Loading unloading operations and vehicle queuing processes at container port

Southwest Region University Transportation Center Loading/Unloading O_perations and Vehicle Queuing Processes at Container Ports SWUTC/95/60017/71249-2 Center for Transportation Research University of Texas at Austin 3208 Red River, Suite 200 Austin, Texas 78705-2650 I Report No SWUTC/95/600 17171249-2 Government Accession No Title and Subtitle Technical Rej)Ort Docwnentation Paj!e Recipient's Catalog No Report Date LoadinglUnloading Operations and Vehicle:Queuing Processes at Container Ports March 1995 Author(s) Performing Organization Report No Max Karl Kiesling and C Michael Walton Performing Organization Code Research Report 60017 and 71249 10 Work Unit No (TRAIS) Performing Organization Name and Address Center for Transportation Research The University of Texas at Austin 3208 Red River, Suite200 Austin, Texas 78705-2650 U Contract or Grant No 0079 and DTOS88-G-0006 13 Type of Report and Period Covered 12 Sponsoring Agency Name and Address Southwest Region University Transportation Center Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135 14 Spousoring Agency Code 15 Supplementary Notes Supported by grants from the Office of the Governor of the State of Texas, Energy Office and from the U.S Department of Transportation, University Transportation Centers Program 16 Abstract This report describes wharf crane operations at container ports In particular, it explores econometric models of wharf crane productivity, as well as simulation and analytical models that focus on the queuing phenomenon at the wharf crane The econometric model revealed factors that significantly affect wharf crane productivity, while all other models, based on extensive time-motion studies, revealed that assumptions of exponential service times are not always appropriate Time distributions were also investigated for the arrival and backcycle processes at the wharf crane All findings were incorporated into simulation and mathematical queuing models for the loading and unloading of container ships 17 KeyWords 18 Distribution statement Queuing, Container, Modelling, Port Operations, Wharf Crane, Time Distribution, Trip Distribution, LoadinglUnloading No Restrictions This docwnent is available to the public through NTIS: National Technical Information Service 5285 Port Royal Road Springfield, Virginia 22161 19 Security Classif.(ofthisreport) Unclassified Form DOT F 1700.7 (8-72) 20 Security Classif.(ofthis page) Unclassified Reprodudion of completed PIlle authorized 21 No of Pages 254 I 22 Price LOADING/UNLOADING OPERATIONS AND VEHICLE QUEUING PROCESSES AT CONTAINER PORTS by Max Karl Kiesling and C Michael Walton Research Report SWUTC/95/60017n1249-2 Southwest Region University Transportation Center Center for Transportation Research The University of Texas Austin, Texas 78712 MARCH 1995 DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program in the interest of information exchange The U S Government assumes no liability for the contents or use thereof ACKNOWLEDGEMENT The authors recognize that support for this research was provided by a grant from the U.S Department of Transportation, University Transportation Centers Program to the Southwest Region University Transportation Center This publication was developed as part of the University Transportation Centers Program· which is funded 50% in oil overcharge funds from the Stripper Well settlement as provided by the State of Texas Governor's Energy Office and approved by the U.S Department of Energy Mention of trade names or commercial products does not constitute endorsement or recommendation for use i ii EXECUTIVE SUMMARY Increased global competition has resulted in shipping ports that are increasingly congested To provide adequate space for the increased traffic, ports must either expand facilities or improve the efficiency of the operations Because many ports are land constrained, the only available option the one investigated in this report~s to improve operational efficiency In exploring ways in which ports can improve efficiency, we analyze the various elements associated with wharf crane operations Looking in particular at the Port of Houston and the Port of New Orleans, we collected historical crane performance records for 1989, including general descriptions of each ship serviced and detailed accounts of how many (and what type of) containers were moved to or from the Ship This information was then used to develop an econometric model to predict the net productivity of the wharf crane based on ship characteristics and on the distribution of container moves expected between the storage yard and the wharf crane While the resulting model proved inadequate for use as a forecasting tOOl, it did identify several variables having statistically significant influence on the net productivity of the wharf crane For example, we learned that the number of outbound container moves, the number of inbound container moves, the type of ship being serviced, the number of ships being serviced simultaneously, and the stevedoring company contracted to service the ship-all have significant impact on crane productivity And although the model is site-specific for the Barbours Cut Terminal in the Port of Houston, we expect that the same variables would have Similar effects at other national container ports iii iv ABSTRACT This report describes wharf crane operations at container ports In particular, it explores econometric models of wharf crane productivity, as well as simulation and analytical models that focus on the queuing phenomenon at the wharf crane The econometric model revealed factors that significantly affect wharf crane productivity, while all other models, based on extensive timemotion studies, revealed that assumptions of exponential service times are not always appropriate Time distributions were also investigated for the arrival and backcycJe processes at the wharf crane All findings were incorporated into simulation and mathematical queuing models for the loading and unloading of container ships v vi TABLE OF CONTENTS CHAPTER INTRODUCTION AND LITERATURE REViEW Growth of Containerization Objectives Literature Review 4' General Port Operations Applicable Queuing Literature Research Approach CHAPTER OVERVIEW OF PORT OPERATIONS 11 Wharf Crane Operations and Delays 11 Storage Yard Operations and Delays 13 Container Storage by Stacking 13 Container Chassis Storage Tractor and Chassis Operations and Delays Conclusions 18 CHAPTER THE PREDICTION OF WHARF CRANE PRODUCTiViTy 19 Factors that Reduce Crane Productivity 19 Data Collection and Reduction 21 General Model and A Priori Expectations 23 Development and Interpretation of ModeL 26 Model Critique 35 Summary 36 CHAPTER DATA ACQUISITION AND ANALySiS 39 Design of Experiment 39 Data Collection Mettlodology 40 Programming the Hewlett-Packard 48SX 40 Data Collection Procedure 42 The Data Set 44 Transfer of the Data to the Macintosh 46 Error Detection and Editing of Data 46 vii Figure B.41 - Cumulative frequency of service times for Mar9p.l data file No distribution tested significantly similar to the field data The Erlang(4 ) distribution is shown below Sample is 38 observations C ~1.00 u 0.90 T ~~ [j 0.80 a t i v ~ 0.70 0.60 e 0.50 F 0.40 r e 0.30 q u 0.20 e 0.10 n c 0.00 y • , 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:00 0:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.42 Cumulative frequency of single move service times for Mar9p.l data file No distribution tested statistically similar to the field data The Erlang(3) distribution is shown with 38 observations c u 1.00 m u 0.90 0.80 a t i 0.70 T + + + v 0.60 -'- ~ e ,&t$~~ Il :~~ 0.50 F 0.40 r e 0.30 q u 0.20 e 0.10 n c 0.00 y 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:000:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.43 - Cumulative frequency of interarrival times for Mar9p.l data file Best fit is the Erlang(2) distribution Sample is 97 observations c ~ u 1.00 m 0.90 u 0.80 a t 0.70 i v 0.60 e 0.50 F r 0.40 t ~ 0 e 0.30 q u 0.20 e n 0.10 c y 0.00 ~~~ ~~ -+ ~ ~ -+ -~ ~ -4 ~ 0:00:00 0:02:00 0:04:00 0:06:00 0:08:00 0:10:00 0:12:00 0:14:00 0:16:00 0:18:00 0:20:00 Time (h:mm:ss) Figure B.44 - Cumulative frequency of single move interarrival times for Mar9p.l data file Best fit is the Erlang(2) distribution Sample is 80 observations c u D_, U 1.00 0.90 0.80 f ~~ a t 0.70 v 0.60 t-) \.oJ e F r e q u e n c y 0.50 0.40 0.30 0.20 0.10 0.00 W -4 + f + + + -+ + + ~ 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:00 0:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.45 - Cumulative frequency of backcycle times for Mar9p.l data file No distribution tested statistically similar to field data The Erlang(7) distribution is shown below with 89 observations c u 1.00 , n .~~ u 0.90 0.80 a t 0.70 ~o ~(JJ o o i v 0.60 ~ t ) e 0.50 F 0.40 r e 0.30 q u 0.20 e 0.10 c 0.00 n y I , 0:00:00 0:02:00 0:04:00 0:06:00 0:08:00 0:10:00 0:12:00 0:14:00 0:16:00 0:18:00 0:20:00 Time (h:mm:ss) Figure B.46 • Cumulative frequency of service times for Mar9p.2 data file Best fit is the Erlang(2) distribution Sample is 128 observations c u 1.00 m 0.90 u 0.80 a t t ~ 0 0 0.70 i ~ Yo) v 0.60 e 0.50 F r 0.40 e 0.30 q u 0.20 e n 0.10 c y 0.00 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:00 0:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.47 - Cumulative frequency of single move service times for Mar9p.2 data file Best fit is the Erlang(2) distribution Sample is 99 observations c u 1.00 n., u 0.90 0.80 a t i ~ ~ o o 0.70 v 0.60 ~ e 0.50 F 0.40 r e 0.30 q u 0.20 e 0.10 c 0.00 " n y , I 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:00 0:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.48 - Cumulative frequency of interarrival times for Mar9p.2 data file Best fit is the exponential distribution Sample is 136 observations cu 1.00 m 0.90 u 0.80 a t ~ VI T ~ m iJO 0.70 i v 0.60 e 0.50 F r 0.40 e 0.30 q u 0.20 e n 0.10 c y 0.00 0:00:00 0:02:30 0:05:00 0:07:30 0:10:00 0:12:30 0:15:00 0:17:30 0:20:00 0:22:30 0:25:00 Time (h:mm:ss) Figure B.49 - Cumulative frequency of single move interarrival times for Mar9p.2 data file Best fit is the exponential distribution Sample is 108 observations c ~ 1.00 u 0.90 I ~~ ~~ 0.80 a N UJ t 0.70 v 0.60 e 0\ 0.50 F 0.40 r e 0.30 q u 0.20 e 0.10 ~ n c 0.00 y ~~~ ~ ~ ~ ~ ~~ ~ r -r -+ -~ 0:00:00 0:02:00 0:04:00 0:06:00 0:08:00 0:10:00 0:12:00 0:14:00 0:16:00 0:18:00 0:20:00 Time (h:mm:ss) Figure B.50 - Cumulative frequency of double move interarrival times for Mar9p.2 data file Best fit is the exponential distribution with 28 observations c • u 1.00 m u 0.90 I 0.80 a t 0.70 i v 0.60 ~ - J ~ e 0.50 F- 0.40 r e 0.30 q u 0.20 e 0.10 • • ••• •• o o 10 ~ • I • ~~ l!!J •~ • n c 0.00 Y -FF"! f f -+ if -! -f + ! ! 0:00:00 0:01 :00 0:02:00 0:03:00 0:04:00 0:05:00 0:06:00 0:07:00 0:08:00 0:09:00 0:10:00 Time (h:mm:ss) Figure B.51 - Cumulative frequency of backcycle times for Mar9p.2 data file No distribution tested statistically similar to the field data The Erlang(3) distribution is shown below with 133 observations c u 1.00 m u 0.90 .~ ~o I 0.80 a ~ t 0.70 v 0.60 e 00 ~ Q ~ o Q o 0.50 F 0.40 r e 0.30 ~ 0.20 e 0.10 n c 0.00 ~, J.~~~ ~ ~ -r -+ -+ ~ y 0:00:00 0:05:00 0:10:00 0:15:00 0:20:00 Time (h:mm:ss) 0:25:00 0:30:00 0:35:00 REFERENCES Whittaker,J.R Containerization Washington: Hemishpere Publishing Company, 1975, 12 Whittaker, J.R Containerization Washington: Hemishpere Publishing Company, 1975,3 Gilman, Sidney Container Logistics and Terminal Resign Washington: International Bank for Reconstruction and Development, 1982,1 Strom, Harold K "Containerization: A Pandora's Box in Reverse?" 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