Journal of Artificial Intelligence and Data Mining, Volume (10), No (1), Year (2022-1) , Pages (127-138)

Title : ( Reward and Penalty Model for the Lighting of Public Thoroughfares Contracts: An Empirical Study in a Distribution Company )

Authors: Reza Ghotboddinh , Heydar Toossian Shandiz ,

Citation: BibTeX | EndNote

Abstract

Lighting continuity is one of the preferences of citizens. The quality of lamps and defect correction time are paramount in lighting continuity. Selecting skilled workers and high-quality lamps has a significant impact on risk reduction during the maintenance contract. Sharing the benefits between contract parties in the public-lighting system to assure a win-win condition supports stakeholders\\\' satisfaction. This research proposed a model to improve strategies for public-lighting asset management. In this regard, the guarantee period and maximum correction time are used for the reward and penalty mechanism. The results show that the commitment to a lifetime guarantee has encouraged the contractor to purchase quality lamps and ultimately receive a reward in 2018 and 2019. Similarly, incentives on the correction time have caused employees to reduce the detection and correction time to less than two days from 2016 to 2019.

Keywords

, Reward, Contract, Distribution, Lighting, Asset Management.
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@article{paperid:1090878,
author = {Reza Ghotboddinh and Toossian Shandiz, Heydar},
title = {Reward and Penalty Model for the Lighting of Public Thoroughfares Contracts: An Empirical Study in a Distribution Company},
journal = {Journal of Artificial Intelligence and Data Mining},
year = {2022},
volume = {10},
number = {1},
month = {January},
issn = {2322-5211},
pages = {127--138},
numpages = {11},
keywords = {Reward; Contract; Distribution; Lighting; Asset Management.},
}

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%0 Journal Article
%T Reward and Penalty Model for the Lighting of Public Thoroughfares Contracts: An Empirical Study in a Distribution Company
%A Reza Ghotboddinh
%A Toossian Shandiz, Heydar
%J Journal of Artificial Intelligence and Data Mining
%@ 2322-5211
%D 2022

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