Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning

Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domain that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks.Comment: Under revie

Perbandingan Teknik Pengkodean Langsung dan Tidak Langsung pada Kasus Penjadwalan Jobshop

Development of technology help human life in problem solving. Scheduling is a one of the problem which could be solved with it. In scheduling research, jobshop case is frequently used to test the scheduling problem solving algorithm. This study provide the comparison between direct encoding and indirect encoding approach. These approach are choices in jobshop secheduling problem research. The apparent differences of these approach are in used technique. Genetic algorithm is used as the testing algorithm. The Cases which will be used are the common cases from OR-Lib. The testing is done by looking the makespan, processing time, and objective value transformation. Testing result shows the direct encoding approach found more small makespan. Whereas indirect encoding approach can found optimal makespan in running with large number of generation

Knowledge-based design of generate-and-patch problem solvers that solve global resource assignment problems

We present MENDER, a knowledge based system that implements software design techniques that are specialized to automatically compile generate-and-patch problem solvers that satisfy global resource assignments problems. We provide empirical evidence of the superior performance of generate-and-patch over generate-and-test: even with constrained generation, for a global constraint in the domain of '2D-floorplanning'. For a second constraint in '2D-floorplanning' we show that even when it is possible to incorporate the constraint into a constrained generator, a generate-and-patch problem solver may satisfy the constraint more rapidly. We also briefly summarize how an extended version of our system applies to a constraint in the domain of 'multiprocessor scheduling'

Optimal Generation Scheduling of Hydropower Plant with Pumped Storage Unit

The generation scheduling problem of hydropower plant in the presence of pumped storage unit is complex. This work has proposed a solution strategy to determine the optimum generation based on the analysis of hourly and annual generation costs. The hourly and annual costs are linear functions of power output and energy generation, respectively. The power output was formulated as a linear function of hydraulic head and discharge rate, without computing the conversion efficiency from a hill diagram. The mathematical relationships between power, hydraulic head, and discharge rate can be determined from loading & efficiency test data and validated by using a field test. The optimum generation was stated as capacity factor and varied with water drawdown of the plant. The optimum time interval and duration of the pumped storage unit can be determined by analyzing the hourly marginal operating costs, simplified by using a composite cost function. The proposed strategy has been simulated with the generation data of the Bhumibol hydropower plant. Keywords: generation scheduling, hydropower plant, pumped storag

Electric Vehicle Scheduling with Capacitated Charging Stations and Partial Charging

This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling problem as a path-based binary program, whose linear relaxation we solve using column generation. We find integer feasible solutions using two heuristics: price-and-branch and truncated column generation, including acceleration strategies. We test the approach using data of the concession Gooi en Vechtstreek in the Netherlands, containing up to 816 trips. The truncated column generation outperforms the other heuristic, and solves the entire concession within 28 hours of computation time with an optimality gap less than 3.5 percent

Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach