Customer Classes Research Articles

Flexible ultrasound-based system for clinical trial of burst wave lithotripsy and pushing of kidney stones

Fragmentation of kidney stones by burst-wave lithotripsy (BWL) has shown promising results in preclinical and clinical trials. Based on initial findings, the system was upgraded to enhance imaging, target stones at deeper depths, fragment small stones, and potentially conduct dusting of stones. A 64-element, single-crystal, phased-array imaging probe is coaxially aligned with the therapy probe for image guidance and therapy feedback. Imaging is controlled through a Verasonics Vantage research ultrasound engine capable of harmonic imaging to enhance stone resolution and contrast. These features improve targeting and endpoint detection, particularly for small stones and fragments. New therapy probes were added to effectively target stones with greater skin-to-stone distance, including a higher (800 kHz) frequency transducer to effectively break <4mm stones into sub-millimeter fragments to facilitate passage. The amplifier was upgraded to a custom class D/E design with increased power required by the therapy transducers and is capable of ultrasonic propulsion and real time monitoring of electrical power. This system will provide capabilities to treat a larger patient population as we begin trials breaking and expelling stones in the clinic setting. [Work supported by NIH-P01-DK043881.]

Improving the economics of battery storage for industrial customers: Are incentives enough to increase adoption?

As adoption of behind-the-meter battery energy storage increases across the United States, implementation continues to lag in the industrial sector. This analysis considers two manufacturing facilities with potential for load shifting to reduce peak demand. Although both facilities have load profiles that demonstrate great potential for regular and programmed demand reduction during peak hours, battery energy storage was deemed prohibitively expensive. A review of several existing utility and state-level policies and incentives determined that few may be rightsized for the industrial customer class. This analysis further considers multiple incentive structures and finds that although incentives increase viability of energy storage, developers must also consider optimization, unique load profiles, and use case to effectively increase adoption of battery energy storage by industrial customers.

Non-technical loss detection in limited-data low-voltage distribution feeders

Non-technical loss (NTL) is the major source of energy loss in distribution networks. Since the direct calculation of NTL is impossible, it is mostly being obtained by calculating the difference between total loss and technical loss (TL). This is rather not possible without adequate equipment for measuring the TL. However, time-worn distribution networks with high limitations and lack of equipment could identify the regions with a high share of NTL to reduce/eradicate the sources of NTL. This strategy can increase efficiency and reduce costs compared to the one-by-one inspection of customers. This paper presents a novel NTL detection procedure based on load estimation in highly limited distribution networks. First, the low-voltage (LV) distribution network will be divided into a few smaller hypothetical sub-networks through a fuzzy c-means (FCM) clustering technique. Then, a meter placement based on the maximum likelihood criterion is deployed to find the best places for installing the existing meters in each cluster. In the next stage, a linear system of equations is utilized to extract the pattern load profiles (PLPs) related to each class of customers for each hypothetical sub-network separately. These PLPs will be used to estimate the transformers’ load in all LV feeders based on their share in the total consumption of the entire network. Finally, NTL possibility per capita will be achieved for each LV feeder by defining an index that estimates the degree of NTL in those areas.

Consumer Behavior while Choosing Air Travel in India

Abstract: The domestic airline industry of India has been witnessing an enormous transformation over the period. Different class of people can avail this service due to modern specifications, affordability rates, ease of reachability, frequency of flights, quality of service, etc. The recent studies depict that the number of customers opting for domestic air travel has a steep increase when compared to the past numbers. This has been possible due to increase in connectivity of different areas and reducing the flight fares to increase the accessibility and reachability for the customers. Reducing fares and increasing connectivity does not only help the customers but it has also helped a lot to contribute to the business of airlines to a very large extent. This research mainly focuses on the consumer behavior while choosing different airlines. There are multiple factors which affect while choosing the appropriate flight company for travelling. There are different class of customers and accordingly the airline companies need to match their preferences. Some consumers want affordable rates whereas some customers want better quality o service. This research will basically give an idea which factors pertain the most while choosing the perfect flight for domestic travel within India. Keywords: Quality Service, IndiGo, SpiceJet, Go Air, Air Asia, Vistara, Air India, Statista, Holidify

Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

Capacity Rationing in Primary Care: Provider Availability Shocks and Channel Diversion

We study capacity rationing by servers facing differentiated customer classes using data from the Veterans Health Administration, which is the largest integrated healthcare system in the U.S. Using more than 11 million health encounters over two years in which the system was capacity constrained, our study provides a comprehensive analysis of the impacts of provider availability shocks on care channel diversion and delays. The outcomes studied include emergency room (ER) visits broken down by type, urgent care center visits, office and phone visits with one’s own versus another provider, post-ER follow-up visits, and ER readmissions. Availability shocks in our analysis are a residualized measure characterizing weeks in which the provider has fewer (or more) office appointments than expected based on typical patterns. The main finding is that moving from two standard deviations above to two standard deviations below in availability shocks increases ER visits by 2.4%, or about 20,000 yearly ER visits. Interestingly, the increase in ER visits is only present for the non-emergent category, indicating differentiated service to emergent and non-emergent care requests; capacity-constrained providers still tend to the patients in most need. Another finding is that provider availability shocks delay and divert post-ER follow-up care. Yet there is no effect on ER readmissions, a severe outcome of delayed or foregone follow-up, indicating that providers ration by priority these follow-up appointments. This paper was accepted by Vishal Gaur, operations management.

Technical and financial analysis of large-scale solar-PV in eThekwini Municipality: Residential, business and bulk customers

Regulatory changes, economic challenges, environmental concerns, and changing public perception have contributed to the profound changes observed globally in the electricity industry. Since 2008, South Africa has been experiencing electric power deficits and outages. This has been due in part to generation capacity constraints, belated investment in new electricity infrastructure, deferred maintenance of existing power assets, load growth in areas which were not adequately planned for, high population and economic growth over the last two decades. This has resulted in peak electricity demand outstripping available power generation capacity, leading to electricity shortages and load shedding, which is now impeding economic growth. In South Africa, forced under frequency load shedding, rising electricity tariffs, energy efficiency, declining cost of solar PV systems, the introduction of Carbon taxes, high cost of unserved energy has led consumers to explore embedded generation options to assist with reducing their energy bills, hence investments in solar PV has become an option to municipal customers. The simple payback period of solar PV systems is an important indicator for customers to ascertain whether to invest in these systems or not. Revenue loss remains a significant concern for municipalities who have historically designed single and two-rate bundled tariffs, which rely on municipalities selling electricity to ensure its business’s sustainability. Municipalities have now proposed new tariff structures designed to minimize the adverse impact of reducing electricity sales from solar PV by creating net billing tariffs with a built in network access charge component based on the customer’s inverter size. Case studies were carried out to better understand the impact on the feasibility of solar PV systems with and without the implementation of these new tariffs and its impact on the customer’s payback periods. A calculation of the levelised cost of electricity and customers rate of return for the different customer classes were also calculated to provide a better picture on the financial feasibility of rooftop solar PV. Results obtained from these case studies indicate lucrative payback periods for customers installing solar PV systems with improved revenue recovery for the municipality.

Capacity Allocation of an Integrated Production and Service System

Manufacturing servitization has become a major trend in industry that is implemented by so called integrated production and service systems (IPSSs) to offer not only products but also their associated services. In this study, we explore the capacity allocation policy for an IPSS that consists of a manufacturing facility and a service center. The IPSS serves two classes of customers, each demanding a specific product produced at the manufacturing facility and, subsequently, the associated service offered by the service center. We formulate this problem of resource allocation as a Markov decision process. Our analysis suggests that it is optimal to serve the customer with the larger cost saving rate and slower service rate, where the cost saving rate equals service rate times the sum of holding cost and waiting cost. Then we explore production control policies coupled with optimal service ones. We demonstrate that, with light service traffic, the system under the optimal production policy becomes similar to a make‐to‐stock system. Such a system can effectively hedge the system uncertainty. Therefore, the optimal production policy for the IPSS is also a hedging point policy. Under this policy, switching and idling curves split the state space into three regions: one without any production, the two others for production of two type products, respectively. We also discuss the optimal control policies of the two extensions of the IPSS (one with multiple types of product and customers, and the other with a single product and two classes of customers) and find that the characters of the optimal production and service policy still hold. Finally, we develop three heuristic integrated scheduling policies. Numerical experiments show that one is very effective.

Admission Control Game with Capacity Borrowing

We study a decentralized admission control problem with capacity borrowing. In this problem, the admission decision is made by each server who is assigned dedicated capacity. However, a server can borrow capacity from other servers if his dedicated capacity is exhausted. The borrower compensates the lender by a payment equal to the ‘‘borrowing cost’’. Hence, the servers share their capacities via borrowing, but they also compete against each other for their pooled capacities. We formulate this problem as a stochastic dynamic game and characterize the equilibrium strategic admission behavior of servers. We show that the critical factors that affect the equilibrium behavior and the system revenue are the magnitude of the borrowing cost, the arrival rate of customers and the revenue difference between different classes of customers. We have identified conditions under which servers maximizing their own payoffs end up making decisions that benefit the system. Our study highlights the important roles that the borrowing cost plays in shaping servers' behavior in service systems and provides concrete guidance for practitioners to design a decentralized admission control system.

Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory

Aggregator-activated demand response (DR) is widely recognised as a viable means for increasing the flexibility of renewable and sustainable energy systems (RSESs) necessary to accommodate a high penetration of variable renewables. To this end, by acting as DR aggregators and offering energy trading capabilities to smaller customers, energy retailers unlock additional sources of demand-side flexibility to ensure the cost-optimal operation of RSESs. Accordingly, a growing body of literature has highlighted the ways in which non-cooperative game theory could be used to reduce the gaps between modelled and real-world results for aggregator-mediated DR schemes. This paper aims to contribute to the trends of giving a realistic grounding to research on distributed DR-integrated energy scheduling by using insights from non-cooperative game theory to determine: (1) the optimal trade-off between importing electricity and utilising DR capacity in grid-tied RSESs, (2) the impact of the price elasticity of DR supply of different customer classes – especially, new sources of electricity demand, such as e-mobility – on the system-level dispatch of DR resources, and (3) the financial implications of harnessing the flexibility potential of a large number of end-consumers across different sectors. Accordingly, the principal goal of the paper is to develop an operational planning optimisation model that can be directly applied to real-world aggregator-mediated, market-based demand-side flexibility provisioning domains. To this end, this paper presents the first DR elasticity-aware, non-cooperative game-theoretic DR scheduling model that: (1) yields the best compromise solution between imported power and dispatched DR resources from the utility’s perspective, (2) characterises the utility-aggregator-customer interactions during the market-based DR trade process with several customer categories involved, and (3) disaggregates the total sectoral load on the system to individual end-consumers, which has potential implications for pre-feasibility and business case assessments. The application of the model to a conceptual micro-grid for the town of Ohakune, in New Zealand, demonstrates its effectiveness in reducing the daily system operational cost (over the critical peak hours) by ~66% and ~47% on a representative summer and winter day, respectively. Importantly, the paper provides statistically significant evidence supporting that activating the flexibility potential of small- to medium-scale end-consumers through specifically defined third-party aggregators in a market-based approach – that is aware of strategic interactions among instrumentally rational economic agents involved in the dispatch and delivery of DR resources – plays a significant role in the cost-optimal transition to 100%-renewable electricity generation systems within the smart grid paradigm.

Preliminarily Screening Geographical Hotspots for New Rooftop PV Installation: A Case Study in Thailand

As rooftop PV deployment accelerates around the world, forecasts of rooftop PV penetration by geographical region and customer group are essential to guide policy and decision-making by utilities. However, most state-of-the-art forecasting tools require detailed data that are often unavailable for developing countries. A simplified analytical tool with limited data is proposed to preliminarily identify the rooftop PV “hotspots”—that is, geographical areas where many new investments into rooftop PV investments are likely to occur. The tool combines the assessment of financial and technical indicator in form of the optimal PV-to-load ratio indicating the maximum penetration of solar PV, and the capital-to-expenditure ratio indicating the ease of such investment. Using Thailand as a case study, the results from this tool show that under the self-consumption and net-billing scheme, the Northern and Northeastern regions are marked as the potential hotspots where the utility’s impact will be realized early or strongly or both. The average LCOE and self-consumption level for all customer classes and regions are in the range of 0.084–0.112 USD/kWh and 41.33–73.13% of PV production, respectively.

Comparison of Efficiency Measures of Non-Priority and Non-Pre-Emptive Priority Queues

When there are different customer classes in a queue system, the j-th class customers have their services before the j+1,j=1,2,… class customers. Such queues are named as queues with priority scheduling. In this study a Markov priority queue system with two customer classes is analyzed both under non-pre-emptive and nonpriority scheduling and the efficiency measures (the expected number of customer in system, the average waiting time in system) are obtained using Little’s Law. The efficiency criteria are compared according to the priority situation. In addition, parameter estimates were compared with simulation results. The simulation was performed using the R program.

Non-preemptive priority [formula omitted] queue with servers’ vacations

We consider a non-preemptive priority M/M/m queue with two classes of customers and multiple vacations. Service times for all customers are exponentially distributed with the same mean, and vacation times follow an exponential distribution. We obtain the vector probability generating function for the stationary distribution of the number of customers in the queue for each class. This is established by deriving a matrix equation for the vector probability generating function of the stationary distribution of the censored Markov process and then studying the analytical properties of the matrix generating function. We also obtain exact expressions for the first two moments of the number of customers in the queue for each class. Finally, as an application, we investigate a customer’s equilibrium strategy and the optimal priority fee associated with social cost minimization for an unobservable M/M/m queue with two priority classes and multiple vacations.

A Multi-Server Heterogeneous Queuing-Inventory System with Class-Dependent Inventory Access

In this paper, we consider a queuing inventory system with heterogeneous customers of K types arriving according to a marked Markovian arrival process. Each class of customers differs by nature of the service they seek and different priorities are assigned for each class resulting in different levels of inventory admitted to exhaust for customers of each class. A single service node is provided for each class with exponential services having class-dependent service rates. All classes of customers are served from a single source of inventory replenished according to (s,S) policy with exponentially distributed lead time. Stability condition and steady state probabilities are obtained by matrix-analytic method. Some important performance measures are also derived. Inventory recycle time was analyzed in detail. Useful cost function and numerical illustrations are also given. The optimization problem is interesting and can be solved in similar real scenario.

Pass-and-swap queues

Order-independent (OI) queues, introduced by Berezner et al. (Queueing Syst 19(4):345–359, 1995), expanded the family of multi-class queues that are known to have a product-form stationary distribution by allowing for intricate class-dependent service rates. This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where, upon a service completion, the customer that completes service is not necessarily the one that leaves the system. More precisely, we supplement the OI queue model with an undirected graph on the customer classes, which we call a swapping graph, such that there is an edge between two classes if customers of these classes can be swapped with one another. When a customer completes service, it passes over customers in the remainder of the queue until it finds a customer it can swap positions with, that is, a customer whose class is a neighbor in the graph. In its turn, the customer that is ejected from its position takes the position of the next customer it can be swapped with, and so on. This is repeated until a customer can no longer find another customer to be swapped with; this customer is the one that leaves the queue. After proving that P&S queues have a product-form stationary distribution, we derive a necessary and sufficient stability condition for (open networks of) P&S queues that also applies to OI queues. We then study irreducibility properties of closed networks of P&S queues and derive the corresponding product-form stationary distribution. Lastly, we demonstrate that closed networks of P&S queues can be applied to describe the dynamics of new and existing load-distribution and scheduling protocols in clusters of machines in which jobs have assignment constraints.

Joint admission, production sequencing, and production rate control for a two-class make-to-order manufacturing system

Today's manufacturing industry faces a number of challenges related to the rapid delivery of products with a high degree of variety. Striking a balance between the effectiveness in capacity utilization and the rapidness in order-fulfillment is a substantial challenge for manufacturing companies. This work aims to provide a theoretical basis from which to address this practical question. To this end, we address the problem of coordinating the admission, production sequencing, and production rate controls for a two-class make-to-order manufacturing system. Formulating the problem as a Markov decision process model, we identify the structural properties of optimal control policies under both discounted and average profit criteria. We show that the cμ rule is optimal for production sequencing and the optimal admission and production rate control policies can be characterized by the state-dependent threshold levels, provided that the production times are not associated with customer class. We also show that the optimal production rates are monotone in the system state, as in the case of a single class queueing system, and that the lower priority class can be preferred to the higher priority class in order admission under a certain condition on the system parameters. Our numerical study demonstrates that a considerable economic benefit can be achieved if the production rate is dynamically controlled between the minimum and maximum rates rather than fixed to the mean rate of these values. Finally, we present a heuristic policy that is described by linear switching functions for the control of order admission and a selection rule for the control of production rate. We compare the performance of our heuristic to the optimal policy using a numerical experiment, revealing that the heuristic provides near optimal solutions to test example problems and is robust to the system parameters.

OPTIMAL ADMISSION AND ROUTING WITH CONGESTION-SENSITIVE CUSTOMER CLASSES

This paper considers optimal admission and routing control in multi-class service systems in which customers can either receive quality regular service which is subject to congestion or can receive congestion-free but less desirable service at an alternative service station, which we call the self-service station. We formulate the problem within the Markov decision process framework and focus on characterizing the structure of dynamic optimal policies which maximize the expected long-run rewards. For this, value function and sample path arguments are used. The congestion sensitivity of customers is modeled with class-independent holding costs at the regular service station. The results show how the admission rewards of customer classes affect their priorities at the regular and self-service stations. We explore that the priority for regular service may not only depend on regular service admission rewards of classes but also on the difference between regular and self-service admission rewards. We show that optimal policies have monotonicity properties, regarding the optimal decisions of individual customer classes such that they divide the state space into three connected regions per class.

Incentive‐based feasibility of a hybrid AC/DC microgrid in Saudi Arabia

The concept of microgrids (MGs) is revolutionizing the current power system. Most of the contemporary MGs are AC based; however, and due to the growth of DC-based machines and household appliances, hybrid AC/DC MGs are noticeably gaining more attention. This paper carries a detailed and comprehensive economic analysis on a proposed hybrid AC/DC MG in the Kingdom of Saudi Arabia, a region where MGs are rarely studied. The aim is to find if such configurations are economically feasible in the region, and what incentives are needed to ensure feasibility. Multiple load categories and renewable generation sources are modelled from real and local data to enhance the practicality of the study. Using the designed MG testbed, the incentive-based economic feasibility study is implemented to analyze the effectivity of the configuration and the proposed testbed in the region. The research concludes that the hybrid framework is not economically feasible due to the subsidized low energy retail prices in the kingdom, which makes customers unwilling to join a MG network that might charge higher rates. A comprehensive financial model is introduced to suggest proper incentives, possibly by the government, to the different customer and income classes to make the AC/DC MG option feasible.

Pricing and free periodic maintenance service decisions for an electric-and-fuel automotive supply chain using the total cost of ownership

Although subsidies have had significant impacts on the electric vehicle (EV) market share, many governments have planned to eliminate subsidies. There is a concern that unsubsidized EVs reduce the EV market share, significantly. However, purchasing an EV instead of a fuel vehicle (FV) might impose a lower total cost of ownership (TCO) on customers, depending on their vehicle usage. In this case, supply chains could optimize their decisions considering which vehicle is affordable for each customer class from the view of TCO. This study investigates optimal pricing and free periodic maintenance service (FPMS) decisions in a two-stage electric-and-fuel automotive supply chain, considering TCO to estimate vehicle market shares under customer classification with different vehicle usage patterns. Two bi-level models are developed and solved through Karush-Kuhn-Tucker equations and a reformulation-and-decomposition algorithm. Sensitivity analyses are performed considering various scenarios on energy prices and ownership periods. Results indicate that the high-usage customers are more likely to purchase an EV if the ownership period is the same for all classes. However, if low-usage customers keep the vehicle for a longer period than the others, they are more likely to purchase an EV. Both providing FPMSs by the manufacturer instead of the retailer and increasing the fuel price over time with a higher rate, compared with the electricity price and the inflation rate, improve the EV market share and reduce the total fuel consumption and emissions. Investment to produce EVs is not economical for a high price of electricity while having low fuel prices.

Demarketing Tourism for Sustainability: Degrowing Tourism or Moving the Deckchairs on the Titanic?

Demarketing is generally recognized as that aspect of marketing that aims at discouraging customers in general or a certain class of customers in particular on either a temporary or permanent basis and has been increasingly posited as a potential tool to degrow tourism and improve its overall sustainability, particularly as a result of so-called overtourism. The paper provides an overview of the various ways in which demarketing has been applied in a tourism context and assesses the relative value of demarketing as a means of contributing to sustainability and degrowing tourism. It is argued that demarketing can make a substantial contribution to degrowing tourism at a local or even regional scale, but that the capacity to shift visitation in space and time also highlights a core weakness with respect to its contribution at other scales. The paper concludes by noting that the concept of degrowth also needs to be best understood as a continuum of which demarketing is only one aspect.