ANALYZING QUAY CRANES JOB SEQUENCE USING STOCHASTIC PROJECT SCHEDULING TECHNIQUE

Abstract

This thesis applies recent advances in the field of project management and persistencymodel to the vessel transhipment time estimation problem under uncertainty. Wedevelop models that take into account the uncertainty of prime mover cycling timeand queuing delay at the yard without assuming a specific probability distribution,while remaining highly tractable and providing insight into the criticality of the pathsin the network. We also develop an effective method to estimate the mean servicetime and its corresponding variance, so that our models can be implemented in realoperations of the container terminal.We first apply the network flow model to the vessel departure time estimation problem in a deterministic manner. The departure time is estimated by finding the criticalpath of the network. The crashing problem is also studied. We show how the allo-cation of additional resource can reduce total transhipment time. When consideringthe uncertainties of the operations of the terminal, we develop a persistency model tohandle the uncertainties without assuming a specific probability distribution. Finally,for the implementation of our proposed models, we suggest a simple but effective algo-rithm to estimate the mean service time and its variance. We compare our estimateddeparture time to the real departure time monitored from an empirical example of thelocal port, and show that our estimation method for the mean and variance leads toa very good performance. Also the results of persistency model are compared withthe results of simulations. We show that the persistency model is robust to all thedistributions and provide a proper upper bound for our problem.