Yasin Gocgun

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Beam search (BS) is used as a heuristic to solve various combinatorial optimization problems, ranging from scheduling to assembly line balancing. In this paper, we develop a backtracking and an exchange-of-information (EOI) procedure to enhance the traditional beam search method. The backtracking enables us to return to previous solution states in the(More)
OBJECTIVES To develop a mathematical model for multi-category patient scheduling decisions in computed tomography (CT), and to investigate associated tradeoffs from economic and operational perspectives. METHODS We modeled this decision-problem as a finite-horizon Markov decision process (MDP) with expected net CT revenue as the performance metric. The(More)
We define a class of discrete-time resource allocation problems where multiple renewable resources must be dynamically allocated to different types of jobs arriving randomly. Jobs have geometric service durations, demand resources, incur a holding cost while waiting in queue, a penalty cost of rejection when the queue is filled to capacity, and generate a(More)
Diverse applications in manufacturing, logistics, health care, telecommunications, and computing require that renewable resources be dynamically scheduled to handle distinct classes of job service requests arriving randomly over slotted time. These dynamic stochastic resource scheduling problems are analytically and computationally intractable even when the(More)
We study a scheduling problem in which arriving patients require appointments at specific future days within a treatment specific time window. This research is motivated by a study of chemotherapy scheduling practices at the British Columbia Cancer Agency (Canada). We formulate this problem as a Markov Decision Process (MDP). Since the resulting MDPs are(More)
In this paper, we study breast cancer screening policies using computer simulation. We developed a multi-state Markov model for breast cancer progression, considering both the screening and treatment stages of breast cancer. The parameters of our model were estimated through data from the Canadian National Breast Cancer Screening Study as well as data in(More)
Objectives To develop a mathematical model for multi-category patient scheduling decisions in computed tomography (CT), and to investigate associated tradeoffs from economic and operational perspectives. Methods We modeled this decision-problem as a finite-horizon Markov decision process (MDP) with expected net CT revenue as the performance metric. The(More)
We study radiation therapy scheduling problem where dynamically and stochastically arriving patients of different types are scheduled to future days. Unlike similar models in the literature, we consider cancellation of treatments. We formulate this dynamic multi-appointment patient scheduling problem as a Markov Decision Process (MDP). Since the MDP is(More)
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