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Title: A decision support method for internal truck employment
Authors: Wang, ZX
Chan, FTS 
Chung, SH 
Niu, B
Keywords: Container terminal
Genetic algorithm
Storage allocation
Yard truck scheduling
Issue Date: 2014
Publisher: Emerald Group Publishing Limited
Source: Industrial management and data systems, 2014, v. 114, no. 9, p. 1378-1395 How to cite?
Journal: Industrial management and data systems 
Abstract: Purpose - The purpose of this paper is to propose a model that determines the strategy of owning and renting trucks in combinations with internal truck scheduling and storage allocation problems in container terminals. Design/methodology/approach - To deal with this complicated problem, a two-level heuristic approach is developed, in which the integration problem is decomposed into two levels. The first level determines the daily operations of the internal trucks, while the second level determines the truck employment strategy based on the calculation in the first level. Findings - The results show that: even if the using cost of owned yard trucks is much lower than the cost of rented yard tucks, terminal companies should not purchase too many trucks when the purchasing price is high. In addition, the empirical truck employment strategies, which are purchasing all the trucks or renting all the trucks, are not cost-effective when compared with the proposed yard truck employment strategy. Originality/value - The paper provides a novel insight for the internal truck employment strategy in container terminals which is the determination of the strategy of employing renting and outsourcing yard trucks to meet operational daily transportation requirements and minimize the long-term cost of employing yard trucks. A mathematical model is proposed to deal with the practical problem. Also, this study presents better solution than empirical method for employing different types of yard truck. Thus, in order to obtain more benefit, terminal companies should employ the proposed yard truck employment strategy.
ISSN: 0263-5577
EISSN: 1758-5783
DOI: 10.1108/IMDS-07-2014-0215
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