Engineering supply chain quality control under different information conditions

The suspension system is an important unit that affects vehicle performance, including handling stability and riding comfort. In order to improve vehicle performances, a novel robust optimal control strategy for the active suspension system in vehicles is proposed. To handle the parameter uncertainties and external random disturbances, the proposed optimal control strategy is designed by using the combination of the linear quadratic regulator (LQR) optimal control and sliding-mode control. First, by using the nominal model of the active suspension system without the uncertain parameters and the external random disturbances, an optimal control performance index is given, and the initial optimal controller is designed by using the LQR control strategy. Then, to handle the parameter uncertainties and the external disturbances, a robust optimal integral sliding-mode control strategy based on the initial optimal controller is designed, which not only can achieve the optimal control objective but also has good robustness to the uncertain parameters and external disturbances. By using the Lyapunov stability theory, stability analysis of the proposed robust optimal integral sliding-mode control strategy is performed. Finally, the collaborative simulation platform based on the Carsim and MATLAB/Simulink is developed. The simulation results illustrate the advantage of the proposed robust optimal control strategy for the suspension system.

Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information

Heterogeneous suppliers’ contract design in assembly systems with asymmetric information

A study was conducted to assess the energy performance of an optimal predictive control strategy for radiant floor district heating systems. A four-zone radiant floor heating system model was developed. The simulated performance of the optimal predictive control strategy was studied. The results showed 10% energy savings compared to a Proportional-Integral (PI) control strategy. Experiments were conducted in a laboratory radiant floor district heating system test facility. The description of the test facility, its operating conditions, and the results obtained are described. Experimental results further confirm the findings from the simulation study. Being simple and energy efficient, the optimal predictive control strategy is a good candidate control strategy for radiant floor district heating systems. Practical application: Energy efficiency is a major issue of interest in the design and operation of sustainable heating systems. The radiant floor district heating systems have been successfully installed and operated in many countries resulting in significant energy savings. The optimal predictive control strategy proposed in this study further enhances the potential for higher energy savings from the district heating systems. The optimal control strategy is simple to implement as it relies on the predicted outdoor air temperature and computes future temperature set-points for the boiler water temperature. A suitably tuned Proportional-Integral (PI) controller can be used to track the optimal set-point thus realizing potential energy savings. A programmable logic controller or a supervisory control system would be appropriate to implement the designed optimal control strategy. The local control can be realized by using industrial PI feedback controllers.

Formal quality programs first appeared in the 1960s through numerous government agencies. Methods by which the contractor administers these programs is often as varied as the projects constructed. Regardless of the method, the result is a cost to the project. Often, conflicts occur between the quality control procedures and the production of a construction project. At times, there is much duplication of quality control effort when there is more than one contractor on a given project. This lends itself to a central quality control system. In order to provide quality in the construction project, adequate attention must be given to the design and working drawings. Prequalification of participants in a project and firm allowances for quality control should result in competitive pricing and better‐constructed projects. Much attention needs to be given to the selection of an engineer, contractor, and, in some cases, construction manager, by the owner, for the best overall end result.

Optimal choice of capacity, toll and government guarantee for build-operate-transfer roads under asymmetric cost information

Quality incentive contracts considering asymmetric product manufacturability information: Piece rate vs. Tournament

Asymmetries of information exist widely in outsourcing market with the development of global economic. The problem of adverse selection with asymmetric information about the agent's production cost in outsourcing of manufacturing industry is studied in the paper. The principal has asymmetric information about agents' production cost and is in inferior position while the agents have their private information and are in superior position. On the basis of the principal-agent theory, two kinds of models are presented respectively under symmetric and asymmetric information. The adverse selection model which can reveal the true cost type of the agent under asymmetric information is set up and the optimal contracts are gotten. The results show that the contracts can attain the objective of inciting the agents to tell truth. Then two kinds of models are compared. The studies we have performed show that the principal experiences his losses owing to the lack of information under asymmetric information. In addition, as the results of our analysis, we conclude that the lower-cost agent obtains extra informational rent besides his reservation utility but the higher-cost agent only gets his reservation utility. The inefficient agent's contract is distorted. Further, the principal's optimal information search interval is given. It is concluded that collecting the agent's private information before signing the contract and offering a menu of contracts are favorable to the principal. Moreover, the principal should weigh the information value and the information search cost. In the end, in view of the given data, the influences of the probability assessment of lower cost on outsourcing contracts are analyzed. The results indicate that there exists efficiency loss under asymmetric information. With the increase of probability assessment on lower cost, the distortion degree of the higher-cost agent's contract is higher, the lower-cost agent obtain less informational rent and profit of the principal under asymmetric information is closer to that under symmetric information

Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized autoregressive conditional heteroskedasticity in mean (GARCH-M (1,1)) was applied to examine whether the risk-return trade-off phenomenon was persistent in crypto-currencies market. Findings The main findings indicate that bitcoin market return or volatility is symmetric informative and has a long memory to persist in the future. Furthermore, the sympatric volatility is found to be more sensitive to its past values (lagged) than to the new shock of the market values. However, asymmetric informative response of volatility to the negative and the positive shocks do not exist in the bitcoin market or, in other words, there is no leverage effect. This suggests that the bitcoin market is in harmony with the efficient market hypothesis (EMH) with respect to the asymmetric information and violated the EMH with regard to the symmetric information. Hence, the market price or return of bitcoin currency could not be predicted by simply exercising such past market information in the short-run investment. In addition, the estimated coefficient of conditional variance or risk premium (λ) in the mean equation of CHARCH–M (1,1) model is positive however, statistically insignificant. This indicates the absence of risk-return trade-off, in which case the higher market risk will not essentially lead to higher market returns. This paper has proposed that an investment in the crypto-currency market is more appropriate for risk-averse investors than risk takers. Originality/value The findings of the study will provide investors with necessary information about the bitcoin market price efficiency, hedging effectiveness and risk management.

This paper presents an optimal Direct Torque Control (DTC) strategy for Doubly Fed Induction Generator based wind turbine. The proposed strategy is considered based on Particle Swarm Optimization (PSO) Algorithm. The PSO is found to be robust and fast in solving nonlinear problems. Motivation for application of PSO approach is to overcome the limitation of the conventional controllers design, which cannot guarantee satisfactory control performance when designed by trial and error. In this work PSO algorithm was used to adjust the hysteresis torque and flux comparators bandwidths and to tune the parameters of Variable Gain PI (VGPI) controller designed for Maximum Power Point Tracking (MPPT) speed control of wind turbine to ensure high performance torque control. The optimal control strategy is considered in detail and it is shown that the use of the optimized controllers reduces rotor flux and electromagnetic torque ripples and improves the system dynamic performances. The effectiveness of the proposed direct torque control based on PSO algorithm (optimal DTC) method is illustrated through simulations on 1.5 MW DFIG based wind turbine. Simulation results illustrate the improved performances of optimal DTC compared with the conventional DTC.

Supply chain contracts under demand and cost disruptions with asymmetric information

PurposeThe aim of this paper is to investigate the effect of information asymmetry on revenue sharing contracts and performance in a dual-channel supply chain. First, the authors model the optimum revenue sharing contract in a dual-channel supply chain under both the full information case and the asymmetric information case. Second, they contrast the optimal decisions of a dual-channel supply chain between the full information case and the asymmetric information case. Third, they explore the impact of asymmetric cost information on the performance of a dual-channel supply chain and investigate the information value.Design/methodology/approachThe authors present two main issues associated with revenue sharing contracts to alleviate manufacturer–retailer conflicts in a dual-channel supply chain. In the first issue, a revenue sharing contract is designed in a dual-channel supply chain under asymmetric cost information conditions, based on the principal-agent model. In the second issue, an optimal revenue sharing contract under full information conditions, based on the Stackelberg game is discussed. They explore the impact of asymmetric cost information on the performance of a dual-channel supply chain and investigate the information value based on comparative static analysis.FindingsFirst, the direct sale price is unchanged and independent of the retailer’s cost construct, but the wholesale price increases and the retail sale price does not decrease under asymmetric cost information. The information asymmetry leads to higher direct sale demand and lower retail sale demand. Second, information asymmetry is beneficial for the retailer, but imposes inefficiency on the manufacturer and the whole supply chain. Third, the performance of the dual-channel supply chain is improved if the retailer’s cost information is shared and the dual-channel supply chain reaches coordination. The retailer is willing to share its cost information if the lump sum side payment that the manufacturer offers can make up the retailer’s reduced profit due to sharing this information.Originality/valueThe authors proposed a contract menus design model in a dual-channel supply chain. They examine how information asymmetry affects optimal policies and performance. They compared the optimal policies under symmetric information and asymmetric information. Conditions under which the partners prefer sharing information are identified. They quantified the information value from the points of partners and the whole system.

This paper analyzes a dual channel supply chain with service cooperation under asymmetric information. We consider the games with asymmetric risk information and symmetric information respectively, derive the corresponding equilibrium strategies, and conduct detailed comparisons and analyses. We study the impact of service quality and asymmetric information on the optimal strategies. We find that the impact of manufacturer's service quality on direct channel price is greater than its on retail price, and retailer's service quality has a greater impact on retail price under with asymmetric information. Retailer would like to share his private information with manufacturer in order to get more profits.

This paper aims to achieve distributed optimal consensus control for discrete-time multi-agent systems (MASs) under composite switching topologies through utilising the iterative Q-learning approach. First, the optimal consensus control strategy, independent of system dynamics information, is developed by using the designed iterative Q-learning approach to guarantee that all follower states can synchronise to the desired trajectory formulated by the leader. Compared with the invariable algebraic or edge-fixed topology structures, the designed Q-learning control algorithm in this paper is implemented based on the composite switching topologies consisting of periodic and stochastic mechanisms. Then, an error dynamic system is established, which transforms the optimal consensus control issue into an optimal regulation issue and removes the influence of the two types of switching topologies. To conquer the problems of lack of model information and the inaccessible analytical solution of Hamilton-Jacobi-Isaacs (HJI) equation, an iterative Q-learning approach is designed according to the reconstructed Q-function Bellman equation. Through further iterative learning, the optimal control strategy can be acquired so as to realise consensus control and ensure the stability of the closed-loop systems. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed distributed optimal control scheme.

The malicious attack in the networked-connected control system has become a key problem to the security issues. The reliable transmission of the signal in industrial control is of prime requirement which ensures the stability of the control system. Recent growth in the networked-connected control system made it more vulnerable to malicious interventions, which degrade the overall performance of the control system. In this paper, an optimal predictive control strategy based on output tracking control is presented to improve the performance parameter of networked-connected control system against network imperfections such as delay, packet loss and malicious attack. The response signal, control signal computed for increasing packet loss rate (15% and 25%) and attack showed slightly more deviation from the value computed for 0% and 5% packet loss rate but it is still converging following optimal predictive control strategy. Finally, a simulation is performed to show the effectiveness of the proposed optimal predictive control strategy.