Abstract

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times.

Highlights

  • Container terminals are important assets in many modern economies

  • This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports

  • The berth allocation problem attempts to find the best time for berthing and the best position for mooring the vessels that arrive at the container terminal

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Summary

Introduction

Container terminals are important assets in many modern economies. They are expensive to build, and difficult to operate. Based on the berthing plan, the second operation, known as the Quay Crane Assignment Problem or QCAP, tries to determine the optimum number of quay cranes to allocate to every vessel so that the throughput of cranes is maximized or, equivalently, their idle time is minimized. The last operation is the quay crane scheduling problem or QCSP It strives to find the optimum order in which to carry out the tasks on vessels in order to minimize their processing time. This depends on how many quay cranes are available to use on every vessel (the output of QCAP), and the berthing time and position of the vessel (the output of BAP).

Problems under consideration: a review
The quay crane scheduling problem
Problem description
Assumptions
Parameters lpbvbv
Binary decision variables
Continuous decision variables
The mathematical model
Numerical examples
Application of the genetic algorithm to BACASP
Solution representation: chromosome
Solution validation
Evaluation of fitness
Generating the next population
Selection process
Crossover operator
Mutation operator
Computational experiments
Conclusion
Full Text
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