Detecting Elevator Traffic Patterns with Neural Networks

Iwata, Masafumi; Hikita, Shiro; Komaya, Kiyotoshi

doi:10.9746/sicetr1965.33.209

Cited by 7 publications

(2 citation statements)

References 1 publication

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“…Many research are, therefore, naturally based on the utilization of if-then rule-bases. For example, if sufficiently effective rule-bases are available, we can decrease passengers' waiting times by switching such rule-bases by the method reported in [1]. Else if the rule-bases are propitious in their configurations but involve some parameters, the rule-bases can be improved through the adjustment in their parameters by the method reported in [2].…”

Section: Introductionmentioning

confidence: 99%

Model-approximated dynamic programming based on decomposable state transition probabilities

Inamoto

Tamaki

Murao

2007

SICE Annual Conference 2007

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In this paper, a discrete model of an elevator system and a design of dynamic programming (DP) method based on that model are shown. The method is followed by a modified DP method on approximated state transition models. The discrete model theoretically considers the causes of a system's probabilistic behavior, and leads such approximation by the reduction of the insignificant constituent of them. In computational illustrations, the two DP methods are applied to two problems of small scale. The results show that the policies obtained by DP methods are both statistically better than an existing elevator allocation rule, and the modified method requires less computational time than the another.

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Section: Introductionmentioning

confidence: 99%

Model-approximated dynamic programming based on decomposable state transition probabilities

Inamoto

Tamaki

Murao

2007

SICE Annual Conference 2007

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“…The latter is more probable if there is no canteen at floor 3. Since it is possible to distinguish the current traffic pattern (11) to some degree by using information such as time, the EOP is separately considered for each representative pattern and experts devise operational rules for each pattern. This fashion is also adopted on the basis of problem instances in this paper.…”

Section: Journal Of Advanced Mechanical Design Systems and Manufactmentioning

confidence: 99%

An Approach Employing Polysemous Rules to Complement Legacy Rules for the Elevator Operation

Inamoto

Ohta

Tamaki

et al. 2010

JAMDSM

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In this paper, an approach to complement legacy rules for the elevator operation is proposed. The approach is derived from the analysis that the elevator operation in the real world often obeys a heuristic rule, and such a rule can be divided into a legacy rule and ad-hoc rules. In the approach, ad-hoc rules are represented as polysemous rules, and a Genetics-Based Machine Learning (GBML) method is applied to acquire such rules. Here, a polysemous rule encodes, not a set of environments' states as the well-known if-then rule does, but a relative attribute vector of an arbitrary elevator. The elevator selection rule based on polysemous rules is simple: if there is a polysemous rule which matches one of attribute vectors of the elevators, select the elevator which corresponds to the matching vector; otherwise select an elevator according to a legacy rule. In computer illustrations, the GBML method is applied to 3 traffic patterns formed by the system's users. It is shown that the resultant polysemous rules seem to complement an existing (legacy) operational rule. Furthermore, polysemous rules, which are selected among those acquired by the GBML method, are successfully applied to harder problems with more elevators than those used in learning.

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Deterministic Optimization Model of Elevetor Operation Problems and An Application of Branch-and-Bound Method

et al. 2003

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Detecting Elevator Traffic Patterns with Neural Networks

Cited by 7 publications

References 1 publication

Model-approximated dynamic programming based on decomposable state transition probabilities

Model-approximated dynamic programming based on decomposable state transition probabilities

An Approach Employing Polysemous Rules to Complement Legacy Rules for the Elevator Operation

Deterministic Optimization Model of Elevetor Operation Problems and An Application of Branch-and-Bound Method

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