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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
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.
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 .
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
Introduction
Source: Carvalho, 2011
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
Loading Operations Characterization
DES Project in Brief
Source: Chwif; Medina 2006
ACD Conceptual Model
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).
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.
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
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
Optimal Number of Cranes x Loading Time
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 %
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
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%.
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.
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