arXiv subjects

Onur Demiray

Publications and source records attributed to Onur Demiray.

2 recordsLinked to original sources

arXiv2023

An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints

This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services.

math.OC↗

arXiv2020

Modeling and Optimizing Resource Allocation Decisions through Multi-model Markov Decision Processes with Capacity Constraints

This paper proposes a new formulation for the dynamic resource allocation problem, which converts the traditional MDP model with known parameters and no capacity constraints to a new model with uncertain parameters and a resource capacity constraint. Our motivating example comes from a medical resource allocation problem: patients with multiple chronic diseases can be provided either normal or special care, where the capacity of special care is limited due to financial or human resources. In such systems, it is difficult, if not impossible, to generate good estimates for the evolution of health for each patient. We formulate the problem as a two-stage stochastic integer program. However, it becomes easily intractable in larger instances of the problem for which we propose and test a parallel approximate dynamic programming algorithm. We show that commercial solvers are not capable of solving the problem instances with a large number of scenarios. Nevertheless, the proposed algorithm provides a solution in seconds even for very large problem instances. In our computational experiments, it finds the optimal solution for $42.86\%$ of the instances. On aggregate, it achieves $0.073\%$ mean gap value. Finally, we estimate the value of our contribution for different realizations of the parameters. Our findings show that there is a significant amount of additional utility contributed by our model.

math.OC↗