Service Time Variability Research Articles

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Abstract The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.

Managing interruptions in appointment schedules via patient notification

Interruptions in appointment schedules are common in practice, yet many clinics use generic appointment scheduling templates and sub-optimal interruption management strategies that fail to address dynamics aspects of daily healthcare operations. In this study, we consider patient no-shows, patient punctuality, service time variability, random walk-in patient arrivals and other common appointment interruptions to determine optimal single-server, intra-day appointment schedules. Using a modified depth first search (DFS) algorithm that relies on a sample average approximation (SAA) technique for objective function evaluation, we minimize a weighted sum of expected waiting times for scheduled and walk-in patients, physician idle time, and physician overtime. Extensive experimentation shows the proposed algorithm solves realistic problems in reasonable time. Furthermore, we propose a sequential notification procedure (SNP) that serves as a dynamic correcting mechanism to address infrequent and unexpected interruptions with a relatively long duration. Considering possible patient responses to notification, SNP notifies scheduled patients in advance to prevent excessive patient waiting and physician overtime. Furthermore, we propose an easy-to-use patient notification heuristic (NH) that can be adopted by clinics using traditional appointment systems. Experimentation shows that both SNP and NH provide significant value, improving operational outcomes. We demonstrate the value of patient notification increases as interruption levels increase and when patients are responsive to notifications.

The Impact of the Variability of Patient Flow and Service Time on the Efficiency of Large-Scale Outpatient Systems

The outpatient services in large-scale hospitals consist of multiple specialty departments, servers, and multiple stages. Single-server and small clinics have been extensively studied, but little attention has been paid to the modeling and evaluation of large-scale outpatient care systems. In this study, complex patient routings in large-scale outpatient systems are specified, and three patient flow variability factors (e.g., return rate, first-lab-visit rate, and second-lab-visit rate) are discussed. The measures, namely, patient satisfaction, resource utilization, and system efficiency, are based on the ratios of ``effective time'' over ``holistic time.'' The purpose of this study is to find the effect of patient flow variability and service time variability on the efficiency of a multistage outpatient system. A discrete-event simulation (DES) model is built, and a real case is presented. A novel method to generate the transition matrix based on patient flow variability is proposed. Our results show that the patient flow variability has a negative impact on patient satisfaction and a positive effect on resource utilization and system efficiency. Service time variability has a negative impact on resource utilization but has a combined effect on patient satisfaction and system efficiency with patient flow variability. The interactions between the two factors are thoroughly discussed.

Off-Platform Threats in On-Demand Services

Problem definition: Online platforms that provide on-demand services are often threatened by the phenomenon of leakage, where customer-provider pairs may decide to transact “off-platform” to avoid paying commissions to the platform. This paper investigates properties of services that make them vulnerable or resistant to leakage. Academic/practical relevance: In practice, much attention has been given to platform leakage, with platforms experimenting with multiple approaches to alleviate leakage and maintain their customer and provider bases. Yet, there is a current dearth of studies in the operations literature that systematically analyze the key factors behind platform leakage. Our work fills this gap and answers practical questions regarding the sustainability of platform. Methodology: We develop two game-theoretical models that capture service providers’ and customers’ decisions whether to conduct transactions on or off the platform. In the first (“perfect information”) model, we assume that customers are equipped with information to select their desired providers on the platform, whereas in the second (“imperfect information”) model, we assume customers are randomly matched with available providers by the platform. Results: For profit maximizing platforms, we show that leakage occurs if and only if the value of the counterparty risk from off-platform transactions exceeds a threshold. Across both models, platforms tend to be more immunized against leakage as provider pool sizes increase, customer valuations for service increase, their waiting costs decrease, or variability in service times are reduced. Finally, by comparing the degree of leakage between both settings, we find that neither model dominates the other across all parameter combinations. Managerial implications: Our results provide guidance to existing platform managers or entrepreneurs who are considering “platforming” their services. Namely, based on a few key features of the operating environment, managers can assess the severity of the threat of platform leakage for their specific business context. Our results also suggest how redesigning the waiting process, reducing service time variability, upskilling providers can reduce the threat of leakage. They also suggest the conditions under which revealing provider quality information to customers can help to curb leakage. Funding: J. Goh’s work was supported by a National University of Singapore Start-Up [Grant R-314-000-110-133] and a 2021 Humanities and Social Sciences Fellowship from the National University of Singapore. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1179 .

Designing variable-sized block appointment system under time-varying no-shows

We consider the tactical problem of optimizing the capacity of a block appointment system with time-varying no-show and random service duration. The problem characteristics are motivated by a real-life case study of two outpatient specialty clinics in the US, where time-of-day variation in appointment attendance was observed. We aim to determine the block size (patients to be assigned in each time period) such that the weighted sum of block-wise waiting- and idle-times (or total cost) are minimized. First, we consider optimizing the capacity of a single block appointment system (SBAS) as a special case of the inverse newsvendor problem, and develop an analytical closed-form solution under the assumption of normally distributed service time. Also, a stochastic integer programming (SIP) model is developed to solve SBAS for any service time distribution. Subsequently, the SIP model is extended to determine the block size of the variable-sized multi-block appointment system (VSMBAS) by treating it as a sequential inverse newsvendor problem. Owing to the computational complexity of the SIP, we employ sample average approximation to estimate the expected total cost. Numerical studies considered several realistic clinic settings, and the results demonstrated that integrating time-varying no-shows for block size determination will considerably improve schedule efficiency as opposed to ignoring it. We also found the cost ratios (waiting-time to idle-time penalty), service time variation, and no-show pattern have a substantial influence on the block size of VSMBAS. Finally, we provide several practical implications based on our analysis.

Planning for resilience in screening operations using discrete event simulation modeling: example of HPV testing in Peru

BackgroundThe World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru.MethodsUsing data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with “what-if” scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system.ResultsOur simulations show how much the screening system’s capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general “rules” for resource and operational planning.ConclusionsMultilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions—how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.

Two-stage no-wait proportionate flow shop scheduling with minimal service time variation and optional job rejection

We consider two-stage no-wait proportionate flow shops with the objective of minimizing service time variation measured by defining the mid-processing point of a job and minimizing the Total Absolute Deviation of mid-processing Points (TADZ). We show that the two-stage no-wait proportionate flow shop with the TADZ objective is solvable in O(nlogn) time. Our findings provide an affirmative answer to an open research question posed by Kovalev et al. (2019) regarding existence of a solvable variant of the two-stage no-wait proportionate flow shop problem with the Total Absolute Deviation of Completion Times (TADC) objective. Moreover, we show when a generic two-stage no-wait proportionate flow shop scheduling problem is solvable in O(nlogn) time. We present practical applications where TADZ is more suitable than TADC as a scheduling objective. We also introduce a new metric defined as the sum of all partial schedule lengths SPSL and show that the two-stage no-wait proportionate flow shop with the SPSL objective is solvable in O(nlogn) time; thus, an additional solvable variant of the two-stage no-wait proportionate flow shop with the SPSL objective is identified and solved. Finally, we consider the option of rejecting a job from the schedule by paying a job-specific penalty for each rejected job and solve the resulting problem with the TADZ objective and the rejection option in O(n3) time by dynamic programming.

Influence of product variety on work allocation and server distribution of flexible manufacturing lines

Mass customization involves medium volume production of a high variety of products where flexible manufacturing systems can cope with it. The work allocation problem of assigning the work content of each workstation in a manufacturing line is formulated as the design problem of capacity allocation across the line by looking for the throughput maximization and/or simultaneous work-in-process containment or minimization. The bowl phenomenon refers to the distribution shape of work across the workstations in unpaced flow line (asynchronous flow) with buffers, experimentally observed and conjectured back in the 1960’s, but with theoretical roots not successfully established up today. The increasing value of flexible capability for mass customization makes worthy the research of the phenomenon in the presence of product variety, beyond the classical model of balanced dedicated flow lines. The three problems of workload, buffer, and server allocation are closely related, and they have been previously studied in queuing networks with metaheuristics. This paper faces the effect of product variety (service time variability) impact on the performance of the flow line, researching the bowl effect through experimental analysis. The results show a main influence of the coefficient of variation of the service times with the secondary effect of the buffer size.

Throughput Capacity Comparison for Airport Pickup and Dropoff Facilities

An airport serves as an interface between ground and air transportation; the efficient processing of ground transportation arrivals and departures is, therefore, an important part of airport operations. At many airports, the current pickup and dropoff locations for taxis and other passenger cars are along the terminal curb or in existing parking facilities, and many of these pickup and dropoff facilities suffer from excessive congestion. In addition, in recent years there has been growth in the use of on-demand ground transportation to and from airports, with the increased adoption of ride-hailing services, resulting in increased congestion. Since most airports are severely space constrained, there is a need to consider pickup and dropoff facilities that are more efficient (in vehicle throughput per unit space) than terminal curbs. In this paper we compare the throughput capacities of different pickup and dropoff facility layouts. We take into account the effect of the facility layout and operational rules on conflicts between the movements of different vehicles, the resulting delays in the movements of vehicles, as well as the spatial requirements of different layouts. We demonstrate the impact of mean service times, variability in service times and vehicle movement times, and operational rules on the relative throughput capacities of different facility layouts.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

Service time variability at manual operated tollbooths under mixed traffic environment: Towards level-of-service thresholds

Abstract Comprehending the variation in service time is essential for evaluating the capacity and level of service of the toll-plaza and developing policies for effective management of tollbooth operations. The present study analyzes the variation in service time using statistical distribution at manually operated toll plazas in India. For this purpose, traffic video data were collected for five toll-plazas in India. The vehicles approaching the toll plaza are divided broadly into seven different categories. The service time of different vehicle classes was extracted manually from the recorded video. The variation in service time for a total of 13 leader-follower pairs (small car as leader and follower) were modeled using statistical distributions. Based on the Chi-squared, Anderson Darling (AD), and Kolmogorov-Smirnoff (KS) test statistics, log-normal distribution was found as the best-fitted distribution among other potential statistical distributions. Investigations revealed that traffic composition, vehicular characteristics, variation in toll rates, drivers' and tollbooth operators’ behavior significantly influence the variability in service time. The important outcome of this study is the development of service time based level of service (LOS) criteria, which were developed using k-mean clustering technique. The developed LOS thresholds can be very useful to field engineers and practitioners for evaluating the existing performance of tollbooth and accordingly facilitating the development of appropriate policies for effective management of toll plaza operations.

Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers.

Even though several clinics serve patients in more than one stage (e.g., visit nurse and then visit doctor) and employ multiple providers in each stage, most of the previous work on appointment system design considers a simplified single-stage single-server clinic. Motivated by a real-life clinic setting, this paper aims to determine the schedule configuration of a hybrid appointment system (i.e., the number of pre-booking and same-day time slots reserved for a physician along with their positions in the schedule) for a two-stage multi-server clinic. A stochastic optimization model is developed to obtain a schedule configuration that minimizes the expected total cost - a weighted sum of excessive patient waiting time, resource idle time, resource overtime, and denied appointment requests. Owing to its computational complexity, we estimate the expected total cost using the sample average approximation method. The proposed model is verified and validated using small test instances and subject matter experts. A case study of a family medicine clinic in Pennsylvania is used to illustrate the proposed approach. The schedule generated by the proposed model results in a significantly lower expected cost compared to the approximated single-stage system's best schedule configuration and clinic's existing configuration. Further, sensitivity analysis is conducted to assess the impacts of no-show rate, service time variation, and cost ratios on the schedule configuration. Our findings demonstrate that the schedule configuration is sensitive to changes in the average no-show rate and cost ratios but is not significantly impacted by service time variation. Several managerial insights are also drawn from our analysis. Finally, we provide directions for future research that also highlights the potential to use the revenue management approach to address the problem under study.

Service Time Variation Analysis at Manually Operated Toll Plazas under Mixed Traffic conditions in India

Service Time Variation Analysis at Manually Operated Toll Plazas under Mixed Traffic conditions in India

Straggler Mitigation at Scale

Runtime performance variability has been a major issue, hindering predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers have been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has drawn significant attention, and numerous papers have analyzed the pain and gain of redundancy under various service models and assumptions on the runtime variability. This paper presents a cost (pain) vs. latency (gain) analysis of executing jobs of many tasks by employing replicated or erasure coded redundancy. The tail heaviness of service time variability is decisive on the pain and gain of redundancy and we quantify its effect by deriving expressions for cost and latency. Specifically, we try to answer four questions: 1) How do replicated and coded redundancy compare in the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting some time and expect it to reduce the cost? 3) Can relaunching the tasks that appear to be straggling after some time help to reduce cost and/or latency? 4) Is it effective to use redundancy and relaunching together? We validate the answers we found for each of these questions via simulations that use empirical distributions extracted from a Google cluster data.

Metamodel-based simulation optimisation for bed allocation

Hospital beds are one of the most critical resources in healthcare institutions. In practice, beds are usually allocated to different departments in advance to accommodate different kinds of patients. Inappropriate decisions in the allocation may lead to the idleness of beds or the high rejection ratio of patients. Hospital managers are under pressure to allocate beds to different departments. High variability in patient arrivals and service times make the allocation problem complex and challenging to solve. To address this problem, a mixed-integer non-linear programming model is formulated, with the objective of minimising the weighted cost of rejecting patients and holding them waiting. To solve this model, a data-driven metamodel simulation optimisation method is proposed, in which metamodels, based on an analytical queuing model and a general function, are proposed and embedded into a general-purpose algorithm Adaptive Hyperbox Algorithm. The metamodels designated for local and global approximation are separately fitted using different sets of simulation observations, which can combine structural information and simulation information, and can provide insightful guidance in solution improvements. A case study is conducted based on the real data collected from a public hospital in Shanghai. Numerical results demonstrate the efficiency of the proposed method.

Performance measurement of a solution for the travelling salesman problem for routing through the incorporation of service time variability

This work assessed the performance of a solution to the problem of assigning service squads, incorporating the variability of service times. The initial problem was modelled as a Travelling Salesman Problem (TSP), whose solution was obtained by the ant colony algorithm, showing the efficient route to be followed by the squad. Assessment of the performance of the solution by discrete event simulation (DES) included the travel time and added the service time. The TSP solution indicated that up to six customer visits could be carried out in an 8hour working day. Validation by DES presented a stable behavior of the variance, regardless of the number of visit sites assigned along the route.

The M/PH/1+D queue with Markov-renewal service interruptions and its application to delayed mobile data offloading

Motivated by delayed mobile data offloading in cellular networks, we consider an M/PH/1 queue with deterministic impatience times (i.e., the M/PH/1+D queue), where the server takes ON and OFF states alternately and does not serve any customers in the OFF states. We assume that the state of the server is governed by an alternating Markov renewal process. For this queue, we analyze various performance measures in steady state. Most of the previous studies on the analysis of delayed mobile data offloading assume that service times, ON- and OFF-period lengths, and impatience times are exponentially distributed, and the effect of their distributions on the queueing performance has not been investigated well. We thus discuss the effects of the variability in the service time distribution, the variability in the ON-period distribution, and the correlation between the ON- and OFF-periods, and we show that those factors, especially the variability in service times, has a great impact on the queueing performance. Furthermore, to demonstrate the modeling capability of Markov-renewal service interruptions, we illustrate such models as the mobility of smartphone users in a two-dimensional area is incorporated, and we show how the queueing performance is affected by the geographical distribution of Wi-Fi access points.

Altering the Environment to Improve Appointment System Performance

Current research on clinic performance is focused primarily on appointment scheduling rather than shaping the clinical environments. The goal of this study is to investigate the impact of environmental factors on the total cost performance of a clinic, measured as a weighted sum of patients’ wait times and a physician’s idle time and overtime. The environmental factors investigated include the variability of service times, the probabilities of no-shows and walk-ins, the number of appointments per session, and the cost ratio of the physician’s time to patients’ time. The effects of these factors are evaluated using a near-optimal rule that already adjusts the patients’ appointment times to minimize the negative effects of these factors so that their residual or true effects on total cost performance can be isolated. As a result, this study provides useful insights for healthcare practitioners in prioritizing their efforts in managing the different sources of variability to further improve the clinic performance beyond the use of an optimal or near-optimal appointment rule. Additional experiments are conducted on the effects of patient and physician unpunctuality, which have been studied to a lesser extent in prior literature.

Chronic pain clinic efficiency analysis: optimization through use of the Gantt diagram and visit diagnoses.

ObjectiveThe aim of this study is to identify scheduling inefficiencies and to develop a personalized schedule based on diagnosis, service time (face-to-face time between the patient and the provider), and patient wait time using a Gantt diagram in a chronic pain clinic.DesignThis is an observational prospective cohort quality improvement (QI) study.SettingThis study was carried out at a single outpatient multidisciplinary pain management clinic in a university teaching hospital.SubjectsNew and established chronic pain patients at the University of Pittsburgh Medical Center (UPMC) Montefiore Chronic Pain Clinic were recruited for this study.MethodsTime tracking data for each phase of clinic visit and pain-related diagnoses were collected from 81 patients on 5 clinic days in March 2016 for patient flow analysis.ResultsA Gantt diagram was created using Microsoft Excel® software. Areas of overbooking and underbooking were identified. Median service times (minutes) differed dramatically based on the diagnosis and were highest for facial pain (23 [IQR, 15–31]) and chronic abdominal and/or pelvic pain (21.5 [IQR, 16–27]) and lowest for myalgia. Abdominal and/or pelvic pain and facial pain median service times consistently exceeded the 15-minute allocation for return visits.ConclusionSchedule efficiency analysis using the Gantt diagram identified trends of overbooking and underbooking and inefficiencies in examination room utilization. A 15-minute appointment for all return patients is unrealistic due to variation of service times for some diagnoses. Scheduling appointments based on the diagnosis is an innovative approach that may reduce scheduling inefficiencies and improve patient satisfaction and the overall quality of care. To the best of our knowledge, this type of scheduling diagram has not been used in a chronic pain clinic.

Effect of environmental constraints on multi-segment coordination patterns during the tennis service in expert performers

ABSTRACTThe aims of this study were to examine the effect of different environmental constraints on kinematic multi-segment coordination patterns during the service and its coordination with service time variability. Ten expert tennis players (Age: 34.1 ± 5.3) volunteered to take part in this study. Participants served 30 times in 3 different conditions: control, target and opposition. The order of conditions was counterbalanced between participants. A wireless 3D motion capture system (STT Co, Spain) was used to measure 7 joint motions, with a 17 degrees of freedom biomechanical model created to capture the entire service action. Results of the principal component analysis showed that 4 synergies were created; however, their roles were changed relative to the perception of the environment. The results of repeated-measures analysis of variance did not show any significant difference on total variance and individual principal components between conditions; however, one synergy pattern significantly predicted the service time variability in both control and opposition conditions. In conclusion, the findings demonstrated that expert performers reduce the joint dimensionality by creating functional synergies in different phases of service and adapt the service action according to the perception of the environment.