Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 09-Special Issue, 2018
Soft Computing Based Optimization of Cargo Vessel Service Time in a South Indian Port *P.Oliver Jayaprakash, Professor, Civil, MSEC, Sivakasi, India, Email:
[email protected] K.Gunasekaran, Professor & Head, Transportation Division, Anna University, Chennai S.Muralidharan, Professor, EEE, MSEC, Sivakasi, India, Email: Email:
[email protected]
Abstract—Multi commodity ports operational performance was judged by revenue through cargo handling, quantum of cargo handled and number of vessels serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over waiting time of vessels for berth allocation and dwell time at the port. Operational characteristics had been studied for five years since2005. Service time of vessel is one of the important factors to quantify the performance. This research focused on building an PSO technique based model to forecast the service time using the limited operational characteristics. Validations of PSO model, comparing MLR model outputs were reported. Keywords---Cargo vessels, Berth service time, Ports, PSO model, Optimization
I.
Introduction
The freight planning at ports is required to focus on planning the operations at the port in isolation and for networking with neighbouring ports and to handle additional ship arrivals. Freight transport planning of cargo movement through ports has emerged as a major challenge for the transportation community over a period of time and is a crucial task to sustain the economic growth of the Country. Current approach of applying the four step model used for person trips, to model freight movements would not effectively reflect commercial scheduling constraints. Conventionally to measure the Port performance, Turnaround time (TAT) was used. But in ports, where, delay was considerable and varying, using TAT has limitations. As ports in developing countries have been facing significant delays, it was established from the statistical performance, the service time models could explain the relationship between port performance and other factors, more efficiently than turnaround time models. In the port of developing countries, the delays, due to crew working behaviour, procedures of port operations and other unforeseen natural disturbances were found to be highly varying and substantial. As port performance is used to plan ahead the infrastructural requirements and port operations, considering turnaround time may result in overestimating the infrastructural requirements and inaction towards delay reduction. Whereas service time, the time during which the port infrastructure would be put to the optimum use if used as a method of describing port performance could be reliably used for planning the Infrastructural requirements. The Service time of vessel and quantity of cargo carried by the vessel had very good correlation for break bulk, bulk and dusty coal. The quantity of cargo carried and number of gangs employed had very good correlation with the service time of break bulk and bulk cargo.
A Particle Swarm Optimization (PSO) It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The algorithm was simplified and observed to be performing optimization. PSO is a meta-heuristic as it makes few assumptions about the problem being optimized and can search very large spaces of candidate solutions. More specifically, PSO does not use the gradient of the problem being optimized, which means PSO does not require that the optimization problem be differentiable as is required by classic optimization methods such as gradient descent and quasi-newton methods. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions and simulating social behaviour. *Corresponding Author: P.Oliver Jayaprakash, Email:
[email protected] Article History: Received: April 19, 2018, Accepted: May 22, 2018 1
Soft Computing Based Optimization of Cargo Vessel Service Time in a South Indian Port
Table 1. Correlation Matrix of Service Time Sl.No.
II.
Variables / Commodities
Service Time of Vessels Carrying Break bulk Cargo
1
Quantity of cargo carried, X1
0.6996
2
Unloading rate, X2
0.3112
3
Gang employed, X3
0.7531
4
Berth occupancy(%),X4
0 .0494
Literuate Review
L.C.Wadhwa (1990) developed a model to establish the relationship between port performance and throughput. He considered a bulk terminal loading under various queuing conditions. He used queuing analysis and simulation studies to suggest several alternatives for improving the port capacity. He considered the queuing delays, turnaround time and queue length. Jose L. Tongzon (1995) measured the port performance in terms of the number of containers moved through a port. He emphasized that the ports overall performance is influenced by the speed of moving cargo into and out of ships at berth, the port charges imposed on ship owners and the actual throughput handled. Strandenes, S. P. and P. B. Marlow (2000) studied the current trends in commercial operations due to port competitiveness which affect port pricing. The authors used public enterprise theory with a welfare economics perspective and moved towards private enterprise pricing. Changes in port pricing affect the port competitiveness in case of short shipping. P.B. Marlow and Ana C. Paixão casaca (2003) suggested a set of new port performance indicators to measure the lean ports and suggested conditions for development of agile ports. Kasypi mokhtar and Muhammad zaly shah (2006) developed a model for the Turnaround time of container ships in terms of the level of port facilities using the vessel call data of Port Klang. El Naggar (2010) devised a methodology to support the decision-making process by developing port infrastructure to meet future demand. In order to determine the optimum berths at Alexandra port in Egypt, the queuing theory was applied. The waiting time of vessels outside the port and in queue was calculated using the queuing model. Ramanjot Kaur (2014) highlighted the importance of the various soft computing techniques such as PSO algorithm, artificial neural networks, evolutionary computation, fuzzy logic and swarm intelligence. These techniques deal with those problems whose solutions are hard, unpredictable and uncertain. Crina Grosan (2012) presented the biological motivation and some of the theoretical concepts of swarm intelligence with an emphasis on particle swarm optimization and ant colony optimization algorithms. The basic data mining terminologies are explained and linked with some of the past and ongoing works using swarm intelligence techniques. Jogeswara Rao (2014) developed a Ongole Breed Cattle Expert System. The Expert systems using machine learning algorithm techniques to advice the farmers through online in villages. PSO Algorithm was taken as base and designed a new algorithm known as Parallel Particle swarm optimization.
III. Methodology followed A Nonlinear Regression based Model for predicting the Service time of Break bulk cargo Vessels at Port.The service time (Tsc) prediction model was developed using NLR technique. The following model eq.1 gives the values. The model is given below; Tsc bb= [83.8+1.97*√Q+0.58* Q-3.9*√ U-0.93*√ Q*√ U+0.47* U-19.6*√ G-1.66*√ Q*√ G +0.28 *√ U*√G +0.11* G -7.32*√ B+8.03*√Q*√ B-3.46*√ U*√ B+6.4)* √ G *√ B- 0.98)* B ]/ [1+0.11*√ Q*√ U+0.27*√ Q*√ G1.37*√ U*√ G-1.14*√ Q*√ B +5.4*√ U* √ B+1.09*√ G*√ B] ……………………………………………………..(1) Parameters chosen for the Correlation Analysis; Q – Capacity of the Vessel in Tons; U – Unloading rate of Crane in Tons; G- Gang Strength in numbers; B- Berth Utilisation in %, Tsc- Total Service Time for Break-bulk cargo ships in days;
Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 09-Special Issue, 2018
Figure 1. Prediction of Vessel Service Time using PSO
IV. Conclusion The analytical experiments were conducted to estimate the Service time requirement of a particular type of vessels using the practical constrains. The need for optimizing the service time of vessel is inevitable, since it affects the waiting time of other vessels arriving to the port to get berthed at port wharfs and berths. The capabilities of the PSO algorithm was utilized to estimate the optimized value of practical constraints such as capacity and quantity carried by the vessel, Unloading rate of the cranes, Strength of gangs- human resources used for evacuating the cargo from the vessels with a prime factor of berth vacancy position in terms of occupancy percentage. The output of the PSO algorithm is given the following Table 2 & Appendix B. Table 2. Optimized Results with BF-PSO Q U G B Tsc sing PSO Tsc - Observed
Trial 1 10174 6616.6 18.5 29.5 1.62 1.59
Trial 2 10080 6496.16 18.10 29.23 1.6380 1.57
Trial 3 11363 6603.47 23.16 54.47 1.74 1.71
Trial 4 10018 5913.78 16.13 15.63 1.72 1.64
Trial 6 10032 5919.81 16.161 15.59 1.72 1.68
Trial 10 10146.83 6486.84 33.62 30.17 1.76 1.70
Break-bulk cargo includes the food grains, copper, iron ore, etc. The vessel to apron cargo transfer and also loading into vessel is generally carried out by onboard cranes of the vessels for break-bulk and bulk cargo Then, from the wharf apron to storage transfer is done by grab cranes and front end loaders on an open lot or in transit shed. Shunt truck or rail loading/unloading takes place at the storage site. The break-bulk vessel service time duration depends on the unloading rate, the strength of gang or manpower, the percentage utilization of neighbouring berths, i.e. congestion of berths or traffic level on port roads and the capacity and swing of the grab cranes.
V. Acknowledgment Authors sincerely extend their gratitude to the Management, Principal of Anna University, Chennai & Mepco Schlenk Engineering College (Autonomous),Sivakasi for their support and motivation during the tenure of this research work.
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L.C.Wadhwa,1990,Capacity and Performance of bulk handling ports, Proceeding of Australian transportation research forum. Vol.15, Part I. Jose L. Tongzon, January 2009, Port choice and freight forwarders, Transportation Research Part E: Logistics and Transportation Review, Vol. 45, Issue 1, Pp. 186-195. 3
*Corresponding Author: P.Oliver Jayaprakash, Email:
[email protected] Article History: Received: April 19, 2018, Accepted: May 22, 2018
Soft Computing Based Optimization of Cargo Vessel Service Time in a South Indian Port
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