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The purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.

This research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.

The authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

This paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

The purpose of this paper is to investigate the major factors influencing retailer’s optimal ordering strategy in a supply chain consisting of one supplier and one retailer, where the retailer is newsvendor-like and capital-constrained, and further explore the issue of supply chain coordination.

Based on bi-objective programming which is modeled under the mean-variance framework, the retailer’s optimal ordering strategy is derived. Furthermore, through comparative analysis between decentralized system and centralized system along with a numerical simulation, this study examines the theoretical conclusions about supply chain coordination.

This study shows that a poor retailer with a high Expected Terminal Wealth Target Threshold (ETWTT) would ignore bankruptcy risk and order more, whereas a rich retailer is relatively conservative. It also reveals that in some cases, the optimal order quantity and performance of decentralized system could be both improved. However, the centralized system can always get more profit than the decentralized one.

This study uses a bankruptcy threshold to describe retailer’s bankruptcy risk, and considers retailer’s wealth status to formulate the model as an innovative bi-objective programming. The type of retailer as rich or poor in terms of his wealth status and asset structure is distinguished. Moreover, the impacts of retailer’s type and ETWTT on ordering strategy are examined.

We study a microfounded search model of exchange in the laboratory. Using a within-subjects design, we consider exchange behavior with and without an intrinsically worthless token object. While these tokens have no redemption value, like fiat money they may foster greater exchange and welfare via the coordinating role of having prices of goods in terms of tokens. We find that welfare is indeed improved by the presence of tokens provided that the economy starts out with a supply of such tokens. In economies that operate for some time without tokens, the later surprise introduction of tokens does not serve to improve welfare. We also explore the impact of announced changes in the economy-wide stock of tokens (fiat money) on prices. Consistent with the quantity theory of money, we find that increases in the stock of money (tokens) have no real effects and mainly result in proportionate changes to prices. However, the same finding does not hold for decreases in the stock of money.

The rapid development of e-commerce has caused not only explosive growth of the express delivery industry, but also ever-greater operational pressures. Models from the sharing economy may provide new ideas for operational improvement. The purpose of this paper is to consider an optimization method that reduces costs and increases efficiency. The proposed method enables a shared distribution system based on revenue-sharing and cooperative investment contracts.

The authors design a two-echelon supply chain (SC) of the shared distribution system with one shared distribution company and N express companies. In this SC, the express companies provide only inter-city transportation, and they outsource internal-city transportation to a shared distribution company. This distribution system differs from that of the traditional express delivery industry. The traditional system of delivery requires large numbers of empty trips (with no load to deliver), because the operating mode of urban distribution has been the franchise. To offer greater efficiency and performance, the authors introduce the sharing economy mode of express delivery. The authors examine the potential of a joint optimal decision-making strategy that involves revenue-sharing and cooperative investment contracts based on an order flow proportion (OFP) and a revenue-sharing factor (RSF). In this shared distribution system, the most important innovation is that all of the express companies jointly invest in and establish a shared distribution company based on OFP or RSF principles.

The profitability of an SC with revenue-sharing contracts based on an OFP system is much higher than that of a decentralized SC, and it is very close to the profitability of a centralized SC. In SCs with revenue-sharing contracts that are based on RSFs, there are many possible combinations of RSFs that can increase the overall profitability. The analyses indicate that the OFP system offers the best solution in designing revenue-sharing contracts based on RSFs.

This study indicates that revenue-sharing contracts based on both OFP and RSF principles can increase overall SC returns by 0.21 to 0.44 percent. In sum total, this improvement could mean a 0.84 to 1.76bn Yuan increase in revenues for the 400+ bn-Yuan express delivery industry.

The authors find that a combination of equity investment and SC coordination contracts makes the cooperation between SC members much more stable. Through this kind of shared distribution system, the scale of economy can further reduce the costs and increase the efficiency of the express delivery industry.

The purpose of this paper is to concern the design of a robust model predictive controller for distributed networked systems with transmission delays.

The overall system is composed of a number of interconnected nonlinear subsystems with time-varying transmission delays. A distributed networked system with transmission delays is modeled as a nonlinear system with a time-varying delay. Time delays appear in distributed systems due to the information transmission in the communication network or transport of material between the sub-plants. In real applications, the states may not be available directly and it could be a challenge to address the control problem in interconnected systems using a centralized architecture because of the constraints on the computational capabilities and the communication bandwidth. The controller design is characterized as an optimization problem of a “worst-case” objective function over an infinite moving horizon.

The aim is to propose control synthesis approach that depends on nonlinearity and time varying delay characteristics. The MPC problem is represented in a time varying delayed state feedback structure. Then the synthesis sufficient condition is provided in the form of a linear matrix inequality (LMI) optimization and is solved online at each time instant. In the rest, an LMI-based decentralized observer-based robust model predictive control strategy is proposed.

The authors develop RMPC strategies for a class of distributed networked systems with transmission delays using LMI-Based technique. To evaluate the applicability of the developed approach, the control design of a networked chemical reactor plant with two sub-plants is studied. The simulation results show the effectiveness of the proposed method.

This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded.

Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs).

The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system.

The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

The purpose of this paper is to deal with profit maximization problem of two-layer supply chain (SC) under fuzzy stochastic demand having finite mean and unknown variance. Buyback policy is employed from the retailer to supplier. The profit of the supplier solely depends on the order size of the retailers. However, the loss of shortage items is related to loss of profit and goodwill dependent. The authors develop the profit function separately for both the retailer and supplier, first, for a decentralized system and, second, joining them, the authors get a centralized system (CS) of decision making, in which one is giving more profit to both of them. The problem is solved analytically first, then the authors fuzzify the model and solve by fuzzy Hausdorff distance method.

The analytical models are formed for both centralized and decentralized systems under non-cooperative and cooperative environment with suitable constraints. A significant assumption on density function, namely Cauchy-type density function, is introduced for demand rate because of its wider range of the retailers’ satisfactions. Fuzzy Hausdorff metric is incorporated within the fuzzy components of the fuzzy sets itself. Using this method, the authors find out closure values of both centralized and decentralized policies, which is an essential part of any cooperative and non-cooperative two-layer SC models. Moreover, the authors take care of the profit values with corresponding ambiguities for both the systems explicitly.

It is found that the centralize policy of SC could only be able to maximize the profit of both the retailers and suppliers. All analytical results are illustrated numerically along with sensitivity analysis and side by side comparative studies between Hausdorff and Euclidean distance measure are done exclusively.

The main focus of attention of the proposed model is given to usefulness of Hausdorff distance. Unlike other distances, Hausdorff distance can take special care on the similarity measures of different fuzzy sets. Researchers have been engaged to use Hausdorff distance on the different fuzzy sets but, in this study, the authors have used it within the components of a same fuzzy set to gain more closure values than other methods.

The use of this Hausdorff distance approach is totally new as per literature survey suggested yet. However, the Cauchy-type density function has not been introduced anywhere in SC management problems by modern researchers still now. In crisp model, the sensitivity on goodwill measures really provides a special attention also.

The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.

To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.

The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.

The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.

The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making behavior of players in a cassava-based bioethanol supply chain under random yield and demand environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply chain resiliency and performance.

The decision-making behavior under three models, namely, centralized model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing coefficients and randomly fluctuating cassava market price on the decision-making behavior and performances are provided.

Though the proposed risk-sharing model cannot achieve the supply chain performance in the centralized model, it does help to encourage the farmers and the company to increase the supply of cassava and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these improvements will be enlarged as the yield uncertainty risk is higher.

The findings will help decision makers in the bioethanol supply chain to understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the development of the bioethanol industry.

The proposed risk-sharing model incorporates the yield uncertainty risk, the random market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in the model.