Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm

Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm

Gamal Abd El-Nasser A. Said (Ain Shams University, Egypt) and El-Sayed M. El-Horbaty (Ain Shams University, Egypt)
DOI: 10.4018/978-1-5225-0058-2.ch007
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Abstract

Seaport container terminals are essential nodes in sea cargo transportation networks. In container terminal, one of the most important performance measures in container terminals is the service time. Storage space allocation operations contribute to minimizing the vessel service time. Storage space allocation problem at container terminals is a combinatorial optimization NP-hard problem. This chapter proposes a methodology based on Genetic Algorithm (GA) to optimize the solution for storage space allocation problem. A new mathematical model that reflects reality and takes into account the workload balance among different types of storage blocks to avoid bottlenecks in container yard operations is proposed. Also the travelling distance between vessels berthing positions and storage blocks at container yard is considered in this research. The proposed methodology is applied on a real case study data of container terminal in Egypt. The computational results show the effectiveness of the proposed methodology.
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Introduction

A container terminal is a zone of the port where container vessels are loaded and unloaded, and where containers are temporarily stored at container yards for variable periods. Container is a large box used for transportation of goods. Thousands of containers are handled in a container terminal everyday by different types of material handling equipment. The docking time of container ships at the port must be as small as possible, with a minimum use of different expensive equipment, managing activities of such high intensity level in a container terminal is a challenging task.

Global containerized trade grew in 2013 taking total volumes to 160 million TEUs; up from 78 million TEUs in 2002 (Review of maritime transport, 2014). Container handling problems are NP-hard; therefore the need of optimization in container terminal operation has become more important recently. Metaheuristics algorithms are used to solve container terminal problems (Steenken, Voß, & Stahlbock, 2004).

When a ship berth is introduced in a container terminal, a number of quay cranes are assigned for the ship to handle containers. The containers are discharged from the ship by quay cranes and transported to container yards by internal trucks; and they are then stored by yard cranes at various locations in the container yard. The loading process starts in the reverse direction of the discharging process, i.e., by transferring containers from yard to quay area (Mak & sun, 2009). Figure 1 shows container handling operations in a container terminal.

Figure 1.

Container handling operations in a container terminal

Container terminals all around the world are keeping and raising their service capacity and level actively to increase their own competitive power because more ports realize the significance of raising their service level, reducing vessel turnaround time and raising the efficiency of loading and unloading of the cargoes in the container terminals. Understanding the operational processes of a container port terminal is essential to increasing efficiency. A seaport container terminal is a place where container vessels are loaded and unloaded, and where containerized cargo is temporarily stored during awaiting a future journey. One significant factor to improve a container terminal competitiveness is to raise the efficiency of loading and unloading operations and reduce vessel turnaround time.

There are different kinds of container transshipment movements: unloading, loading, receipt, delivery, and marshaling. Unloading is container movement from ship to yard. Loading is container movement from yard to ship. Receipt is container movement from the hinterland “main land” to the yard. Delivery is container movement from the yard to the hinterland. Again, it happens either by truck through a gate. Marshalling is container movement within the yard. Yard cranes store and retrieve containers within yard blocks. The containers inside the yard are divided into: import, export, hazardous, empty, and transient containers. Import containers are brought by ship, export containers are sent out by ship, and transient containers are brought by ship and also sent out by ship. In most container terminals, a large portion of the container terminal turnaround time is spent on discharging and loading containers for a ship. The most effective method to reduce the terminal turnaround time is to improve the productivity of handling activities (Stahlbock & Voß, 2008).

In container terminals, quay cranes, yard cranes and trucks are used in different parts of a container terminal to transfer or transport containers from one location to another. Quay cranes handle containers by running at the berth to transfer containers between ships and trucks. Yard cranes are moving in the yard to transfer containers between trucks and the container yard for container storage or retrieval. Trucks are highly mobile devices for transporting containers from one location to another.

Key Terms in this Chapter

TEU: TEU stands for Twenty-Foot Equivalent Unit which can be used to measure a ship cargo carrying capacity or a container terminals handling capacity. A standard size is twenty-foot (length=20 ft, width=8 ft, height=8 ft).

Nonlinear Programming (NLP): Nonlinear programming is a mathematical technique for finding optimal solutions to optimization problems in which either the objective function or some of the constraints are nonlinear functions.

Integer Programming (IP): An IP in which all variables are required to be integers is called a pure integer programming problem. An IP in which only some of the variables arc required to be integers is called a mixed integer programming problem (MIP).

Containerization: Containerization is a system of intermodal freight transport using containers. Containers can be loaded and unloaded, stacked, transported efficiently over long distances, and transferred from one mode of transport to another. The handling system is completely mechanized so that all handling is done with cranes and special trucks.

Nondeterministic Polynomial Time Problem (NP): The complexity class P is the set of problems that can be solved with worst-case polynomial time-complexity. The complexity class NP is the set of problems that can be non-deterministically accepted in worst-case polynomial time.

Quadratic Assignment Problem (QAP): QAP is a combinatorial optimization problem. QAP can be described as assignment problem of n facilities to n locations, which minimizes the sum of the total quadratic interaction cost, the flow between the facilities multiplied with their distances, and the total cost associated with allocating a facility to a certain location.

Continuous Optimization: In continuous optimization the variables used in the objective function are continuous variables. In discrete Optimization, some or all of the variables are restricted to be discrete variables.

Container Ships: Container ships are ships that carry all of their load in standard size intermodal containers. Container ships are actually specifically designed and equipped to carry containers, with a design which is intended to hold containers securely with a minimum of wasted space. Containerships vary considerably in size. Container ship capacity is measured in (TEU).

Stochastic Optimization: The process of maximizing or minimizing the value of a mathematical function when one or more of the input parameters is subject to randomness. Stochastic optimization algorithms were designed to deal with highly complex optimization problems.

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