Identifying the Robust Number of Intelligent Autonomous Vehicles in Container Terminals

Kavakeb, Shayan; Nguyễn, Trung Thành; Yang, Zaili; Jenkinson, Ian

doi:10.1007/978-3-662-45523-4_67

Cited by 5 publications

(13 citation statements)

References 9 publications

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“…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.…”

Section: Introductionmentioning

confidence: 56%

“…Examples of such environments are manufacturing factories, warehouses and container terminals. In [2], we proposed an extension on FSEA to improve its performance in terms of computational time and finding robust solutions. In this paper, to evaluate the performance of the algorithm, we choose container terminals, one of the most common and important ESTTs, as case studies.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…In other situations, they may want to have a robust average value or a robust mode value. If a worst-case scenario is desired, the worst performance of the solution on the samples is considered the fitness of the solution so that the EA can evolve to find a solution with the better worst-case scenario; otherwise, based on certain performance measures [2] the results of evaluation on n samples should produce the fitness of the solution.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Kavakeb

Nguyễn

Benmerikhi

et al. 2014

The 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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Abstract-In this paper, we propose an improved memetic algorithm by combining an evolutionary algorithm (EA) with Monte Carlo simulation (MCS) to identify the robust number of vehicles in environments with shuttle transport tasks (ESTTs). ESTTs are very common settings where vehicles shuttle between pickup and delivery points to transport goods. Examples of ESTTs are military bases, warehouses, manufacturing floors, and container terminals. In this study, MCS works as a local search to take into account risks of disruptions and guide the EA towards more reliable solutions. The disruptions arise from changes in the travel time of vehicles which can be caused by any breakdown, collision, accident and deadlock. Identifying the robust number of vehicles can improve sustainability and reliability of ESTTs against possible changes. This paper improves our previous attempts in which we studied a combination of an EA and MCS. Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from having an accurate estimation of the robust solutions within reasonable time. This paper improves the performance of the EA to make it possible to reach high quality solutions in reasonable time to make it practical for the realworld applications. Firstly, it proposes a new sampling technique to generate samples that reflect the worst-case scenarios better. This helps the EA to find better robust solutions using the fewer number of samples. Secondly, it proposes a new adaptive sampling technique to adjust the number of samples during evolution. To evaluate the algorithm we tested it in one of the most common ESTTs: real-world container terminals. Experimental results show that by such improvements the performance of the EA is improved significantly, making the proposed algorithm perfectly usable for its real-world case studies.

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Section: Introductionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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