Thursday , June 21 2018

Optimization of Container Stowage in a Yard Block Using a Genetic Algorithm

Cristina SERBAN1, Doina CARP2
1
Ovidius University of Constanta,

Mamaia Bld. 124, Constanta, 900527, Romania
cgherghina@gmail.com
2 Constanta Maritime University,
Mircea cel Batran Str. 104, Constanta, 900663, Romania
doina.carp@gmail.com

Abstract: One indicator for efficient management in a port is the time spent by a ship in the port quays. The time allowed for loading-unloading into a specialized quay is mentioned in the management contract. Because the cost of the overtime is very high, it is very important to have a special plan to unload the container ship in a short time. Given the number of containers to be unloaded from a vessel and the initial state (in regards of number of slots) of a block, the genetic algorithm that we propose in this paper finds the plan of container stacking in the block, whilst the objective function is to minimize summation of handling time of yard crane in placing the containers in the available storage cells of the stacking area. The performance of the proposed method is evaluated through several sets of tests on control parameters of the algorithm.

Keywords: containers, cranes, stacking, genetic algorithms

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CITE THIS PAPER AS:
Cristina SERBAN, Doina CARP, Optimization of Container Stowage in a Yard Block Using a Genetic Algorithm, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(1), pp. 123-130, 2016.

  1. Introduction

One of the objectives of an efficient management plan in a port is to reduce the berthing time of vessels. The port efficiency is determined by the main container terminal operations, namely the vessel berthing operation, (quay or yard) crane unloading/loading operations and container shifting and storage operations.

In order to develop better operational strategies and investment plans, researches identified several optimization problems along the years and developed different tools based on intelligent techniques in order to achieve optimal solutions (Figure 1).

fig1

Figure 1. The most common optimization problems in container terminals

The Container Stacking Problem(CSP) consists in relocating the containers to ensure easy access to them so that the yard cranes don’t have to do further reshuffles at the expected time of transfer (e.g. [3], [10]). The CSP is classified as a three dimensional bin packing problem in [6], and a genetic algorithm is proposed to solve it. The Berth Allocation Problem (BAP) and the Quay Crane Assignment Problem (QCAP) refer to the allocation of docks and quay cranes to incoming vessels under several constraints and priorities (see [11], where the CSP, BAP and QCAP problems are considered and a computer-based decision support system that integrates the solution of the three problems is provided).

The Quay Crane Scheduling Problem (QCSP) main objectives are first to find the assignment of tasks to quay cranes and then to determine the tasks sequence for each quay crane in order to minimize the handling time while

respecting certain constraints (a feasible solution to this problem is found in [8] using a probabilistic technique inspired from ants behaviour). An extension of QCSP is the Integrated Quay Cranes, Vehicles and Platforms Scheduling Problem (IQCVPSP) considered in a split-platform automated storage/retrieval system (see [5], where a genetic algorithm is proposed to solve it).

In this paper we consider a Stowage Planning Problem which occurs during the unloading task of a vessel. In this respect, a genetic algorithm is proposed and optimal solutions for this problem are found in relatively low computational time.

Section 2 gives the main features of the problem considered. In Section 3 we briefly describe the basic principles of genetic algorithms, then we thoroughly depict the one that we proposed. This section also states the results of the several tests that we have been executed on the control parameters of the proposed genetic algorithm. Conclusion remarks and recommendations for further research directions are presented in Section 4.

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https://doi.org/10.24846/v25i1y201613