Search

Operational decisions made by manufacturers and retailers are often affected by the power relationships of both vertical and horizontal competitions in a supply chain. Although many studies have been carried to assess how the power structure impacts supply chains, most of them only focus on the power dynamics in the vertical competition or horizontal competition. This paper systematically examines the impacts of bidimensional power structure on a supply chain. Considering a triadic setting that is made of two rival retailers and a common manufacturer, we compare the equilibriums for the supply chain and individual firms with five different possible power structures. The analysis results show that the bidimensional power dynamics have a significant impact. Furthermore, the impacts of the bidimensional power structure are also dependent on the nature of customer demand. This paper provides important management implications that are supportive for companies to make strategic and operational decisions. [ABSTRACT FROM AUTHOR]

Mass customization (MC) programs are commonly seen in the real world. Many companies offer consumer returns and quick delivery for their MC products to the market. Thus, the quick response (QR) strategy is crucial for MC supply chains. Prior studies in related areas only examine MC supply chains with limited features and topics, such as measures for win–win coordination or profit risk analysis, but without a comprehensive consideration of these aspects together. Besides, the systems enhancement schemes are not yet explored. This paper aims to fill these gaps and generate novel insights. Among the findings, we show that a higher salvage value of the consumer returned items and a higher salvage value of the unused inventories both can reduce the value of QR for the supplier. We illustrate how a simple two-part tariff contract and a sophisticated hybrid contract can achieve win–win coordination with different levels of flexibility. We also conduct a profit risk analysis on the MC supply chain. Finally, we demonstrate how three practical measures, namely technology investment, product design improvement, standardization of component, can further improve the MC supply chain system’s performance. [ABSTRACT FROM AUTHOR]

The proper supply of end-of-life (EOL) items plays a critical role in the success of remanufacturing systems. In many real-world practices, a remanufacturer has multiple links with independent third party collectors, while these links should be simultaneously coordinated. However, most previous studies mainly focus on reverse supply chain (RSC) coordination in a one-to-one setting (single link). Moreover, the competition among third party collectors affects the acquisition of EOL products and this is an important issue. In this paper, an RSC consisting of a single remanufacturer and duopolistic competing third party collectors is explored and a new mechanism is proposed to achieve channel coordination across multiple links. To be specific, we consider the case when the collectors compete on the acquisition prices offered to the consumers and the remanufacturer decides on the pricing and environmental effort decisions with two considerations: 1) increasing return of EOL items and 2) boosting demand of remanufactured products. The optimal decisions of RSC members and performance of RSC are analyzed under different behaviors of competing collectors. Moreover, a multilateral two-part tariff contract is proposed to coordinate the decisions across multiple links. The results indicate that the proposed contract improves not only profits of the remanufacturer and competing collectors but also the collection quantity of EOL items and demand of remanufactured products, which helps improve the environment. [ABSTRACT FROM AUTHOR]

Nowadays, governments all around the world have implemented rules and launched legislation to enhance environmental sustainability. Supply chain systems are hence operated under different forms of legislation, such as the carbon tax or extended producer responsibility tax. In this paper, we examine the newsvendor model-based supply chain systems with the consideration of all common forms of environmental taxes. We highlight how the retailer’s risk attitude and the number of consumer returns affect: 1) supply chain operations; 2) performances of the environmental taxes; and 3) supply chain coordination (i.e., optimization). To be specific, we derive the optimal inventory decisions of the retailer when she is risk neutral and risk averse, respectively. We then uncover how environmental taxes and consumer returns affect the retailer’s optimal inventory decisions under different risk attitudes. We further explore the supply chain coordination challenge under three different contracts, characterize their flexibility in coordinating the channel and discuss the impacts of the environmental taxes and consumer returns on each coordination mechanism. Our analytical results show that the two-part tariff contract can achieve coordination for the case with a risk neutral retailer only, while markdown sponsor (MDS) contract and revenue-sharing policy (RSP) can achieve coordination for both risk neutral or risk averse retailer cases. Besides, we reveal that the examined contracts can coordinate the supply chain with a risk neutral retailer more flexibly than that with a risk averse retailer. Finally, by comparing between the MDS contract and RSP, we find that the environmental taxes and the consumer returns will affect the coordination mechanism differently toward the risk neutral and risk averse retailers. Impacts brought by the consumer returns are also explored. [ABSTRACT FROM AUTHOR]

In this paper, we consider the capacity requirements allocation problem of a buyer between an incumbent supplier and a new supplier over a two-period horizon. The buyer allocates some of its capacity requirements to the incumbent supplier in the first period. This enables the incumbent supplier to reduce its cost in the second period due to the phenomenon of production learning. The buyer also makes supplier development investments in the new supplier in the first period. In addition, both the incumbent and the new entrant make process improvement efforts in the first period, which enhance the effective capacity available in the second period. Finally, the buyer allocates the rest of its capacity requirements between the two suppliers in the second period. We find that the buyer has various sourcing strategies to maintain a competitive supply base. We also find that the optimal supplier development investments and sourcing strategies depend on the collaboration relationship between the buyer and the new supplier. [ABSTRACT FROM AUTHOR]

This paper explores supply chain systems coordination challenges in the presence of multiple heterogeneous risk sensitive retail buyers using the commonly seen markdown contract under both information symmetric and asymmetric settings. For each setting, we explore two scenarios. The first scenario allows the upstream manufacturer to freely set a separate contract to each risk averse retailer, whereas the second scenario specifies that the manufacturer has to grant the same contract to each risk averse retailer under the fair trade rule. We analytically show that the markdown contract which can achieve “perfect coordination” only exists in the first scenario (without the fair trade rule) under the information symmetric setting. For all the other scenarios, we find that perfect coordination cannot be achieved by the markdown contract, and hence we develop the computational algorithms to help identify the markdown contract parameter(s) which can achieve the “best possible coordination.” In addition, we reveal that the manufacturer’s risk attitude does significantly affect the achievability of perfect coordination. The findings of this paper also provide analytical evidence to show that the fair trade rule would do more harm than good for supply chain systems optimization under simple supply contracts. [ABSTRACT FROM PUBLISHER]

In recent years, the rapid development of cryptocurrencies and their underlying blockchain technology has revived Szabo’s original idea of smart contracts, i.e., computer protocols that are designed to automatically facilitate, verify, and enforce the negotiation and implementation of digital contracts without central authorities. Smart contracts can find a wide spectrum of potential application scenarios in the digital economy and intelligent industries, including financial services, management, healthcare, and Internet of Things, among others, and also have been integrated into the mainstream blockchain-based development platforms, such as Ethereum and Hyperledger. However, smart contracts are still far from mature, and major technical challenges such as security and privacy issues are still awaiting further research efforts. For instance, the most notorious case might be “The DAO Attack” in June 2016, which led to more than $50 million Ether transferred into an adversary’s account. In this paper, we strive to present a systematic and comprehensive overview of blockchain-enabled smart contracts, aiming at stimulating further research toward this emerging research area. We first introduced the operating mechanism and mainstream platforms of blockchain-enabled smart contracts, and proposed a research framework for smart contracts based on a novel six-layer architecture. Second, both the technical and legal challenges, as well as the recent research progresses, are listed. Third, we presented several typical application scenarios. Toward the end, we discussed the future development trends of smart contracts. This paper is aimed at providing helpful guidance and reference for future research efforts. [ABSTRACT FROM AUTHOR]

This paper explores a dual-channel closed-loop supply chain (CLSC) system consisting of a manufacturer and a retailer. The manufacturer is the CLSC Stackelberg leader. We investigate the effect of exerting marketing effort on the optimal decisions and profits of supply chain members by considering several marketing effort supported models, namely the models when the manufacturer is the investor, the retailer is the investor, and the centralized CLSC system, respectively. We propose a two-part tariff contract for coordinating the CLSC. We analytically reveal that the two-part tariff contract can coordinate the CLSC when the manufacturer is the investor but unable to coordinate supply chain when the retailer is the investor. Finally, we present the numerical analysis to uncover insights on how the consumers’ preference toward the direct channel affects the optimal decisions in the dual-channel CLSC system. [ABSTRACT FROM AUTHOR]

As climate concerns, low natural gas prices, and renewable technologies increase the electric power sector's dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas–electricity models exist. This paper reviews the state of the art for hybrid gas–electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources. [ABSTRACT FROM PUBLISHER]

Wind power producers participating in day-ahead electricity markets are compelled to pay imbalance costs if they do not generate the same amount of power as they had bid for. These imbalance costs comprise a significant proportion of their income. To reduce the risk of such financial losses, this paper employs the idea of trading in a separate prediction market, as a hedging method. In prediction markets, participants trade shares associated with a certain outcome of an event. We propose that the wind power producers might participate in a prediction market to trade the future value of the wind power and by taking an opposite position in comparison to the electricity market, the imbalance costs will be offset through payouts in the prediction market. Wind power is modelled as a stochastic variable and an optimal trading strategy is developed where the trading volume in the prediction market is analytically derived and formulated by minimising the maximum possible loss and pricing of shares is determined via indifference utility condition. The results suggest that the proposed method limits the loss values and improves the risk measures. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of Security-Constrained Unit Commitment (SCUC) which is a long-standing challenge in power system engineering faced by system operators and utility companies on a daily basis. We consider a detailed variant of this problem that suffers from complexities posed by the presence of binary variables, the uncertainty of renewable sources and security constraints. A convex relaxation is formulated which is capable of finding feasible solutions within a provable distance from global optimality. We demonstrate the performance of this approach on detailed and challenging instances of SCUC with IEEE and PEGASE benchmark cases from Matpower. The proposed approach is able to handle over 12,000 binary variables and 2 million continuous variables with significant improvement in solution quality over commonly-used off-the-shelf solvers and other methods of convex relaxation. [ABSTRACT FROM AUTHOR]

A multiperiodic structure always underlies the procurement problem in practice. Option contracts are widely used to make a firm capable of responding flexibly to high demand uncertainty. However, so far, the impact of different option contracts on the multiperiod decisions and performance of a firm has been not discussed. Motivated by this problem, in this paper we analyze the multiperiod procurement problem within the framework of wholesale price, call, put, and bidirectional option contracts. Based on the dynamic programming theory, we characterize the structure of the firm's optimal replenishment policy for each period in four cases. We then develop a heuristic to approximate the corresponding policy parameters. Through system comparisons of four cases, we examine the value of option contracts for the firm and discuss the best option contract type for the firm. The results show that the firm will gain higher profits after the option contracts are introduced. Moreover, if the unit purchase and exercise costs of the bidirectional option are equal to those of the unilateral option, then bidirectional option contracts are preferred over unilateral option contracts. If the unit purchase cost of the bidirectional option is higher than that of the unilateral option, unilateral option contracts may be more profitable than bidirectional option contracts. Furthermore, if the unit backlogging cost is high and the unit inventory holding cost is low, call option contracts are better than put option contracts; if the unit backlogging cost is low and the unit inventory holding cost is high, then put option contracts work better than call option contracts. [ABSTRACT FROM AUTHOR]

The structure of the operational costs of gas fired units (GFU) in real power systems is much more complex than what standard unit-commitment (UC) models consider in the state-of-the-art formulations. On the one hand, when a generation company owning several GFUs procures its gas -either totally or partially- at the gas spot market, the final fuel price will depend on the total gas purchases. This fact couples the operation of all the GFUs as it is impossible to build individual cost functions for each generator. On the other hand, as any other gas consumer, the generation company has to pay the gas Third Party Access (TPA) tariffs. The generation company must contract in advance the monthly TPA capacity for each gas exit point and depending on such decision, the final variable cost of the GFUs will be different. This paper states the importance of modeling properly both issues and presents a novel mathematical formulation that can be embedded in a self-UC model. The presented example case highlights the benefits of the proposed formulation. [ABSTRACT FROM AUTHOR]

Supplier development (SD) has become an emerging supply chain management practice for firms focusing on their core strategic advantages by outsourcing a significant portion of production or construction activities. Various incentive mechanisms are available for suppliers to improve their capabilities to meet owners’ expectations. However, a comparative analysis is needed to understand how successful they are. This paper investigates the incentive mechanisms for SD in a construction supply chain that consists of one owner and one supplier. Considering the owner's dominant position in the negotiation of a supply contract, a principal–agent model with the objective of optimizing the owner's profit and a Stackelberg game model designed to optimize the profits of both the owner and supplier are proposed and analyzed. Two commonly used incentive methods, including purchase price incentive and cost sharing, are analyzed and compared. The analysis and comparison are illustrated by a numerical example. The primary contribution of this paper is the analysis of the impact of incentive methods on quality improvement for the supplier and a sensitivity analysis of the supplier's internal and external factors, such as purchase quantity, market demand, and manufacturing cost structures, which are useful managerial insights for SD practices in mega construction projects. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of the Plug-in Electric Vehicles charging costs in housing sector. A review of the optimal strategies is proposed and as such six algorithms are presented: three smart unidirectional and three smart bidirectional charging algorithms where the Vehicle-to-Grid (V2G) and the Vehicle-to-Home (V2H) concepts were exploited. In addition an innovative V2G algorithm named Optimal Logical Control (V2G-OLC) is introduced in this paper. This latter is dedicated to the French energy billing system within the peak/base hour's contract. All the algorithms are tested over a set of 1000 data composed of real elements with four different daily energy price profiles. The results show a great efficiency of the V2G-OLC algorithm compared to the traditional optimal charging strategies. Indeed, the results show a reduction of 47.94% of the average charging cost with a unitary selling/buying price ratio in comparison to the simple charging at 230V-32A. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy resources in power systems, it is necessary to overcome the limitations of these resources in terms of the supply and demand balance in high-voltage power systems. A viable solution is employing zero net energy community microgrids, which can manage the demand locally and minimize the impact to the power grid. To this end, this paper proposes an optimal scheduling method for a zero net energy community microgrid with customer-owned energy storage systems (CES). It is assumed that the microgrid operator operates in the CES market based on a bilateral contract. The CES aggregators constitute the CES and participate in the market. The aggregated CES (ACES) is considered as a single energy storage system in the proposed scheduling method. Numerical examples are presented to demonstrate the effectiveness of the proposed method.  [ABSTRACT FROM AUTHOR]

Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wide range of industrial services, how to execute smart contracts efficiently becomes a big challenge nowadays. Due to the existence of Byzantine nodes, existing mature concurrency control protocols in database cannot be employed directly, since the mechanism of executing smart contracts varies a lot. Furthermore, even though smart contract execution follows a two-phase style, i.e., the primary node executes a batch of smart contracts in the first phase and the validators replay them in the second phase, existing parallel solutions merely focus on the optimization for the first phase, rather than the second phase. In this paper, we propose a novel two-phase concurrency control protocol to optimize both phases for the first time. First, the primary executes transactions in parallel and generates a transaction dependency graph with high parallelism for validators. Then, a graph partition algorithm is devised to divide the original graph into several sub-graphs to preserve parallelism and reduce communication cost remarkably. Finally, we propose a deterministic replay protocol to re-execute the primary’s parallel schedule concurrently. Moreover, this two-phase protocol is further optimized by integrating with PBFT. Theoretical analysis and extensive experimental results illustrate that the proposed scheme outperforms state-of-art solutions significantly. [ABSTRACT FROM AUTHOR]

As the speed of cellular networks has steadily increased so has user consumption of video content. As such, mobile TV deployments over cellular networks have become increasing attractive. Typically mobile TV deployment schemes utilize a subscription-based model. In this model, the advertisements are “baked” into the TV program, i.e., the service provider’s role is typically limited to that of providing a content distribution network. As such, the service provider’s revenue is limited per the contracts negotiated with the content providers. In this paper, we present the design and game theoretic analysis of a non-subscription ad-supported mobile TV system. In our system, we show how to incentivize viewership statistic collection via fair allocation of resources among the TV channels, on a per TV show basis. In addition, we present a real-time auction which enables service providers the ability to directly schedule advertisements/earn revenue based on an accurate set of viewership statistics. [ABSTRACT FROM AUTHOR]

This paper develops an equilibrium-based model for ex ante analysis of capacity withholding under financial virtual divestiture in a day-ahead market, including one dominant producer and a number of small producers considered as a price-taking competitive fringe. We model the offering problem of the dominant producer as a bilevel optimization problem. In the upper-level problem, the dominant producer derives optimal strategy of physical capacity withholding restricted by operational limits of its own generating units and conditions stated in the financial virtual divestiture. In the lower-level problem, market operator clears the day-ahead market on an hourly basis. We recast the bilevel optimization problem as a mathematical programming with equilibrium constraints problem and convert it to a mixed integer linear programming problem. We show how the application of the financial virtual divestiture may mitigate market power in the day-ahead market, through a case study. The numerical results demonstrate that the dominant producer can exploit technological diversification of its own generating units to exercise market power, even in the presence of this market power mitigation policy. [ABSTRACT FROM PUBLISHER]

This paper studies information sharing in a supply chain where a contract manufacturer (CM) acts as both an upstream partner and a downstream competitor to an original equipment manufacturer (OEM). To better explore the effect of information sharing on a coopetitive supply chain, we consider a sequential game with the OEM as the Stackelberg leader under two different wholesale pricing methods, which is close to the practical situation. Our analysis shows that when the wholesale price is exogenously given, information sharing is beneficial for the OEM but hurts the CM, and a side-payment contract is designed to induce Pareto-optimal information sharing. When the wholesale price is a decision variable, the CM benefits from information sharing while the OEM does not and a discount-based wholesale price contract is designed to induce truthful information sharing. The numerical examples analysis further finds that the OEM benefits from the exogenous wholesale price scenario while the CM benefits from the endogenous wholesale price scenario. The study sheds lights on the decision-making processes for operating and managing the coopetitive supply chain and extends the research scope of information sharing. Our findings also provide some scientific references about information sharing in daily production. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed the wide implementation of many sophisticated supply contracts in the industry, such as the price, rebate, and return (P2R) contract. The P2R contract is offered by the manufacturer to the retailer. In this paper, a practice-based approach is adopted in which we first report the use of P2R contracts in the real world from industrial interviews. Based on the meanings of risk as revealed from the interviews, we proceed to construct analytical models and derive the minimum risk coordinating P2R (PMR*) contract, which yields the minimum level of risk for the manufacturer and coordinates the supply chain. We analytically show that the manufacturer's expected profit is increasing with the return price and the rebate value under the PMR* contract. We reveal that different risk measurements may lead to different PMR* contracts. In particular, when the price-dependent demand distribution takes the additive form, we interestingly find that the manufacturer can become riskfree in coordinating the supply chain with the PMR* contract; however, it is not the case for the multiplicative case. Furthermore, we observe that the demand distribution's shape affects the setting of the PMR* contract. Managerial implications are also discussed. [ABSTRACT FROM AUTHOR]

Sustainability is a prevalent topic as the consumers and the governments are much more concerned about the environmental pollution. To reduce the carbon emission, in real world practice, governments have executed different forms of policies to motivate the firms to operate in a sustainable manner. This paper aims to examine the value of bargaining contract when the supply chain member invests in the sustainability projects, and explore the effectiveness of the direct (e.g., cash grant) and indirect (e.g., an increased environmental tax rate) subsidies in reducing the total carbon emission. We first derive the optimal pricing and sustainability effort under the wholesale pricing contract when either the manufacturer or the retailer makes the sustainability investment. We then consider the supply chain coordination under the two-part tariff, revenue sharing, and bargaining contracts. We uncover that only the bargaining contract can coordinate the supply chain and a Pareto improvement is achieved when the bargaining power of the manufacturer is in a specific range. Finally, we reveal that both the direct and indirect subsidies can increase the sustainability effort but they are not always an effective tool to reduce the total carbon emissions. [ABSTRACT FROM AUTHOR]

In mid-term planning, the objective of an electricity retailer is to procure the electricity energy to supply its demands, while efficient control of the financial risks under uncertainties is as vital as maximizing profit. To meet these challenges, this paper proposes and compares different stochastic-based risk measures including minimax regret (MMR), chance-constrained (CC), and conditional value-at-risk (CVaR) criteria, which are useful tools for assessing the risk levels, considering whether a retailer is a risk-taking or a risk-averse participant. To obtain an optimal procurement strategy, robust versions of implemented approaches are applied. To show the real performance of the proposed procurement solutions, postoptimization procedures with different price behaviors are performed to carry out experimental results on the basis of realistic data. The results show that for the same robustness degree of strategy, CVaR is less conservative than MMR and CC is less conservative than CVaR. [ABSTRACT FROM AUTHOR]

Blockchain technique, with the novelties of decentralization, smart contract, security and cooperative autonomy, is expected to play great effects on promoting the development of energy local networks (ELNs). This paper presents an automated demand response (ADR) framework for decentralized scheduling and secure peer-to-peer (P2P) trading among energy storage systems in ELNs. Different from most existing works that trade electricity over long distances and through complex meshes, this proposed work performs decentralized and automated demand response through energy sharing of P2P executors. We explore for the first time the benefits of a promising blockchain to conduct the overall ADR framework and increase the P2P trading security. To achieve decentralized scheduling without relying on a central entity, a price-incentive noncooperative game theoretic model is introduced to produce equilibrium solutions for energy storage systems. Moreover, we develop a schedulable ability evaluation system to match trading pairs involving buying and selling nodes. On this basis, a state-machine-driven smart contract mechanism is built to realize P2P trading without reliance on a trusted third party. To illustrate the implementation details of the ADR method, a distributed algorithm is designed. Case studies are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A system operator asks a group of sensors toexert costly effort to collect accurate measurements of a value of interest over time. At each time, each sensor is asked to report its observation to the operator, and is suitably compensated for the costly effort it exerts. Since both the effort and the observation are private information for the sensor, a naive payment scheme which compensates the sensor based only on its self-reported values of the effort and the measurements will lead to both shirking and falsification of outcomes by the sensor. In this paper, we design an appropriate compensation scheme to incentivize the sensors to both exert costly effort and then reveal the resulting observation truthfully. To this end, we formulate the problem as a repeated game and propose a compensation scheme that employs stochastic verification by the operator coupled with an algorithm to assign a reputation to each sensor. By including the history of the behavior exerted by the sensor in determining present payments, we show that the operator can incentivize higher effort as well as more frequent truthtelling by the sensors. [ABSTRACT FROM AUTHOR]

The recently approved regulation about self-consumption in Spain will dramatically change the level of penetration of photovoltaic distributed generation combined with storage devices at residential level in short term. In this paper, we will study the impact of the massive installation of such technologies under the constraints imposed by the new regulation, over the terminal distribution network. As it will be demonstrated, this impact will be drastic in terms of energy demand, voltage profile, and load curves. [ABSTRACT FROM AUTHOR]

This paper focuses on who should control the inventory and who should lead the supply chain under consignment contract. The supply chain consists of one supplier and one retailer, and inventory inaccuracy occurs on the retailer's side. Vendor-managed inventory (VMI) and retailer-managed inventory (RMI) models are analyzed in two different supply chain power structures: where the supplier is the Stackelberg leader (referred as SVMI and SRMI) and where the retailer is the Stackelberg leader (referred as RVMI and RRMI). The inventory availability and the channel cost sharing rate are examined to investigate the impact of these two factors on supply chain performance under four cases. It is important to find that whatever the values of the inventory availability and the channel cost sharing rate may be, the retailer always prefers SVMI. However, there is a threshold value of inventory availability (channel cost sharing rate) such that the supplier prefers SVMI only if the inventory availability (channel cost sharing rate) is larger (less) than the threshold value; and it prefers RRMI only if the inventory availability (channel cost sharing rate) is less (larger) than the threshold value. Therefore, both leading retailer and leading supplier have the incentive to act as the follower. [ABSTRACT FROM AUTHOR]

Network use-of-system (UoS) tariffs play an essential role in the deregulated power markets to recover network investment and maintenance costs from network users, and send economic signals to influence the users' behaviors in using systems. The tariffs are essential to the business operation of both network operators and users. They, however, could have great volatility due to the uncertainties from both external and internal factors. Such uncertain tariffs could bring severe adverse impacts to network users, which justifies that there is an urgent need to reduce the tariff volatility and improve its predictability. This paper for the first time investigates the variations in network tariffs that cause great risks to network users and then designs financial tools to reduce the volatility. The causing factors of the variations are categorized into global and local groups according to their different features. After introducing the process from network planning, charging to revenue reconciliation, the paper discusses the benefits of hedging for network users. In order to reduce tariff volatility, it proposes a novel risk management strategy to maintain the consistency of tariffs. It is achieved by designing long-term contracts using financial hedging. The value of hedge contracts is decided by three key factors: hedged load/generation percentage, hedged price, and risk premium. The hedged part is charged at hedged prices and the non-hedged part is charged at actual prices, on top of which customers needs to pay extra for the risk premium. The paper also designs an optimal decision-making tool to assist network users to manage long-term contracts in order to reduce total tariff costs. As demonstrated in the case study, the proposed long-term products can effectively reduce tariff volatility for network users and create a safe environment for their business operation. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation. [ABSTRACT FROM PUBLISHER]

This paper studies the effects of channel leadership and information asymmetry on supply chain coordination (SCC) of a single-manufacturer (M) single-retailer (R) supply chain, which sells a product with a general stochastic price-dependent-demand function. Three all-units quantity discount (QD) contracts are examined: 1) the manufacturer-led QD (MQD); 2) the retailer-led QD (RQD); and 3) the vendor managed inventory QD (VQD), which represent different channel leaderships. The problems are constructed as multistage sequential games. We find that any VQD that coordinates the supply chain has an equivalent RQD counterpart, and hence only MQD and RQD need to be examined for SCC. For the symmetric information scenario, both MQD and RQD can achieve coordination and the respective conditions for RQD are distribution free, but not for the MQD case. Only RQD, but not MQD, can always achieve Pareto improvement. For the asymmetric information scenario, MQD achieves coordination only if the actual supply chain's optimal quantity is known by M, but this condition is not needed for RQD. There is a unique RQD that achieves SCC, and hence the profit division between channel members is fixed. To overcome this inflexibility issue, we demonstrate that R can offer a menu of two RQDs to M. However, Pareto improvement is not always guaranteed by using RQD. Since QD contracts are widely adopted in practice, this paper lays the foundation to advance our knowledge on how channel leadership affects performance under both the information symmetric and asymmetric cases. [ABSTRACT FROM PUBLISHER]

With the continued scaling of transistor feature size, the cost of IC development has been escalating. The economics of semiconductor IC development favors high volume manufacturing, while high volume cannot be attained without developing an IC that serves many applications. Some of these applications are in low-margin Internet of Things (IoT) devices, where an SoC cannot command a high price. Consequently, without the ability to customize IC features after production, its lowest-priced application will determine an IC’s price. This motivates the manufacturers to develop chips with provisions for post-manufacturing IC customization. This paper explores how an IC can be programmed repeatedly and securely using blockchain-based smart contracts to allow users to upgrade or rent features. The availability of such a system could, for example, allow a buyer to upgrade her processor from a low-end to a high-end part by making an additional payment to the manufacturer. Implementing such a system will require remote device management capabilities. Remote device management presents unique design considerations, such as, necessity for transparency of the actions that a device takes on behalf of a user; the requirement of a trusted arbiter; and provision for management of these devices beyond the intended lifespan. To overcome the challenges of transparency, longevity, and the necessity of a trusted arbiter, we propose embedded smart contracts in concert with a blockchain. Our proposed smart contract takes the device feature configuration request as input and outputs the corresponding configuration. To support remote, secure, and authorized updates, we propose an on-die hardware module which communicates with the smart contract and enforces its functionalities. This was prototyped using a programmable system-on-chip working in concert with Ethereum blockchain. The prototype demonstrates the feasibility and practicality of the proposed solution. [ABSTRACT FROM AUTHOR]

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, due to the diverse behaviours of mobile users, unreliable or malicious users and platforms could provide untrusted data, which affects the quality of sensing service. Besides, mobile users are reluctant to participate in sensing tasks without sufficient incentives. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality and stimulates mobile users to join sensing tasks. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms. [ABSTRACT FROM AUTHOR]

Increasing demands of multimedia contents brings a great profit to the content providers, but also a challenge of how to efficiently delivery contents to make users have a good Quality of Experience (QoE). Take the advantage of owning wireless infrastructures, the telco operator is motivated to build a content platform for entering the market of content. In this paper, we consider a content platform belonging to the telco operator, which can provide periodically-updated contents with small Age of Information (AoI), namely, fresh contents. The content update consumes radio resource resulting in a trade-off between the AoI and the latency. We adopt the contract theory to monetize contents considering the above two factors in a realistic asymmetric information scenario. Necessary and sufficient conditions are derived to ensure the feasibility of the contract. We further propose the optimal update schemes and the corresponding fees, which maximizes the utility of the operator. Simulation reveals that the proposed contract enables the users, who attach importance to the freshness, to obtain frequently updating contents. [ABSTRACT FROM AUTHOR]

This paper develops a decision making framework for mid-term scheduling problems of the large industrial consumers. The proposed approach is based upon the recently introduced class of stochastic programming problems, established by the concept of second-order stochastic dominance (SSD). In this paper, it is assumed that the electricity price and the rate of availability (unavailability) of the generating unit (forced outage rate) are the sources of uncertainty in the decision-making problem. In the developed SSD-constrained stochastic programming problem, the consumer risk is managed by minimization of the pool procurement, as the objective function, and economic issues (e.g., cost minimization) are considered in the SD constraint. Furthermore, while most approaches optimize the cost subject to an assumed demand profile, our method enforces the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e., three months (12 weeks), so that the total production will remain constant. Simulation results for a typical cement factory (as an industrial consumer) and comparison to a mean-risk approach with CVaR risk measure, revealed the interesting results and benefits of the proposed approach. [ABSTRACT FROM PUBLISHER]

Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information. [ABSTRACT FROM AUTHOR]

In this paper, a novel economic approach, based on the framework of contract theory, is proposed for providing incentives for LTE over unlicensed channels (LTE-U) in cellular networks. In this model, a mobile network operator (MNO) designs and offers a set of contracts to the users to motivate them to accept being served over the unlicensed bands. A practical model in which the information about the quality-of-service (QoS) required by every user is not known to the MNO and other users is considered. For this contractual model, the closed-form expression of the price charged by the MNO for every user is derived and the problem of spectrum allocation is formulated as a matching game with incomplete information. For the matching problem, a distributed algorithm is proposed to assign the users to the licensed and unlicensed spectra. The simulation results show that the proposed pricing mechanism can increase the fraction of users that achieve their QoS requirements by up to 45% compared to classical algorithms that do not account for users requirements. Moreover, the performance of the proposed algorithm in the case of incomplete information is shown to approach the performance of the same mechanism with complete information. [ABSTRACT FROM AUTHOR]

This paper proposes an incentive-based coordination mechanism between a wind energy supplier (WES) and a conventional energy supplier (CES) to achieve a Pareto improvement. To comply with its day-ahead schedules and hedge against the intermittent wind energy generation, the WES is allowed to outsource a back up power capacity from the CES via making a bilateral contract. However, unanimous agreement cannot always be achieved since each party plays on its own interest. We employ the concept of swing option contracts to further encourage the suppliers to reach an agreement of the contract. On the one hand, the WES can leverage the uncertainty of wind output by covering possible energy shortage from the CES. On the other hand, the CES can optimally allocate its energy capacity by participating into the electricity market and offering capacity to backup the shortage of energy from the WES. The bidding problem for each supplier is formulated as a multi-stage stochastic programming model, with the objective of maximizing the expected profit while maintaining a low level of risk. Unlike the traditional two-stage approach, the proposed multi-stage model can effectively capture the impact of rebidding process in the real-time market. We incorporate conditional-value-at-risk as a risk measure to characterize the effect of risk perception of suppliers on their bidding decisions. Meanwhile, a game theory based approach is developed to obtain the contract items between the suppliers. Implementation results on real cases are provided to illustrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. [ABSTRACT FROM AUTHOR]

How to stimulate the devices to participate is a key problem in the device-to-device (D2D) communications, especially when there are multiple base stations in practice. On one hand, the base station needs to lower the price to compete with other base stations for attracting more devices. On the other hand, the base station needs to increase the price to obtain more benefit. In this paper, we propose an optimal multiprincipal multiagent contract design to tackle such a challenge. Specifically, we formulate the multiprincipal contract design problem as a game theoretic optimization problem, where the objective of each principle (base station) through contract design is to maximize its overall utility obtaining from the trade with agents (devices) subject to the individual rationality and incentive compatibility constraints. By solving the game theoretic optimization problems, we can obtain the equilibrium contract where the contracts of different principals reach an equilibrium and any deviation from a single principal will lead to the degradation of its own utility. Moreover, under the equilibrium, both the individual rationality and incentive compatibility constraints are satisfied, i.e., all agents will choose the corresponding contract according to their types to reach the optimal nonnegative utilities. Simulation results show that compared with the monoprincipal contract design, both the utilities of devices and the social welfare can be improved with the multiprincipal contract design. [ABSTRACT FROM AUTHOR]

Cooperative communication is a promising technique to mitigate channel impairment and improve spectral efficiency. Due to the mobility of relay nodes (RNs) and the unfavourable effects of wireless channels, channel conditions may change as time passes. Therefore, how to design proper long-term incentive mechanisms for RNs in such dynamic communication environments is an essential issue. This paper investigates the contract incentive method underasymmetric informationscenario, where the relay channel condition and cost are the RNs’ private information. To capture the dynamic characteristic of the RNs’ cooperative information during the long-term cooperation, two dynamic contract mechanisms are introduced into long-term relay incentive with different RNs’ relay information structures. The optimal contract is derived to maximize the source's expected utility for bothindependent asymmetric informationandcorrelated asymmetric information. Numerical results are presented to evaluate the effectiveness of the proposed dynamic contract-based incentive mechanism. [ABSTRACT FROM AUTHOR]

The paper considers a bidirectional power flow model of the electric vehicles (EVs) in a charging station. The EVs can inject energy by discharging via a vehicle-to-grid (V2G) service, which can enhance the profits of the charging station. However, frequent charging and discharging degrade battery life. We propose a menu-based pricing scheme, where the charging station selects a price for each arriving user (EV owner) for the amount of battery utilization, the total energy, and the time (deadline) that the EV will stay. The user can accept one of the menus or rejects all. The user reaches its decision based on the utilities. The charging utilizes its own limited renewable energy, and the conventional energy bought from the grid to serve the users. We show that there exists aprior-freepricing mechanism, which maximizes the ex-post social welfare for a myopic scenario. It selects a lower price for the menu containing a higher V2G service. However, theprior freepricing mechanism does not necessarily maximizes the expected profit. We show that the pricing strategy, which sets the price for a menu at a fixed value greater than the marginal cost of serving the menu, maximizes the expected profit for a wide class of utility functions. In the menu-based pricing, when the harvested renewable energy is small the users have higher incentives for the V2G service. We, numerically, show that the charging station's profit and the user's surplus both increase as V2G service is efficiently utilized by the pricing mechanism. [ABSTRACT FROM AUTHOR]

Long-term contracting is the traditional governance mechanism to deal with the transaction costs associated with the specificity of gas industry assets. Long-term contracts have been often used to allocate risks among players, and to that end, they often include take-or-pay provisions. These clauses specify that buyers take the volume risk, as they are obliged to pay for a minimum amount of gas consumption. In exchange, buyers pay a predefined price, supposedly lower than the risk-neutral expectation of short-term gas prices. In that view, if the buyer is able to resell that gas in the short term, the contract is an effective hedge against short-term volatility. Otherwise, the contract does not act as a hedge but it becomes a sunk cost. The corresponding power producers' behavior involves not only output decisions but also financial decisions. To analyze that situation, this paper develops a new quantitative methodology that allows comparing risk-neutral valuations of gas and power markets decisions. We test the model in a real-size system, and show the additional cost of the power system associated with a possible illiquidity of the short-term gas market. [ABSTRACT FROM PUBLISHER]

Portfolios of renewable electricity sources are interesting risk-management mechanisms for trading in electricity contract markets. When formed by players belonging to different companies, their stability relies on the way the benefit generated by the optimal portfolio is allocated. The challenge of finding a fair and efficient allocation can be mathematically formulated in terms of finding the Core of a cooperative game, which in turn is stated as an optimization problem with a set of constraints that exponentially grows with the number of participants, quickly becoming computationally intractable. Moreover, the right-hand-side of each constraint relies on a given coalition value, which in our case is obtained by a two-stage stochastic optimization model. This paper presents an efficient methodology based on mixed —linear programming and Benders decomposition to find the Nucleolus share of large-scale renewable portfolios. Case studies are presented with data from the Brazilian power system. [ABSTRACT FROM PUBLISHER]

The increasing integration of uncertain and volatile renewable power generation poses challenges to the operation of the interdependent electricity and gas markets. In this paper, a robust operational equilibrium solution method for the interactive markets of power and gas systems is proposed, where the bidirectional interactions include both firm energy and reserve contracting, and the impacts of the uncertainties of wind generation outputs on the two markets are characterized. The line pack as well as bidirectional flow characteristics of gas pipelines are depicted in the gas system model so as to improve its operational flexibility. To guarantee the robustness of market equilibrium against uncertainties, the power and gas market clearing models become two-stage robust ones. Column-and-constraint generation (C&CG) based and the best response algorithms are devised to clear the two markets as well as to coordinate them, respectively. Simulation results validate the effectiveness of the proposed robust operational equilibrium model and the performance of the devised solution methodology. [ABSTRACT FROM AUTHOR]

This paper deals with wind power offering strategies in day-ahead markets. A new plan is proposed in which a wind power producer participates in the day-ahead market while employing demand response (DR) to smooth its power variations. In this context, a new DR scheme is presented through which the wind power producer is able to achieve DR by establishing various DR agreements with DR aggregators. The proposed offering plan involves two stages: the first stage clears on the day-ahead market. The wind power producer decides on day-ahead offers as well as DR agreements with the aggregator. The second stage takes place on the balancing market. In a successive approach, the wind power producer determines its energy trading for each period until the whole day is covered. Additionally, proper DR agreements for each period are confirmed here. The proposed plan is formulated in a stochastic programming approach, where its validity is assessed on a case of the Nordic market. [ABSTRACT FROM PUBLISHER]

In time series analysis research, there is a strong interest in discrete representations of real valued data streams. One approach still considered state-of-the-art is the Symbolic Aggregate Approximation (SAX) algorithm. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the standard normal curve to choose partitions to discretize the data. The SAX approach chooses partitions on the standard normal curve that would produce an equal probability for each symbol. This procedure is generally valid as a time series is normalized to have $\mu = 0$ and $\sigma = 1$ . However, there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). We show in this paper that when PAA is applied, the distribution of the data is altered, resulting in a shrinking standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the standard deviation of the data contracts, the mean remains the same, however the distribution is no longer standard normal and therefore the partitions based on the standard normal curve are no longer valid for the assumption of equal probability. [ABSTRACT FROM PUBLISHER]

The penetration of distributed renewable energy sources in the distribution grid is increasing considerably in the last years. This is one of the main causes that contributed to the growth of technical problems in both transmission and distribution systems. An effective solution to improve system security is to exploit the flexibility that can be provided by distributed energy resources (DER), which are mostly located at the distribution grids. Their location combined with the lack of power flow coordination at the system operators interface creates difficulties in taking advantage of these flexible resources. This paper presents a methodology based on the solution of a set of optimization problems that estimate the flexibility ranges at the distribution and transmission system operators (TSO-DSO) boundary nodes. The estimation is performed while considering the grid technical constraints and a maximum cost that the user is willing to pay. The novelty behind this approach comes from the development of flexibility cost maps, which allow the visualization of the impact of DER flexibility on the operating point at the TSO-DSO interface. The results are compared with a sampling method and suggest that a higher accuracy in the TSO-DSO information exchange process can be achieved through this approach. [ABSTRACT FROM AUTHOR]

As an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, blockchain has attracted intensive attention in both research and applications in recent years. The key advantage of this technology lies in the fact that it enables the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources. In this paper, we presented a systematic investigation of blockchain and cryptocurrencies. Related fundamental rationales, technical advantages, existing and potential ecosystems of Bitcoin and other cryptocurrencies are discussed, and a six-layer reference model of the blockchain framework is proposed with detailed description for each of its six layers. Potential applications of blockchain and cryptocurrencies are also addressed. Our aim here is to provide guidance and reference for future research along this promising and important direction. [ABSTRACT FROM AUTHOR]

This paper sets out a framework for decision making in emergency operations for power transformers, which rely on interruption contracts as decision variables that take into account the risk of accepting some level of violation of the transformer's operating limits. The risk of failure is calculated by measuring the useful life of the transformer and the presence of dissolved gases. The useful life is estimated by means of the Arrhenius model of thermal damage, while incipient faults can be detected by dissolved gas analysis. The risk of transformer failure is estimated by examining the data cited above, on the basis of information theory concepts. It is formulated as a multiperiod optimization problem with linear objectives and nonlinear constraints and restricted to the risk of accepting transformer overloads. The results are validated by means of an adapted version of the IEEE14 system, which is shown to be effective as a tool for emergency decision making in the case studies included here. [ABSTRACT FROM PUBLISHER]

Many countries use auctions to select agents to which transmission concessions are awarded. Where multiple concessions are auctioned each year, there are potential benefits, for transcos and grid users, in using auction protocols allowing risk-averse bidders to explicitly consider complementary packages of transmission facilities: combinatorial and simultaneous descending auctions. We investigate the use of these protocols for transmission auctions, with aid of mixed-integer linear optimization models developed for this task and realistic case studies, while focusing on the treatment of the exposure problem. [ABSTRACT FROM AUTHOR]