Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]

Abstract

Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks -the case studies of container terminals http://researchonline.ljmu.ac.uk/1851/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LJMU Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain.The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size.

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

“…This paper extends our previous attempts [1,2] in terms of the performance of the evolutionary algorithm (EA) and quality of solutions. In [1], we proposed a memetic algorithm by combining an EA with Monte Carlo simulation (MCS), named Fleet Sizing Evolutionary Algorithm (FSEA), to identify the robust number of vehicles in environments where vehicles shuttle between pickup and delivery points to transport goods.…”

“…In [1], we proposed a memetic algorithm by combining an EA with Monte Carlo simulation (MCS), named Fleet Sizing Evolutionary Algorithm (FSEA), to identify the robust number of vehicles in environments where vehicles shuttle between pickup and delivery points to transport goods. Examples of such environments are manufacturing factories, warehouses and container terminals.…”

“…To identify the robust number of vehicles, the possible source of uncertainties should be identified and taken into account. In [1,2], uncertainties in the travel time of vehicles were considered to be the main source of uncertainties which have a significant impact on the optimal number of vehicles. The travel time of vehicles in container terminals is under the risk of being unexpectedly changed due to many disruption factors such as breakdowns, deadlocks and collisions.…”

“…In [1,2], to encapsulate uncertainties in solutions, FSEA used MCS to evaluate the robustness of solutions during evolution. To do so, whenever FSEA wants to evaluate the fitness of one solution it replicates n samples of that particular solution by incorporating uncertainties in the travel time of vehicles.…”

Multi-Objective Evolutionary Algorithms: Foundation, Development and Open Issues

“…Further, the representation should be suitable for the nature of the problems which might have variables in the discrete, real-valued or mixed formats. A recent example can be seen in Shayan et al [36].…”