A simulation model of pedestrian flow is introduced based on Mixed Logical Dynamical (MLD) system approach. Typical phenomena in the evacuation flow are generated with the proposed model and, further, it is verified that improved outflows can be reached by obstacles placed asymmetrically in front of the exits. The feature of the congestion in the evacuation is also discussed in terms of the flow coefficient.
In this paper, we try to model for ‘Naiji System’ which is a unique corporation between a maker and suppliers in Japan. We propose Mass Customization Production Planning & Management System (MCPS) based on unfulfilled-order-rate by using Advance Demand Information, which is called ‘Naiji’. This model is formulated as a nonlinear stochastic programming problem which minimizes the sum of production cost and inventory holding cost subject to the set of probabilistic constraint and some linear production constraints. We propose the new upper bound SOn (ρmin) to estimate the unfulfilled-order-rate more strictly. The procedure to find a good solution is developed by solving the linear programming problem repeatedly on the basic solution strategy that is ‘relaxation’. A computational load to obtain a solution by the proposed indicator is shown to be very small. Finally, an availability of the procedure is shown.
An efficient learning method using Fuzzy ART with Genetic Algorithm is proposed. The proposed method reduces the number of trials by using a policy acquired in other tasks because a reinforcement learning needs a lot of the number of trials until an agent acquires appropriate actions. Fuzzy ART is an incremental unsupervised learning algorithm in responce to arbitrary sequences of analog or binary input vectors. Our proposed method gives a policy by crossover or mutation when an agent observes unknown states. Selection controls the category proliferation problem of Fuzzy ART. The effectiveness of the proposed method was verified with the simulation of the reaching problem for the two-link robot arm. The proposed method achieves a reduction of both the number of trials and the number of states.