Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Alsoufi, Ghazwan; Yang, Xinan; Salhi, Abdellah

doi:10.1057/s41274-017-0226-3

Cited by 14 publications

(7 citation statements)

References 32 publications

(38 reference statements)

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“…The QCs are rubber-tired at some MCTs and rail-mounted on others [21]. The rubber-tired QCs (also known as moveable QCs) can move freely and cross each other, while the latter cannot [22]. Hence, there is a need for some non-crossing constraints for models that consider rail-mounted QCs.…”

Section: Seaside Operationsmentioning

confidence: 99%

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

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Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well‐known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in‐depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area.

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Section: Seaside Operationsmentioning

confidence: 99%

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

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“…The value of M can be set differently in different constraints. For constraints (10) to (13), it is sufficient to set the value of M to the largest distance between any two stacks. For constraints (16) to (23), the value of M needs to be greater than the makespan.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…More research was done on crane scheduling problems in container terminal operations, mostly at container ports. These include yard crane scheduling problems (e.g., Lim et al [3]; Ng [4]; Li et al [5]; Li et al [6]), quay crane scheduling problems (e.g., Liu et al [7]; Lee et al [8]; Chen et al [9]; Alosufi et al [10]), and combined problems (e.g., Zhang et al [11]). Guo et al [12] studied a crane scheduling problem in a railroad container terminal.…”

Section: Introductionmentioning

confidence: 99%

Model and Heuristic Solutions for the Multiple Double-Load Crane Scheduling Problem in Slab Yards

Zhao

Liu

Tang

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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This paper studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointerbased discrete differential evolution (PDDE) algorithm was developed with a dynamic programming algorithm embedded to solve the one-crane sub-problem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4-hour) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly, and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use.Note to Practitioners-Bridge cranes are commonly used to move heavy items in manufacturing and logistics systems. Usually, more than one crane runs on a common track. The latest versions of such cranes, such as those used in slab yards in the steel industry, can hold two items simultaneously. Operations scheduling of multiple double-load cranes involves assignment of tasks to the cranes, combination of tasks to double-load operations, and sequencing of the tasks, considering the noncrossing constraint between cranes. Effective solution of this complex problem can help fully utilize the crane capability, increase productivity, and reduce energy consumption. This paper models this problem and develops a heuristic solution that combines differential evolution and dynamic programming. Experiment results show that the algorithm is effective and efficient for practical use in slab yards. It may also be applicable to other systems using similar cranes.

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“…GA is a well established meta-heuristic, among many others, (Salhi 2017), which has been shown to be very effective on combinatorial optimisation problems of the same type as BACASP, such as QCASP, (Alsoufi et al. 2017; Vazquez-Rodríguez and Salhi 2006a, b). Hence the choice.…”

Section: Mathematical Formulationmentioning

confidence: 99%

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

2017

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This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times.

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Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Cited by 14 publications

References 32 publications

A survey on computational intelligence approaches for intelligent marine terminal operations

A survey on computational intelligence approaches for intelligent marine terminal operations

Model and Heuristic Solutions for the Multiple Double-Load Crane Scheduling Problem in Slab Yards

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

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