Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Issue 1 (January 2015)

• Record Type: Journal Article
• Authors: He, Junliang; Huang, Youfang; Yan, Wei
• Is Part Of: Advanced engineering informatics. Volume 29:Issue 1(2015:Jan.)
• Page Start: 59
• Page End: 75
• Publication Date: 2015-01
• Subjects: Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2014.09.003
• Languages: English
• ISSNs: 1474-0346

Abstract

<abstract xml:lang="en" abstract-type="author" id="ab005"> <title id="st210">Abstract</title> <sec> <p id="sp0005">Green transportation has recently been the focus of the transportation industry to sustain the development of global economy. Container terminals are key nodes in the global transportation network and energy-saving is a main goal for them. Yard crane (YC), as one type of handling equipment, plays an important role in the service efficiency and energy-saving of container terminals. However, traditional methods of YC scheduling solely aim to improve the efficiency of container terminals and do not refer to energy-saving. Therefore, it is imperative to seek an appropriate approach for YC scheduling that considers the trade-off between efficiency and energy consumption. In this paper, the YC scheduling problem is firstly converted into a vehicle routing problem with soft time windows (VRPSTW). This problem is formulated as a mixed integer programming (MIP) model, whose two objectives minimize the total completion delay of all task groups and the total energy consumption of all YCs. Subsequently, an integrated simulation optimization method is developed for solving the problem, where the simulation is designed for evaluating solutions and the optimization algorithm is designed for exploring the solution space. The optimization algorithm integrates the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method.</p> </sec> </abstract>