Some Studies on the Principles and Mechanisms for Loading and Unloading

Recommended Models

This section presents a summary of recommended models for loading operations, unloading operations and combined loading and unloading operations for different number of trains. Table ý6. presents the recommended models and associated savings for loading, unloading and combined loading-unloading processes for a single train. The computation of savings has been carried out with respect to the existing operation procedures being followed at ICD, Tughlakabad. It can be seen from Table ý6. that by using the models recommended in this study, a substantial savings in operation costs can be achieved. Table ý6., Table ý6., and Table ý6. present a summary of recommend models, cost savings, savings in resources for loading, unloading, and combined loading and unloading processes for two, three and four trains respectively. The computations of savings in cost, time and resources (trucks) is presented in Table ý6., Table ý6., and Table ý6.. It can be seen from these Tables that substantial savings can be achieved by using the models developed in this study.

Table ý6. Recommended models for loading and unloading one train

Loading operationsMin ValueRecommended ModelSavings (minute)Percentage savingsMakespan (minutes)204.5STF213.5 ¨C 204.5 = 73.4%Cost (Rs.)1563LBT2176-1563=61339,2%Waiting time (minutes)333LBT1235-333=902270%trucks5LBT9-5=480%Unloading operationMakespan (minutes)201.5FAT214-201.5=12.56.2%Cost (Rs.)2146STF2732-2146=58627%Waiting time (minutes)322FAT1235-322=913283.5%trucks5FAT, STF9-5=480%Combined operationMakespan (minutes)406STF(L)FAT(UL)428-406=225.4%Cost (Rs.)3709LBT(L)STF(UL)4908-3709=119932%Waiting time (minutes)655LBT(L)FAT(UL)2400-655=1745266%trucks10LBT(5L)FAT(UL)18-10=880%Table ý6. Recommended models for loading and unloading two trains

Loading OperationsMin ValueRecommended ModelSavings (minute)Percentage savingsMakespan (minutes)423.5STF432.5-423.5=92.1%Cost (Rs.)3384LBT5059.5-3384=1675.549.5%Waiting time (minutes)1151LBT3779-1151=2628 228%trucks6FAT12-6=6100%Unloading operationMakespan (minutes)421FAT437-421=163.8%Cost (Rs.)4400STF6375-4400=1975 44.8%Waiting time (minutes)814STF3856-814=3042 373.7%trucks5STF12-5=7 140%Combined operationMakespan (minutes)844.5STF(L)FAT(UL)869.5-844.5=

25 2.9%Cost (Rs.)7748LBT(L)STF(UL)11434.5-7748= 3686.547.5%Waiting time (minutes)1965LBT(L)STF(UL)7635-1965=5670 288.5%trucks12(7+5)LBT(L)FAT(UL)24-12=12 trucks100%Table ý6. Recommended models for loading and unloading three trains

Loading OperationsMin ValueRecommended ModelSavings Percentage savingsMakespan (minutes)599.5STF608.5-599.5 = 91.5%Cost (Rs.)4746.5LBT8209.5-4746.5=346377.2%Waiting time (minutes)1664LBT7093-1664=5429326%trucks6LBT15-6=9 150%Unloading operationMakespan (minutes)598.5LTF622-598.5=23.53.9%Cost (Rs.)6265STF10075-6265=379260%Waiting time (minutes)1139STF8209.5-1139=7070620%trucks5STF15-5=10200%Combined operationMakespan (minutes)1200.5STF(L)FAT(UL)1230.5-1200.5=302.4%Cost (Rs.)11011LBT(L)STF(UL)17325-11011=6314 57.3%Waiting time (minutes)2803LBT(L)STF(UL)5302.5-2803= 12500446%trucks13(8+5)

or(6+7)STF(L+UL) or FAT(L)LBT(UL)30-13=17130%Table ý6. Recommended models for loading and unloading four trains

Loading OperationsRecommended ModelSavings Percentage savingsMakespan (minutes)761.5STF770.5-761.5 = 91.18%Cost (Rs.)5604LBT11621-5604 =6017107%Waiting time (minutes)1396.5LBT112232-1396=9836704 %trucks5LBT18-5=13260%Unloading operationMakespan (minutes)760FAT774-760=141.8%Cost (Rs.)8784.5FAT13974-8784.5= 5189.559%Waiting time (minutes)2863FAT11287.5-2863= 8424.5294%trucks7FAT18-7= 11157%Combined operationMakespan (minutes)1521.5STF(L)FAT(UL)1544.5-1521.5=23 1.5%Cost (Rs.)14388.5LBT(L)FAT(UL)25595-14388 =11206 77.8%Waiting time (minutes)4259.5LBT(L)FAT(UL)22520-4259.5 =18260.5 428.7%trucks12(5+7)LBT(L)FAT(UL)36-12=24200%

Table ý6. Computations for savings in cost for one train

Saving per train (Rs.)Daily Saving for 8 trains (Rs)Annual saving (Rs.)Loading61349041789960Unloading58646881711120Combined119995923501080

Table ý6. Computations for time savings for unloading one train

Saving per train (minutes.)Daily Saving for 8 trains(minutes)Annual saving (minutes/day.)Loading75620440=14.2 daysUnloading12.510036500=25.35 daysCombined2216760955=42.33 days

Table ý6.Computations for resource savings for one train

Saving per train (truck)Daily Saving for 8 trainsAnnual saving (truck.)Loading43211680 Unloading43211680Combined86423360

Conclusions and future work

This chapter summarises the research reported in this thesis, outlining the limitations of the research and providing recommendations for future research. Containerization of general cargo has been increasing steadily over the last three decades. Starting with 76 million twenty feet equivalent unit (TEU) in year 1988, world container turnover has reached more than 544 Million TEU in year 2008. As a result of the increasing volume of the world container turnover, container terminals have become an important component of the global transportation network. Due to and rising competition, handling an enormous volume of containers speedily and accurately is of paramount importance to a container terminal. To achieve better operations efficiency, a container terminal needs to address the complicated process of transporting containers between various containers handling equipment.

The transportation of containers between the quayside and the yard-side can be decomposed into a number of separate sub-processes according to the type of equipment involved. In most container terminals, trucks are commonly used for transporting containers. Determining the sequence of containers to be handled by trucks is one of the major issues in terminal planning. This sequencing decision directly affects the operation efficiency of both the quayside and the yard-side to a large extent. Unnecessary truck waiting times would lead to a longer terminal turnaround time; terminals need to develop optimal truck schedules to ensure a high terminal throughput. Scheduling problems have been studied extensively in the operations research and management science literature but very few studies have been carried out to study scheduling problems of container transporting equipment such as trucks.

The research presented in this thesis describes the development and application of models to solve truck scheduling problem that is the problem of scheduling a fleet of trucks to handle a set of non-preemptive jobs with sequence-dependent processing times and different arrival time to minimize the makespan. In this research, development and implementation of several kinds of models for optimal scheduling of loading and unloading operations in container terminals has been presented. A generic model based on mixed integer programming (MIP) has been developed and its effectiveness in scheduling container loading and unloading operations has been studied. In addition to MIP model, four generic models based on different scheduling algorithms have been developed in this research. The performance of these models has been evaluated in terms of the quality of solution achieved by them. Comparison of the performance of MIP model with that of other models has revealed that although MIP model is able to produce optimal solutions but its execution time is quite high. Due to the high execution time requirements, it was concluded that MIP model has limited practical applicability to the real world problems. It was decided to apply greedy algorithm for truck scheduling problem in ICD, Tughlakabad. The main motivation behind using greedy algorithm is that easy to implement in real-world situations because it is highly efficient in terms of time requirements. Following this introduction, section presents the achievements of the research. section describes the limitations and section presents a few pointers towards the future work.

Achievement of the thesis

The research presented in this thesis is made up of three distinct parts. The first part of the thesis presents the literature review related to the application of optimization techniques to container terminal operations. Review of container terminal processes has also been presented in the first part of the thesis. In the second part, development of a MIP model has been described. Application of MIP model to a number of container loading and unloading problems has also been described in the second part of the thesis. Finally, development and application of models based on different scheduling mechanisms to several real world problems has been presented. Major achievements of the thesis highlighting the contributions at each stage are presented below.

Following the introduction to global and Indian port scenario, chapter 2 of the thesis, presents a review of optimization techniques for improving the efficiency and productivity of container terminals. A review of container terminal processes is also presented in chapter 2 of the thesis. Chapter 3 of the thesis describes the characteristics of the Port of Singapore. The factors that are responsible for making Singapaore the leading port in the world have been discussed. Operations at Container Corporation of India’s ICD at Tughlakabad in New Delhi have been described in chapter 3 of this thesis. Various facilities available at ICD, Tughlakabad have also been described. A summary of control, handling and scheduling operations has been presented in tabular form in chapter 3. It was observed that except for container stock and booking position, none of the operations are scheduled or planned using the real time planning of logistics. Therefore, there is an urgent need to develop models for improving the efficiency of terminal operations in ICD, Tughlakabad.

In Chapter 4 of the thesis, formulation of truck dispatching problem has been presented. A generic model based on MIP has been developed using AMPL programming language. Four different problems with combinations of number of trucks and number of trucks were solved using MIP model. It was concluded that the execution time requirements are quite high for problems of larger size. Therefore, application of MIP model to solving real world problems is not practical. In chapter 5 of the thesis, heuristic models based on greedy and reverse greedy algorithms have been described. Four different models based on shortest time first (STF), longest time first (LTF), last busy truck (LBT), and first available truck (FAT) scheduling mechanisms have been developed, and described in this chapter.

Application of models based on STF, LTF, LBT, and FAT to four real-world case studies based on the data collected from CONCOR for ICD, Tughlakabad has been described in chapter 6 of the thesis. The data pertaining to job processing and travel times as obtained from CONCOR is presented in tabular form in the chapter. Using the real world data, optimal scheduling of container loading and unloading jobs has been carried out. Comparison of optimal results achieved by each of the four models has also been made. Major achievements of the research carried out in this thesis may be summarized as follows:

A comprehensive review of literature related to application of optimization techniques for improving container terminal operations has been carried out with a view to provide ready reference for any future work.

A comprehensive review of processes being followed, and equipment being used in container terminals has been carried out in order to put the work carried out in this research in context.

Real-world data pertaining to processing time and travel time for each container has been procured from CONCOR, and has been used to test the developed models

A generic MIP model that is transportable to any other container terminal with minimal changes has been developed for the truck scheduling problem

Analysis of several computational experiments carried out using the MIP model has revealed that the execution time requirements are quite high for solving even moderate size problems, thereby ruling out the application of MIP model to large real-world problems

Four generic models based on STF, LTF, FAT, and LBT scheduling mechanisms have been developed and their practicality in solving complex problems has been demonstrated through application to four real-world problems.

It has been demonstrated that the application of recommended models for unloading different number of trains as presented in section could lead to substantial savings in the cost of operations

A distinct practical advantage of the model developed in this work are that they are transportable to any other container terminal without any difficulty

Models developed in this work can be used by terminal operations managers for optimizing container terminal processes

Models recommended in this work provide a wider choice to the terminal managers as well as to customers

Finally, implementation of models developed in this work is straightforward due to the ease of interfacing

Limitations of the research

This section presents the limitations of the research presented in this thesis.

The location for storing containers in the storage yard has not been taken into account by the models developed in this work. Each container was randomly assigned to a location in the storage yard. The time taken by the yard crane has not been separately considered. Instead, it was included in the travel time of the jobs. To prevent congestion and accidents in the terminal, the speed of each truck was restricted to 15 km/hr. Assigning higher speeds could have led to decrease in the makespan. The capacity of each truck has been assumed to be one container although some trucks could carry two number of TEU containers. Further, due to space limitations each train could carry a maximum 90 TEU containers.

Recommendations for further research

To conclude the thesis, the following recommendations are made for further research which could lead to further improvement in efficiency and productivity in container terminals.

In practice, one important issue is how to determine a storage location for each unloading container. In the models described here, the storage location is not considered as a decision variable. Considering storage location as a decision variable is likely to improve the efficiency of terminals but it makes the problem highly complex. Another aspect that could be considered in future work is to identify routes for each truck with the objective to minimize the makespan and at the same time avoid congestion in the terminal. This would lead to decrease in the cost of operations. In this work, the time taken by the yard crane to load and unload the containers was assumed to be included in the travel time. In future work, the time taken by the yard for loading and unloading may be considered separately.

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