Computational intelligence approaches for optimizing operations in smart ports
Date Issued
October 2022
Author(s)
Advisor
Abstract
Over the last couple of decades, demand for seaborne containerized trade has increased
significantly and it is expected to continue growing over the coming years. As an important
node in the maritime industry, a marine container terminal (MCT) should be able to
tackle the growing demand for international sea trade. The increasing number of ships and
containers creates several challenges to MCTs, such as congestion, long waiting times before
ships dock, delayed departures, and high service costs. The berth allocation problem
(BAP) and the quay crane assignment problem (QCAP) are two of the most important
optimization problems in container terminals at ports worldwide. A BAP concerns allocating
berthing positions to arriving ships to reduce total service cost, waiting times,
and delays in vessels’ departures. The latter concerns assigning optimal number of quay
cranes to docked vessels. From both the port operator’s and the shipping lines’ point of
view, minimizing the time a vessel spends at berth and minimizing the total cost of berth
operations are considered fundamental objectives with respect to terminal operations.
This dissertation initially focuses on the BAP, with the objective of reducing the total
service cost, which includes waiting cost, handling cost, and several penalties, such as a
penalty for late departure and a penalty for non-optimal berth allocation. First, the BAP
is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an
NP-hard problem and cannot be solved by exact optimization methods in a reasonable
time, a metaheuristic approach, namely, a cuckoo search algorithm (CSA), is proposed to
solve the BAP. To validate the performance of the proposed CSA-based method, we use
two benchmark approaches, namely, the genetic algorithm (GA) and the optimal MILP
solution. Next, we conduct several experiments using a benchmark data set as well as
a randomly-generated larger data set. Simulation results show that the proposed CSA
algorithm has higher efficiency in allocating berths within a reasonable computation time
than its counterparts.
Furthermore, we extend the study of BAP, which considers a single quay (straight line) for
berthing ships, to multiple quays, as found in many ports around the globe. Multi-quay
BAP (MQ-BAP) adds the additional dimension of assigning a preferred quay to each arriving
ship, rather than just specifying the berthing position and time. Here, we address
MQ-BAP with the objective of minimizing the total service cost, which includes minimizvii
ing the waiting times and delays in the departure of ships. MQ-BAP is first formulated
as a MILP and then solved using three computational intelligence (CI)-based approaches,
namely, CSA, GA, and particle swarm optimization (PSO). In addition, the exact MILP
method is also implemented for comparison purposes. Several experiments are conducted
using real data from the Port of Limassol, Cyprus, which has five quays serving commercial
vessel traffic. The comparative analysis and experimental results show that the CSAbased
method outperforms the other CI-based methods, while achieving near-optimal results
in affordable time for all considered scenarios.
Eventually, this dissertation investigates, for the first time, multi-quay combined BAP and
QCAP, and solves it using CI approaches. First, a mathematical model has been developed
based on a real port scenario and real constraints. Then, based on the developed model, we
solve multi quay combined BAP and QCAP using exact method and CI approaches, i.e.,
CSA, GA, and PSO. Validation and performance evaluation of the developed modeling
framework and the proposed methods are performed through extensive experiments with
real data. The real dataset is collected from the Port of Limassol, Cyprus. In addition, the
dataset contains data for multiple quays (five), two of which are container terminals and
the other three are passenger or general cargo terminals. The experimental results reveal
that the exact method can solve the problem only when one week dataset is used; however,
our newly adopted CI-based methods for MQ combined (BAP and QCAP) problem are
able to solve large instances (i.e., one month) with small computation time.
To summarize, this dissertation develops several CI based methodologies for several BAP
formulations (stand-alone BAP, MQ-BAP, and MQ combined BAP and QCAP) in real
world environments with several practical constraints. The proposed methods have been
tested and evaluated extensively using real data against benchmark approaches. Numerical
findings from experiments confirm the effectiveness of the proposed solutions. Therefore,
the proposed CI-based methods can serve as promising decision support tools and assist
terminal operators while developing berth allocation plans. The latter (MQ combined
BAP and QCAP) will also assist port operators with the development of a fully-specified
berth schedule, for container ships as well as for other general cargo or passengers ships,
to ensure that the ships will be moored and departed in a timely manner.
significantly and it is expected to continue growing over the coming years. As an important
node in the maritime industry, a marine container terminal (MCT) should be able to
tackle the growing demand for international sea trade. The increasing number of ships and
containers creates several challenges to MCTs, such as congestion, long waiting times before
ships dock, delayed departures, and high service costs. The berth allocation problem
(BAP) and the quay crane assignment problem (QCAP) are two of the most important
optimization problems in container terminals at ports worldwide. A BAP concerns allocating
berthing positions to arriving ships to reduce total service cost, waiting times,
and delays in vessels’ departures. The latter concerns assigning optimal number of quay
cranes to docked vessels. From both the port operator’s and the shipping lines’ point of
view, minimizing the time a vessel spends at berth and minimizing the total cost of berth
operations are considered fundamental objectives with respect to terminal operations.
This dissertation initially focuses on the BAP, with the objective of reducing the total
service cost, which includes waiting cost, handling cost, and several penalties, such as a
penalty for late departure and a penalty for non-optimal berth allocation. First, the BAP
is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an
NP-hard problem and cannot be solved by exact optimization methods in a reasonable
time, a metaheuristic approach, namely, a cuckoo search algorithm (CSA), is proposed to
solve the BAP. To validate the performance of the proposed CSA-based method, we use
two benchmark approaches, namely, the genetic algorithm (GA) and the optimal MILP
solution. Next, we conduct several experiments using a benchmark data set as well as
a randomly-generated larger data set. Simulation results show that the proposed CSA
algorithm has higher efficiency in allocating berths within a reasonable computation time
than its counterparts.
Furthermore, we extend the study of BAP, which considers a single quay (straight line) for
berthing ships, to multiple quays, as found in many ports around the globe. Multi-quay
BAP (MQ-BAP) adds the additional dimension of assigning a preferred quay to each arriving
ship, rather than just specifying the berthing position and time. Here, we address
MQ-BAP with the objective of minimizing the total service cost, which includes minimizvii
ing the waiting times and delays in the departure of ships. MQ-BAP is first formulated
as a MILP and then solved using three computational intelligence (CI)-based approaches,
namely, CSA, GA, and particle swarm optimization (PSO). In addition, the exact MILP
method is also implemented for comparison purposes. Several experiments are conducted
using real data from the Port of Limassol, Cyprus, which has five quays serving commercial
vessel traffic. The comparative analysis and experimental results show that the CSAbased
method outperforms the other CI-based methods, while achieving near-optimal results
in affordable time for all considered scenarios.
Eventually, this dissertation investigates, for the first time, multi-quay combined BAP and
QCAP, and solves it using CI approaches. First, a mathematical model has been developed
based on a real port scenario and real constraints. Then, based on the developed model, we
solve multi quay combined BAP and QCAP using exact method and CI approaches, i.e.,
CSA, GA, and PSO. Validation and performance evaluation of the developed modeling
framework and the proposed methods are performed through extensive experiments with
real data. The real dataset is collected from the Port of Limassol, Cyprus. In addition, the
dataset contains data for multiple quays (five), two of which are container terminals and
the other three are passenger or general cargo terminals. The experimental results reveal
that the exact method can solve the problem only when one week dataset is used; however,
our newly adopted CI-based methods for MQ combined (BAP and QCAP) problem are
able to solve large instances (i.e., one month) with small computation time.
To summarize, this dissertation develops several CI based methodologies for several BAP
formulations (stand-alone BAP, MQ-BAP, and MQ combined BAP and QCAP) in real
world environments with several practical constraints. The proposed methods have been
tested and evaluated extensively using real data against benchmark approaches. Numerical
findings from experiments confirm the effectiveness of the proposed solutions. Therefore,
the proposed CI-based methods can serve as promising decision support tools and assist
terminal operators while developing berth allocation plans. The latter (MQ combined
BAP and QCAP) will also assist port operators with the development of a fully-specified
berth schedule, for container ships as well as for other general cargo or passengers ships,
to ensure that the ships will be moored and departed in a timely manner.
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