Research papers that are using SEE Data – Technion and Out-of-Technion Publications starting from 2013 year, listed chronologically and alphabetically
|catalog key||year||paper type||data type|
|rp1||2020||Altman D., Yom-Tov G., Olivares M., Ashtar S., Rafaeli A. Do Customer Emotions Affect Worker Speed? An Empirical Study of Emotional Load in Online Customer Contact Centers, Forthcoming in MSOM, 2020, online appendix.|
Problem Definition: Research in Operations Management has focused mainly on system-level load, ignoring the fact that service agents and customers express a variety of emotions that may impact service processes and outcomes. We introduce the concept of emotional load—the emotional demands that customer behaviors impose on service agents—to analyze how customer emotions affect service worker’s behavior.
|conference paper||online chats|
|rp2||2020||Berkenstadt G., Gal A., Senderovich A., Shraga R., Weidlich M. Queueing Inference for Process Performance Analysis with Missing Life-Cycle Data.ICPM 2020: 57-64.|
Abstract: Measuring key performance indicators, such as queue lengths and waiting times, using event logs serve for improvement of resource-driven business processes. However, existing techniques assume the availability of complete life cycle information, including the time a case was scheduled for execution (aka arrival times). Yet, in practice, such information may be missing for a large portion of the recorded cases. In this paper, we propose a methodology to address missing life-cycle data by incorporating predicted information in business processes performance analysis. Our approach builds upon techniques from queueing theory and leverages supervised learning to accurately predict performance indicators based on an event log with missing data. Our experimental results using both synthetic and real-world data demonstrate the effectiveness of our approach.
|rp3||2020||Carmeli N., Yom-Tov G.B., Boxma O. State-Dependent Estimation of Delay Distributions in Fork-Join Networks|
Problem definition: Developing estimators for delay distributions in a queueing network with a Fork-Join structure.
|rp4||2020||Castellanos A., Yom-Tov G.B., Goldberg Y. Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data|
Abstract: In the quest to improve services, companies offer customers the opportunity to interact with agents through contact centers, where the communication is mainly text-based. This has become one of the favorite channels of communication with companies in recent years. However, contact centers face operational challenges, since the measurement of common proxies for customer experience, such as knowledge of whether customers have abandoned the queue and their willingness to wait for service (patience), are subject to information uncertainty. We focus this research on the impact of a main source of such uncertainty: silent abandonment by customers. These customers leave the system while waiting for a reply to their inquiry, but give no indication of doing so, such as closing the mobile app of the interaction. As a result, the system is unaware that they have left and waste agent time and capacity until this fact is realized. In this paper, we show that 30%–67% of the abandoning customers abandon the system silently, and that such customer behavior reduces system efficiency by 5%–15%. To do so, we develop methodologies to identify silent-abandonment customers in two types of contact centers: chat and messaging systems. We first use text analysis and an SVM model to obtain the actual abandonment level. We then use a parametric estimator and develop an expectation-maximization algorithm to estimate customer patience accurately, as customer patience is an important parameter for fitting queueing models to the data. We show how accounting for silent abandonment in a queueing model improves dramatically the estimation accuracy of key measures of performance. Finally, we suggest strategiesto op erationally cope with the phenomenon of silent abandonment.
|working paper||online chats; online messaging|
|rp33||2020||Chen M., Baron O., Mandelbaum A., Wang J., Yom-Tov G.B., Arber N. Waiting Time Management in a Healthcare Open-Shop Network, Manlu Chen video INFORMS 2020 Virtual Conference.|
Abstract: Motivated by a large healthcare clinic that provides periodic health screening services, we study how to improve the system performance of an open-shop service system using priority and routing policies combined with information technologies. We consider the system performances from both macro system-level perspective, such as total wait, and micro station-level perspective, such as excessive waits at single stations. We empirically evaluate automation efforts done by the clinic to improve the streamline of patients using automatic routing and queue communication using SMS notifications and find that such practices have negligible impacts on system performance. We, therefore, use a stylized queueing model and simulations to study priority policies for open-shop networks that consider customers' profiles. We demonstrate that such policies may outperform the station-level FCFS policy by simultaneously improving both the macro- and micro-level performance measures. One of the challenges we uncover is the tension between pooling consid erations that promote last-minute decisions and behavioral considerations that promote early decisions to enable customers to smooth transfer from one station to another. To overcome this tension, we propose a buffer strategy that postpones the routing decisions, which leaves the system more exibility and gathers more information for decision making. We demonstrate the efficacy of this policy in improving and balancing these performance measures.
|working paper||healthcare clinic|
|rp5||2020||Daw A., Castellanos A., Yom-Tov G.B., Pender J., Gruendlinger L. The Co-Production of Service: Modeling Service Times in Contact Centers Using Hawkes Processes|
Abstract: In customer support centers, a successful service interaction involves a dialogue between a customer and an agent. Both parties depend on one another for information and problem solving, and this interaction defines a co-produced service process. In this paper, we propose, develop, and compare new stochastic models for the co-production of service in a contact center. To this end, we develop and examine alternative Hawkes process models. More specifically, our models incorporate both dynamic busyness factors that depend on the agent workload (e.g. concurrency) as well as dynamic factors that depend on the inner-mechanics of the interaction (e.g. number of words each party wrote). To understand how well our Hawkes models describe the message-timestamps, we compare the goodness-of-fit of these models on contact center data from industry.
|working paper||online messaging|
|rp15||2020||Mandelbaum A., Momcilovic P., Trichakis N., Kadish S., Leib R., Bunnell C.A. (2020) Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center. Management Science 66(1):243-270.|
Abstract: Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means - and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups.
|rp7||2020||Westphal M., Yom-Tov G.B., Parush A., Carmeli N., Shaulov A., Shapira C., Rafaeli A. A Patient-Centered Information System (myED) for Emergency Care Journeys: Design, Development, and Initial Adoption, JMIR Form Res 2020;4(2):e16410. (DOI:10.2196/16410)|
Background: Medical care is highly complex in that it addresses patient-centered health goals that require the coordination of multiple care providers. Emergency department (ED) patients currently lack a sense of predictability about ED procedures. This increases frustration and aggression. Herein, we describe a system for providing real-time information to ED patients regarding the procedures in their ED medical journey.
|rp6||2020||Yom-Tov G.B., Yedidsion L., Xie Y. An Invitation Control Policy for Proactive Service Systems: Balancing Efficiency, Value and Service Level, M&SOM, 2020 (DOI: 10.1287/msom.2019.0852) (Link to the manuscript:Proactive_service_systems_2_2_2019).|
Problem definition: We study the problem of designing a dynamic invitation policy for proactive service systems with finite customer patience under scarce capacity. In such systems, prior knowledge regarding customer value or importance is used to decide whether the company should offer service or not. Academic/practical relevance: Proactive service systems are becoming more popular, as data availability and machine-learning techniques are developed to forecast customer needs. However, very little is known about the efficient use of such tools to promote and manage service systems. Methodology: We use fluid approximation and the Filippov convex method to analyze system dynamics and develop approximations for important performance measures. Results: We show that whereas prioritizing customers in descending order of their r-µ ranking (as long as there are idle servers in the system) is optimal on the fluid level, refinements are necessary in the presence of abandonment on the stochastic level. We propose an r-µ-N policy to account for customer patience. Managerial implications: Our policy can be used to promote service effectivenes and allow decision makers the means to trade off service level against costs in such systems explicitly. Using a case study of a transportation service provider, we show that such a policy can triple revenue compared with random arrivals (nonproactive policy).
|conference paper||online chats|
|rp25||2020||Yu Q. When Providing Wait Times, It Pays to Underpromise and Overdeliver, Harvard Business Review. (Digital Article), 2020.|
Summary: Virtual queues, or systems that allow you to hold your place without physically standing in line, have become commonplace in restaurants, call centers, and many other businesses — and how you build those systems can have a major impact on the customer’s experience. In this piece, the author shares key takeaways from over a decade’s worth of research on how companies can optimize their virtual queuing systems. Specifically, she suggests that providing wait time estimates can reduce customers’ average wait time, and that providing pessimistic estimates (i.e., telling customers they will have to wait longer than they actually will) can improve the customer experience. In addition, the research shows that providing more frequent progress updates also improves the customer experience, and that customers who wait for longer than expected will take longer when their turn finally arrives (suggesting that pessimistic estimates can also help businesses to increase throughput).
|digital article||call centers|
|rp24||2020||Yu Q., Allon G., Bassamboo A. The Reference Effect of Delay Announcements: A Field Experiment, accepted, Management Science, 2020.|
Abstract: We explore whether customers are loss averse in time and how delay information may impact such reference-dependent behavior using observational and field-experiment data from two call centers of an Israeli bank. We consider settings with no announcements and announcements of different accuracy levels. We face two key challenges: (1) we do not directly observe the reference points customer use as any other studies using field data; and (2) it is difficult to separate the reference-dependent behavior from the potential non-linear waiting cost of customers. To address these challenges, we develop a dynamic decision model with consumer learning, through which we infer the reference point each customer used during any given call. The reference points may be different across different customers and evolve across different calls of the same customers. We also exclude the alternative explanation by showing that our main reference-dependent models better explain the observed customer abandonment than models where customers have non-linear waiting cost. Our results indicate that customers are loss averse regardless of the availability or accuracy of the announcements, when their waiting time is relatively long (90s or longer). While delay announcements do not alter the nature that customers are loss averse, accurate announcements may affect customers' belief about the offered waiting time and thus impact the reference points. Through counterfactual studies, we demonstrate that providing delay announcements improves the call center performance given the loss aversion behavior observed in our data. Interestingly, as customers become more loss averse, the value of providing delay announcements decreases.
|journal article||call centers|
|rp8||2019||Carmen R., Yom-Tov G., Van Nieuwenhuyse I., Joubert B., Ofran Y. The role of specialized hospital units in infection and mortality risk reduction among patients with hematological cancers, PLoS ONE 14(3): e0211694. (https://doi.org/10.1371/journal.pone.0211694)|
|rp9||2019||Rafaeli A., Altman D., Yom-Tov G.B. Cognitive and Emotional Load Influence Response Time of Customer-Service Agents: A Large Scale Analysis of Chat Service Conversations. Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS-52), January 8-11, 2019, Hawaii, US, 10 pages. (DOI:10.24251/hicss.2019.231) (Link to the manuscript: HICSS-52_0190)|
Abstract: We highlight two psychological aspects of the load in service work – cognitive load (amount of information customers present) and emotional load (emotions customers present), and examine their effects on response time of service agents, in service conversations conducted using text-based chats. Using operational data of 145,995 chat service conversations, we show that cognitive load and emotional load increase agent response time both between and within service conversations. Our analyses unpack common assumptions that number of customers is identical to amount of work load, and shed light on customer-agent dynamics both between and within service conversations. In studying text-based service communication, which is rapidly expanding and insufficiently studied, we open up exciting opportunities for further research.
|conference paper||online chats|
|rp10||2019||Senderovich A., Beck J.C., Gal A., Weidlich M. Congestion Graphs for Automated Time Predictions. AAAI 2019: 4854-4861. |
Abstract: Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in congested systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (e.g., workload and resource availability), must be considered. These features are typically gathered using process knowledge, (i.e., insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of congestion graphs, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.
|rp11||2019||Senderovich A., Booth Kyle E.C., Beck J.C. Learning Scheduling Models from Event Data. ICAPS 2019: 401-409.|
Abstract: A significant challenge in declarative approaches to scheduling is the creation of a model: the set of resources and their capacities and the types of activities and their temporal and resource requirements. In practice, such models are developed manually by skilled consultants and used repeatedly to solve different problem instances. For example, in a factory, the model may be used each day to schedule the current customer orders. In this work, we aim to automate the creation of such models by learning them from event data. We introduce a novel methodology that combines process mining, timed Petri nets (TPNs), and constraint programming (CP). The approach learns a sub-class of TPN from event logs of executions of past schedules and maps the TPN to a broad class of scheduling problems. We show how any problem of the scheduling class can be converted to a CP model. With new instance data (e.g., the day’s orders), the CP model can then be solved by an off-the-shelf solver. Our approach provides an end-to-end solution, going from event logs to model-based optimal schedules. To demonstrate the value of the methodology we conduct experiments in which we learn and solve scheduling models from two types of data: logs generated from job-shop scheduling benchmarks and real-world event logs from an outpatient hospital.
|rp12||2019||Senderovich A., Di Francescomarino C., Maggi F.M. From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring. Inf. Syst. 84: 255-264 (2019).|
Abstract: Predictive process monitoring (PPM) is a research area that focuses on predicting measures of interest (e.g., the completion time) for running cases based on event logs. State-of-the-art PPM techniques only consider intra-case information that comes from the case whose measures of interest one wishes to predict. However, in many systems, the outcome of a running case depends on the interplay of all cases that are being executed concurrently, or can be derived from the characteristics of cases that are executed in the same period of time. For example, in many situations, running cases compete over scarce resources, and the completion time of a running case can be derived from the number of similar cases running concurrently. In this work, we present a general framework for feature encoding that relies on a bi-dimensional state space representation. The first dimension corresponds to intra-case dependencies and utilizes existing feature encoding techniques. The second dimension encodes inter-case features using two approaches: (1) a knowledge-driven encoding (KDE), which assumes prior knowledge on case types, and (2) a data-driven encoding (DDE), which automatically identifies case types from data using case proximity metrics. Both approaches partition the event log into sets of cases that share common characteristics, and derive features according to these commonalities. We demonstrate the usefulness of the proposed framework with an empirical evaluation carried out against two real-life datasets coming from an outpatient hospital process and a manufacturing process.
|rp13||2019||Senderovich A., Weidlich M., Gal A. Context-aware temporal network representation of event logs: Model and methods for process performance analysis. Inf. Syst. 84: 240-254.|
Abstract: Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works have several limitations, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log. It is based on Allen’s interval algebra, comprises the pairwise temporal relations for activity executions, and potentially incorporates the context in which these relations have been observed. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we further develop a framework for measuring performance fitness. Under this framework, TNR-based process discovery is guaranteed to dominate existing techniques in measuring performance characteristics of a process. In addition, we show how contextual information in terms of the congestion levels of the process can be mined in order to further improve capabilities for performance analysis. To illustrate the practical value of the proposed models, we evaluate our approaches with three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms, while congestion learning is able to accurately reconstruct congestion levels from event data.
|rp14||2019||Shraga R., Gal A., Schumacher D., Senderovich A., Weidlich M. Inductive Context-aware Process Discovery. ICPM 2019: 33-40.|
Abstract: Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A "control-flow first" approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We evaluate the approach using synthetic and real-world datasets and show that the resulting models are superior to state-of-the-art discovery methods in terms of measures that are based on multi perspective alignments.
|rp27||2019||Webb E., Yu Q., Bretthauer K. Linking Delay Announcements, Abandonment, and Service Time, under revision for the special issue at OR.|
Abstract: Using call center field data, we examine the reference-dependent impact of delay announcements on both customer abandonment behavior and time spent in service. Delay announcements induce a reference point telling customers how long they must wait for service. Customers behave differently before and after this reference point. Our novel contributions include demonstrating the effects of delay announcements on service time, as well as simultaneously considering both reference-dependent abandonment behavior and reference- dependent service times. One additional minute spent waiting after the reference point increases the hazard rate of abandonment by up to 52%, a much larger effect than the hazard rate decrease from having one additional minute to go before the reference point. For those customers who do not abandon, we also observe reference-dependent behavior during service. Conditional on the same total wait in queue, one additional minute spent waiting after the reference point triggers up to 21 extra seconds in service. While longer total waiting time in queue also leads to longer service time, its effect is much smaller than this reference-dependent effect. Reference-dependent behaviors in abandonment and service impact system-level congestion in opposite directions. Therefore, managers should consider both effects when considering operational decisions, including staffing and customer prioritization.
|journal article||call centers|
|rp28||2019||Whitt W., Zhang X. Periodic Little’s Law, OPERATIONS RESEARCH Vol. 67, No. 1, January–February 2019, pp. 267–280.|
Abstract: Motivated by our recent study of patient flow data from an Israeli emergency department (ED), we establish a sample path periodic Little’s law (PLL), which extends the sample path Little’s law (LL). The ED data analysis led us to propose a periodic stochastic process to represent the aggregate ED occupancy level, with the length of a periodic cycle being 1 week. Because we conducted the ED data analysis over successive hours, we construct our PLL in discrete time. The PLL helps explain the remarkable similarities between the simulation estimates of the average hourly ED occupancy level over a week using our proposed stochastic model fit to the data and direct estimates of the ED occupancy level from the data. We also establish a steady-state stochastic PLL, similar to the time-varying LL.
|rp30||2018||Bastianin A., Galeotti M., Manera M. Statistical and economic evaluation of time series models for forecasting arrivals at call centers, Empirical Economics, 2018.|
Abstract: Call centers’ managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars: (i) flexibility of the loss function; (ii) statistical evaluation of forecast accuracy; and (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second-moment modeling is important for both point and density forecasting and that the simple seasonal random walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.
|journal article||call centers|
|rp16||2018||Senderovich A., Shleyfman A., Weidlich M., Gal A., Mandelbaum A. To aggregate or to eliminate? Optimal model simplification for improved process performance prediction. Inf. Syst. 78: 96-111 (2018). |
Abstract: Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance questions about business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting the impact of their design choices in terms of performance measures. In this work, we thus offer a technique for controlled performance-driven model reduction of GSPNs, using structural simplification rules, namely foldings. We propose a set of foldings that aggregate or eliminate performance information. We further prove the soundness of these foldings in terms of stability preservation and provide bounds on the error that they introduce with respect to the original model. Furthermore, we show how to find an optimal sequence of simplification rules, such that their application yields a minimal model under a given error budget for performance estimation. We evaluate the approach with two real-world datasets from the healthcare and telecommunication domains, showing that model simplification indeed enables a controlled reduction of model size, while preserving performance metrics with respect to the original model. Moreover, we show that aggregation dominates elimination when abstracting performance models by preventing under-fitting due to information loss.
|rp17||2018||Yom-Tov G.B., Gurtz T. Delay Guarantee Planning of Call-Back Options in Time-Varying Service Systems. In Winter Simulation Conference, December 9-12, 2018, Gothenburg, Sweden (DOI:10.1109/WSC.2018.8632519). (Link to the manuscript call_back).|
Abstract: Many service centers offer a “call-back” option, in which customers entering the queue are informed of the anticipated (on-line) wait and can choose to wait either online or off-line till an agent contacts them. We show that such a policy has the potential of both improving service performance and server utilization, by balancing the load between overloaded and underloaded periods. However, our analysis suggests that companies need not offer that service at all times, and that the delay guarantees proposed should be planned according to the anticipated load throughout the day. In order to optimize the operation of such a system,we develop an Iterative Simulation Algorithm to determine what delay guarantees the company should offer in a time-varying environment. Those guarantees depend on service level targets the company wishes to provide and the delay sensitivity of the online vs. off-line customers.
|conference paper||call centers|
|rp18||2017||Senderovich A., Weidlich M., Gal A. Temporal Network Representation of Event Logs for Improved Performance Modelling in Business Processes. BPM 2017: 3-21Â (best paper award!) |
Abstract: Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works are severely limited, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log, based on Allen’s interval algebra, as a complete temporal representation of a log, which enables simultaneous discovery of control-flow and performance information. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we develop a framework for measuring performance fitness. Under this framework, we provide guarantees that TNR-based process discovery dominates existing techniques in measuring performance characteristics of a process. To illustrate the practical value of the TNR, we evaluate the approach against three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms.
|rp29||2017||Whitt W., Zhang X. A data-driven model of an emergency department, Operations Research for Health Care, 12 (2017) 1–15.|
Abstract: This paper develops an aggregate stochastic model of an emergency department (ED) based on a careful study of data on individual patient arrival times and length of stay in the ED of the Rambam Hospital in Israel, which was used in a large-scale exploratory data analysis by Armony et al. (2015). This data set is of special interest because it has been made publicly available, so that the experiments are reproducible. Our analysis confirms the previous conclusions about the time-varying arrival rate and its consequences, but we also find that the probability of admission to an internal ward from the ED and the patient length-of-stay distribution should be time varying as well. Our analysis culminates in a new time-varying infinite-server aggregate stochastic model of the ED, where both the length-of-stay distribution and the arrival rate are periodic over a week.
|rp26||2017||Yu Q., Allon G., Bassamboo A. How do Delay Announcements Shape Customer Behavior? An Empirical Study, Management Science, 63(1): 1-20, January 2017.|
Abstract: In this paper, we explore the impact of delay announcements using an empirical approach by analyzing the data from a medium-sized call center. We first explore the question of whether delay announcements impact customers’ behavior using a nonparametric approach. The answer to this question appears to be ambiguous. We thus turn to investigate the fundamental mechanism by which delay announcements impact customer behavior, by constructing a dynamic structural model. In contrast to the implicit assumption made in the literature that announcements do not directly impact customers’ waiting costs, our key insights show that delay announcements not only impact customers’ beliefs about the system but also directly impact customers’ waiting costs. In particular, customers’ per-unit waiting cost decreases with the offered waiting times associated with the announcements. The results of our counterfactual analysis show that it may not be necessary to provide announcements with very fine granularity.
|journal article||call centers|
|rp19||2016||Senderovich A., Weidlich M., Yedidsion L., Gal A., Mandelbaum A., Kadish S., Bunnell C.A. Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Inf. Syst. 62: 185-206 (2016).|
Abstract: Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today׳s economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.
|rp20||2015||Armony M., Israelit S., Mandelbaum A., Marmor Y.N., Tseytlin Y., Yom-Tov G.B. On Patient Flow in Hospitals: A Data-Based Queueing-Science Perspective, Stochastic Systems, Vol. 5, No. 1, 146-194, 2015 (DOI: 10.1214/14-SSY153). (Link to the manuscript: Patient flow (May 2015) Extended Version) |
Abstract: Hospitals are complex systems with essential societal benefits and huge mounting costs. These costs are exacerbated by inefficiencies in hospital processes, which are often manifested by congestion and long delays in patient care. Thus, a queueing-network view of patient flow in hospitals is natural for studying and improving its performance. The goal of our research is to explore patient flow data through the lens of a queueing scientist. The means is exploratory data analysis (EDA) in a large Israeli hospital, which reveals important features that are not readily explainable by existing models.
|rp21||2015||Senderovich A., Weidlich M., Gal A., Mandelbaum A. Queue mining for delay prediction in multi-class service processes. Inf. Syst. 53: 278-295.|
Abstract: Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Information recorded by systems during the operation of these processes provides an angle for operational process analysis, commonly referred to as process mining. In this work, we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We present predictors that treat queues as first-class citizens and either enhance existing regression-based techniques for process mining or are directly grounded in queueing theory. In particular, our predictors target multi-class service processes, in which requests are classified by a type that influences their processing. Further, we introduce queue mining techniques that derive the predictors from event logs recorded by an information system during process execution. Our evaluation based on large real-world datasets, from the telecommunications and financial sectors, shows that our techniques yield accurate online predictions of case delay and drastically improve over predictors neglecting the queueing perspective.
|journal article||call centers|
|rp31||2014||Goldberg Y., Ritov Y., Mandelbaum A. Predicting the Continuation of a Function with Applications to Call Center Data. Journal of Statistical Planning and Inference 147 (2014) 53–65.|
Abstract: We show how to construct the best linear unbiased predictor (BLUP) for the continuation of a curve, and apply the proposed estimator to real-world call center data. Using the BLUP, we demonstrate prediction of the workload process, both directly and based on prediction of the arrival counts. The Matlab code and all data sets in the presented examples are available in the supplementary material.
|journal article||call centers|
|rp22||2014||Senderovich A., Weidlich M., Gal A., Mandelbaum A. Queue Mining - Predicting Delays in Service Processes. CAiSE 2014: 42-57.|
Abstract: Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Recently, work on process mining showed how management of these processes, and engineering of supporting systems, can be guided by models extracted from the event logs that are recorded during process operation. In this work, we establish a queueing perspective in operational process mining. We propose to consider queues as first-class citizens and use queueing theory as a basis for queue mining techniques. To demonstrate the value of queue mining, we revisit the specific operational problem of online delay prediction: using event data, we show that queue mining yields accurate online predictions of case delay.
|conference paper||call centers|
|rp23||2014||Senderovich A., Weidlich M., Gal A., Mandelbaum A. Mining Resource Scheduling Protocols. BPM 2014: 200-216.|
Abstract: In service processes, as found in the telecommunications, financial, or healthcare sector, customers compete for the scarce capacity of service providers. For such processes, performance analysis is important and it often targets the time that customers are delayed prior to service. However, this wait time cannot be fully explained by the load imposed on service providers. Indeed, it also depends on resource scheduling protocols, which determine the order of activities that a service provider decides to follow when serving customers. This work focuses on automatically learning resource decisions from events. We hypothesize that queueing information serves as an essential element in mining such protocols and hence, we utilize the queueing perspective of customers in the mining process. We propose two types of mining techniques: advanced classification methods from data mining that include queueing information in their explanatory features and heuristics that originate in queueing theory. Empirical evaluation shows that incorporating the queueing perspective into mining of scheduling protocols improves predictive power.
|conference paper||call centers|
|rp32||2013||Aksin Z., Ata B., Emadi S., Su C. Structural estimation of callers' delay sensitivity in call centers. Management Science, 59(12), December 2013, 2727-2746.|
Abstract: We model the decision-making process of callers in call centers as an optimal stopping problem. After each waiting period, a caller decides whether to abandon a call or continue to wait. The utility of a caller is modeled as a function of her waiting cost and reward for service. We use a random-coefficients model to capture the heterogeneity of the callers and estimate the cost and reward parameters of the callers using the data from individual calls made to an Israeli call center. We also conduct a series of counterfactual analyses that explore the effects of changes in service discipline on resulting waiting times and abandonment rates. Our analysis reveals that modeling endogenous caller behavior can be important when major changes (such as a change in service discipline) are implemented and that using a model with an exogenously specified abandonment distribution may be misleading.
|journal article||call centers|