A genetic algorithm approach for quay crane scheduling with non-interference constraints at Narvik container terminal

Hakam, Moulay Hicham; Solvang, Wei Deng; Hammervoll, T.

doi:10.1080/13675567.2012.741220

Cited by 21 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang and Rose [1] and Al-Dhaheri et al [19] used a GA together with a simulation approach to solve the crane scheduling problem and to obtain an optimal integrated crane schedule. A GA was also used to study the quay crane scheduling problem [20][21][22]28] and its extended problems, such as berth allocation and quay crane assignment problem [23] , quay crane assignment and scheduling problem [25] , quay crane scheduling problem with draft and trim constraints [29] , and integrated quay crane and yard truck scheduling problem [31] . Other problems using GAs include the scheduling problems of multiple vehicles/cranes along a common lane [24,26,27,30] and the yard crane scheduling problem with a noninterference constraint in a container terminal [32] .…”

Section: Literature Reviewmentioning

confidence: 99%

Trajectory Predictions with Details in a Robotic Twin-Crane System

Zhao

Lodewijks

et al. 2022

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

Nowadays, more automated or robotic twin-crane systems (RTCSs) are employed in ports and factories to improve material handling efficiency. In a twin-crane system, cranes must travel with a minimum safety distance between them to prevent interference. The crane trajectory prediction is critical to interference handling and crane scheduling. Current trajectory predictions lack accuracy because many details are simplified. To enhance accuracy and lessen the trajectory prediction time, a trajectory prediction approach with details (crane acceleration/deceleration, different crane velocities when loading/unloading, and trolley movement) is proposed in this paper. Simulations on different details and their combinations are conducted on a container terminal case study. According to the simulation results, the accuracy of the trajectory prediction can be improved by 20%. The proposed trajectory prediction approach is helpful for building a digital twin of RTCSs and enhancing crane scheduling.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Trajectory Predictions with Details in a Robotic Twin-Crane System

Zhao

Lodewijks

et al. 2022

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

show abstract

“…Using metaheuristics, the resolving of the crane split problem can be automated. The authors in [16][17][18][19] have used genetic algorithm for solving crane split problems. Besides the genetic algorithm, which is the most popular metaheuristic inspired by nature, there are two research papers [20,21] in which the authors have used particle swarm optimization algorithm inspired by nature to resolve this crane split problem.…”

Section: Crane Splitmentioning

confidence: 99%

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

2018

View full text Add to dashboard Cite

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.

show abstract

“…Many existing papers possess some of these properties. The single dimensional structure and the makespan objective alone are frequently found in the literature (Diabat and Theodoru 2014;Javanshir and Seyedalizadeh-Ganji 2010;Lee and Chen 2010;Lee and Wang 2010;Lee et al 2008;Lim et al 2007;Tang et al 2014;Zhu and Lim 2006), along with papers containing problems with more similar features, such as those of Guan et al (2013), Hakam et al (2012), Liu et al (2006), andRodriguez-Molins et al (2014). Each of these problems contains four of the same features as the TRPP, but again, differences are significant.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling twin robots in a palletising problem

Thomasson

Battarra

Erdoğan

et al. 2017

International Journal of Production Research

View full text Add to dashboard Cite

This paper introduces the Twin Robot Palletising Problem (TRPP) in which two robots must be scheduled and routed to pick up and deliver products at specified locations along a rail. The robots are initially located at the opposite ends of the rail and must preserve a minimum safe distance from one another. The objective is to minimise the makespan, defined as the time required to complete all operations and for both robots to return to their starting positions. The paper presents a proof of NP-Hardness of the TRPP, as well as two mixed integer linear programming models. Local search operators are introduced, before an iterated local search and a genetic algorithm are developed, in which a linear-time scheduling algorithm and dynamic programming are utilised to evaluate the quality of solutions. Extensive computational results demonstrate the limits of the mathematical models, the effectiveness of the metaheuristics, and the savings obtained by using twin robots instead of a single one.

show abstract

A genetic algorithm approach for quay crane scheduling with non-interference constraints at Narvik container terminal

Cited by 21 publications

References 10 publications

Trajectory Predictions with Details in a Robotic Twin-Crane System

Trajectory Predictions with Details in a Robotic Twin-Crane System

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

Scheduling twin robots in a palletising problem

Contact Info

Product

Resources

About