Search

Population growth and climate change have strained existing water supplies requiring municipalities to shift towards demand management strategies to ensure reliable water provisions. Particularly in the residential sector, water conservation measures and incentives have been utilized to reduce demand during short-term shortages. As water conservation programs are now being commonly utilized as a way to ensure enough water will be available for continued growth, the impacts on a basin-wide scale have yet to be established. By changing the relative water demand for indoor and outdoor uses within a municipality, the amount of water being consumed can thereby reduce the effluent available for downstream communities. This research investigates how the timing of water conservation, water conservation strategy, and population growth impact water availability in a shared basin. A generalized system dynamics model reflecting typical residential water use and availability patterns similar to the southwest United States was utilized. We found that when upstream municipalities focus their initial reductions on non-consumptive demands, downstream municipalities reliant on upstream return flow have to increase their conservation rate to meet demands and maintain population growth. When most of the basin's population is in upstream municipalities, the more influence their change in water use has on downstream water availability. Therefore, consumptive conservation should be the priority of basin-wide conservation programs to ensure return flow is sufficient to satisfy the demands of downstream municipalities. Master of Science Population growth and climate change have reduced water supplies for many cities across the United States. This has led to increased water conservation at the household level to reduce the amount of water needed for everyday uses. Efforts like reduced lawn watering and shorter showers have been helpful during short-term dry spells but more cities are using long-term strategies like high efficiency appliances and the removal of green lawns to "free up" enough water for population growth. However, these changes in water use can impact how much water returns to the river and goes downstream for the next city to use. This research looks at how changes in the speed of water conservation, water conservation strategy, and population impacts water availability in a shared basin. Our stylized model reflects typical residential water use and availability patterns similar to the southwest United States. We found that the faster cities within a basin reduce their indoor water use, the greater downstream cities have to reduce their water use in order to have enough available water to meet demands. Also, when most of the basin's population is in upstream cities, the harder it is for the downstream city to have enough water to maintain target population growth. However, reducing outdoor water use first is the best way for every city that shares the same river to stay satisfied.

Understanding and predicting each individual's real-time travel destination given the origin information in urban public transportation systems is crucial for personalized traveler recommendation, targeted demand management, dynamic traffic operations and so on. Existing methods are often based on modeling the regular travel patterns through analyzing the long-term personal travel information. They are suitable for destination prediction of individual regular trips with regular travel patterns, but may not work well for occasional trips with strong randomness and uncertainty, especially for the individuals with a few historical travel data. In this paper, we focus on more challenging issue about destination prediction of occasional trips. We design a general Multi-View Deep Learning Framework (MDLF) based on the data-driven insight that a location where a user will destine to is not only related to the user's own travel preference to the location, but also influenced by crowd's travel preference and the region's characteristics of the location under certain spatiotemporal contexts. The destination of an individual's occasional trip can be predicted by combining all these complementary influencing factors. The novelty of MDLF is mainly reflected in two aspects. The first is the effective feature extraction from multiple and complementary views. The second is that a CNN (Recurrent Neural Network) based deep learning component for predicting each occasional trip's destination by calculating a moving trend score for each possible destination. We evaluate the MDLF based on two real-world smart card datasets collected by AFC (Automatic Fare Collection) Systems. The experimental results demonstrate the superiority of MDLF against other competitors.

The decreasing cost of solar photovoltaics (PVs) and battery storage systems is driving their adoption in the residential distribution system, where more consumers are becoming prosumers. Accompanying this trend is the potential roll-out of home energy management systems (HEMSs), which provide a means for prosumers to respond to externalities such as energy price, weather, and energy demands. However, the economic operation of prosumers can affect grid security, especially when energy prices are extremely low or high. Therefore, it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely, the network operator, prosumer, and aggregator. In this paper, a novel transactive energy (TE)-based operational framework is proposed. Under this framework, aggregators interact with the distribution grid operator through a negotiation process to ensure network security, while at the lower level, prosumers submit their schedule to the aggregator through the HEMS. If network security is at risk, aggregators will send an additional price component representing the cost of security (CoS) to the prosumer to stimulate further response. The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

Beijing is a city with severe water shortage, and the rapidly growing economy and population has led to an increasing water demand for households. Thus, water conservation has become the focus of urban water supply and demand management. This study analyzed the impact of water saving devices on urban residential water consumption and water supply security in Beijing. The results show that the popularization of water saving devices brings significant water conservation potential to households in Beijing, with 22% water saving efficiency in 2019. It is predicted that the per capita urban residential water demand of Beijing in 2030 will be 201 lpd (liters per capita per day), but the water consumption may decrease to 163 lpd, 148 lpd or 133 lpd, respectively, in three proposed water saving scenarios (S1 to S3). Compared with water demand, the water saving efficiency was calculated at 19%, 26% and 34%, respectively. Meanwhile, the total urban residential water consumption under the four scenarios in 2030 would be 1.372, 1.242 or 1.113 billion m3, respectively. However, the Comprehensive National Water Resources Plan proposes that urban residential water consumption in 2030 should be limited to 1.12 billion m3. Therefore, only in the enhanced water saving scenario (S3) can the total urban residential water consumption meet the household demand and achieve the goal of security controlling. In addition, this paper analyzes the cost-effectiveness of water saving devices promotion policy and proposes some further measures to ensure the water supply security and develop the water conservation potential in Beijing households.

This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains. A demand management model for industrial park considering the integrated demand response of combined heat and power (CHP) units and thermal storage is firstly proposed. Specifically, by increasing the electricity outputs of CHP units during peak-load periods, not only the peak demand charge but also the energy charge can be reduced. The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units. The heat dissipation of thermal storage, thermal delay effect, and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park. The proposed model is formulated as a multi-period alternating current (AC) optimal power flow problem via the second-order conic programming formulation. The alternating direction method of multipliers (ADMM) algorithm is used to compute the proposed demand management model in a distributed manner, which can protect private data of all participants while achieving solutions with high quality. Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge, and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.

In this study, a Pareto efficient incentive-based real-time pricing model was designed for balanced energy consumption scheduling (ECS) in a smart grid. In this model, the energy consumption of each subscriber is monitored and updated in real-time by an individual smart meter, and a cost-effective ECS is determined. The most recent research has not considered a balanced distribution of costs and profits to the participants. In general, there is a trade-off between service providers and subscribers. A service provider tries to maximize its profit, and a subscriber tends to minimize its cost. Therefore, the well-adjusted cost and profit distribution of a service provider and subscribers is considered by controlling the incentive degree in a Stackelberg game. The multiobjective genetic algorithm is applied to show the Pareto efficient solutions of a service provider and subscribers. Furthermore, welfare is introduced as the third objective in proposing a practical solution. It is used to select one of the multiple Pareto efficient solutions. Our model decreases subscriber costs by 9.1% and the peak-to-average ratio (PAR) by 33.2%, on average, compared with non-scheduling. The model also reduces the PAR by 11.3% and increases the provider’s profit by 34.9% and total welfare by 60.0%, on average, compared with day-ahead scheduling.

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.

Teaching water economics in a desert environment is less complex than in other environments. Economic methods dealing with the interaction between surface and groundwater, surface water pollution, water for nature, and hydropower are not in need in desert environments. The course focuses on demand-side policies such as quantity restriction, rate setting, technology adoption, and comparisons of policies oriented toward reducing water demand and addressing the wasteful use of water. Often adopting water-saving technologies have the ability to reduce demand without decreasing users’ utility. We discuss the right of access to water and fairness. The course addresses the rationale for supply-side policies implemented in Gulf Cooperation Council (GCC) countries. We emphasize the environmental impacts and energy requirements of desalination as limits. Alternative sources of water for cities, such as the expansion of existing agricultural water markets to encompass trade between farmers and cities is explored. The course looks into the social impediments of treated wastewater recycling in farming. The course is taught since 2011 to undergraduate students in the form of lectures and lab work with the support of videos and flipped classroom. During the last weeks of the course, each student presents to the class a paper on the main issues addressed., Request Teaching Notes/Supplemental Materials: https://form.jotform.com/91565767216163

Reducing the cost of electricity consumption by industrial enterprises is the most important area of increasing the operational efficiency of their activities. The article is devoted to the issue of reducing the cost of paying for the service component of the transport component of purchased electrical energy from industrial enterprises that have technological connection to the electrical networks of electricity producers. The article makes an empirical study of the features of the pricing of payment for the services of the transport component of purchased electrical energy for industrial enterprises connected to the electric grids of electricity producers with the identification of factors influencing the overestimation of the cost of paid electricity, and calculating such overestimations using the example of a typical schedule of electricity consumption of a machinebuilding enterprise for various regions Russia. On the basis of the developed author's indicators (tariff coefficient for electricity transportation by the level of GNP, index of tariff coefficient for electricity transportation, weighted average price for electricity transportation, index of weighted average price for electricity transportation, integral index of efficiency of GNP tariffs) study of the effectiveness of the application of tariffs for the transport of electricity for industrial enterprises connected to the electric networks of electricity producers. Based on the calculated indicators, the article groups the regions into three main groups, with the development of recommendations for managing the cost of purchasing electricity by the component of the cost of the transport component of purchased electricity in each group. As the most optimal option for reducing the cost of electricity transportation, the author proposes the introduction of demand management for electricity consumption, which will reduce the costs of industrial enterprises that pay for the transport component of purchased electricity at unfavorable tariff configurations.

The study aimed to determine the shortcomings and identify factors that can improve the effective implementation of demand management and supply chain performance management in the department of community safety and transport management. The study, followed positivism paradigm in which quantitative research method was used to collect and analyse data. A survey questionnaire was used to collect data from the participants. This questionnaire was paper based and online (Google Forms). The sample was composed of the managers drawn from the department of community safety and transport management. The findings of the study revealed that when implemented effectively, demand management and SCPM will improve the entire SCM system. Also, the study revealed that there are drawbacks to these two. These relates to non-collaborative planning, non-adherence to legislations, misunderstanding of these concepts. The study finally revealed that there are factors that can improve the implementation of demand management and SCPM, which include: the monitoring of suppliers through contract management activities and others.

This paper examines the potential role of Mobility-as-a-Service (MaaS) as a transport demand management (TDM) tool to influence commuting mode choice. A stated choice experiment was conducted among employees in the Netherlands to capture the effect of integrating TDM measures with MaaS on mode choice behavior. We found that the train and car-sharing attributes’ variations are influential factors on employees’ mode choice behavior. Moreover, the mobility package price played a significant role in mode choice even though most employees in the Netherlands receive a full or partial travel allowance from their employers. Similarly, the increase in parking tariffs was an influential measure for car users who used street/garage parking spaces. However, the effects were not equal for all respondents. Young, low-income and multi-modal commuters are more susceptible to travel behavior changes than old, high-income, and car-dependent employees. In conclusion, MaaS could be seen as a promising element in TDM strategies combining carrots (enablers) and sticks (deterrents), albeit for specific groups users.

Demand management is expected to reduce emissions from energy systems and support the utilization of renewable energy sources. In this paper, the focus is on the viability of educational buildings’ mechanical ventilation systems’ participation in electric demand management. The results suggest that when load shedding lasts for a short duration, the ventilation machine load seems more promising than expected for electric demand management, as even 60% of its electric power could be granted to such markets. Prolonging the load-shedding duration increases the risk of the indoor carbon dioxide (CO2) concentration exceeding the limit for good indoor air quality. This paper contributes to the academic community by providing information for the assessment of the demand management potential of buildings and eventually their significance in decarbonizing the electric energy system and filling research gaps concerning the impact of implementing demand management that involves a reduction in ventilation rate.

For energy sustainability, the integrated energy distribution system (IEDS) is an efficient and clean energy system, which is based on the coordinated operation of a power distribution network, a gas distribution network and a district heating system. In this paper, considering the damage of natural disasters to IEDS, a demand management strategy is proposed to improve resilience of IEDS and ensure stable operation, which is divided into three stages. In the first stage, the electricity, natural gas and thermal energy are co-optimized in the simulating fault state to develop the importance ranking of transmission lines and gas pipelines. In the second stage, the natural disasters are classified as surface natural disasters and geological natural disasters. According to the types of natural disasters, the demand management strategy includes semi-emergency demand management scheme and full-emergency demand management scheme in the electrical resilience mode and the integrated resilience mode, respectively. In the third stage, the non-sequential Monte-Carlo simulation and scenario reduction algorithm are applied to describe potential natural disaster scenarios. According to the importance ranking of transmission lines and gas pipelines, a demand management strategy is formulated. Finally, the proposed strategy is applied on an IEEE 33-bus power system and a 19-node natural gas system. Its effectiveness is verified by numerical case studies.

PurposeThe purpose of this paper is to evaluate and understand the factors that contribute to the enhancement in the process of supply chain activities, specifically among manufacturing industries.Design/methodology/approachThis paper follows the quantitative approach by disseminating a structured questionnaire to supply chain practitioners working in manufacturing industries. A sample of 109 responses is gathered from senior employees involved in sales and operations planning. Exploratory and confirmatory factor analyses are performed to evaluate the reliability and validity of the model. Lastly, SmartPLS is used to test the hypotheses proposed in the study.FindingsThe findings revealed that demand sensing and managing practices positively impact supply chain performance by creating dynamic distribution capabilities. It is observed that distribution capabilities integrate both efficient and effective handling of resources. However, demand management practice has insignificant relationship with supply chain performance.Research limitations/implicationsThe authors suggest that supply chain management must expand in firms, and importance must be given as supply chain performance can provide competitive advantage in order to sustain in today's competitive market.Practical implicationsThe findings can help decision-makers working in the planning process by identifying the right tools to sense the market and react accordingly.Originality/valueThis paper is an initial effort to understand the dynamic distribution capabilities by establishing the link with the studied variables, especially in the manufacturing industries in Pakistan.

The advent of autonomous vehicles (AVs) is likely to introduce new mobility experiences for travelers. In particular, AVs would allow travelers to get off at the destinations and then drive themselves elsewhere to park rather than cruise for parking or park at a location with a high parking fee. The self-parking capability is likely to increase the utility of private-owned AVs (PAVs) and make this mobility option more attractive than human-driven vehicles. The present study investigates the dynamics of travelers shifting to PAVs from other transport modes and its negative impact on road traffic congestion. To this end, we propose an agent-based demand model which considers different travel cost components depending on crucial travel attributes such as trip purpose and activity duration. The estimated demand is then fed into a mesoscopic traffic simulation model to examine the resulting road traffic conditions. As charging private vehicles for the congestion they cause is an effective tool for demand management and congestion alleviation, we also integrate a distance-based pricing scheme into the overall modeling framework to investigate its impact on mode choice and transport network performance. A case study is conducted in Melbourne, Australia to demonstrate the proposed methodology. The results indicate that the distance-based pricing scheme can effectively limit the usage of PAVs and reduce traffic congestion, especially in the city center and peripheral suburbs.

Road transportation is the main source of greenhouse gas (GHG) emissions in Iran. Different road transport emissions reduction strategies (RTERS) can be defined to address this environmental problem. The present study aims to define a comprehensive set of RTERS according to the socio-economic conditions of Iran. Then, the defined strategies are evaluated using a new hybrid decision-making technique developed by integrating the conventional Q-methodology (QM), best–worst method (BWM) and evidential reasoning (ER) approach. The main advantages of the developed methodology include: simple Q-sorting, the capability of checking inconsistency ratio of judgments and dealing with uncertainty. A set of thirty-three RTERS was defined and then categorized into the following seven main strategies, including travel demand management (TDM), public transport management (PTM), traffic flow management (TFM), education, urban planning and development (UPD), and governmental rules and regulations (GRR). These strategies were assessed based on the views of twenty-three experts and government officials. Five perspectives on the importance of RTERS were derived using BWM and QM: 1—PTM-FCM, 2—UPD, 3—TDM, 4—FCM-PTM and 5—FCM. Finally, the ER approach was employed to aggregate these viewpoints. The aggregated perspective highlights that improving fuel quality and HOV systems are the most effective RTERS, while the mitigation policies based on deterrent rules and regulations are the least effective strategies. Accordingly, some managerial implications and suggestions are provided in this study.

In e-commerce, customers are usually offered a menu of home delivery time windows of which they need to select exactly one, even though at least some customers may be more flexible. To exploit the flexibility of such customers, we propose to introduce flexible delivery time slots, defined as any combination of such regular time windows (not necessarily adjacent). In selecting a flexible time slot (out of a set of windows that form the flexible product), the customer agrees to be informed only shortly prior to the dispatching of the delivery vehicle in which regular time window the goods will arrive. In return for providing this flexibility, the company may offer the customer a reduced delivery charge and/or highlight the environmental benefits. Our framework also can accommodate customized flexible slots where customers can self-select a set of regular slots in which a delivery may take place.\ud The vehicle routing problem (VRP) in the presence of flexible time slots bookings corresponds to a VRP with multiple time windows. We build on literature on demand management and vehicle routing for attended home delivery, as well as on flexible products. These two concepts have not yet been combined, and indeed the results from the flexible products literature do not carry over directly because future expected vehicle routing implications need to be taken into account. The main methodological contribution is the development of a tractable linear programming formulation that links demand management decisions and routing cost implications, whilst accounting for customer choice behavior. The output of this linear program provides information on the (approximate) opportunity cost associated with specific orders and informs a tractable dynamic pricing policy for regular and flexible slots. Numerical experiments, based on realistically-sized scenarios, indicate that expected profit may increase significantly depending on demand intensity when adding flexible slots rather than using only regular slots.\ud \ud \ud

As each day passes by, the number of flights expands and the need for travel quality services continues to grow. In consequence, for an airline to remain in market, better revenue management is required such that the same quality of service is offered at reasonable prices. Since cost cut plans may be disrupted by the need to uphold quality of service, demand and sales management plans can be one of the best techniques for the management of revenues in airlines. To excel in providing safe and most comfortable flights at sensible prices, airlines should objectively employ advanced techniques to reduce the costs and maximize the earnings, which can be achieved via seat allocation and pricing of different classes. Toward achieving such an objective, in this work, we seek to find the best prices of two different seating classes. Here, two economy classes are considered for pricing and quantification, where the optimal number of seats of each class is expressed in a closed form and the prices that will maximize the size of sales are computed accordingly. Using a multiplicative demand model and the newsvendor approach, the best discount rates and the optimal number of seats could be found. The proposed model is tested using real flight data; it was found that for a normally distributed demand, the sales can be increased by allocating the right number of seats for both the regular and the discount seats. Not only the prices can maximize the sales, specific discount ratios were found to improve the sales too. The model can assist revenue managers to increase the sales via the right selection of the number of economy seats and prices.

Problem definition: Many healthcare providers and payers offer teletriage, a telemedicine service where concerned patients can get advice about their health condition. In theory, teletriage should help patients with an acute illness choose an appropriate provider, reducing unnecessary and duplicate provider visits, yet no study has assessed the overall costs and benefits of teletriage at a system level. Academic/practical relevance: We model the use of teletriage in managing healthcare demand and determine in which cases teletriage is efficient and effective. Our model extends the academic literature on service operations and provides practical suggestions for healthcare payers. Methodology: We use a Markov decision process to model a patient’s choices during an acute illness episode, where the illness severity is partially observable to the patient and triage is subject to errors. We parameterize the model using data from the literature and provide both structural and numerical results. Results: Patients with high uncertainty about their state would use the teletriage service, which may improve their cost outcomes. However, when teletriage is added, the rate of arrivals to the emergency department (ED) may increase when the overtriage rate is above 5%, the lowest value observed in the literature. Patient’s choices and the copayment of other services affect the cost-effectiveness of teletriage. Managerial implications: There are several reasons why adding teletriage to the healthcare system could produce a negative cost outcome. Teletriage should not necessarily be free, which would encourage use by patients with low levels of uncertainty and actually increase the payer’s total cost. The overtriage rate is a key driver of performance and must be managed carefully.

In South Africa, more than 3.5 million households live without access to modern energy. The Government acknowledged the impossibility of universal grid electrification in the desire time frame (2025–2030). This study reviews the struggling rural off-grid initiative in South Africa to unpack effective, sustainable rural electrification approaches. It is based on a thorough evaluation of fellow African successful off-grid initiatives and the country’s energy transition that gave rise to its exiting energy landscape. It was revealed that the conventional electrification rate of 5 to 10% annual is not sufficient for the country to accomplish universal grid access by 2030. It requires a minimum of 20% electrification rate per annum at an enormous budget increase of over 200%, amounting to an excess of USD 500 million to achieve universal grid access by 2025. The following recommendations were proposed to address the existing unsustainable rural electrification practice in the country. A new energy policy that mandates the supply of 50 kWh/month electricity through renewable energy source is required. The policy should clearly stipulate incentives for local and provincial municipalities partaking in renewable energy initiatives after independently verified demand reduction. Furthermore, the new policy should also allow for innovative entrepreneurs and programmes to collaborate with said municipalities to achieve its mandate. Provision of learnerships in the Higher Education sector to support the envisaged increased need for technical expertise on renewable energy systems, optimisation, and smart metring should be enforced in the new policy. Lastly, a concerted approach to educating existing and potential consumers on the benefits of being unaffected by load shedding and control of their own demand management should be embarked on in a national drive.

Mantab Rasa Madiun is a home-based business in the city of Madiun that manages and supplies soybean tempe products with a capacity of 150-200 kg of soybeans per day. Providing product availability in the market is quite a complicated issue for Mantab Rasa, especially for food products that have a perishable product life. The delay in conveying information to the production department causes the accuracy of the production quantity to be unsuccessful so that many requests cannot be fulfilled or products that are not absorbed. Strategies to solve the problem include the formulation of a method of identifying market demand per day so that the response of the production section to meet demand is almost accurate. The method offered is to collect demand data from sales through the WA group so that the demand data can be centralized and easy to enter into the forecasting application formulation of demand data then forecast with an MS Excel-based application. From the forecasting results, it is then included in the MRP-Like time phasing formula with the aim that operational managers can more easily decide how many kg of raw and soaked soybeans to boil today. The systematics that have been made have been socialized to partners and tested for 7 days. The results obtained are the managers have a recapitulation of centralized daily demand data and quickly get a decision on how much soybeans are processed to meet demand., Mantab Rasa Madiun merupakan salah satu usaha rumahan di kota Madiun yang mengelola dan memasok produk tempe kedelai dengan kapasitas 150-200 kg kedelai per hari. Menyediakan ketersediaan produk di pasar merupakan persoalan yang cukup rumit bagi Mantab Rasa, terutama untuk produk-produk makanan yang memiliki umur produk singkat (perishable product). Keterlambatan dalam menyampaikan informasi ke bagian produksi menyebabkan ketepatan jumlah produksi tidak berhasil diperoleh sehingga banyak permintaan yang tidak dapat dipenuhi atau produk yang tidak terserap. Strategi untuk menyelesaikan permasalahan diantaranya adalah rumusan metode identifikasi permintaan pasar per hari sehingga respon bagian produksi memenuhi permintaan mendekati akurat. Cara yang ditawarkan adalah mengumpulkan data permintaan dari para sales melalui grup WA sehingga data permintaan dapat terpusat dan mudah untuk dimasukkan ke aplikasi perumusan forecasting data permintaan kemudian diramalkan dengan sebuah aplikasi berbasis MS Excel. Dari hasil peramalan kemudian dimasukkan dalam rumusan yang MRP-Like time phasing dengan tujuan pengelola operasional dapat lebih mudah memutuskan berapa kg kedelai mentah dan rendaman yang harus direbus hari ini. Sistematika yang dibuat telah disosialisasikan kepada mitra dan dilakukan uji coba selama 7 hari. Hasil yang diperoleh adalah pengelola memiliki rekapitulasi data permintaan haria terpusat dan cepat mendapatkan mengambil keputusan berapa jumlah kedelai yang diolah untuk memenuhi permintaan.

Transport network capacity enhancement is a significant aspect of urban transport planning and demand management, and a suitable measurement of the network capacity is of considerable importance. In this paper, the network capacity with second-best constraints (NCSC) is investigated to meet some specific development requirements of urban transport networks. Herein, the network capacity is restricted to an inferior “second-best solution”, due to various concerns/constraints regarding the public transport mode share, serviceability, and emissions, etc. For the sake of presentation, these constraints are termed as second-best constraints, and the NCSC problem can also be referred as second-best network capacity (SBNC) problem. A bi-level model is formulated to analyse the NCSC problem. The upper-level model maximizes the total origin-destination (OD) demand, which incorporates the second-best constraints into consideration. The lower-level model is a transport network equilibrium model, which measures the network performance under a given OD demand pattern. To better investigate some important second-best constraints (e.g., public transport mode share) and also the demand elasticity, the modelling framework is extended to a multimodal transport network. An exact solution method is developed for the NCSC problem; wherein, a modified improved gradient projection (MIGP) algorithm is designed for the lower-level multimodal flow equilibrium problem, and a tailored sensitivity analysis-based (SAB) method is employed for solving the NCSC problem. The proposed models and solution methods are verified by numerical examples, demonstrating that NCSC can be an efficient tool for transport planning and management.

Police forces, like much of the UK public sector, have struggled to meet the demand placed upon them because of real-term funding cuts and increases in some types of demand. Where increases in resource are not possible, attention has to be paid to the reduction of demand or the increase in effective capacity through efficiency gains. Within the literature, ‘failure demand’ is seen as unnecessary demand, caused by errors and repeated work that could be eliminated. This article reports on the analysis of failure demand at one police force as a method of sustainably reducing demand. The findings suggest 30% of non-urgent demand entering the system could be avoided. However, not all of this avoidable demand has been eliminated so far. The article explores the necessary system changes to reduce failure demand and the management challenges to achieve effective change.

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-...

Information distortion tends to inflate from downstream to upstream, and results in demand planning errors and this is followed by inaccurate forecasting. This situation is referred to as the bullwhip effect phenomenon. Demand management is also determined based on either a centralized or decentralized distribution strategy. Both strategies will influence the accuracy of demand planning and its effect on the bullwhip phenomenon. Unfortunately, research investigating the relationship between centralization and decentralization strategies for demand and the bullwhip effect is still limited. To answer this shortcoming, this paper has two scenarios for its analysis. First, forecasting is done to determine the accuracy of demand planning, indicated by the smallest forecast error value. Based on the results of the analysis, it is known that single exponential smoothing with alpha 0.5 is the best forecasting method. The second step is to measure the bullwhip effect; the results show that the coefficient is less than one. This coefficient indicated that the company underperformed in the fulfillment of its customers’ needs. Theoretically, this paper extends the literature on demand management in the supply chain by considering centralized and decentralized strategies.

Funding Information: This work has been supported by the Strategic Research Council at the Academy of Finland grant number 327771. Publisher Copyright: © 2022 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we approach a fair and inclusive transition to a circular economy from a perspective of daily rhythms and illustrate these concepts using examples from the electricity and transport sectors. The circular economy discourse endorses sharing and efficiently using capital assets. To be effective, such strategies further need to manage the timing of demand. Dynamic pricing is frequently used to manage, for example, the demand for electricity services and to match demand with the capacity of production. Congestion charges for road usage similarly aim to shape peak demand. The economic and environmental benefits of these schemes have been demonstrated, but the social consequences remain underexplored. Dynamic prices forge new everyday rhythms and contribute to “shift consumption” by limited access during peak demand. We inquire whether, and to what extent, the existing schemes of dynamic pricing consider issues of equality, equity and fairness when offering time-dependent and dynamic tariffs, and highlight the policy implications of promoting a circular economy with effective and more socially just management of the timing of demand.

[EN] The electrification of final energy uses is a key strategy to reach the desired scenario with zero greenhouse gas emissions. Many of them can be electrified with more or less difficulty, but there is a part that is difficult to electrify at a competitive cost: heavy road transport, maritime and air transport, and some industrial processes are some examples. For this reason, the possibility of using other energy vectors rather than electricity should be explored. Hydrogen can be considered a real alternative, especially considering that this transition should not be carried out immediately because, initially, the electrification would be carried out in those energy uses that are considered most feasible for this conversion. The Canary Islands' government is making considerable efforts to promote a carbon-free energy mix, starting with renewable energy for electricity generation. Still, in the early-mid 2030s, it will be necessary to substitute heavy transport fossil fuel. For this purpose, HOMER software was used to analyze the feasibility of hydrogen production using surplus electricity produced by the future electricity system. The results of previous research on the optimal generation MIX for Grand Canary Island, based exclusively on renewable sources, were used. This previous research considers three possible scenarios where electricity surplus is in the range of 2.3-4.9 TWh/year. Several optimized scenarios using demand-side management techniques were also studied. Therefore, based on the electricity surpluses of these scenarios, the optimization of hydrogen production and storage systems was carried out, always covering at least the final hydrogen demand of the island. As a result, it is concluded that it would be possible to produce 3.5 x 10(4) to 7.68 x 10(4) t of H-2/year. In these scenarios, 3.15 x 10(5) to 6.91 x 10(5) t of water per year would be required, and there could be a potential production of 2.8 x 10(5) to 6.14 x 10(5) t of O-2 per year., This study has been in part supported by the "Catedra de Transicion Energetica Urbana, Universitat Politecnica de Valencia".

PurposeThe recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.Design/methodology/approachThis study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.FindingsThe results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.Practical implicationsThe findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.Originality/valueThis study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

Metro system has been increasingly recognized as a backbone of urban transportation system in many cities around the world. To improve the demand management and operation efficiency, it is crucial to have accurate prediction of real-time metro passenger flow. However, the forecast performance is often subject to the complex spatial and temporal distributions of the metro passenger flow data. To this end, we developed a novel dual attentive graph neural network that can effectively predict the distribution of metro traffic flow considering the spatial and temporal influences. Specifically, two directed complete metro graphs (i.e., inbound and outbound graphs) and the weighted matrix of them are proposed to characterize the inbound (entering the system) and outbound (leaving the system) passenger flow, respectively. The weighted matrix of inbound graph is estimated based on the historical origin-destination demand and that of the outbound graph is estimated based on the similarity metrics between every two stations. Moreover, to capture the dependencies between inbound and outbound flows, multi-layer graph spatial attention networks that incorporate the spatial context are applied to exploit the dynamic inter-station correlations. Then, the acquired dependency features integrated with external factors, such as weather conditions, are filtered by temporal attention and fed into a sequence decoder to produce short-term and long-term passenger flow predictions. Finally, a series experiments are conducted based on a comprehensive empirical dataset. Findings indicated that the proposed model does not only well predict the metro passenger flow, but also effectively detect the emergencies and incidents of metro system.

The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.

The factors influencing trip departure time are taking more importance in urban planning practice since congestion is increasingly being addressed by travel demand management (TDM) strategies. In this paper we formulate and estimate a joint travel mode-departure time model for commuting trips using combined revealed preference (RP) and stated choice (SC) data. The RP data considered nine alternative modes and up to 11 time periods, and the level-of-service data were obtained at an unusual level of precision using GPS measurements. The travel time, cost and cost divided by the wage rate coefficients were fairly similar in both the RP and SC environments, suggesting equal error variances for both datasets. The only parameters that differed between each type of data were those associated with the schedule delay early (SDE) and late (SDL) variables required by Small’s Scheduling Model. This may be due to the potentially different temporal perspectives between RP choices (longer term decisions) and SC decisions, arguably shorter term given the nature of the experiment and the context presented in it (implementation of a congestion charging policy and a flexible working-hours scheme). The models were used to forecast the impacts of a hypothetic congestion charging scheme in Santiago, showing that the schedule delay coefficients derived from the SC context produced a smoother and less-peaked temporal distribution of travel demand than the RP parameters.

With growing concerns about travel demand management practices in overcrowded metro systems, it is considered that time-dependent pricing strategies are effective for dealing with the crowding occurring during peak commuting hours. In this study, a bi-level optimisation framework is used to consider the peak avoidance behaviour of commuters in the development of time-dependent pricing strategies. The behavioural sensitivity of commuters to pricing factors is investigated in terms of departure time and mode shift decisions based on a stated preference survey conducted in Beijing, China. The proposed bi-level programming model comprises a multi-objective optimisation model at the upper level and a nested logit-based stochastic user equilibrium model at the lower level. Based on an empirical case study of the Batong line in Beijing metro, nine optimal time-dependent pricing strategies are tailored by representative decision preferences, yielding up to 13.97% decrease in the peak ridership during rush hours.

Incentive-based travel demand management (IBTDM) strategies utilize rewards to redistribute travel demand across space and time. Such congestion-alleviation solutions are usually managed by small private companies with constraint budgets. Aside from spending money on incentives, running promotional campaigns to achieve the gains in market share is essential for maintaining the financial health of IBTDM programs. Therefore, the budget allocation between the two counterparts—incentive and marketing expenditure—needs to be wisely determined. Based on the bottleneck model, this paper proposes an optimal budget allocation scheme considering the impact of a budget constraint and market penetration. It was found that the constraint budget should be prioritized to attract those with lower marketing costs in general. In situations with an insufficient budget and when marketing costs were lower for attracting lower-income individuals, IBTDM decision-makers should focus on those lower-income individuals at first. This mitigates inequity issues to some extent. Therefore, policy makers or planners should pay more attention to marketing cost when developing a marketing plan and try to reduce marketing cost to make full use of incentive budget.

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

The circular economy concept can enhance sustainability through restructuring consumption and production patterns using innovative designs and business models. This core premise is highly relevant for the interlinked water and food supply sectors in arid regions, which are threatened by natural scarcity and resource overuse. This paper transfers the idea of the circular economy into the practice of the water and food sectors using the example of the region of the Gulf Cooperation Council (GCC). It develops a framework for identifying circular economy strategies and issues applicable to basic supply sectors. In analyzing the value chain and circular strategies of the water and food sectors, the circular economy idea has resulted in numerous industrial applications. The range of applications is illustrated in the key industries of wastewater and local food production. Expanding the reuse options for municipal wastewater and valorizing organic waste represent important circular economy directions for the basic supply sector of the GCC. Incorporating these ideas is positive, but a more comprehensive set of measures is needed to generate low-carbon and low-metabolism economic development in the region. In addition to the current sporadic supply-side initiatives, there is a need for non-technical circular economy strategies related to demand management and waste reduction.

Demand management aiming to optimize system cost while ensuring user compliance in an urban traffic network is a challenging task. This paper introduces a cooperative demand redistribution strategy to optimize network performance through the retiming of departure times within a limited time window. The proposed model minimizes the total time spent in a two-region urban network by incurring minimal disruption to travelers’ departure schedules. Two traffic models based on the macroscopic fundamental diagram (MFD) are jointly implemented to redistribute demand and analyze travelers’ reaction. First, we establish equilibrium conditions via a day-to-day assignment process, which allows travelers to find their preferred departure times. The trip-based MFD model that incorporates individual traveler attributes is implemented in the day-to-day assignment, and it is conjugated with a network-level detour ratio model to incorporate the effect of congestion in individual traveler route choice. This allows us to consider travelers with individual preferences on departure times influenced by desired arrival times, trip lengths, and earliness and lateness costs. Second, we develop a nonlinear optimization problem to minimize the total time spent considering both observed and unobserved demand—that is, travelers opting in and out of the demand management platform. The accumulation-based MFD model that builds on aggregated system representation is implemented as part of the constraints in the nonlinear optimization problem. The results confirm the resourcefulness of the model to address complex two-region traffic dynamics and to increase overall performance by reaching a constrained system optimum scenario while ensuring the applicability at both full and partial user compliance conditions.

To enhance industrial park's economic gains and effectively allocate its electricity bill among industrial users with combined heat and power (CHP) units and photovoltaic (PV) panels, this paper proposes a distribution locational marginal price (DLMP)-based bi-level demand management approach. The upper level optimizes dispatching decisions of industrial users with the objective of minimizing their energy bills, and the lower level is a DLMP-based market clearing problem to minimize the two-part tariff cost of the industrial park operator. In order to solve the proposed bi-level model efficiently, it is first equivalently converted into a single-level mathematical programming with equilibrium constraints (MPEC), and then reformulated as a mixed-integer second-order conic programming (MISOCP) model by linearizing bilinear terms. Numerical results demonstrate the effectiveness of our proposed bi-level method in lowering industrial park's electricity bill and achieving effective allocation among users.

While occupancy-based control and other demand management control strategies have demonstrated potential, little is known about how these changes in heating, ventilation, and air-conditioning (HVAC...

The growth rate of electric vehicle sales is increasing every year, analytical companies provide optimistic forecasts for the number of electric vehicles on the roads in the future, and regulators are changing rates for future growth. In this regard, the full implementation of the concept of smart networks, which could facilitate the transition of old networks to a new reality, is becoming increasingly relevant. This article is aimed at studying the influence of electric vehicles on the technical indicators of the electric network of small cities, as well as an analysis of the implementation of the demand management system.

The article studies meteorological barriers to the use of public bicycle systems. The meteorological conditions have always been considered important in influencing bike-usage. Weather changes may lead to shifts in the preferred mode of travel. Studies mostly focus on the average effects and overlook their role in creating barriers. However, each meteorological factor (temperature, humidity, wind speed, precipitation, etc.) can individually disrupt the usage, irrespective of other factors' level. We conduct the necessary condition analysis for climate and weather-related factors, using hourly bicycle usage data for two years at Capital Bikeshare, Washington, D.C., to find barriers to public bicycle system usage. The analysis is performed separately on regular and casual users revealing interesting results. We find that season is not an effective barrier to bike usage. Only an extreme weather condition cause bottleneck. The elements of weather affect the usage of bikes by affecting the comfort and risk associated with riding. Lower temperature creates constraints for all users, whereas only regular bikers are constrained by low humidity. The differences in the necessary condition of regular and casual riders reveal the contrast in the perception of the two groups' comfortable riding conditions, making it essential to design the system accordingly. The results will help plan public bike systems in other cities and help in demand management at established locations. The demand projections are to be recalibrated using the bottlenecks that set in at different levels of temperature, humidity, and windspeeds. The results help in considerations about starting year-round rental services at other locations.

Background Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients. Method Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management. Results The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p p p p p Conclusions Managing the needs of Covid-19 patients while also caring for other patients requires a response from the entire healthcare system. Our findings could have two important implications for effectively managing health-care demand during and after the Covid-19 pandemic. First, as a result of a naturally progressive learning process, the resource balance between Covid-19 and non-Covid-19 patients improves over time. Second, it appears that demand management to control the flow of patients necessitates targeted interventions that combine agile structures with decentralization. Finally, untested integration models risk slowing down the response, giving rise to significant costs without producing effective results.

Purpose Many flights in Europe today are routed on longer routes either due to capacity shortages in the network or because Airspace Users (AUs) choose longer routes to avoid countries with high en-route charges. We analyse how better demand-capacity balancing of ATM can reduce detours and hence emissions. Approach We model the effects of two demand-capacity balancing mechanisms: network-oriented capacities and trajectory-independent ATM charges. The idea is that capacity decisions, which consider both network effects and emissions, reduce capacity shortages in the system, and thus also unnecessary re-routings. Also, if AUs pay ATM charges that are independent of flight path, they choose the most fuel-efficient and environmentally-friendly route. We test both mechanisms in a case study with 4000 flights. Findings We find that network-oriented capacities can reduce emissions from detours by 64%, with an increase in capacity cost of just 5%. We also find that current en-route airspace charges create around 320k tons of additional CO2 per year from detours of intra-European flights alone, which could be avoided by using trajectory-independent ATM charges. Such a charging scheme would need to be accompanied by changes in capacity that reflect the shift in demand towards shorter trajectories. Contribution Our research complements existing studies on trajectory optimization (which focuses on airborne flights) by evaluating how better capacity and demand decisions in the planning phase of ATM impact flight efficiency and emissions. In contrast to existing research, we include capacity constraints in the network when modeling the routing of flights. We also extend the methodology for ATM capacity planning by including emission cost in the decision. Finally, we formulate clear policy implications.

Research purpose We develop and test a comprehensive approach that combines innovative capacity as well as demand management concepts for European air traffic management (ATM), comparing different levels of centralized vs. decentralized decision making. Methodology We develop different institutional designs for decision making in ATM and use case studies of different size to analyze the effects on the cost of capacity provision, delays, and greenhouse gas emissions. The largest case study is based on actual traffic and airspace configuration data, combined with literature based cost information. Results Based on previous research we expect significant improvements in cost-efficiency and positive impacts on other areas, especially reductions in delays and CO2 emissions. This allows us to derive recommendations for the future role of the network manager in European ATM. Contributions We cover all areas of capacity provision (en-route as well as terminal airspace), all temporal levels (strategic, pre-tactical, and tactical), and also analyze different options for cross-border capacity provision. Therefore, our approach is more comprehensive than previous research, and we thoroughly included stakeholder feedback.

16 Pags.- 5 Figs. © Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, 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., The following projects provided funding for this research: EFA210/16/PIRAGUA and EFA346/19/ADAPYR co-funded by the European Regional Development Fund (ERDF) through the Interreg V Spain-France-Andorre Programme (POCTEFA 2014–2020) of the European Union, INNOMED co-funded by the Agencia Estatal de Investigación of Spain in the framework of the ERA-NET WaterWorks2015 co-funded Call (project INNOMED). This ERA-NET is an integral part of the 2016 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI) as a result of a joint collaborative effort with the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE JPI). Also, this work was supported by Fundación Biodiversidad of the Spanish Ministerio para la Transición Ecológica.

The scope of thiswork is to provide unobtrusive means to accuratelydepict the thermal comfort and well-being level of the occupants making them predictable energy wise and allow pertinent personalizedfeedback notifications or actions towards energy demandmanagement while preserving their corresponding preferences.Specifically, a low-cost wearable wrist device, collects occupant’sphysiological, motion and indoor environmental condition datausing unobtrusive appropriate sensors. Then, accounting the clothingconditions and the mean radiant temperature the occupant’sthermal comfort level as well as the indoor air quality is assessedon the scale provided by ANSI/ASHRAE 55-2010 Standard. Thermalcomfort and indoor quality levels are critical not only for healthmatters but also for the productivity of the occupants since it affectshumans’ efficiency and the mood as well. Being aware of the occupant’sthermal comfort level as well as the indoor quality level andthe outdoor conditions essential actions can be scheduled regardingthe energy demand.

Актуальність теми. Формування локальної електроенергетичної системи за концепцією Smart Grid потребує якісно нової архітектури системи енергозабезпечення та нових принципів функціонування – як на рівні технічних рішень, так і механізмів організаційної взаємодії на технологічному рівні та фінансових розрахунків. Microgrid – це концепція малої розосередженої генерації електроенергії, яка забезпечує перехід від централізованої системи до використання різноманітних типів джерел енергії, найбільш прийнятних за даними природних умов та особливостями функціонування конкретних споживачів. Об’єкти джерел розосередженої генерації (ДРГ) – це нові елементи електротехнічної системи з новими динамічними характеристиками та можливостями керування, які вимагають комплексних досліджень та глибокого аналізу в кожному конкретному випадку. Один із важливих напрямків розвитку Microgrid є побудова та забезпечення ефективного функціонування інтегрованих електротехнічних систем, створених із взаємопов'язаних навантажень, пристроїв акумулювання електроенергії та ДРГ, у тому числі й відновлюваних джерел енергії (ВДЕ). Така цілісна система може працювати паралельно з централізованою електроенергетичною системою або окремо від неї. Метою дисертаційної роботи є: оптимізація режимів роботи систем Microgrid шляхом комбінування ВДЕ (СЕС та ВЕС) з дизель-генераторами на основі динамічної тарифікації за станом. Для досягнення мети були поставлені та вирішені такі питання: 1. Методологія оптимізації режимів роботи систем Microgrid на основі динамічної тарифікації за станом. 2. Аналіз гібридних систем Microgrid з дизель-генератором. 3. Аналіз і особливості динамічної тарифікації. 4. Побудова моделей Microgrid з врахуванням техніко-економічних показників. 5. Визначення вартості витрат первинного палива та поточних тарифів на електроенергію Microgrid. 6. Алгоритм оцінки фінансових та технічних показників ефективності роботи Microgrid в динамічних режимах. 7. Розробка алгоритма оптимізації системи Microgrid. Об’єктом дослідження є перехідні та усталені процеси в автономній системі електроживлення. Предметом дослідження є динамічна тарифікація у автономній системі електроживлення з урахуванням зміни рівня генерованої електроенергії під час перехідних процесів. Метод дослідження. Науково-методичну основу виконаних досліджень склали такі методи: системного аналізу, математичного моделювання, алгоритми та методи оцінки нерівномірності споживання електричної енергії, методи комп’ютерного моделювання. Наукова новизна роботи одержаних результатів проведеного в дисертаційній роботі дослідження полягає в такому: 1. Досліджено методологію оптимізації графіків потужності електроспоживання з використанням потужності Фризе QФ у разі керування режимом електроспоживання та впровадження програм з керування попитом на електричну енергію. 2. Розширено систему показників адекватного врахування складових втрат електроенергії на основі модифікації та декомпозиції потужності Фризе QФ за рахунок оцінювання впливу факторів нерівномірності споживання електричної енергії на електроенергетичну систему на довільному інтервалі часу. 3. Запропоновано метод оцінки впливу нерівномірності споживання електричної енергії на рівень втрат у системі електропостачання, в якому на відміну від існуючих використано модифіковані показники потужності Фризе QФ та поширення визначення потужності Фризе на довільний інтервал часу. 4. Для підвищення техніко-економічних показників в Microgrid з ДГ було запропоновано використати електровартісну модель енергогенеруючої системи, що дозволяє розраховувати як динамічну зміну генерованої потужності, так і динамічну зміну її вартості та вартості первинного палива. 5. Розроблено алгоритм розрахунку витрати первинного палива для двох варіантів керування з оптимальним розподіленням потужності та рівномірним за допомогою широкоформатного комп’ютерного програмування. Практичне значення одержаних результатів полягає у комплексному розв’язанні задачі оцінювання ефективності керування попитом в СЕП та оптимізації графіків навантаження і режимів роботи обладнання з мінімізацією витрат на електричну енергію. Actuality of theme. The formation of the local power system according to the Smart Grid concept requires a qualitatively new architecture of the power supply system and new principles of operation - both at the level of technical solutions and mechanisms of organizational interaction at the technological level and financial calculations. Microgrid is a concept of small dispersed generation of electricity, which provides a transition from a centralized system to the use of different types of energy sources, the most acceptable in terms of natural conditions and characteristics of specific consumers. Scattered Generation Source (DRG) objects are new elements of the electrical system, with new dynamic characteristics and control capabilities that require comprehensive research and in-depth analysis in each case. One of the important directions of Microgrid's development is the construction and operation of integrated electrical systems created from interconnected loads, electricity storage devices and DRG, including renewable energy sources (RES). Such an integrated system can operate in parallel with a centralized power system separate from it. The purpose of the dissertation is: optimization of Microgrid operating modes by combining RES (SES and WPP) with diesel generators based on dynamic state tariffs. To achieve this goal, the following issues were raised and resolved: 1. Methodology for optimizing the modes of operation of Microgrid systems based on dynamic charging by status. 2. Analysis of hybrid systems Microgrid with diesel generator. 3. Analysis and features of dynamic charging. 4. Construction of Microgrid models taking into account technical and economic indicators. 5. Determining the cost of primary fuel costs and current electricity tariffs Microgrid. 6. Algorithm for evaluating the financial and technical performance of Microgrid in dynamic modes. 7. Development of an algorithm for optimizing the Microgrid system. The object of research is the transient and steady-state processes in the autonomous power supply system. The subject of the study is the dynamic charging in an autonomous power supply system taking into account changes in the level of generated electricity during transients. Research method. The scientific and methodological basis of the research was the following methods: systems analysis, mathematical modeling, algorithms and methods for estimating the unevenness of electricity consumption, methods of computer modeling. The scientific novelty of the results of the research conducted in the dissertation is as follows: 1. The methodology of optimization of power consumption graphs with the use of Frieze QF power in the case of power consumption mode management and implementation of programs to manage electricity demand has been studied. 2. The system of indicators of adequate consideration of components of electricity losses based on modification and decomposition of Frieze QF power by expanding the impact of factors of uneven electricity consumption on the power system at any time. 3. A method for estimating the impact of uneven electricity consumption on the level of losses in the power supply system is proposed, which, in contrast to the existing ones, uses modified Frieze power indicators QF and extends the Frieze power definition to any time interval. 4. To improve the technical and economic performance of Microgrid with DG, it was proposed to use the electric cost model of the energy generating system, which allows to calculate both the dynamic change of generated power and the dynamic change of its cost and the cost of primary fuel. 5. An algorithm for calculating the consumption of primary fuel for two control options with optimal power distribution and uniform with the help of large- format computer programming. The practical significance of the obtained results lies in the complex solution of the problem of estimating the efficiency of demand management in EPS and optimization of load schedules and operating modes of equipment with minimization of electricity costs.

The Smart Grid concept is being implemented and it is a close reality, its application has been accelerated by the reduction of the electronic components costs, the Internet of Things evolution and the necessity to develop more efficient solutions for electric vehicles and distributed generation. The main projects around the world are starting with the implementation of Smart Homes, due to the greater easy technical solution and high compensation. These implementations are accompanied by technical and economic barriers, due to the increase in data generation and the increase products price. This paper focuses on the study of the Sub-GHz, WiFi, and ZigBee wireless networks, making a technical and economic analysis of these networks. Experimental and simulation analysis were used to evaluate these technologies, allowing to simulate a complete Smart Home environment for energy and demand management, applying a communication node for each energy point. The economic analysis relates the direct cost of technology module and their energy consumption, based on rates from the local distributor of the residence. In conclusion, the document establishes the best wireless technology to apply in energy and demand management in smart homes environments, with higher transmission success rates and better cost benefit.

Incentive-Based Traffic Demand Management (IBTDM) provides monetary incentives to encourage commuters to alter their departures spatially or temporary with the goal of alleviating congestion. With the proliferation of smartphone technology, mobility apps have become ideal platforms for carrying out IBTDM. Tremendous amounts of empirical app usage data have been collected, but research into the behavioral insights of IBTDM remains limited. It is unclear who IBTDM's target users should be, and which users are the most likely to be stable (actively use the app) and behaviorally sustainable (willing to contribute to congestion alleviation). This study aims to profile the socio-demographics of such favored users based on behavioral and socio-demographic data collected by the Metropia app. The Ensemble Empirical Mode Decomposition (EEMD) method was used for usage trend detection. The detected usage trends were then used in pattern classification to identify stable and sustainable users. Next, binary logistic regression was adopted to explore the socio-demographic characteristics of each category of users. It was found that factors including home work days, household annual income, household size and schedule flexibility played important roles in users' usage patterns and departure time decisions. Specifically, home work days and household annual income co-influenced app usage patterns. Household size and schedule flexibility were the main determinants of departure time behavior. The findings of this research can be used to guide administrators of budget-constrained IBTDM programs who need to wisely allocate their marketing budget to increase penetration among favored users as to maximize the utility of the program.

Health financing in Indonesia has increased from 27 US $ (2003) to 115 US $ (2017) and will continue to increase as the population increases. The JKN era is also a burden where health financing managed by BPJS is always deficit every year. Hospitals as health service providers must be able to carry out operational cost efficiency as mandated by the JKN era, namely quality control and cost control. One of the cost efficiency methods is the implementation of lean management in hospital operational processes, especially the supply chain management process (procurement, inventory control, distribution planning and demand management). This study aims to evaluate the improvement process and cost efficiency after the implementation of lean management in the supply chain management process at the hospital. This study used a single holistic case study method with the unit of analysis at the Lean team level at Panti Rapih Hospital, Yogyakarta. Research subjects are employees in the team who are directly involved in the implementation of lean management. Selection of research subjects based on purposive sampling. Total sample of 7 respondents for in-depth interview process. The research instrument used an interview guide. The result of this research is that lean management which is applied in the logistic unit of the neat orphanage hospital can reduce waste such as motion, inventory, and waiting time. In addition, another benefit that can be felt by management is increasing cost efficiency. The conclusion of this study is that lean management is recommended to be applied in health services such as hospitals.