Universidade Estácio de Sá Engenharia de Produção

Transcription

1 Universidade Estácio de Sá Engenharia de Produção MODELING AND SIMULATION OF PIPELINE LOADING OPERATIONS ONTO BARGES: A CASE STUDY OF RESOURCE ALLOCATION USING DISCRETE EVENT SIMULATION Fabricio Cardoso de Vasconcellos Flávia Cristina da Silva Duarte Henrique Alves Serpa Prof. Dr. Marcelo Prado Sucena Prof. Dr. David Fernandes Cruz Moura

2 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.

3 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.

4 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

5 Introduction Source: Carvalho, 2011

6 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

7 Loading Operations Characterization

8 DES Project in Brief Source: Chwif; Medina 2006

9 ACD Conceptual Model

10 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).

11 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 barge; Mean Loading Time: 18 load.

12 Validation Issues Simulation Model: Pipe Loading Mean Time:18.075,87 min = 301,25 h to load pipes. Number of Loaded pipes: I.C (95%) = [12.242, ] At a given load out operation: Actual System: Time average = 18 hours Number of loaded pipes = t(hours) = 708 * 301,25 = 17,32 h

13 Scenario Analysis Loading Time X # of Cranes Tempo de operação (min) Número de guindastes (unidades) Fonte: Próprio autor

14 Optimal Number of Cranes x Loading Time

15 Scenario Analysis Queue Time & Cranes % % % 43 % % 0 Cenário 1 Cenário 2 Cenário 3 0 % Tempo % Utilização Guindaste Tempo de Fila Guindaste

16 Economic Analysis Equipment Rates Scenario 1 Scenario 2 Scenario 3 Trucks (unities) Cranes (unities) Total loading time (h) Cranes rental (US$) 2, , , , Reach Stacker rental (US$) Barge berthing tax (US$) Tug berthing tax (US$) Storage yard rental (US$) Manpower (US$) Trucks rental (US$) Total (US$) 2, , , , , , , , ,120, , , , , , , , , , , , , , , Scenario 2: 44% reduction when compared to #1 Scenario 3: 47% reduction when compared to #1

17 Final Remarks Investigation 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%.

18 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.