Index Polices for Patient Scheduling and ATM Replenishment

Markov Decision Processes (MDP) are one of the most commonly used stochastic models to solve sequential decision making problems. The optimal solution to many real-world problems cannot be achieved due to the curse of dimensionality. It is common to use a heuristic policy called the index policy, which is obtained by applying one-step policy improvement to a simple initial policy. The index policy performs close to the optimal policy and is easily implementable, which makes it attractive to use in practice. In this dissertation, we first introduce the background information on MDP and index policies in Chapter 1. We then study their applications in two problems: the appointment scheduling problem with patient preferences, and the automated teller machine (ATM) replenishment problem. In Chapter 2, we build an MDP model to design appointment scheduling policies in the presence of patient preferences. We model the patient preferences by assuming that each patient has a set of appointment days that are equally acceptable to the patient. We consider a service provider which receives the appointment-booking requests and makes an appointment decision one at a time. The objective is to minimize the long-run average cost while responding to the patients' booking requests based on their preferences. We propose the index policy and show it performs close to the optimal policy in the two-day horizon and outperforms other benchmarks in the multi-day horizon. In Chapter 3, we build an MDP model to design ATM replenishment schedules, while balancing the cost of replenishments and the cost of stock-outs. We propose a method to establish a relationship between the service level and the cost of a stock-out. We also assume that the replenishment cost is a sub-modular function of the set of ATMs that are replenished together. We derive the index policy, prove it has the same structural properties as the optimal policy, and show it performs close to the optimal policy when there are two or three ATMs. When there are a large number of ATMs, we show the index policy outperforms a benchmark policy through a simulation study and a real-world data-set.Doctor of Philosoph

Exploratory research into supply chain voids within Welsh priority business sectors

The paper reports the findings resulting from the initial stages of an exploratory investigation into Supply Chain Voids (SCV) in Wales. The research forms the foundations of a PhD thesis which is framed within the sectors designated as important by the Welsh Assembly Government (WAG) and indicates local supplier capability voids within their supply chains. This paper covers the stages of initial data gathering, analysis and results identified between June 2006 and April 2007, whilst addressing the first of four research questions. Finally, the approach to address future research is identified in order to explain how the PhD is to progress

Data-driven Platform and Digital Operations

The objective of this dissertation is to study the emerging operations issues on data-driven platforms and digital operations. With the increasing availability of data and the development of information technologies, platforms process a large amount of data in order to efficiently make daily operational decisions. Understanding human behaviors and the human-algorithm connection is instrumental to the success of this process. In my research, I implement field experiments and use structural models to study in-warehouse worker behavior and out-of-warehouse customer behavior in the last mile of logistics. In Chapter 1, “The Impacts of Algorithmic Work Assignment on Fairness Perceptions and Productivity: Evidence from Field Experiments”, we study in-warehouse worker behavior. We study how algorithmic (vs. human-based) task assignment processes change task recipients\u27 fairness perceptions and productivity. In a 15-day-long field experiment with Alibaba Group in a warehouse where workers pick products following orders (or “pick lists”), we randomly assigned half of the workers to receive pick lists from a machine that ostensibly relied on an algorithm to distribute pick lists, and the other half to receive pick lists from a human distributor. Despite that we used the same underlying rule to assign pick lists in both groups, workers perceive the algorithmic (vs. human-based) assignment process as fairer by 0.94-1.02 standard deviations. This yields productivity benefits: receiving tasks from an algorithm (vs. a human) increases workers\u27 picking efficiency by 15.56%-17.86%. These findings persist beyond the first day when workers were involved in the experiment, suggesting that our results are not limited to the initial phrase when workers might find algorithmic assignment novel. We replicate the main results in another field experiment involving a nonoverlapping sample of warehouse workers. We also show via online experiments that people in the U.S. also view algorithmic task assignment as fairer than human-based task assignment. We demonstrate that algorithms can have broader impacts beyond offering greater efficiency and accuracy than humans: introducing algorithmic assignment processes may enhance fairness perceptions and productivity. This insight can be utilized by managers and algorithm designers to better design and implement algorithm-based decision making in operations. In Chapter 2, “The Value of Logistic Flexibility in E-commerce”, we study out-of-warehouse customer behavior in the last mile of logistics. We use the opening of hundreds of pick-up stations as a natural experiment to study the impact of these stations on consumers. We find that the introduction of pick-up stations has increased total sales by 3.9%. In contrast with past literature, we show that shipping time reduction is not the driving factor on the impact of pick-up stations. Yet, the logistic flexibility introduced by pick-up stations explains the sales impact. To explicitly examine how logistic flexibility affects consumers\u27 decisions on purchases, we develop and estimate a structural model of consumer choice. In our model, consumers value two types of logistics flexibility---the flexibility to pick up their items at their preferred time, denoted as the value of time flexibility, and the flexibility to delay pickup decisions until after packages arrive at a local station, denoted as the value of choice flexibility. We show that the value of time flexibility accounts for 76.2% of the impact on sales, while the value of choice flexibility accounts for the remaining 23.8%. Using our estimated model, we develop a counterfactual strategy in building pick-up stations that could achieve the sales lift with 56.4%-63.6% fewer stations. Last but not least, using our estimated time flexibility, we also develop a novel shipping strategy without pick-up stations that could improve sales by 8.4%. Our estimates suggest that our counterfactual logistic strategies could increase consumer welfare by 2.0%-10.0%