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The role of Laboratory Medicine in patient care has evolved in the last decades. The same has occurred to the laboratory model, which has evolved from a traditional model where the laboratory is merely involved in clinical decision-making to a leading model where the laboratory is not only involved but also determines decision-making. The advent of new technologies and automation of processes have enabled laboratory professionals to focus on the first and last phase of the analytical process namely, test ordering and decision-making based on laboratory results. These phases are more error-prone than the analytical phase, and where action must be taken to improve the quality of patient care. We share our experience in the design and establishment of laboratory test demand management interventions that facilitated diagnosis of occult disease, improved adherence to clinical guidelines, and optimized patient safety. A description is provided of key points in the management of laboratory test over/underutilization. The objective of this review is to promote the involvement of laboratory professionals in the design and implementation of demand management interventions and in the development of the new Leader Laboratory model. [ABSTRACT FROM AUTHOR]

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Demand management of electric vehicles (EVs) in shared residential parking lots presents challenges for sustainable transportation systems. Especially, in shared parking lots where multiple EVs share the same parking space, such as residential apartments. This is challenging due to involvement of various factors such as limited number of chargers, limited capacity of transformer, and diverse driving behavior of EV owners. To address this issue, this study proposes an optimal demand management framework that addresses limited chargers, transformer capacity, and diverse driving behavior to promote sustainable EV integration. By estimating driving behavior, energy consumption, and utilizing a linear programming-based optimization model, power allocation to EVs is optimized based on multiple factors. A satisfaction index is introduced to measure the satisfaction level of the EV community, further emphasizing user-centric sustainability. Performance analysis includes factors such as power usage, charger utilization, and community satisfaction. The performance of the proposed method is compared with a conventional method and the proposed method increase the satisfaction index of the community by up to 10%. In addition, sensitivity analysis is used to explore the impact of factors like charger availability, EV penetration, and transformer capacity limits. Results show that with 3% EV penetration, satisfaction levels exceed 75%, reaching over 80% with five chargers and 6% EV penetration. [ABSTRACT FROM AUTHOR]

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products. [ABSTRACT FROM AUTHOR]

Nowadays IOT becoming popularize in all the application especially in the power system network for data monitoring from the Hybrid power distribution system. Because of easy adaptability of IOT technology, it find its place in data monitoring for the remote system and data can also be logged in the cloud server for analysis of the system under surveillance. By having data enabled IOT system, which will make the complete system smarter in terms of monitoring and analysis of the performance of the power distribution network. In these research paper, concept IOT technology for data monitoring of grid connected Hybrid system consist of PV source for the typical educational institute was developed and implemented in the campus. Apart from the data monitoring, controlling of the critical loads connected to this hybrid system was developed using Programmable Logic Controller (PLC). The MyQtt based cloud server was used to store the data pushed from the IOT device and user interactive mobile Application was developed using MIT inventor to monitor the data in the mobile itself, the command from the Mobile app was given to the PLC to control the loads. The energy data from the Multi-function energy meter (MFM) is pushed to PLC through gateway of Raspberry Pi. In this paper, Raspberry PI was used as IOT device and ILC 131 ETH PLC was used to control the loads. Performance of IOT device along with PLC was monitored for 3 months and results obtained were satisfactory.

With the aim of diversifying different energy sources and achieving net zero emissions, hybrid renewable energy sources (HRES) represent the future of the world. However, several HRES simulation software do not integrate the Photovoltaic/thermal (PV/T) system. This article designs an optimal design model for a tri-hybrid Photovoltaic/thermal/hydroelectric (PV/T/H) system for a rural locality in the North Cameroon region. The two Demand Side Management (DSM) strategies used reveal that the DSM strategy significantly reduced the energy cost by 59 % and the emission by CO2 22 % compared to the No-DSM mode. Although the use of battery storage (BSS) is used in both cases, the optimal solutions obtained thanks to the multi-objective optimization method implemented on Matlab led to 418 PV/T panels, 2 MH generators, 2 diesel generators (DG) and 217 PV/T panels and 1 DG for DSM and No-DSM mode respectively. This study is a demonstration of the effect of dynamic tariffs and active demand management technologies on PV/T/H modeling and optimization. It also reveals the need to hybridize PV/T with other energy systems to increase performance and achieve net zero emissions.

The increasing scarcity of water sources near urban areas, coupled with urbanization and population growth, necessitates the development of solutions that optimize water demand management, which entails a more accurate estimation of consumption patterns and implementing measures that promote the rational use of water. A stochastic residential water demand model was built based on the superposition of pulses of constant intensity and variable arrival time and duration applied to the microcomponent consumption at a 1-minute resolution, reflecting the aggregate system contributions to the total instantaneous demand. The model can support the planning of water supply systems (WSSs) and was calibrated using data from Brazilian institutes combined with a literature review of statistical data on users and end uses of water in Brazilian dwellings. Innovatively, it proposes using water demand forecasting to apply demand management measures evaluating the replacement of conventional devices for water-saving ones by analyzing hydrographs of 50–50,000 households, in addition to detailing the influence of the K2 peak factor. The results demonstrated a new water consumption profile with about a 40% reduction in water demand. A new equation for K2 is proposed as an alternative to estimate the demand for small populations fittingly. HIGHLIGHTS The model can support the planning of water supply systems (WSS).; From its use, it is possible to obtain essential characteristics for the dimensioning and operation of supply networks, such as maximum, average, and minimum flows, peak hours, per capita consumption, and consumption per appliance, among others.; A new formulation for the peak coefficient is available from the data used in the model.;

Omnichannel retailing is of growing importance. Yet, retailers lack knowledge about how to set prices overtime in their different channels, that is, in-store and online, which gives rise to a dual-channel pricing problem. The retailing issue is even more salient when consumers are prone to a psychological element, that is, when a reference price exerts influence. This article fills the gap by offering an analytical model of intertemporal price setting for dual channel pricing problem. We present an optimal control framework of dynamic pricing when (1) consumer behaviour is prone to a reference price and (2) the online channel is subject to the last-mile delivery cost. Analytical results, which hold for a general (nonlinear) reference-dependent demand formulation, inform about the relationships between the store and online prices over time and also about the market power of the retailer in each channel. Numerical results describe the features of three different phases of the planning horizon. The managerial recommendations show how a retailer sets differentiated dynamic pricing policies when offline and online channels are integrated. Such recommendations pave the way to more profitable omnichannel management. [ABSTRACT FROM AUTHOR]

Providing adequate water supply to the growing number of urban residents will be a challenge faced by many utility managers throughout the remainder of this century. Though traditionally, water managers have looked toward supply‐based solutions (e.g., expanding reservoirs), recent trends indicate a shift toward demand‐side management (e.g., encouraging conservation behaviors). Here, we present an agent‐based model (ABM) that simulates water supply as a function of the local climatic conditions and water consumption, which is, in part, determined based on water conservation attitudes. Our results indicate the ABM performs well (normalized root mean squared error <10%) for the study area. Further, we explore various hypothetical demand management scenarios by changing the water conservation attitudes of the households (i.e., the archetypes). This scenario testing reveals a statistically significant improvement to water availability after successfully changing water conservation attitudes to be more participatory. Ultimately, this study aims to understand the nuances of water conservation attitudes and aid utilities in their goal to better manage urban water demand. [ABSTRACT FROM AUTHOR]

It has been challenging for firms to effectively manage demand when they release products of one generation after another one. Motivated by the observations from the smartphone industry, this paper investigates the effectiveness of two demand management strategies in the presence of a product rollover: the upgrade program and price markdown policy. Under an upgrade program, a firm allows customers to upgrade their on‐hand product to a new generation product that will be released in a future time. Under a markdown pricing policy, the firm offers a discount for the currently available product so as to induce waiting customers to make immediate purchases. The two demand management strategies target different groups of customers and have distinct impacts on customers' choices. Starting from the time‐varying choice behavior of a heterogeneous group of customers, we study the optimal pricing decisions involved in the two strategies. Specifically, when customers are myopic in the sense that they only make a one‐time purchasing decision upon arrival, we show that the firm should offer the upgrade program only when the innovation level of the new product is relatively high, and the firm's optimal upgrade price can increase over time. Generally, the firm should offer the upgrade program during the early selling period and adopt markdown pricing as the release date of the new product approaches. Numerical experiments reveal that the dynamic upgrade program and markdown pricing policies can help improve profit significantly. When customers are strategic in the sense that they can monitor the selling prices and make dynamic purchasing decisions until they buy a unit of product, we examine two coping strategies that a firm can adopt, and investigate how the strategic monitoring behavior may influence a firm's optimal selling decisions and profit. [ABSTRACT FROM AUTHOR]

To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated incentive and routing decisions under uncertainty. We model this problem as a two-stage stochastic program with decision-dependent uncertainty. In the first stage, a retailer decides which customers to incentivize. However, customers' reaction to the incentive is stochastic: they may accept the offer and switch to pickup point delivery, or they may decline the offer and stick with home delivery. In the second stage, after customers' final delivery choices are revealed, a vehicle route is planned to serve customers via the delivery option of their choice. We develop an exact branch-and-bound algorithm and propose several heuristics to improve the algorithm's scalability. Our algorithm solves instances with up to 50 customers, realizing on average 4%–8% lower last-mile delivery costs compared with the commonly applied approaches in the industry that do not use incentives or offer incentives to all customers. We also develop a benchmark policy that gives very fast solutions with a 2% average optimality gap for small instances and up to 2% average cost increase compared with the heuristic solutions. Funding: This project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [Grant 765395]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0287. [ABSTRACT FROM AUTHOR]

This paper investigates the planning of virtual ward (VW) capacity including the remote monitoring of frail and elderly patients. The main objective is to optimize VW hub locations across a region in the United Kingdom. Furthermore, assigning the optimal number of clinicians to different regions needs to be considered. We develop a mathematical model that minimizes the setup and travel costs of VW hubs and staff. Our experimental analysis evaluates different levels of demand considering postcode areas within different Trusts, also known as Health Boards, in the National Health Service (NHS). Furthermore, our experiments provide insights into how many hub locations should be deployed and staffed. This can be used to individually find the number of remote monitors and clinicians for each facility as well as the system overall. [ABSTRACT FROM AUTHOR]

Demand management of electric vehicles (EVs) in shared residential parking lots presents challenges for sustainable transportation systems. Especially, in shared parking lots where multiple EVs share the same parking space, such as residential apartments. This is challenging due to involvement of various factors such as limited number of chargers, limited capacity of transformer, and diverse driving behavior of EV owners. To address this issue, this study proposes an optimal demand management framework that addresses limited chargers, transformer capacity, and diverse driving behavior to promote sustainable EV integration. By estimating driving behavior, energy consumption, and utilizing a linear programming-based optimization model, power allocation to EVs is optimized based on multiple factors. A satisfaction index is introduced to measure the satisfaction level of the EV community, further emphasizing user-centric sustainability. Performance analysis includes factors such as power usage, charger utilization, and community satisfaction. The performance of the proposed method is compared with a conventional method and the proposed method increase the satisfaction index of the community by up to 10%. In addition, sensitivity analysis is used to explore the impact of factors like charger availability, EV penetration, and transformer capacity limits. Results show that with 3% EV penetration, satisfaction levels exceed 75%, reaching over 80% with five chargers and 6% EV penetration.

Introduction: Anxiety disorders are impactful mental health conditions for which evidence-based treatments are available, notably cognitive-behavioral therapies (CBTs). Even when CBTs are available, demand-side factors limit their access, and actors in a position to perform demand management activities lack a framework to identify context-appropriate actions. Methods: We conducted a constructivist grounded theory study in Quebec, Canada, to model demand management targets to improve access to CBTs for anxiety disorders. We recruited key informants with diverse experiences using purposeful, then theoretical sampling. We analyzed data from 18 semidirected interviews and 20 documents through an iterative coding process centered around constant comparison. Results: The resulting model illustrates how actors can target clinicaladministrative processes fulfilling the demand management functions of detection, evaluation, preparation, and referral to help patients progress on the path of access to CBTs. Discussion: Modeling clinical-administrative processes is a promising approach to facilitate leveraging the competency of actors involved in demand management at the local level to benefit public mental health. [ABSTRACT FROM AUTHOR]

This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid. This study focuses on a school with twelve classes, classifying the electrical components of the total load into three priority profiles: green, orange, and red. The developed approach involves implementing demand management for the hybrid microgrid through Bayesian inference, emphasizing goal-directed decision making within embodied or active inference. The Bayesian inference employs three parameters as inputs: the total production of the hybrid system, the load demand, and the state of charge of batteries to determine the supply for charge consumption. By framing decision making and action selection as variational Bayesian inference, the approach transforms the problem from selecting an optimal action to making optimal inferences about control. The results have led to the creation of a Bayesian inference approach for the new demand management strategy, applicable to load profiles resembling those of commercial and service institutions. Furthermore, Bayesian inference management has successfully reduced the total unmet load on secondary and tertiary priority charges to 1.9%, thereby decreasing the net present cost, initial cost, and energy cost by 37.93%, 41.43%, and 36.71%, respectively. This significant cost reduction has enabled a substantial decrease in investments for the same total energy consumption. [ABSTRACT FROM AUTHOR]

The article explores a demand management approach to vitamin D testing over an 8-year period, implementing a mandatory additional information step during ordering. The study reveals a significant reduction in overall vitamin D orders, with potential cost savings estimated between 101292 to 516704 Euros. Topics include the impact on testing frequency, financial savings, and the importance of collaboration with stakeholders for a successful intervention.

One of the phenomena brought about by the genesis of supply chain management (SCM) in the South African public procurement sector arena is demand management (DM). If properly implemented, departments may be able to plan their procurement activities and has the potential to convert a procurement office from a mere reactive function into a fit for purpose office which will be responsive to the needs of its stakeholders in a more efficient and effective manner. Despite its glorious intensions, many government departments are still grappling with the implementation of DM function. The guiding question is: what are the obstacles preventing demand management functions from being implemented successfully? To find answers, the study applies a qualitative research approach, and information is obtained from literature review. Conceptual analysis is considered to analyse the data. The study aims to demystify the concept and provide a practitioner's perspective regarding the implementation of DM. The motivation of the study is that the findings may assist procurement officials on how to implement DM effectively serving the needs of SCM. It may also serve as springboard in the body of knowledge, thus being used as a point of reference and continue to illuminate public discourse in the public sector procurement environment. [ABSTRACT FROM AUTHOR]

This study proposes a trip- and area-based tradable credit scheme (TCS) for congestion management in the context of the morning commute problem using a trip-based Macroscopic Fundamental Diagram model with heterogenous travelers. In our proposed TCS, the regulator distributes credits to all travelers and designs a time-varying and trip-based credit tariff. Credits are traded between travelers and the regulator via a credit market at the price determined by credit demand and supply interactions. The TCS is incorporated into a day-to-day modeling framework to examine travelers' learning process, network state evolution, and credit market properties. The conditions for existence of an equilibrium solution and uniqueness of the equilibrium credit price are established analytically. Simulation results validate the analytical properties, demonstrate that the proposed TCS yields identical social welfare as the congestion pricing while maintaining revenue neutrality, and show the superiority of a trip-based TCS to (trip agnostic) area-based TCS. [ABSTRACT FROM AUTHOR]

The research field of attended home delivery (AHD) and attended home service (AHS) problems has experienced fast growing interest in the last two decades, with the rapid growth of online platforms and e-commerce transactions. The COVID-19 pandemic has reinforced that interest, raising further challenges and opportunities that have to be tackled by innovative methodologies and policies. The aim of this work is to provide an extensive literature review on the state of the art for AHD and AHS problems, with a particular focus on real-world applications. A discussion of promising future research directions is also provided. [ABSTRACT FROM AUTHOR]

In this paper we analyse the effectiveness of fiscal policies with respect to macroeconomic stabilisation during the COVID-19 pandemic in the small open economy of Slovenia. Using SLOPOL11, an econometric model of the Slovenian economy, we simulate the development of the its economy during the 2020s under alternative assumptions about future pandemic-related shocks. We also determine optimal fiscal policies to combat the effects of the pandemic and to stabilise the economy under two scenarios for the future course of developments under COVID-19. Our simulations show that those public spending measures that entail both demand- and supply-side effects are more effective at stimulating real GDP and increasing employment than pure demand-side measures. Successful stabilisation policies should thus contain a supply-side component in addition to a demand-side component. [ABSTRACT FROM AUTHOR]

This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2 values over 90%.

In a 500-household pilot, we tested an innovative approach to water demand management, implemented in collaboration with a water utility in a large city in the Middle East and North Africa (MENA) region. We provided a novel intervention, called a Water Savings Credit (WSC), which granted participants volumetric rebates on their water bills for their reductions in water consumption. WSCs were effective at encouraging conservation in our pilot in Marrakech. Our approach has the benefits of a price incentive, without the political risk of a tariff increase. For urban water utilities that provide highly subsidized services, this approach could ultimately pay for itself, or potentially result in net financial savings. Our approach may be especially effective in the countries of the MENA region, as the region has a high rate of subsidization for water services, and because it is facing increasing water scarcity from economic growth, urbanization, and climate change. HIGHLIGHTS A Water Savings Credit (WSC) is a novel intervention that provides a volumetric reward for reduced consumption.; WSCs were effective at encouraging conservation in our pilot in Marrakech.; WSCs have the potential to be more politically acceptable than rate increases and can protect equitable access while encouraging conservation.;

IntroductionAnxiety disorders are impactful mental health conditions for which evidence-based treatments are available, notably cognitive-behavioral therapies (CBTs). Even when CBTs are available, demand-side factors limit their access, and actors in a position to perform demand management activities lack a framework to identify context-appropriate actions.MethodsWe conducted a constructivist grounded theory study in Quebec, Canada, to model demand management targets to improve access to CBTs for anxiety disorders. We recruited key informants with diverse experiences using purposeful, then theoretical sampling. We analyzed data from 18 semi-directed interviews and 20 documents through an iterative coding process centered around constant comparison.ResultsThe resulting model illustrates how actors can target clinical-administrative processes fulfilling the demand management functions of detection, evaluation, preparation, and referral to help patients progress on the path of access to CBTs.DiscussionModeling clinical-administrative processes is a promising approach to facilitate leveraging the competency of actors involved in demand management at the local level to benefit public mental health.

Ride-sharing systems strive to provide affordable on-demand mobility in urban areas by effectively consolidating incoming transportation requests. To ensure that transportation offers meet travelers’ individual time requirements and constraints, service operators offer multiple pickup times from which travelers can choose. Designing such pickup time offers is challenging due to the uncertainty of both the requirements of the requesting traveler and the efficient fulfillment of future demand. We propose a parametric cost function approximation to balance between maximizing the probability that a traveler will choose an offered pickup time and minimizing the expected vehicle routing effort. We demonstrate the effectiveness of the proposed approach in a comprehensive computational study and provide managerial insights, particularly with respect to the value of information on traveler pickup time requirements.

Purpose: Delays in outpatient specialist neurologist care for people with epilepsy are common despite recommendations for prompt access. There is evidence to suggest that there are interventions that can minimise waitlists and waiting time. However, little is known about whether such interventions can result in sustained improvements in waiting. The aim of this study was to determine the extent to which an intervention to reduce waiting in an epilepsy specialist outpatient clinic demonstrated sustained outcomes two years after the intervention was implemented. Methods: This observational study analysed routinely collected epilepsy clinic data over three study periods: pre-intervention, post-intervention and at two-year follow-up. The intervention, Specific Timely Assessment and Triage (STAT), combined a short-term backlog reduction strategy and creation of protected appointments for new referrals based on analysis of demand. After the initial intervention, there was no further active intervention in the following two years. The primary outcome was waiting measured by 1.) waiting time for access to a clinic appointment, defined as the number of days between referral and first appointment for all patients referred to the epilepsy clinic during the three study periods; and 2.) a snapshot of the number of patients on the waitlist at two time points for each of the three study periods. Results: Two years after implementing the STAT model in an epilepsy clinic, median waiting time from post-intervention to two-year follow-up was stable (52–51 days) and the interquartile range of days waited reduced from 37 to 77 days post-intervention to 45–57 days at two-year follow-up, with a reduction in the most lengthy wait times observed. After a dramatic reduction of the total number of patients on the waitlist immediately following the intervention, a small rise was seen at two years (n = 69) which remained well below the pre-intervention level (n = 582). Conclusion: The STAT model is a promising intervention for reducing waiting in an epilepsy clinic. While there was a small increase in the waitlist after two years, the median waiting time was sustained.

The article focuses on the hidden costs of load shedding in South Africa. Topics include the economic impact of load shedding on short-term and long-term growth, the strain on social cohesion, and the quantification of costs related to discretionary electricity use, such as water heating, electric vehicles, and poorly insulated dwellings.

With the rapid uptake of renewable energy generation and increasing price volatility, there are multiple opportunities emerging for businesses to earn additional revenue and reduce electricity bills by implementing demand response. However, commercial and industrial consumer implementation of demand response is not well understood and largely absent in energy management guidelines, which focus on reducing energy consumption and driving energy efficiency. Based on interviews with managers from 24 commercial and industrial businesses, we describe a practical implementation framework for demand response. The framework identifies unique implementation features for demand response - the activation steps. Energy management guidelines may have underemphasised approaches to demand response and inclusion of its unique features for businesses because benefits to be gained from demand response have focussed on benefits for the utilities in the electricity system, not benefits for individual businesses. The article concludes there is an opportunity for market operators to encourage organisations who produce energy management guidelines to include demand response, to promote awareness of the opportunities for businesses and provide practical guidance for implementation, therefore providing support for greater renewable energy penetration and reduced energy costs for businesses. [ABSTRACT FROM AUTHOR]

Abstract Smart water enables utilities, regulators, and customers to make more timely and informed decisions about how they use and regard their water resources. It has been developed to assist demand management by influencing customer behavior and reducing network leakage, lowering energy consumption, and avoiding deploying assets that are not actually needed. Smart water has seen an evolution toward monitoring wastewater applications. Challenges include the need for common operating standards and more cohesive national policy frameworks. As a result, smart water adoption occurs on a utility‐by‐utility basis.

This paper investigates the planning of virtual ward (VW) capacity including the remote monitoring of frail and elderly patients. The main objective is to optimize VW hub locations across a region in the United Kingdom. Furthermore, assigning the optimal number of clinicians to different regions needs to be considered. We develop a mathematical model that minimizes the setup and travel costs of VW hubs and staff. Our experimental analysis evaluates different levels of demand considering postcode areas within different Trusts, also known as Health Boards, in the National Health Service (NHS). Furthermore, our experiments provide insights into how many hub locations should be deployed and staffed. This can be used to individually find the number of remote monitors and clinicians for each facility as well as the system overall.

This paper proposes a rolling horizon (RH) approach to deal with management problems under dynamic demand in planning horizons with variable lead times using system dynamics (SD) simulation. Thus, the nature of dynamic RH solutions entails no inconveniences to contemplate planning horizons with unpredictable demands. This is mainly because information is periodically updated and replanning is done in time. Therefore, inventory and logistic costs may be lower. For the first time, an RH is applied for demand management with variable lead times along with SD simulation models, which allowed the use of lot-sizing techniques to be evaluated (Wagner-Whitin and Silver-Meal). The basic scenario is based on a real-world example from an automotive single-level SC composed of a first-tier supplier and a car assembler that contemplates uncertain demands while planning the RH and 216 subscenarios by modifying constant and variable lead times, holding costs and order costs, combined with lot-sizing techniques. Twenty-eight more replications comprising 504 new subscenarios with variable lead times are generated to represent a relative variation coefficient of the initial demand. We conclude that our RH simulation approach, along with lot-sizing techniques, can generate more sustainable planning results in total costs, fill rates and bullwhip effect terms. [ABSTRACT FROM AUTHOR]

The Measures for Administration of Government Procurement Demands formulated by the Ministry of Finance in 2021 is the latest regulation of government procurement behavior. This paper first sorts out the implementation points of the key links in the new specification, such as demand investigation, demand preparation, demand review and procurement plan implementation, and analyzes the problems existing in the actual work such as demand investigation, demand review, procurement method and pricing mode. Then, based on COSO internal control theory, the causes of problems are explored from the perspectives of demand management control environment, risk assessment, control activities, monitoring activities and information communication mechanism. Finally, it is proposed to increase policy publicity, study the government procurement demand management model, establish case guidelines and other improvement measures. [ABSTRACT FROM AUTHOR]

The ongoing research on tradable credit schemes (TCS) in mobility has captured the attention of policymakers, authorities and academics. Even though several studies have demonstrated the potential of such schemes, key policy design aspects that are relevant for real world implementation have been overlooked. There exists insofar no blueprint for an actual application of a TCS. We therefore propose a single all-encompassing framework for designing a TCS based on three dimensions: ownership , transfer and consumption. This framework has been developed through the extraction of policy design questions in the existing literature on TCS as well as alternative demand management schemes. Literature on the design of central bank digital currencies (CBDC) and digital tokens has been used to derive additional decisions regarding technical implementation and regulation. The decisions that are incorporated in the framework facilitate the comparison of different scheme designs in a three-dimensional space, addressing aspects of security, anonymity, centralization and technical feasibility. Hence, it provides policymakers and responsible authorities with a comprehensive framework on the essential decisions that need to be made upon practical application of a tradable credit scheme. It also serves as a means to classify existing work and develop new (or more realistic) variants of TCS that can be studied in future research endeavors. Based on the proposed framework, we devise an extensive research agenda describing directions for future research on tradable credit schemes across a variety of themes. • Framework on essential policy design decisions in tradable mobility credit schemes. • Framework facilitates comparison of scheme designs in a three-dimensional space. • Can serve as means to classify existing work and investigate new variants of TCS. • Extensive research agenda describing directions for future research on TCS. [ABSTRACT FROM AUTHOR]

Observed and projected consequences of climate change on streamflow generated in the Pyrenees threatens the long-term sustainability of water resources systems downstream, especially those with high irrigation demands. To tackle this challenge, the participation of stakeholders in defining potential adaptation strategies is crucial to building awareness and capacity for the community, providing agreed solutions, and reducing conflict. However, there is also a need for a top-down approach to incorporate other, large-scale, or innovative adaptation strategies. This article describes a bottom-up-meets-top-down approach to estimate the optimal implementation intensity of adaptation strategies under different climate scenarios on a complex water resources system. Future streamflow projections were used in a water allocation model combined with a Markov Chain Monte Carlo sampling process to obtain optimal combinations of measures to meet different sustainability objectives. The methodology was applied to the Gállego-Cinca River system in NE Spain, which relies on water from the Pyrenees. A stakeholder workshop identified storage development and irrigation modernisation as the preferred adaptation options. However, the modelling results show that more storage in the basin, especially on-farm reservoirs, is not enough to maintain current sustainability levels. This will enable the adoption of demand management measures that optimise water use despite not being among stakeholder preferences. [ABSTRACT FROM AUTHOR]

To promote sustainable urban mobility and reduce environmental pollution, transportation policies worldwide aim to decrease reliance on fossil fuels. This requires reducing private car use through policy instruments such as urban vehicle access regulations (UVARs) and promoting alternative sustainable transport technologies such as electromobility. Considering that the deployment of such regulations and the market penetration of battery electric vehicles (BEVs) is still low in Hungary, this study aimed to examine the willingness of urban dwellers in Budapest, Hungary, to adopt battery electric vehicles (BEVs) upon implementation of an UVAR measure. The study analysed the BEV adoption intention of 409 urban residents who participated in an UVAR study in 2022. The results show that age is a significant factor, with individuals aged 35–44 most likely to adopt BEVs. However, other socio-demographic characteristics did not significantly affect willingness to adopt BEVs. Additionally, pro-environmental behaviour or attitude did not significantly predict BEV adoption. Based on these findings, this study highlights the importance of considering multiple interrelated factors and provides policy insights for promoting sustainable transportation technology adoption. [ABSTRACT FROM AUTHOR]

Islanded rural microgrid require continuous resource monitoring. Demand response schemes have been phenomenal in managing loads. However, urban demand response schemes are well equipped with market prices and peak time penalties to control deferrable loads. In rural microrids, regular loads such as fans, lights and water pumps are normally used that do not fall under category of deferrable loads. In addition, full liberty of utilizing regular load at any time, lack of awareness and no information of storage reserves make task of load management more complex. In this research fully automated two layered demand response scheme is designed for regular operating loads. The first layer control is load mode control. The mode of operation is decided on the state of charge (SoC) of battery. In second layer, fuzzy controller is designed on the consumer's routines, SoC and ambient temperature as membership function. Results are assessed in terms of consumers comfort and availablity of SoC. The load operation in automated demand response remained indentical to actual rutine operation as per consumer's desire with 5 to 7% deviation. In all modes of operation SoC levels remained 15% higher and heavy load operated 13.5% more compare to relevant study. [ABSTRACT FROM AUTHOR]

Transportation electrification is considered a green alternative to internal combustion vehicles. However, higher penetration of electric vehicles (EVs) can cause several technical challenges to the power systems, including local equipment overloading. This is especially challenging for distribution systems due to the direct connection of EVs with them. To mitigate equipment overloading, by managing EV loads locally, a dynamic pricing model is proposed in this study. First, a satisfaction function is devised for EVs considering the sensitivity of different EV owners to the battery state of charge and urgency of recharge. Then, a welfare function is developed for the EV fleet operator (EFO) considering the net load of EVs and the electricity price of the upstream grid. To this end, a welfare maximization problem is formulated considering the welfare of EVs and EFO. The problem is then decomposed into the EFO sub-problem and EV sub-problem to preserve privacy. Finally, a distributed mechanism is developed to solve the sub-problems iteratively without revealing private information. The performance of the proposed method is analyzed for a residential apartment complex in terms of load management and convergence for different day types (working days and holidays). Simulation results have shown the efficacy of the proposed method in mitigating the overloading of transformers during peak load hours.