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Yusuke MORIHIRO Toshiyuki MIYAMOTO Sadatoshi KUMAGAI
This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP is a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). In our former study, a cooperative algorithm, called the triple loop method, with autonomous distributed agents has been proposed. The Improving initial Task Assignment and Avoiding Deadlock method (ITAAD) is a faster algorithm than the triple loop method. In this paper, we propose a new vehicle routing method for the ITAAD. Results of computational experiments show effectiveness of the proposed routing method.
Yusuke MORIHIRO Toshiyuki MIYAMOTO Sadatoshi KUMAGAI
This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). As an approach to this problem, a cooperative algorithm with autonomous distributed agents has been proposed. The algorithm is able to plan deadlock-free routes even though the buffer capacity is less, but includes reformability at the point that computation time of that case increases drastically. This paper proposes an initial task assignment method to reduce computation time on planning routes. Results of computational experiments show effectiveness of the proposed method.
Toshiyuki MIYAMOTO Norihiro TSUJIMOTO Sadatoshi KUMAGAI
Recently, there are so many researches on Autonomous Distributed Manufacturing Systems (ADMSs), where cooperation among agents is used to solve problems, such as the scheduling problem and the vehicle routing problem. We target ADMSs where an ADMS consists of two sub-systems: a Production System (PS) and an Autonomous Transportation System (ATS). This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for ATSs under conditions of given production schedule and finite buffer capacity. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW), and this paper gives a mathematical formulation of the problem. This paper, also, proposes a cooperative algorithm to obtain suboptimal solutions in which no deadlocks and buffer overflows occur. By computational experiments, we will examine the effectiveness of the proposed algorithm. Computational experiments show that the proposed algorithm is able to obtain efficient and deadlock-free routes even though the buffer capacity is less.
Toshiyuki MIYAMOTO Sadatoshi KUMAGAI
Autonomous distributed manufacturing systems(ADMS) consist of multiple intelligent components with each component acting according to its own judgments. The ADMS objective is to realize more agile and adaptive manufacturing systems. This paper presents the introduction of context-dependent agents (CDAs) in ADMS, and switch strategies depending on system conditions to achieve better performance can be realized by agents that use the same strategies under all system conditions. For the real-time job scheduling problem, the present paper recalls a basic CDA architecture, and presents the results of an extensive empirical evaluation its performance relative to other rule-based schemes based on several common indices for real-time dispatch.
Toshiyuki MIYAMOTO Daijiroh ICHIMURA Sadatoshi KUMAGAI
The present paper addresses the design of manufacturing systems. A resource planning and task allocation problem is proposed, and a multi-agent system for this problem is discussed. In the multi-agent system, an agent exists for each resource and for each operation. The proposed multi-agent system improves the quality of resulting plans by the learning of these agents.
Toshiyuki MIYAMOTO Bruce H. KROGH Sadatoshi KUMAGAI
Autonomous distributed manufacturing systems (ADMS) consist of multiple intelligent components with each component acting according to its own judgments. The ADMS objective is to realize more agile and adaptive manufacturing systems. This paper presents the introduction of context-dependent agents (CDAs) in ADMS that switch strategies depending on system conditions to achieve better performance than can be realized by agents that use the same strategies under all system conditions. For the real-time job scheduling problem, the paper presents a basic CDA architecture and the results of an extensive empirical evaluation of its performance relative to other rule-based schemes based on several common indices for real-time dispatch.