Energy-Efficient Train Operation with Given Time

Zhang, Yan; Wang, Zhengbin; Tang, Qiaomei; Wang, Zilan

doi:10.1061/41184(419)486

Cited by 1 publication

(2 citation statements)

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“…Equation (12) shows that the start time of elevator j in round 1 is the loading in time of the passengers to all elevators, while Equation (13) shows that the finish time of elevator j in round 1 is the start time of round 1 plus the total loading time of all floors that the elevator stops at, plus the transportation time from floor l to floor M jk . Equation (14) shows that the start time of elevator j in round k is the finish time of elevator j in round k − 1 plus transportation time from M j,k−1 to floor l, plus the loading time of passengers in round k. Equation (15) shows that the finish time of elevator j in round k is the start time of round k plus the total loading time of all floors that the elevator stops at, plus the transportation time from floor l to floor M jk . Equation (16) shows that all elevators must finish moving in the last round within the limited operating time.…”

Section: N Jkmentioning

confidence: 99%

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Minimizing Energy Usage and Makespan of Elevator Operation in Rush Hour Using Multi-Objective Variable Neighborhood Strategy Adaptive Search with a Mobile Application

2023

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The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.

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Section: N Jkmentioning

confidence: 99%

“…In addition, the stopping pattern of the elevator can significantly impact its energy consumption. A lift that stops on every floor will consume more energy than one that only stops on every other floor [14]. Therefore, optimizing the stopping pattern of the elevator can lead to significant energy savings.…”

Section: Introduction 1backgroundmentioning

confidence: 99%