For a service-oriented architecture-based system, the problem of synthesizing a concrete model (i.e., a behavioral model) for each peer configuring the system from an abstract specification — which is referred to as choreography — is known as the choreography realization problem. In this paper, we consider the condition for the behavioral model when choreography is given by an acyclic relation. A new notion called re-constructible decomposition of acyclic relations is introduced, and a necessary and sufficient condition for a decomposed relation to be re-constructible is shown. The condition provides lower and upper bounds of the acyclic relation for the behavioral model. Thus, the degree of freedom for behavioral models increases; developing algorithms for synthesizing an intelligible model for users becomes possible. It is also expected that the condition is applied to the case where choreography is given by a set of acyclic relations.
The present paper discusses an assembly line balancing problem(ALBP). ALBP discussed up to now does not consider rack spaces where tools or parts are stored. We introduce an extended resource planning and assembly line balancing problem that takes the rack space into account. An exact search method for solving the problem by using a graph structure, and a heuristics for the method are proposed. The proposed method is evaluated by computational experiments.
In the present paper, we propose an on-line operation planning method for a demand bus system with multiple buses. It is necessary to solve a passengers assignment problem and a routing problem in real time in operating the demand bus system. We propose an agent-based planning method. In the proposed method, an agent exists for each bus, and it solves the routing problem of the bus by a heuristic rule based method, and solves the assignment problem by auctions and negotiations among agents. By computational experiments, we will examine effectiveness of the proposed method.
In automated transport applications, the design of a task allocation policy becomes a complex problem when there are several agents in the system and conflicts between them may arise, affecting the system's performance. In this situation, to achieve a globally optimal result would require the complete knowledge of the system's model, which is infeasible for real systems with huge state spaces and unknown state-transition probabilities. Reinforcement Learning (RL) methods have done well approximating optimal results in the processing of tasks, without requiring previous knowledge of the system's model. However, to our knowledge, there are not many RL methods focused on the task allocation problem in transportation systems, and even fewer directly used to allocate tasks, considering the risk of conflicts between agents. In this paper, we propose an option-based RL algorithm with conditioned updating to make agents learn a task allocation policy to complete tasks while preventing conflicts between them. We use a multicar elevator (MCE) system as test application. Simulation results show that with our algorithm, elevator cars in the same shaft effectively learn to respond to service calls without interfering with each other, under different passenger arrival rates, and system configurations.
Control of CO2 emissions which is the main factor of global warming is one of the most important problems in the 21st century about preservation of earth environment. Therefore, efficient supply and use of energy are indispensable. We have proposed distributed energy management systems (DEMSs), where we are to obtain optimal plans that minimize both of costs and of CO2 emissions through electrical and thermal energy trading. In this paper, we evaluate trading methods for the DEMSs by computational experiments.
Multi-Car Elevator (MCE) systems, which consist of several independent cars built in the same shaft, are being considered as the elevators of the next generation. In this paper, we present MceSim, a simulator of MCE systems. MceSim is an open source software available to the public, and it can be used as a common testbed to evaluate different control methods related to MCE systems. MceSim was used in the group controller performance competition: CST Solution Competition 2007. This experience has proven MceSim to be a fully functional testbed for MCE systems.
In recent years, the installation of distributed energy resources such as photovoltaic(PV) generations progresses. However, in a distribution system with many distributed energy resources, the voltage of the distribution line rises due to the increase of reverse power flow. Therefore, many optimization methods related to voltage control are widely studied. Particularly, the application of distributed optimization is attracting attention because the centralized control is not suitable due to the security issue for consumers and the difficulty of calculating for a large scale system. The conventional studies control real power outputs and/or reactive power outputs of power conditioning systems to minimize power losses or maximize the total real power output in the line. However, the fairness among the consumers is not considered there. In this paper, we propose a distributed optimization model using the alternating direction method of multipliers (ADMM) and the reactive power control to assure the fairness of real and reactive power outputs among consumers.
Scheduling problems belong to NP-hard and are not easily solved in large systems. In recent years, the development of optimization methods in multi-agent systems has been remarkable. In this paper, we consider a large-scale system as a multi-agent system and discuss a method of solving a scheduling problem using consensus among agents. We propose a distributed method using the alternating direction method of multipliers and evaluate the method using a small-scale instance of the scheduling problem.
This chapter discusses energy planning in a small district composed of a set of corporate entities. Although the term “energy planning” has a number of different meanings, the energy planning in this chapter stands for finding a set of energy sources and conversion devices so as to meet the energy demands of all the tasks in an optimal manner. Since reduction of CO2 emissions which are the main factor of global warming is one of the most important problems in the 21st century about preservation of the earth environment, recent researches on energy planning consider reducing impacts to the environment(Cormio et al., 2003; Dicorato et al., 2008; Hiremath et al., 2007). On the other hand, corporate entities with energy conversion devices become possible to sale surplus energy by deregulation about energy trading. Normally conversion devices have nonlinear characteristics; its efficiency depends on the operating point. By selling energy to other entities, one may have an opportunity to operate its devices at a more efficient point. We suppose a small district, referred to be a “group”, that composed of independent plural corporate entities, referred to be “agents”, and in the group trading of electricity and heat energies among agents are allowed. We also suppose that a cap on CO2 emissions is imposed on each agent. Each agent performs energy planning under the constraints on CO2 emissions and by considering energy trading in the group. An agent may take various actions for reduction: use of alternative and renewable energy sources, use of or replacement to highly-efficient conversion devices, purchase of emission credits, and so on. Use of alternative and renewable energy sources and purchase of emission credits are easier ways to reduce CO2 emissions. However, there is no guarantee to get sufficient amount of such energy or credit at an appropriate price, because the amount of such energy and credit is limited and their prices are resolved in the market. On the other hand, installing a highly-efficient conversion device comes expensive. Another way to reduce CO2 emissions is energy trading among agents. Suppose that one agent is equipped with an energy conversion device such as boilers, co-generation systems, etc. If he operates his device according to his energy demands only, the operating point of the device cannot be the most efficient one. Energy trading among agents makes it possible to seek efficient use of devices, and as a result to reduce CO2 emissions. When we attempt to minimize energy cost under the constraints on CO2 emissions in the group, it is not difficult by considering the entire group as one agent. But it is another matter 2
