Manufacturing & Service Operations Management.