Desired Service Level Research Articles

Next-Generation Performance Measures for Pavement Management Decision Support

Since 2012, federal legislation has promoted the use of performance-based decisions for managing the nation’s highway system. The Moving Ahead for Progress in the 21st Century Act established seven national goal areas and required establishment of national performance measures for pavements. Fulfillment of these requirements has strengthened highway agencies’ data-driven investment decisions, maximizing returns on public investment and maintaining highway infrastructure assets in a state of good repair. While condition-based performance measures meet the current legislative requirement, there is a need to identify “next-generation” measures that can be integrated into pavement investment decisions to drive sustainable, long-term performance management of pavements. This paper discusses lifecycle and financial performance measures that can be used in concert with existing condition-based measures to better inform pavement management decision-making. Lifecycle performance measures characterize and monetize the long-term investment strategies associated with providing the desired level of service for a highway asset. These measures proactively encourage activities that reduce the long-term cost of managing the system. Financial performance measures indicate whether an adequate level of investment is being made to offset the asset value depreciation or satisfy treatment needs to not only meet condition targets today, but to sustain those into the future. The performance measures were validated through pilot implementations at three state highway agencies and the lessons learned from the validation efforts are documented in this paper. The paper also highlights considerations for implementing next-generation pavement performance measures, as well as the potential benefits to agencies from their implementation.

Multi-objective continuous review inventory policy using MOPSO and TOPSIS methods

In the dynamic business environment of today, decision-making involves considering various conflicting aspects. Inventory planning problems aim to determine how much and when to order products to satisfy customer demand at the lowest possible cost while maintaining a desirable service level. These problems can be formulated as a MOPSO algorithm, which is used to handle multiple objectives in a continuous review stochastic inventory control system (r, Q). Unfortunately, most multi-objective inventory models have been solved by aggregating objectives using specific weights or by optimizing only one objective and treating the others as constraints. Considering the complexity of real-world inventory control problems, which involves conflicting objectives such as minimizing cost and maximizing service level, the need arises to employ more precise optimizers that can generate better and more diverse non-dominated solutions of reorder point and order size system. In this paper multi-criteria decision-making framework that combines MOPSO algorithm and TOPSIS method to generate a Pareto front of non-dominated solutions and rank them based on decision makers' preferences. Initially, the original MOPSO is applied to the multi-objective inventory control problem, and then the mutation operator is integrated into the MOPSO to maintain diversity in the swarm and explore the entire search space. Next, the leader selection strategy called the geographically-based system (Grids) is replaced by the crowding distance factor to choose the global optimal particle as a leader. Additionally, the ε-dominance concept is employed to limit the archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. In conclusion, this work not only pioneers a cutting-edge approach to multi objective inventory control but also underscores its practical value. By facilitating the generation of superior solutions that cater to diverse decision-maker preferences, our methodology resonates deeply with real-world challenges and sets a new benchmark for effective inventory planning.

Watercraft tracking and monitoring system towards safe and resilient marine communication systems

In this research, Sea-condition Emergency Alert and Warning Apparatus for Vessel Safety (SEAWAVES) is a watercraft tracking and monitoring system developed and deployed to monitor watercraft and sea conditions to provide a safer and more resilient marine communication system. The proposed method puts together an application of contemporary technology using the fused location provider Application Programming Interface (API) to retrieve the device’s current location. In addition to managing the underlying location technologies, like Global Positioning System (GPS) and Wi-Fi, the fused location provider offers a straightforward API that you may use to specify the desired level of service. The work includes calibrating the compass, GPS, and Global System for Mobile Communication (GSM) for watercraft tracking and monitoring using Android sensors of mobile phones. The GPS provides the present site of the watercraft; General packet radio service (GPRS) sends the tracking information to the server and thus an alert message generated is transmitted to the registered monitoring systems. This scheme is deployed in the watercraft to monitor location and sea conditions, which are to be determined on the web page and supervised in real-time. Hence, if the watercraft is at a critical level, alert messages will be sent to the monitoring system, and the registered mobile phone number of the watercraft. Also a warning display and sound are produced by the device. The SEAWAVES tracking and monitoring system could be recommended for use onboard a watercraft throughout the country for effective management and monitoring of the performance of such watercraft, and low-cost instrumentation as a stand-alone system through modern technologies.

Service mega-disruptions: a conceptual model and research agenda

PurposeThis paper aims to formally conceptualize service mega-disruptions as any far-reaching and unforeseen general environmental stressor or threat that impacts a service organization’s ability to provide a desired level of service. The authors differentiate sudden large-scale general environmental threats from traditional service failures in scope and scale of impact via number of customers and sectors affected and duration and speed of the disruption.Design/methodology/approachThis paper draws from service recovery theory to build a conceptual model of service mega-disruptions. The resulting conceptual model maps service failure recovery strategies against a service mega-disruption recovery approach to examine consumer response to changes in service value. This work further articulates additional research needs including conceptualization, measurement and methods as traditional drivers of service recovery and the value of the service experience change in response to service mega-disruptions.FindingsThis work proposes a research agenda to investigate whether service mega-disruptions can bypass the need for service recovery due to a consumer self-moderating process. As past research shows, the less control a service provider has over a failure, the more customers attribute fault to the situation and transfer blame away from an organization. This paper suggests that this self-moderating process disrupts the need for service providers to court forgiveness for a failure with perceptions of similarity and controllability providing an alternate pathway to customer forgiveness. Similarly, it is suggested that service mega-disruptions play a role in transforming service ecosystems into tighter, more contractual systems with less agency for service providers and poorer ability to adjust to market conditions. The duration and longevity of effects on service providers’ control, agency and ability to adjust following a service mega-disruption must be researched further.Originality/valueThis paper builds theory to develop a conceptual model of service mega-disruptions and their role in customer engagement and reshaping the service ecosystem. This paper culminates in the proposition of a research agenda that aims to build research capacity among services marketing scholars as service providers’ coordination and market conditions are challenged by service mega-disruptions.

The impact of stockout‐based switching on fill rates

AbstractThe possibility of product substitution due to a stockout provides retailers with a buffer against lost sales but can also complicate the management of inventory because both substitute and primary demand affect inventory levels. Since most retailers make their inventory decisions around a desired service level (e.g., fill rate), it is important to understand how customer substitution behavior can influence different measures of customer service. In this study, an extensive theoretical framework is presented and used to develop a decision tree approach for predicting realized item and category fill rates. We find that item fill rate is primarily a function of the target service level of the focal item and the willingness to switch from an alternate item to the focal item. Category fill rate is influenced by the target service level of both items, with willingness to switch amplifying their effect. The decision tree approach is found to be an accurate predictor in most cases, however, it tends to overestimate item fill rate when the willingness to substitute from the alternate item increases. The approach also accurately predicts category fill rates, outside of scenarios with asymmetric substitutability.

Terra Incognita: California Transit Agency Perspectives on Demand, Service, and Finance in the Age of COVID-19

The COVID-19 pandemic upended transit use, finance, and management. To investigate these effects two years into the pandemic, we conducted 21 semi-structured interviews with senior managers at transit agencies in the most populous U.S. state, California. We found that the pandemic generated many operational and managerial challenges for transit agencies. Ridership plummeted, then slowly recovered, but is still well below pre-pandemic levels at most agencies. Commuter trips to and from major job centers were especially slow to return. In response to decreased demand, public health concerns, and uncertain finances, many agencies cut services and spending early on. As a result, fare revenues declined, in some cases precipitously. However, federal pandemic relief funds proved essential in filling budgetary gaps, stabilizing finances, preventing layoffs, and maintaining services. Other transit subsidies mostly bounced back robustly. Our interviews suggest that, though California transit agencies experimented with free fares, few fareless programs were made permanent. Their challenges include considerable uncertainty associated with future travel demand, looming financial shortfalls at systems that formerly had high farebox recovery and are still drawing on federal pandemic funds to backfill their fare revenue losses, and protracted labor shortages of drivers and mechanics that are preventing many systems from providing desired levels of service.

Minimizing the makespan for the two-machine flow shop scheduling problem with random breakdown

AbstractThis paper studies a two-machine flow shop scheduling problem with availability constraints due to a breakdown on the first machine. The starting time of the breakdown is considered stochastic and follows a known probability distribution. A service-level constraint is introduced to model the guarantee with which the obtained schedule takes into account the stochastic nature of the breakdown’s starting time. The objective is to find a solution that minimizes the makespan while satisfying the desired service level. The studied problem is strongly NP-hard. We develop two mixed integer linear models that linearize the non-linear model. Using interval modeling of the breakdown, we propose lower bounds and a valid inequality that are used to strengthen both models. When the lower bounds and the valid inequality are applied, the performance of both models is greatly improved by reducing the gap and reaching optimality for more instances. We also introduced two heuristics that exploit the proposed interval modeling. The computational results indicate that both models are able to reach optimality for the 10-job instances. Moreover, the comparisons results between both models with the two heuristics showed their effectiveness.

Subsidy strategy for reserving flexible capacity of emergency supply production

AbstractThis paper investigates a government's subsidy strategy for motivating a manufacturer to set up a flexible production line for emergency supplies. Four subsidy strategies are proposed to ensure a desired service level in case of an emergency: zero subsidy, a fixed subsidy, a marginal subsidy, and a hybrid subsidy. We develop a game theoretical model to examine how the government can induce a manufacturer to set up a flexible production line that can respond promptly to an emergency, based on the manufacturer's cost structure (fixed and marginal costs). We find that when the marginal profit of an emergency product is higher than that of the manufacturer's regular product, a fixed (marginal) subsidy is the dominant strategy if the manufacturer's fixed (marginal) cost is high, while a hybrid subsidy strategy is dominant if both costs are high. When the marginal profit of an emergency product is lower than that of the manufacturer's regular product, neither a fixed subsidy nor a zero subsidy will be the dominant strategy. We also find that a marginal subsidy can ensure the effectiveness of the strategy, while a fixed subsidy helps improve strategy efficiency. We use government subsidy strategies implemented for Chinese COVID‐19 emergency supplies as examples to demonstrate the effectiveness and efficiency of the subsidy strategies under the proposed framework. We also extend the discussion by considering the manufacturer's social consciousness.

Service staffing with delay probabilities

The square-root staffing model generates the number of servers s by summing the “offered load” k (minimum staffing level) and the “safety staff” (square root of k multiplied by the parameter β reflecting the desired service level). We treat the service level as one minus the delay probability (Erlang-C). Due to its iterative nature, we present a model for the Erlang-C which allows us to construct an approximate closed-form solution for the number of servers at the desired service level.

A nonparametric approach for setting safety stock levels

In practice, lead time demand (LTD) can be skewed, multi‐modal or highly variable, and these factors compromise the validity of typical approaches used for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely used statistical procedure. Existing bootstrap approaches for inventory management either operate directly on observed LTD or assume deterministic lead times, permitting direct application of the bootstrap approach for univariate quantile estimation. As LTD is a convolution of multiple random demands over a random lead time, a multivariate bootstrap approach is required. As we demonstrate, when lead times are stochastic, our multivariate approach provides improved safety stock estimates. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile—components of the safety stock calculation—highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation of our approach with our industry partner resulted in an inventory investment reduction of $1.17 million combined with an overall increase in service level. Our approach is general and can be implemented without modification in other settings.

A Holistic View on the Adoption and Cost-Effectiveness of Technology-Driven Supply Chain Management Practices in Healthcare

Healthcare supply chains are complex systems facing challenges in delivering high-quality care while maintaining cost-effectiveness. Research on inventory management and control, the heart of the healthcare supply system, has demonstrated that these targets can be reached by managing inventories efficiently while aiming at desired service levels. Although this can be enabled by adopting various supply chain management practices, the healthcare sector appears to lag compared to other industries. Seeking to investigate these aspects, this study draws on operations management literature, identifies and categorizes technology-driven supply chain management practices that are applied in hospital operations, develops a holistic conceptual framework delineating the key factors influencing their adoption in healthcare facilities, and examines their aggregated impact on financial performance. The research model is tested using structural equation modeling with survey data collected from Greek public hospitals. The study results indicate that technological readiness, organizational readiness, perceived benefits, and hospital size significantly influence the adoption of these practices in hospital supply chains. Moreover, they show a statistically significant association between the supply chain management practices applied and improved hospital cost performance, suggesting greater urgency for hospitals to exploit them fully. Theoretical and practical implications of the findings are discussed.

Gravel Loss Prediction Model Using Beta Regression

Unpaved roads consist of considerable portions of the roadway network in many countries. These roads play crucial roles in the development of infrastructure systems, advancements of socio-economic activities and improvements of the agricultural and production sectors. Thereby, unpaved roads benefit the underdeveloped, rural, and remote neighborhoods, and act as lifelines for these geographically disadvantaged communities. Frequent and regular maintenance activities keep the roadway system operational at a desired level of service. Resurfacing is one of the major maintenance treatments for unpaved roads. A gravel loss prediction model (GLPM) can evaluate the impacts of varying magnitude of resurfacing treatments on the roadway performance. Thus, a GLPM can provide valuable insights for roadway maintenance budget scheduling and decision-making tasks. In this paper, the backgrounds, input requirements, and output results of three popular GLPMs were reviewed. These models were (i) Highway Development and Management Model 4 (HDM-4), (ii) South African Technical Recommendation for Highways Model 20 (TRH-20), and (iii) Australian Road Research Board (ARRB) Model. In addition, the practicality of roadway resurfacing frequency charts which were developed based on these models was also evaluated. This study determined that the existing GLPMs and the corresponding roadway resurfacing frequency charts were often unreliable and impractical. In this study, a beta regression (BR) analysis methodology was utilized to develop and calibrate a GLPM for Iowa. Because of its simplified yet effective nature, the BR model outperformed the popular GLPMs and offered a practical approach to quantify annual roadway gravel loss.

Pickup and delivery problem with hard time windows considering stochastic and time-dependent travel times

Due to the uncertain nature of the traffic system, it is not trivial for delivery companies to reliably satisfy customers’ time windows. To guarantee the reliability of the pickup and delivery service under stochastic and time-dependent travel times, we consider a pickup and delivery problem with hard time windows considering stochastic and time-dependent travel times. We propose a chance-constrained model where the operational cost and the service’s reliability are considered. To quantify the service reliability, every node is associated with a desired node service level, and there exists a global service level, both measured by success probabilities. We present an estimation method for arrival times and success probabilities under stochastic travel and service times. We propose an exact solution approach based on a branch-price-and-cut framework, where a labeling algorithm generates columns. Computational experiments are conducted to assess the effectiveness of the solution framework, and Monte Carlo simulations are used to show that the proposed method can generate routes that satisfy both node and global service levels.

Hydraulic and Water Quality Reliability of Water Distribution System under Uncertain Conditions

Reliability assessment plays a significant role in the planning, design, and operation of water distribution systems (WDS). Herein, pressure, flow, chlorine concentration, and water age are simulated by continuity, energy, and transport equations; and hydraulic, water quality, and system reliability assessments are proposed based on a comparison between simulated hydraulic and water quality conditions and the desired service level. Because the independent parameters in hydraulic and water quality simulations cannot be estimated accurately, a hydraulic and water quality reliability assessment based on Monte Carlo simulation was applied to a network under three uncertain levels under steady- and unsteady-states. The effects of uncertain parameters on hydraulic and water quality reliability under steady- and unsteady-states were evaluated and compared to obtain the most significant parameters. The results indicated that the mean hydraulic reliability decreases with an increase in the uncertainty level for various nodes under steady- and unsteady-states, while the regulation is not suitable for water quality reliability. Pipe diameters have the most significant effects on hydraulic reliability under steady-state conditions, and mean hydraulic reliability under unsteady-state conditions. Nodal demand, bulk decay coefficient, and wall decay coefficient have effects on water quality reliability under steady-state conditions, while the roughness coefficient and pipe diameter also have effects on mean water quality reliability under unsteady-state condition. The results obtained can give managers more information about network reliability under uncertain conditions.

Spare parts control strategies for offshore wind farms: A critical review and comparative study

Offshore Wind Farm (OWF) downtime causes huge financial loss to the stakeholders. One of the major concerns for them is to reduce the downtime of the offshore wind turbine as much as possible. To do this, inventory managers must keep the required number of spare parts in the inventory. It is important to forecast the type and amount of spare parts ahead of time. The maintenance team tries to figure out failure symptoms to predict the approximate time for failure. This prediction helps to purchase and stock spare parts systematically. There is a trade-off between the ordering cost, holding cost, and shortage cost. Proper inventory planning saves a manager from placing expensive emergency orders and also an extended period of holding spare parts. The desired service level should be determined earlier, based on which spare parts planning is done. In this paper, some prominent spare parts models have been studied, findings have been systematically presented, compared against some key determinant factors, critical analysis has been performed and the applicability of the models has been discussed. More than a 100 research articles on spare parts have been reviewed and major contributions from the most relevant articles in OWF have been presented in this paper. One advanced spare parts modeling reported up to 51% cost reduction compared to traditional spare parts planning. Another integrated spare parts planning reported 27% savings. This critical review aims to suggest some guidelines for the managers and other associates of wind farms about the effective and efficient spare parts management technique from the beginning of the turbine installation to the end of its life cycle.

Capacity and resource allocation in flexible production networks by a game theory model

In many production applications, plants that produce multiple products with random demands share the required items among suppliers. The decision of how to allocate requests between suppliers to achieve the desired level of customer service is relevant to the efficiency of the production network. The literature highlighted how the long chain has the same level of performance as the full flexible network. This research proposes a decision model based on the game theory model to improve the performance of the production network. The model uses the Gale-Shapley algorithm with low computational complexity to share the demand among the suppliers. A simulation environment allows the evaluation of the proposed model in different conditions, and the model is compared to the dedicated, full flexibility, and long chain models. The numerical results show how the proposed model improves the efficiency of the production environment by keeping the number of connections with the supplier closer to the long chain model.

The Influence of Healthcare Service Quality on Public University Students’ Satisfaction and Behavioural Intention: Moderating Role of Trust

Healthcare service quality has been extensively studied over the past few decades. However, the topic remains imperative as it is the main contributor to patients’ satisfaction. It is considered as the yardstick to examine the desired service level that should be delivered to patients. Due to its importance, research was initiated to examine the influence of healthcare service quality on patients’ satisfaction by looking at the moderating effect of trust. The study was conducted in UiTM Selangor, Malaysia, involving 1,894 respondents who have experienced using the healthcare service provided by the institution. Based on the multiple regression analysis, all dimensions of healthcare service quality, namely reliability, assurance, tangibles, empathy and responsiveness, are significant to influence satisfaction and behavioural intention of patients. For satisfaction, trust moderates the relationship between empathy and satisfaction. For behavioural intention, trust moderates the relationship between tangibles and behavioural intention and the relationship between empathy and behavioural intention. The implications of the study are highlighted in this article.

Data based analysis of order processing strategies to support the positioning between conflicting economic and logistic objectives

The order processing strategy directly influences the economic and logistic objectives of manufacturing companies. In industrial practice, the order processing strategy is usually chosen based on a few primarily qualitative decision criteria or employees’ experience. The increasingly volatile markets require a differentiated decision of the order processing strategy for each product and a continuous review of the decisions made. The high number of factors influencing the decision of order processing strategy and a wide range of products offered by manufacturing companies make it impossible to manually make a fast and at the same time holistic decision. The digitalization and the increasing availability of data and its quality provide the basis for developing a decision support system.This paper presents an approach for a data based analysis of order processing strategies. As logistic models reflect the interdependencies of conflicting economic and logistic objectives, they are applied to derive the costs of the strategies based on desired adherence to the delivery time and the desired service level of the finished goods store. In addition to the description of the procedure for comparing different order processing strategies, the transfer of the theoretical model into a software demonstrator is outlined. A case study is presented to show the practicality of the proposed approach.

Capacity and Link Budget Management for Low-Altitude Telemedicine Drone Network Design and Implementation

Network planning involves link budget and capacity management to ensure scalability since adequate capacity is necessary to support coverage expansion and traffic demand increases. The primary objective in network standardization is to minimize implementation costs while maintaining a desired level of service. The harsh outdoor environment of millimeter-wave propagation is subject to various uncontrollable factors that can significantly affect network performance and reliability. This is particularly problematic for drones that support life-saving telemedicine applications, especially when many accidents take place under heavy rainfall, making standardization particularly challenging. Our study compares a number of deployment options such as variation of modulation schemes and differences of carrier frequencies between 10, 30, and 50 GHz. The selected frequencies cover a broad range of options for 5G implementation that serves as a backbone network for standardizing communication networks for mission-critical telemedicine drone services.

Investigating the impact of COVID-19 on user perception for deriving policies and intervention areas for urban bus service in India

The paper aims to investigate the impact of COVID-19 pandemic on the perception of travelers and identify new policies and intervention areas for improving urban bus service. This was carried out based on the perception data collected from travelers in the years 2018 (before pandemic) and 2020(in post lockdown scenario). Data was collected from the state of Kerala, India, and Importance-Satisfaction Analysis (ISA) with fuzzy c-means clustering was carried out to identify improvement areas for both scenarios. Results show a substantial increase in the number of priority areas as compared to the pre-pandemic scenario. Bus service attributes such as cleanliness, crowding level, and pedestrian environment emerged as new intervention areas of bus service during the pandemic. This is a clear reflection of the increased awareness of travelers about the prevailing pandemic scenario and the need for improvement in attributes related to social distancing and hygiene. The higher priority to information related attributes of bus service clearly shows the need for improvement in such attributes for the safe planning of trips during the pandemic. The work also estimated desired service levels of quantitative attributes to enhance the satisfaction levels of users. An evaluation of five bus routes in Kochi based on the desired levels shows the underperformance of attributes such as headway, punctuality, and crowding level. While the findings are city-specific, the methodology and experience may be adopted to enhance the service quality of buses and other public transport.