Solving the 3D stowage planning problem integrated with the quay crane scheduling problem by representation by rules and genetic algorithm

Azevedo, Aníbal Tavares de; Neto, Luiz Leduíno de Salles; Chaves, Antônio Augusto; Moretti, Antônio Carlos

doi:10.1016/j.asoc.2018.01.006

Cited by 41 publications

(31 citation statements)

References 32 publications

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“…Concerning stowage problems closely related to that studied here, Parreño et al [12] also used GRASP for the slot planning problem, but in recent papers other approaches have been successfully applied: particle swarm optimization [24], genetic algorithms [25][26][27], simulated annealing [27], or hill climbing [27].…”

Section: A Grasp Algorithmmentioning

confidence: 99%

Solution Strategies for a Multiport Container Ship Stowage Problem

Parreño-Torres

Álvarez-Valdés

Parreño

2019

Mathematical Problems in Engineering

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The multiport container ship stowage problem consists in determining the position of the containers on board a ship along its route with the objective of minimizing the number of unproductive moves required in the loading and unloading operations at each port. This paper presents an integer programming model for the problem and proposes several sets of valid constraints that bring its LP-relaxation closer to an integer solution. Moreover, it presents a GRASP algorithm that generates stowage plans with a minimal number of unproductive moves in a high percentage of medium and large-size instances. An extended computational analysis has been performed in which, to the best of the authors’ knowledge, the efficiency of integer programming models for the problem is tested for the first time. With respect to GRASP, the computational results show that it performs well on different sized datasets.

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Section: A Grasp Algorithmmentioning

confidence: 99%

Solution Strategies for a Multiport Container Ship Stowage Problem

Parreño-Torres

Álvarez-Valdés

Parreño

2019

Mathematical Problems in Engineering

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“…Chen [21] reviewed and summarized the flexible methodology for quay crane allocation and scheduling. Azevedo et al [22] raised a framework for solving the 3D stowage planning problem for container ships integrated with the scheduling of quay cranes problem. Msakni [23] put forward two exact methods to solve the quay crane scheduling problem where a task is defined as handling a single container and subject to different technical constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Song

2019

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Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

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“…Whenever a metaheuristic procedure is used, coding and decoding the solution of mathematical problem is required [18][19][20][21]. Due to page limitation, the encoding scheme is refferd to the literature [12][13][14].…”

Section: Red Deer Algorithm (Rda)mentioning

confidence: 99%

“…The efficient operation of the terminal depends on the proper planning of the container movement, called "stowage planning". Recently, Azevedo [12] addressed the integrated problem of the 3D stowage planning problem and quay cranes scheduling problem in container vessels. More recently, an integrated berth allocation and quay crane assignment and scheudling was developed by Kasam and diabet [13].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Constraint (13) prevents interference of contractors with their ship assignments. Similar to the constraint (12), the indexing of contractors and ships is arranged in ascending order according to their position. Constraint (14) indicates that if the contractor q is not assigned to the vessel m, there is no possibility of assigning cranes of contractor q to the vessel m, and if the contractor q is assigned to the vessel m, the cranes of the contractor q can be assigned to the vessel m. Constraint (15) indicates that each contractor can be assigned to at most one vessel, and constraint (16) ensures that each vessel is assigned exactly to one contractor for servicing.…”

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confidence: 99%

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Addressing a Coordinated Quay Crane Scheduling and Assignment Problem by Red Deer Algorithm

Fazli

Fathollahi-Fard

Tian

2019

IJE

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A B S T R A C TNowadays, there is much attention for planning of container terminals in the global trade centers. The high cost of quay cranes motivates both scholars and industrial practitioners especially in the last decade to develop novel optimization models to address this dilemma. This study proposes a coordinated optimization model to cover both Quay Crane Scheduling Problem (QCSP) and Quay Crane Assignment Problem (QCAP) as among the first attempts in this area. Another main contribution of this paper is to apply a recent nature-inspired algorithm called Red Deer Algorithm (RDA). The RDA revealed its performance for a variety of combinatorial optimization problems in different real-world applications. This is the first attempt in the literature to employ this recent metaheuristic to solve the proposed Coordinated Quay Crane Scheduling and Assignment Problem (CQCSAP). Finally, an extensive comparison discussion is considered to reveal the main benefits of the proposed optimization model and solution algorithm.

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Solving the 3D stowage planning problem integrated with the quay crane scheduling problem by representation by rules and genetic algorithm

Cited by 41 publications

References 32 publications

Solution Strategies for a Multiport Container Ship Stowage Problem

Solution Strategies for a Multiport Container Ship Stowage Problem

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Addressing a Coordinated Quay Crane Scheduling and Assignment Problem by Red Deer Algorithm

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