Call centers with a postponed callback offer

We study a call center model with a postponed callback option. A customer at the head of the queue whose elapsed waiting time achieves a given threshold receives a voice message mentioning the option to be called back later. This callback option differs from the traditional ones found in the literature where the callback offer is given at customer’s arrival. We approximate this system by a two-dimensional Markov chain, with one dimension being a unit of a discretization of the waiting time. We next show that this approximation model converges to the exact one. This allows us to obtain explicitly the performance measures without abandonment and to compute them numerically otherwise. From the performance analysis, we derive a series of practical insights and recommendations for a clever use of the callback offer. In particular, we show that this time-based offer outperforms traditional ones when considering the waiting time of inbound calls

Call Center Experience Optimization: A Case for a Virtual Predictive Queue

The evolution of the call center into contact centers and the growth of their use in providing customer-facing service by many companies has brought considerable capabilities in maintaining customer relationships but it also has brought challenges in providing quality service when call volumes are high. Limited in their ability to provide service at all times to all customers, companies are forced to balance the costs associated with hiring more customer service representatives and the quality of service provided by a fewer number. A primary challenge when there are not enough customer service representatives to engage the volume of callers in a timely manner is the significant wait times that can be experienced by many customers. Normally, callers are handled in accordance with a first-come, first-served policy with exceptions being skill-based routing to those customer service representatives with specialized skills. A proposed call center infrastructure framework called a Virtual Predictive Queue (VPQ) can allow some customers to benefit from a shorter call queue wait time. This proposed system can be implemented within a call center’s Automatic Call Distribution (ACD) device associated with computer telephony integration (CTI) and theoretically will not violate a first-come, first served policy

Queue Management in a Government Agency: the case of IRN

Public agencies are, in some cases, the sole providers of essential services to citizens and often suffer from long queues and criticality. Since queues result from higher demand than the available service capacity, Queue Management starts with Capacity and Demand Management. Queue Theory and Queue Psychology contribute to a better waiting experience, aiming at the comfort of those waiting and the regular operation of the service. This internship report analyzes the queue management of a Portuguese government agency. The internship occurred at the "Instituto dos Registos e do Notariado, I.P." (IRN), a Portuguese Ministry of Justice government agency. The research for this report took place at the IRN headquarters and the Civil Identification Department of the Campus of Justice. Case-study research was conducted with semi-structured Interviews as the primary source of evidence, complemented by direct observations, direct participation, and documentation. The conducted root-cause analysis identified the lack of workforce, the citizens' aging process, the CC's sensitive information, and the accumulation of services during the pandemic restrictions as the root causes for the queues at the registry offices. Also, communication difficulties, long waiting hours, lack of waiting conditions, and high human workload were found. IRN's strategies to deal with queues were to reinforce service capacity during peak demand, offer alternative digital services, optimize services, and improve queue organization in the registry offices. The data suggest that SSTs can improve queues; as such, a set of suggestions for implementing SST in registry offices is presented.Os organismos públicos são, em alguns casos, os únicos prestadores de serviços essenciais para os cidadãos, e muitas vezes sofrem com longas filas de espera e criticismo. Uma vez que as filas são o resultado da procura ser mais elevada que a capacidade de serviço disponível, a Gestão das Filas começa com a Gestão da Capacidade e da Procura. A Teoria e Psicologia das Filas de Espera contribuem para uma melhor experiência de espera, visando o conforto de quem aguarda e o normal funcionamento do serviço. Este relatório de estágio analisa a gestão de filas de espera de uma agência governamental portuguesa. O estágio ocorreu no "Instituto dos Registos e do Notariado, I.P.". (IRN), uma agência governamental do Ministério da Justiça Português. A investigação deste relatório realizou-se na sede do IRN e no Departamento de Identificação Civil do Campus da Justiça. A investigação deste estudo de caso utilizou entrevistas semiestruturadas como fonte primária de informação, complementada por observações diretas, participação direta, e documentação. A análise das causas fundamentais identificou a falta de funcionários, o processo de envelhecimento dos cidadãos, a informação confidencial do CC, e a acumulação de serviços durante as restrições pandémicas como as causas fundamentais para as filas de espera nas conservatórias durante o período de estágio. Foram também encontradas dificuldades de comunicação, longas filas de espera, falta de condições de espera, e elevada sobrecarga de trabalho. As estratégias do IRN para lidar com as filas de espera foram o reforço da capacidade de serviço durante o pico da procura, a oferta de serviços digitais alternativos, a otimização dos serviços e melhorias na organização das filas nas conservatórias. Os dados sugerem que as Tecnologias de Self-service (SST) podem melhorar as filas de espera, como tal, é apresentado um conjunto de sugestões para a implementação de SSTs nas conservatórias

Improving Patient Flow through Early Bed Requests at UNC Hospital ED: A Discrete-Event Simulation Study

Emergency department (ED) overcrowding is a widely recognized problem in the United States due to numerous legal, economic, and operational factors. This difficulty has motivated a significant body of clinical and academic interest in applying operations research (OR) techniques to improve patient flow in EDs. In particular, discrete event simulation has been extensively utilized to study each of the three steps of ED patient flow (into, within, and out of the ED). The flexibility of simulation makes it particularly useful for examining each stage and comparing alternatives to reduce overcrowding. Many approaches to addressing ED overcrowding focus on ways to increase bed capacity for patients requiring service since beds are the bottleneck resource in many EDs. The problem of bed capacity can be addressed at each stage of patient flow. For example, many works have considered the effect of physician-at-triage (PT) altering the flow within the ED by treating low acuity patients with low resource requirements in a separate clinic to preserve more beds for severe patients. In contrast, early bed request attempts to improve flow out of the ED by reducing “bed-block”, the utilization of ED beds by patients who have completed service but board in the ED until an in-patient bed is available. Early boarding is the process of identifying at the time of triage patients who will later be admitted as in-patients. The ED “calls ahead” to request a bed from the appropriate ward so that the in-patient bed is ready when, or soon after, the patient completes service in the ED. In theory, such a policy has great potential to deliver system-wide improvements since many studies recognize bed-block and patient flow out of the ED as major drivers in long LOS and wait durations and “one of the most well-known operational problems to afflict an ED”. This paper describes the development and application of a simulation model for the UNC Hospitals ED to examine the effect of implementing an early bed request policy. Section 2 describes the patient flow at UNC Hospitals ED, the simulation model, the proposed early bed request policy, and how this policy is incorporated into the simulation model. Section 3 provides details on the data used, the estimation of input parameters, and validation of the model. Section 4 reports the results of simulation experiments on early bed request policies.Bachelor of Scienc

Three Essays on Modeling Consumer Behavior and Its Operations Management Implications.

Traditionally, models used in operations management have considered the firm side of the problem by making simplifying assumptions on demand or market. In practice, however, consumers or agents in the market actively make decisions or choices based on self interest. This dissertation aims to analyze how insights and results from traditional models are affected when we account for such active decision making by consumers or the market. In Chapter II, we study how the customers' decision of joining the queue to receive a service varies by the individual incentive as well as the firm's capacity decision, which also depends on the firm’s selfishness. By considering three customer types: individual, collective, and social, and two firm types: profit maximizing and welfare maximizing, we are able to disentangle the effects of selfishness of the customers and the firm, and the interactions between these two in equilibrium. Among other results, we find that there can be a ``benefit of selfishness'' to consumers and the system, in contrast to the price of anarchy literature. In Chapter III, we discuss the customers' redemption behavior of loyalty points and its impact on the seller's pricing and inventory rationing strategy. We model the customer choice between cash or loyalty points by characterizing consumers in three dimensions: the reservation price, the point balance, and their perceived valuation of points. Applying this choice model into the seller's dynamic pricing model, we characterize the seller's optimal strategy that specifies the optimal price, the control of reward sales (black-out), and the redemption points. In Chapter IV, we study the customers’ substitution behavior when their preferred product is not available, and the seller's assortment optimization problem. Motivated by the exogenous demand model and the recently developed Markov chain model, we propose a new approximation to the random utility customer choice model called rescaled multi-attempt model. The key feature of our proposed approach is that the resulting approximate choice probability can be explicitly written. From a practical perspective, this allows the decision maker to use an off-the-shelf solver to solve a general assortment optimization problem with a variety of real-world constraints.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133387/1/hakjin_1.pd

Capacity Planning for Heterogeneous Patient Populations in Primary Care and Specialty Networks

Access to primary care has a direct impact on morbidity and mortality, and is strongly influenced by indirect waiting time: the delay between the requested and allotted appointment day. Our models describe the heterogeneous appointment seeking patterns of a primary care patient panel using stochastic processes parameterized to reflect the diversity of primary care visit rates in the US. For capacity planning, we estimate the distribution of daily appointments, and show that the distribution variability can be reduced by heuristics that use patient flexibility regarding the day of the appointment. For delays, we demonstrate that in a first-come, first-served system, patients who need the most frequent appointments suffer the greatest delays, motivating the need to reserve slots for high-visit patient classes. To further understand the inequity in delay, we model the primary care appointment system as a Discrete-Time Markov Chain. We derive an analytical expression for delay in terms of the patient’s probability of daily visit. We show that conditions for monotone mapping of the probability of visit to delay are intractable and give numerical results that support monotonicity. In our last chapter, we expand our scope to include specialty care networks. Using patient-level longitudinal data from the Medical Expenditure Panel Survey, we model the sequence of appointments with multiple specialty types and the time intervals between such appointments as a Markov Renewal Process (MRP). We use comorbidity count to model patient heterogeneity and extract the MRP parameters for each patient subgroup. Next, we adapt the steady state results to provide an analytical expression of the expected appointment fill-rate by specialty and patient subgroups. Our analytical results demonstrate that patients with higher comorbidity count typically have a lower fill-rate because of shorter lead time between appointments thereby necessitating either overtime or reserved slots to ensure timely access. We further simulate appointment seeking patterns of a nationally representative panel of patients in the specialty network and estimate the distribution of daily appointment requests for each specialty. Similar to the primary care case, we show that heuristics that leverage patient flexibility regarding the day of the appointment can reduce variability in appointment requests for each specialty

Analytic modelling and resource dimensioning of optical burst switched networks

The realisation of optical network architectures may hold the key to delivering the enormous bandwidth demands of next generation Internet applications and services. Optical Burst Switching (OBS) is a potentially cost-effective switching technique that can satisfy these demands by offering a high bit rate transport service that is bandwidth-efficient under dynamic Internet traffic loads. Although various aspects of OBS performance have been extensively investigated, there remains a need to systematically assess the cost/performance trade-offs involved in dimensioning OBS switch resources in a network. This goal is essential in enabling the future deployment of OBS but poses a significant challenge due to the complexity of obtaining tractable mathematical models applicable to OBS network optimisation. The overall aim of this thesis lies within this challenge. This thesis firstly develops a novel analytic performance model of an OBS node where burst contention is resolved by combined use of Tuneable Wavelength Converters (TWCs) and Fibre Delay Lines (FDLs) connected in an efficient share-per-node configuration. The model uses a two-moment traffic representation that gives a good trade-off between accuracy and complexity, and is suitable for extension to use in network modelling. The OBS node model is then used to derive an approximate analytic model of an OBS network of switches equipped with TWCs and FDLs, again maintaining a two-moment traffic model for each end-to-end traffic path in the network. This allows evaluation of link/route loss rates under different offered traffic characteristics, whereas most OBS network models assume only a single-moment traffic representation. In the last part of this thesis, resource dimensioning of OBS networks is performed by solving single and multi-objective optimisation problems based on the analytic network model. The optimisation objectives relate to equipment cost minimisation and throughput maximisation under end-to-end loss rate constraints. Due to non-convexity of the network performance constraint equations, a search heuristic approach has been taken using a constraint-handling genetic algorithm

COST-EFFICIENT RESOURCE PROVISIONING FOR CLOUD-ENABLED SCHEDULERS

Since the last decade, public cloud platforms are rapidly becoming de-facto computing platform for our society. To support the wide range of users and their diverse applications, public cloud platforms started to offer the same VMs under many purchasing options that differ across their cost, performance, availability, and time commitments. Popular purchasing options include on-demand, reserved, and transient VM types. Reserved VMs require long time commitments, whereas users can acquire and release the on-demand (and transient) VMs at any time. While transient VMs cost significantly less than on-demand VMs, platforms may revoke them at any time. In general, the stronger the commitment, i.e., longer and less flexible, the lower the price. However, longer and less flexible time commitments can increase cloud costs for users if future workloads cannot utilize the VMs they committed to buying. Interestingly, this wide range of purchasing options provide opportunities for cost savings. However, large cloud customers often find it challenging to choose the right mix of purchasing options to minimize their long-term costs while retaining the ability to adjust their capacity up and down in response to workload variations. Thus, optimizing the cloud costs requires users to select a mix of VM purchasing options based on their short- and long-term expectation of workload utilization. Notably, hybrid clouds combine multiple VM purchasing options or private clusters with public cloud VMs to optimize the cloud costs based on their workload expectations. In this thesis, we address the challenge of choosing a mix of different VM purchasing options in the context of large cloud customers and thereby optimizing their cloud costs. To this end, we make the following contributions: (i) design and implement a container orchestration platform (using Kubernetes) to optimize the cost of executing mixed interactive and batch workloads on cloud platforms using on-demand and transient VMs, (ii) develop simple analytical models for different straggler mitigation techniques to better understand the cost of synchronization in distributed machine learning workloads and compare their cost and performance on on-demand and transient VMs, (iii) design multiple policies to optimize long-term cloud costs by selecting a mix of VM purchasing options based on short- and long-term expectations of workload utilization (with no job waiting), (iv) introduce the concept of waiting policy for cloud-enabled schedulers, and show that provisioning long-term resources (e.g., reserved VMs) to optimize the cloud costs is dependent on it, and (v) design and implement speculative execution and ML-based waiting time predictions (for waiting policies) to show that optimizing job waiting in the cloud is possible without accurate job runtime predictions

Establishing agent staffing levels in queueing systems with cross-trained and specialized agents

The determination of the right number of servers in a multi-server queueing system is one of the most important problems in applied queueing theory. The problem becomes more complex in a system that consists of both cross-trained and specialized servers. Such queueing systems are readily found in the call centres (also called contact centres) of financial institutions, telemarketing companies and other organizations that provide services to customers in multiple languages. They are also found in computer network systems where some servers are dedicated and others are flexible enough to handle various clients' requests. Over-staffing of these systems causes increased labour costs for the underutilized pool of agents on duty, while under-staffing results in reduced revenue from lost customers and an increase in queue times. The efficient design and analysis of these systems helps management in making better staffing decisions. This thesis aims to develop models for establishing agent staffing levels in organizations with cross-trained and specialized staff with a view to minimizing cost and maintaining a desirable customer satisfaction. The work investigates the effect of various traffic loads on the number of agents required and the cost. It also considers how using specialized agents, flexible agents and a combination of both categories of agents affects the system. It uses a contact centre that has agents with monolingual, bilingual and trilingual (English, French and Spanish) capabilities to do the study