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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]

In this paper, multiproduct supply chain network model is developed with system opti-mization perspective. Each kind of products has an individual cost function and, at the same time,contributes to its own and other product's cost function in an individual way. The well-known equi-librium algorithm is extended to nd system optimization pattern for such multiproduct supply chainnetwork.

• 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]

In this paper, multiproduct supply chain network model is developed with system optimization perspective. Each kind of products has an individual cost function and, at the same time, contributes to its own and other product's cost function in an individual way. The well-known equilibrium algorithm is extended to find system optimization pattern for such multiproduct supply chain network. [ABSTRACT FROM AUTHOR]

This paper deals with a parallel machine scheduling problem whose objective is to minimize a regular step total cost function. A real world application of the problem is presented, and Mixed Integer Linear Programming models are described for the cases with and without release dates, as well as a dedicated constraint generation procedure. Experimental results are reported and discussed to evaluate the relevance of the different approaches on well-known special cases and the general case. [ABSTRACT FROM AUTHOR]

In this paper, we develop multi-product supply chain network models with explicit capacities, before and after their horizontal integration. In addition, we propose a measure, which allows one to quantify and assess, from a supply chain network perspective, the synergy benefits associated with the integration of multi-product firms through mergers/acquisitions. We utilize a system-optimization perspective for the model development and provide the variational inequality formulations, which are then utilized to propose a computational procedure which fully exploits the underlying network structure. We illustrate the theoretical and computational framework with numerical examples. This paper is a contribution to the literatures of supply chain integration and mergers and acquisitions. [ABSTRACT FROM AUTHOR]