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This paper aims to design a vehicle routing problem for collection of reusable packages. With the operations of reuse and/or recycling systems, effective systems for reverse logistics which disposed products are collected from customers are needed. In this paper, a vehicle routing problem is simultaneously considered for pickup and delivery goods with time windows and for collection of reusable package without time windows. In this paper, we formulate a vehicle routing problem with time windows, propose the heuristics using ACO, and clarify the effectiveness of our proposed method from some numerical examples.

This paper aims to propose a planning method on a sequence of finished cars for mounting optional components in distribution centers. In automobile manufacturers, finished cars are transported from the final assembly plant to the distribution centers by pure car carriers. In the distribution centers, finished cars are mounted optional components, and transported to the dealers. In this paper, a heuristic method for scheduling amounting order of finished cars is proposed by an Ant Colony Optimization(ACO).

This paper aims to propose a heuristic method for a transportation plan of finished cars by pure car carriers.In automobile manufacturers, finished cars are transported by trailers and pure car carriers.In this paper, we propose a heuristic method using an ant colony optimization(ACO),and clarify the effectiveness of our proposed method.

This paper aims to propose a heuristic method for a transportation plan of finished cars by pure car carriers. In automobile manufacturers, finished cars are transported by trailers and pure car carriers. In this paper, we propose a heuristic method using an ant colony optimization (ACO), and clarify the effectiveness of our proposed method.

This paper aims to propose a heuristic method for a transportation plan of finished cars by pure car carriers. In automobile manufacturers, finished cars are transported by trailers and pure car carriers. In this paper, we propose a heuristic method an ant colony optimization (ACO), and clarify the effectiveness of our proposed method.

Ant Colony Optimization (ACO)はDorigoに提案されて以降，様々なアルゴリズムの拡張が行われている。従来のACOでは個々のアリが独自の情報を持つことは無く，グローバルな情報のみに従って探索を行っていた。本稿では個々のアリの記憶情報を探索に利用するACOを提案する。さらに，個々のアリの記憶が一定確率で忘れられるケースも考える。また 性能比較実験にはTSPライブラリーの標準テスト問題を使い，拡張アルゴリズムの有効性を示す。, Since Ant Colony Optimization (ACO) algorithm was introduced by Dorigo in 1992, several researchers have enhanced it. Each ant in basic ACO algorithm has no long-term memory; it searches using only pheromone information. In this paper, we propose a variant of ACO algorithm that uses external memory of each ant to seek an optimum solution. Moreover, it incorporates not only the case in which each ant’s memory is permanent but also the case in which the memory is lost with a certain probability. The effectiveness of our proposed algorithm is demonstrated by testing with benchmark test problems from the TSP library (TSPLIB).

The characteristic of pulmonary is different, and the setting of the respirator is decided by the experience and the perception of the doctor now. Therefore, the suitable setting of ventilation conditions is needed for every patient. The static P-V curve expresses the characteristic of human lungs, and that is needed for the parameter setting of the respirator. Thus we must estimate the pulmonary elastance and the static P-V curve. In our previous work, the fuzzy logic technique has been proposed to estimate the pulmonary elastance that is a nonlinear function by fuzzy combination of first order functions. However, manual operation is needed to set suitable range of the fuzzy variables in this technique. In this paper, an optimization algorithm is proposed for fuzzy variables's range setting, and the effectiveness of the suggested technique is illustrated by an estimation example using real patient data.

The characteristic of pulmonary is different, and the setting of the respirator is decided by the experience and the perception of the doctor now. Therefore, the suitable setting of ventilation conditions is needed for every patient. The static P-V curve expresses the characteristic of human lungs, and that is needed for the parameter setting of the respirator. Thus we must estimate the pulmonary elastance and the static P-V curve. In our previous work, the fuzzy logic technique has been proposed to estimate the pulmonary elastance that is a nonlinear function by fuzzy combination of first order functions. However, manual operation is needed to set suitable range of the fuzzy variables in this technique. In this paper, an optimization algorithm is proposed for fuzzy variables's range setting, and the effectiveness of the suggested technique is illustrated by an estimation example using real patient data.