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.