Stowage and Transport Optimization in Ship Planning

Steenken, Dirk; Winter, Thomas Nelson; Zimmermann, Uwe

doi:10.1007/978-3-662-04331-8_35

Cited by 21 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Areaj À jcrossjmaxðutilÞ Winter (1999) Steenken et al (2001) Areaj À jcross; savej À P w through Lim et al (2002Lim et al ( , 2004a) Bayj À jcrossjmaxðcomplÞ Lim et al (2004bLim et al ( ,c, 2007 Zhu and Lim (2006) Lee et al (2007 Meisel and Wichmann (2008) problem formulation that includes time windows in the QCSP with container groups is presented by . In this model QCs can possess multiple time windows for serving a vessel, as observed in practice under a variable-in-time QC-to-vessel assignment.…”

Section: Problem Classification Referencementioning

confidence: 99%

See 1 more Smart Citation

A survey of berth allocation and quay crane scheduling problems in container terminals

Bierwirth

Meisel

2010

European Journal of Operational Research

674

314

View full text Add to dashboard Cite

Section: Problem Classification Referencementioning

confidence: 99%

“…Winter (1999) and Steenken et al (2001) assign bay areas to QCs such that the maximum difference regarding the utilization of any two cranes is minimized. The authors show that crane scheduling on the basis of bay areas leads to a partitioning problem that can easily be solved optimal for instances of practical size.…”

Section: Other Problemsmentioning

confidence: 99%

A survey of berth allocation and quay crane scheduling problems in container terminals

Bierwirth

Meisel

2010

European Journal of Operational Research

674

314

View full text Add to dashboard Cite

“…Few papers in the literature were devoted to the study of the QCSP with bay areas. Steenken et al (2001) addressed the problem with the aim of minimizing the difference in the use of any two cranes. They showed that for instances of practical size the problem leads to a partitioning problem that can be easily solved by straight-forward enumeration.…”

Section: Related Workmentioning

confidence: 99%

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Rouky¹,

Abourraja²,

Boukachour³

et al. 2019

10.5267/j.ijiec

View full text Add to dashboard Cite

This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP), where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO) meta-heuristic hybridized with a Variable Neighborhood Descent (VND) local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

show abstract

“…Chen et al [5] and Lee et al [8] treat the dynamic case of varying crane areas and present heuristic solution procedures at indented berths. For the static case, respective polynomial time dynamic programming (DP) procedures for equally partitioning the workload of successive container slots along a straight line into disjunct crane areas are provided for the fields of scheduling quay cranes in container ports [10, 12], rail‐mounted gantry cranes in rail‐road terminals [2], and in rail‐rail terminals [3]. However, the problem of determining fixed crane areas becomes much more complex if two independent sets of cranes operate in parallel, for example, from opposing berths on different sides of a vessel.…”

Section: Problem Descriptionmentioning

confidence: 99%

Determining crane areas for balancing workload among interfering and noninterfering cranes

Boysen

Emde

Fliedner

2012

Naval Research Logistics

View full text Add to dashboard Cite

This article treats the problem of subdividing an area for storing containers such that the workload is evenly shared among the cranes operating the resulting subareas. We consider two crane sets: while noncrossing constraints between cranes of the same set need to be observed, cranes of different sets do not interfere. Such a problem setting is, for instance, relevant for scheduling the (un‐)loading of vessels by parallel quay cranes operating on opposing berths or in container yards with cross‐over cranes. We formalize the resulting optimization problem, prove computational complexity, and present exact and heuristic solution procedures. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

show abstract

Stowage and Transport Optimization in Ship Planning

Cited by 21 publications

References 9 publications

A survey of berth allocation and quay crane scheduling problems in container terminals

A survey of berth allocation and quay crane scheduling problems in container terminals

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Determining crane areas for balancing workload among interfering and noninterfering cranes

Contact Info

Product

Resources

About