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

Inamoto, Tsutomu; Tamaki, Hisashi; Murao, Hajime; Kitamura, Shinzo

doi:10.1541/ieejeiss.123.1334

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…4, No.3, 2010 it can be modeled as a kind of production scheduling problem (8) by regarding travels of passengers as jobs, hall-calls and car-calls as operations, and cars as machines. Such models make it possible to apply mathematical programming methods, such as the Branch-and-Bound method (9) . However, such assumption is impractical, so the EOP is usually treated as dynamic and the simulation program-based framework, in which passengers appear according to specific traffic patterns, is adopted.…”

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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [10] the authors assume that the advanced information in a look-ahead window can be obtained and show by numerical tests that the advanced information is valuable. There has been also research for the full information problem [11][12][13][14] , in which the arrival time, origins and destinations of all the passengers (including arrived and not arrived passengers) are all assumed known. The more information we have, the better we can schedule the elevator.…”

Section: Introductionmentioning

confidence: 99%

“…problem, we know the best performance that the scheduling algorithm could achieve. The optimal solution of the full information problem is "helpful to estimate the effectiveness of the utilization of look-ahead information" and "also useful in valuating the performance of the existing rules for elevator operation", as pointed out in [12].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization for single car scheduling of elevator systems with full information

Shen

Zhao

2009

2009 4th IEEE Conference on Industrial Electronics and Applications

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We concentrate on the single car, full information elevator problem. Here "full information" means that the arrival time, the origins and destinations of passengers are all assumed known beforehand. The importance of studying full information problem lies in that we can know the value of the future information and evaluate the existing scheduling methods for the elevator system. We aim to find the best solution of serving the passengers, and the performance is measured by the average service time. The problem is modeled into a graph and our goal is converted to finding a path on the graph corresponding to the best performance. An algorithm of Ant Colony Optimization (ACO) is applied. Different from applying ACO to solve the Traveling Salesman Problem, we have the time factor and the constraints in our problem. The modeling of the problem and the handling of the constraints are the contributions of this paper. Our method is compared with Branch and Bound method (BB) which can obtain the optimal solution and a popular heuristic rule in the literature named selective collective operation. The results of our ACO on small scale problems are very near to optimal, and for middle and large scale problems, our results are much better than the selective collective operation. These results show the effectiveness of our ACO.

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“…In recent years, some attentions have been paid to the research on the full information problem assuming that the future information is available [5]- [8]. The importance of studying the full information problem lies in the fact that the optimal solution of the full information problem is "helpful to estimate the effectiveness of the utilization of lookahead information" and "also useful in evaluating the performance of the existing rules for elevator operation", as pointed out in [7]. If we know the full information can help improve the performance a lot, in the future the elevator should be designed to take use of the future information.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] the authors have shown the near future information is valuable. In [6] and [7] the full information problem has been solved by the Branchand-Bound (B&B), with the basic assumption that the time is discrete and the events only happen at the discrete time points.…”

Section: Introductionmentioning

confidence: 99%

A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

Shen

Zhao

2008

SICE Journal of Control, Measurement, and System Integration

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A new Branch and Bound method is given for the scheduling of the group elevator system with full information. Full information means that not only the parameters of the elevator systems but also the arrival time, origins and destinations of all the passengers who are to be served are known beforehand. The performance obtained by solving the full information problem is the best performance that the elevator scheduling algorithm can achieve and then can be used to measure how good an elevator scheduling algorithm is. The method can handle the continuous time event and is based on the concept of "trip", which refers to the movement of the car without changing the direction and with at least one passenger being served.

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

Cited by 7 publications

References 4 publications

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

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

Ant Colony Optimization for single car scheduling of elevator systems with full information

A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

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