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Bag-of-Tasks (BoT) applications consisting of multiple tasks are widely used in various fields.Different from the traditional deadline constraint in scheduling problems,a due date constraint allows the BoT applications to finish more than a predetermined due date,but would result in the tardiness penalty.In this case,in order to reduce the total cost,the efficient harmony search (EHS) algorithm is proposed to optimize the scheduling tasks in hybrid clouds.The algorithm obtains the initial task sequence by random search and fine tuning.By improving the harmony search steps and the way of generating new harmony memory,a large number of high-quality harmony can be obtained in one search process,which greatly improves the efficiency of harmony search and speeds up the convergence speed of the algorithm.Through continuous iteration,the global optimal solution is obtained,that is to say,BoT application scheduling scheme has the lowest total cost.Experimental results show that compared with other algorithms,the proposed algorithm has significant improvement in performance,which can effectively reduce the total cost of scheduling BoT applications.

We consider a real-time demand response system in smart grid distribution to solve real-time pricing’s (RTP) total cost minimization problem. This problem has not yet to be discussed especially at the level of neighborhood area networks. While the available RTP-alike schemes are too stand-alone to solve all the problems systematically, this paper proposes a mathematical energy scheduling model for RTP demand response, and based on which, a distributed scheduling algorithm of energy consumption for total cost minimization is proposed. Simulations are conducted to find the minimum total cost under different sets of parameters. [ABSTRACT FROM AUTHOR]

• We develop a total cost minimization model to optimize service frequency and vehicle size for automated bus systems. • Crowding discomfort externalities, time-dependent demand, denied boardings, and stochastic travel times are modeled. • An application-focused study offering an extensive evaluation of the service and cost implications of the deployment of automated buses. • Extensive experiments are performed for two real-life case studies in Germany and Chile, and numerical results are widely analyzed. • In the presence of crowding discomfort externalities, the frequency is increased at a higher rate for automated bus fleets than for human-driven bus fleets. • The deployment of automated bus systems can significantly mitigate crowding-related problems for users. Public transport is considered as one of the most suitable candidates to benefit from autonomous driving technologies. In this research, we develop a mathematical modeling framework to optimize service frequency and vehicle size for automated bus systems, while accounting for both user and operator costs. We explicitly consider travel time stochasticity, time-dependent passenger flows, vehicle capacity limitations (extra waiting time due to denied boarding), and in-vehicle discomfort externalities for both sitting and standing passengers at a microscopic level. We attempt to provide a thorough assessment of the service and cost implications of the deployment of automated buses. Hence, a broad range of experiments are simulated by combining different deployment cases: (i) vehicle technology (human-driven or automated vehicles), (ii) travel time assumptions (deterministic or stochastic travel times), and (iii) crowding externalities (considering or ignoring in-vehicle crowding costs). The model applicability is assessed on two real-world bus corridors in Regensburg (Germany) and Santiago (Chile). Results show that, with crowding externalities, optimal vehicle size is increased at a similar rate for both human-driven and automated bus services, whereas optimal service frequency is increased at a higher rate for automated buses. Thus, under optimal levels of supply, automated vehicles are operated with lower occupancy levels than human-driven vehicles, increasing the quality of service. Besides, the deployment of automated bus systems can significantly alleviate or eliminate denied boardings. The effects of automation on travel time volatility and dwell time regularity are studied. The consideration of stochastic travel times increases optimal frequencies at a higher rate for automated services relative to human-driven vehicles. Interestingly, we find that even though the operator benefits from automation are more pronounced in high-income countries (due to a greater potential for human driving cost savings), the final outcome is counterbalanced by the actual public transport demand level, because large user cost savings from automation are reachable in crowded routes even in situations in which labor costs are lower (as in developing countries). [ABSTRACT FROM AUTHOR]