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Universidade Estácio de SáEngenharia de Produção
MODELING AND SIMULATION OF PIPELINE LOADING OPERATIONS ONTO BARGES: A CASE STUDY OF RESOURCE ALLOCATION
USING DISCRETE EVENT SIMULATION
Fabricio Cardoso de VasconcellosFlávia Cristina da Silva Duarte
Henrique Alves SerpaProf. Dr. Marcelo Prado Sucena
Prof. Dr. David Fernandes Cruz Moura
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Agenda
§ Introduction§ Loading Operations Characterization;§ Discrete Event Simulation Architecture;§ Case Study Construction Steps§ Verification & Validation;§ Technical Scenario Analysis (What-If);§ Economic Scenario Analysis;§ Final Remarks.
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Introduction
Problem: Reduction of Loading Time of Pipelines onto Barges
Methodology in Brief: Discrete Event Simulation (DES)-driven resource allocation (trucks, reach stackers, and cranes) analysis of different investment scenarios .
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Motivation
Several bottlenecks in a Brazilian pre-salt area surronding port (São Sebastião):
● Berthing average utilization rate: 18 hours – 50% higher than optimal values presented in literature
● Queue generation at the berth area – Almost 10% of the overall containers freight costs
● Loss of port calls
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Introduction
Source: Carvalho, 2011
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Technique Choice Reasoning
DES Advantages
§ New process configurations verification§ Design of novel operational proceedings§ System evaluation for different timing
conditions§ Easy process bottlenecks identification§ Model reproducibility§ Low cost investment
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Loading Operations Characterization
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DES Project in Brief
Source: Chwif; Medina 2006
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ACD Conceptual Model
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Case Study Steps
§ Reach Stacker Loading Time Intervals– Weibull (Fixo= 1, α= 3.48, β= 0.736);
§ Truck traveling time intervals – Weibull (Fixo= 2, α= 15.2, β= 1.11);
§ Truck weighting time intervals – Pearson 5 (Fixo= 1, α= 3.48, β= 0.736);
§ Crane loading time intervals – Pearson 5 (Fixo= 5, α= 5.53, β= 4).
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Computational Model Construction
Software Simul8 Scenario Representation: § Actual logical sequence of pipes (B, C, D, A);§ Actual proportion of plain pipes (93%) and anode pipes (7%);§ Attendance of 4 pipes at a given time on each resource(RS, trucks, cranes, and port scale);§ Each load: 708 pipelines @ barge;§ Mean Loading Time: 18 hour @ load.
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Validation Issues§Simulation Model:
● Pipe Loading Mean Time:18.075,87 min = 301,25 h to load 12.313 pipes. ● Number of Loaded pipes: I.C (95%) = [12.242, 12.321]● At a given load out operation:
t(hours) = 708 * 301,25 = 17,32 h12.313
Actual System:● Time average = 18 hours● Number of loaded pipes = 12.313
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Scenario Analysis Loading Time X # of Cranes
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
2000018595
9352
7891 7854 7848
Número de guindastes (unidades)
Tem
po d
e op
eraç
ão (m
in)
Fonte: Próprio autor
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Optimal Number of Cranes x Loading Time
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Scenario Analysis Queue Time & Utilization @ Cranes
Cenário 1 Cenário 2 Cenário 30
0.2
0.4
0.6
0.8
1
1.2
Tempo % Utilização Guindaste Tempo de Fila Guindaste
78 %
14 %
43 %
0 %
99 %
50 %
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Economic Analysis
Scenario 2: 44% reduction when compared to #1 Scenario 3: 47% reduction when compared to #1
Equipment Rates Scenario 1 Scenario 2 Scenario 3
Trucks (unities) 4 4 4
Cranes (unities) 1 2 3
Total loading time (h) 302 152 129
Cranes rental (US$) 2,700 135,900.00 136,800.00 174,150.00
Reach Stacker rental (US$)Barge berthing tax (US$)Tug berthing tax (US$)Storage yard rental (US$)Manpower (US$)Trucks rental (US$)Total (US$)
2,700249.5053.00753.5013,840.50490.00
135,900.0012,551.872,668.6737,919.87696,634.4798,653.331,120,228.23
68,400.006,317.001,343.1719,085.50350,623.9749,653.33632,223.48
58,050.005,361.561,139.9316,197.56297,569.0342,140.00594,608.08
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Final RemarksInvestigation of the resource allocation issue such as trucks, cranes and reach stackers at the Port of São Sebastião, to propose a reduction in total loading time of pipelines on barges.
Verification and Validation of an actual port model
What-if scenario analysis to enhance productivity and suggest improvements in resource allocation.
Brief economic analysis of the suggested scenarios proposed by the simulation model
Conclusion: Scenario 3 - better performance, but comprises space reduction for the safe movement of equipments, people and products. Scenario 2, therefore, constitutes the best option, as it showed a 50% reduction in the total loading time and cost reduction of approximately 44%.
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Final Remarks
We conclude that performance evaluation by means of a discrete event simulation methodology allowed the assessment of alternative investment scenarios, constituting
itself as a fundamental tool for the characterization of a port terminal of pipeline loading, diagnosing problems and identifying possible improvement opportunities.