Genetic Algorithm Implementation

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Sea Port Container Terminal

There exist many complex systems in today’s world and we need to understand and identify the drawbacks / weakness in existing systems, try to remove drawbacks those can be seen as “bottlenecks” and ultimately improve the performance from a system’s thinking perspective. Various solutions and ideas exist in managing complexity.
Similarly, a sea port terminal is a complex system that includes multiple in-out operations of trade and works as the essential intermodal interfaces in the global transportation network also. Ceyhun Guven et al. (2014) said that a container terminal is an interim storage area, where vessels dock on berths, unload inbound containers and load outbound container. Often sea ports are servicing vessels for handling cargo which is increasing day by day [8].
There are three types of containers in stacking area, inbound, outbound and transshipment containers at the seaport terminal. The inbound container is a container that unloads from ship and store in the yard. Nathan Huynh and Jose M. Vidal (2010) stated that the inbound/import containers are discharged from a vessel. They are stacked in the allocated space without any segregation [6]. The outbound container is a container that waits for loading on the ship. Transshipment container is a container that unload from one ship and temporary store until to load on another ship. The stacking area is divided according to types of containers.

Container stacking yard

The container stacking yard is an important strategic area that affects the overall performance of the terminal. Stacking yard is one of the seaport’s core facilities for container storage in order to prevent delay in berthing time. The incoming containers into the storage yard are separated into several blocks that consist of several bays, rows, and tiers. The maximum stacking height (tiers) depends on the yard crane’s height. In most of the cases, the average tiers are 03. Most of the container terminals make blocks according to containers’ attributes. This storage involves a criterion for container stacking to minimize the reshuffling and extra movement of the yard cranes. Therefore, Chuanyu Chen et al. stated that the proper planning and well-designed storage yard can largely improve the port performance by efficient space utilization [10].

RTG Crane

A rubber-tired gantry crane (RTG) or yard crane is a mobile gantry crane which is used for intermodal operations (pick up, transfer and store) to their stacking positions in the block of the stacking yard. According to Nathan Huynh and Jose M. Vidal (2010), Most U.S seaport terminals use rubber-tired gantry (RTG) cranes often referred to as yard cranes to load and unload containers in the yard blocks [6]. RTG crane has some types. One is ARTG (automated RTG). That is operated by an automatic system. Another one is Manual RTG which is operated by manually. The third one is a reach stacker that is introduced by Konecranes. The Konecranes Company launched the world’s first hybrid reach stacker recently [41].
The most of the container terminals have used RTG/yard crane in the yard area. Some seaport container terminals like Helsingborg seaport container terminal are using Reach Stacker instead of RTG in the stacking yard to reduce the cost. Konecranes reach stackers are equipped with powerful, low-emission engines while reducing fuel consumption. It can handle 10-45 tons heavy containers [41].
Figure 2.2: Reach stacker in yard at Helsingborg port

Sea Port of Helsingborg

The Swedish Maritime Administration has established the significance of the port of Helsingborg as being a national interest of Sweden. Helsingborg port is as a logistics hub. It is a Sweden’s second largest container port. It is located in a booming part of the Nordic region. More than 350,000 TEU pass through the Helsingborg port every year. This port has 13 reach stackers, nine are used for loading and unloading trucks, and others are mobile cranes and one 16 ton fork lift truck to handle containers filled with rolls of steel plate [44].
This port is a second largest port of Sweden and easy to approachable for us to visit and investigate the actual problems that occurred and what’s the reasons of these problems and how can we solve them. We have visited this port on 28-04-2015 and asked questions related to our problem, took an understanding of the stacking system and its flaws and improvable areas. Through this, we can perform this work in the better way and get the efficient results.

PROBLEM DESCRIPTION

It is the most common at many container terminals that the retrieval containers are not properly stacked in the yard and we cannot avoid the containers’ reshuffles. Amir Hossein Gharehgozli et al. (2014) stated that a reshuffle is the removal of a container stacked on top of the desired container [9].
This thesis’s work addresses the land-side container handling operation in the yard to address the issue of unproductive moves or reshuffling during stacking of inbound containers and to determine the most effective and best possible solution to maximize the efficiency of stacking system. There are two types of container handling operation, one is stacking and another is retrieval. If the containers are stacked on best possible location then the retrieval time of container, waiting time, and cost of the delivery truck will also be reduced. According to Ceyhun Guven et al. (2014), a reshuffling move of the container is an unproductive move during stacking or retrieving operations and hence adding to the overall transportation cost. An efficient container handling (storage and retrieval) at the terminals is highly significant for reducing transportation costs and keeping shipping schedules [8]. According to Jose M. Vidal and Nathan Huynh (2010), the reshuffling container is time-consuming and increases a ship’s berthing time [6]. Yang J. H. and K. H. Kim (2006) stated that the allocation and reshuffle of containers are both time consuming and expensive, which is one of the most critical issues that decrease the productivity of container operations [30].
To help mitigate the complex decision making, one way of improving the performance of existing resources is an intelligent container stacking. According to Ndeye Fatma Ndiaye et al. (2014), efficient management of the storage space is essential to ensure the productivity of a port [12]. The container stacking is based on various rules, policies and priorities to have the unaffected shipping schedules. Wei Jiang, Yun Dongand and Lixin Tang (2011) stated that the efficient stacking strategy can minimize the number of containers’ reshuffles [11]. Since each move of the yard crane implies cost which needs to be minimized. Therefore, an adaptive algorithm that can provide a near/best possible solution to this problem. According to a preliminary investigation, container stacking using delivery date has not been investigated previously. The delivery date is the key metric in assigning priorities to containers for transportation.
The policy for stacking inbound containers is based on delivery date. Through this, costly repositioning and unnecessary container handling in the yard can be minimized, while containers with dwell time are stacked in a separate location in the yard. The objective of this improvement is to find the exact or minimum reshuffle location for incoming containers in the yard, yard space utilization within a shorter time and hence improving the accuracy to minimize the cost.
The figure 3.1 is showing the idea about inbound and outbound container flow, stack, bay, and tier. All these are common terms which we have used in this work.

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Scope of work

The focus of this work is on container stacking in the yard area of the Helsingborg port. The main focus is on the following points of container stacking system.
1. Find the exact or less reshuffle location to store the incoming container in the yard.
2. Avoid the costly extra movement of the cranes in the yard
3. Avoid late delivery of the container to the destination through truck
This thesis is on handling a block of 150 containers having the following properties.
• Equal size
• Heavy-weight
• Non-refrigerated
According to the delivery date of the containers, a Genetic Algorithm is used to find the best possible storage location for incoming containers in the yard. For this purpose, a fitness value will be taken into account.
As early discussed, stacking area is the main part of the container terminal that affects the overall performance of the terminal and increasing the cost of container handling also. Our opinion is that if we shall manage this part of the terminal in a good manner through finding the best possible location before stacking then the overall performance will be improved automatically. According to Tao Chen (1999), one major consequence would be a higher number of unproductive container movements taken in the terminal operations thus influencing overall operations efficiency [44]. This solution will assist to stacking area management to avoid costly repositioning of containers and save the time. Container without a delivery date is known as the dwell container. The latter is not considered in this thesis.

Aim and Objectives

The aim of this thesis is to improve the accuracy in stacking system by finding the exact or less reshuffled location for the incoming container in the yard. It will be helpful for stacking area management to reduce the cost of container handling in the yard. The overall cost of containers handling will also be reduced.
To achieve this aim, the following objectives will be considered.
• Analyze the existing “stacking system” at seaport container terminal, Helsingborg, Sweden
• Investigate the already provided solution(s) of reshuffling problem during stacking
• Find the best optimization technique for best or near possible solution
• Suggest the best possible solution(s) for this problem
• Test the suggested solution which is based on suggested algorithm (GA)
• Analyze the results with respect to the desired solution.
• Write the report on the basis of tested results

Contribution

The main contribution in this work is discrete-event simulation model for inbound container stacking in the yard and the Genetic Algorithm with high fitness value parameters that is used to determine the appropriate location for inbound container stacking to minimize the container’s reshuffle. The results of GA have been compared to Tabu Search’s results. This design is based on the delivery date of the container. The integration between the fitness value and handling cost has been shown by using GA. A proposed mathematical model to show the integration between the cost and the number of moves in the container yard.
The other contribution is a data classification method to classify the literature on container handling at the seaport container terminals. Most of the articles in this literature review are published in 2012 to 2015. The purpose to focus on recent work is to consider newly and updated research work in this field. We have classified the literature on the basis of following KPI’s (Time, cost and container’s reshuffle) for the minimization problem which will be beneficial for readers to find the literature on their relevant KPI easily.

Table of contents :

CHAPTER 1 
Introduction
CHAPTER 2 
Background
CHAPTER 3 
Problem Description
3.1 Scope of the work
3.2 Aim and Objectives
3.3 Contribution
CHAPTER 4 
Literature Review
CHAPTER 5 
Research Methodology
5.1 Research Questions
5.2 Research Process
5.3 Methodology…
5.3.1 Simulation Method
5.4 Genetic Algorithm Implementation
5.4.1 Reason/s to select the GA for solution
5.4.2 GA parameters
5.5 Tabu Search selection for comparison with GA
5.6 Mathematical Model
5.7 Conceptual Design Model
5.7.1 Flowchart Diagram
5.7.2 Sequence Diagram
CHAPTER 6 
Simulation Experiment
6.1 Simulation Model
6.2 Input parameters for simulation
6.3 Run experiment
CHAPTER 7 
Results
CHAPTER 8
Validation and verification
CHAPTER 9 
Analysis and Discussion
CHAPTER 10 
Conclusion and Future work
REFFERENCES
Appendix A Classified data from Literature review
Appendix B Literature review year wise distribution
Appendix C Glossary

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