Febri Zukhruf, Russ Bona Frazila, Wijang Widhiarso

Corresponding email: febri.zukhruf@ftsl.itb.ac.id

Corresponding email: febri.zukhruf@ftsl.itb.ac.id

**Published at : ** 21 Apr 2020

**IJtech :** IJtech
Vol 11, No 2 (2020)

**DOI :** https://doi.org/10.14716/ijtech.v11i2.2090

Zukhruf, F., Frazila, R.B., Widhiarso, W., 2020. A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design.

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Febri Zukhruf | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132, Indonesia |

Russ Bona Frazila | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132, Indonesia |

Wijang Widhiarso | Faculty of Information Technology, Multi Data Palembang Bachelor Program, Palembang 30113, Indonesia |

Abstract

This study
compared swarm-based algorithms in terms of their effectiveness in improving
the design of facilities in container terminals (CTs). The design was conducted
within the framework of stochastic discrete optimization and involved
determining the number of equipment needed in CTs by considering variations in
demand and the productivity of facilities—issues that are rarely elaborated in
CT design. Variations were identified via Monte Carlo simulation characterized by a particular
distribution. The conflicting issue due to increments in equipment investment
that possibly cause the distribution delays was also modeled, specifically in
relation to the increasing number of trucks used in terminals. Given that the
optimization problem is typified by numerous combinations of actions, the
swarm-based algorithms were deployed to develop a feasible solution. A new
variant of glowworm swarm optimization (GSO) was then proposed and compared
with particle swarm optimization (PSO) algorithms. The numerical results showed
that the performance of the proposed GSO is superior to that of PSO algorithms.

Design of container terminal facilities; Glowworm swarm optimization; Particle swarm optimization; Stochastic optimization.

Introduction

As an
essential part of annually expanding global trade, the container shipping
industry has been compelled to extensively develop container terminals (CTs) by
investing in large-scale equipment and advanced hardware for tackling container
flows (Mishra et al., 2017). This
development has correspondingly increased the complexity of CT operations,
which encompass interactions among resources, entities, and activities. Such
interactions begin at the seaside, where a vessel requires assistance from a
tugboat for berthing. After berthing, quay cranes (QCs) simultaneously handle
containers and transport them to a loading dock or transport vehicles. Multiple
transport vehicles then convey the containers to a stacking yard, where smooth
distribution is considerably facilitated by the existence of an internal road
network. Cumulatively, these interactions reflect seaport performance, which is
manifested in different forms that range from operational performance (Cartenì 2012 Luca, 2012) to environmental
performance (Budiyanto et al., 2019).

The
above-mentioned interactions equally contribute to the complexity of CT
operations, which is hardly represented in analytical models (Dragovi? et al., 2017).

The complexity of CT operations can likewise
be viewed as an optimization problem, whose resolution lies in selecting the
action that best enhances the performance of CTs. Given that CTs operate under
uncertainties (i.e., variations at the demand and supply sides), this study
also established a stochastic optimization model that directly incorporates
uncertainty into the decision-making process. In this
model, variations in vessel size are the uncertainties manifested in the demand
side, whereas fluctuations in equipment productivity represent the
uncertainties in the supply side. The stochastic modeling also considered
the QCs, container truck-trailer units (TTUs), and container yard equipment
[i.e., rubber tyred gantry crane (RTGC)] employed in CT operations. Because an
increment in TTUs used potentially causes delays at land-side area, this
research integrated estimations of delays in travel time by applying the Bureau
of Public Roads (BPR) function.

Optimization in CTs may be embodied by an
enormous number of problem combinations, so the issue was resolved in this
research through a metaheuristic approach, which comes in several types, such
as genetic algorithms, tabu search, simulated annealing, and swarm-based
algorithms. Swarm-based algorithms are grounded in the
natural behaviors of swarm entities, such as a flock of birds [i.e.,
particle swarm optimization (PSO)] and a colony of glowworms [i.e., glowworm
swarm optimization (GSO)]. Because of the excellent
performance of these algorithms, they have been widely used in solving various
optimization problems. However, to the best of our knowledge, little
research has been devoted to the performance comparison of swarm-based
algorithms intended to address the CT optimization problem, specifically the
stochastic type. To fill this void, the present study evaluated the
effectiveness of these algorithms in enhancing the design of CT facilities. The
comparison revolved specifically around the latest variants of PSO and a
version of GSO within the framework of a binary optimization problem.

The rest of the paper is organized as follows.
Section 2 describes CT operations and discusses the optimization modeling
framework. Section 3 elaborates on swarm-based algorithms and presents the case
study on the performance of these approaches. Section 4 concludes the paper
with a summary.

Conclusion

This research investigated the performance of swarm-based algorithms in
the design of CT facilities. To this end, a new variant of binary GSO and the
latest types of binary PSOs (i.e., PBPSO and MPBPSO) were incorporated into the
framework of stochastic discrete optimization. Taking
into account uncertainty issues and possible additional delays due to
increments in the number of facilities, the swarm-based algorithms were used to
determine the number of additional facilities required for CT operations.
The results revealed that an increase in the number of trucks and gantry cranes
improves CT performance. The numerical experiment showed that the binary
version of GSO realizes better optimization results and computational times
than those achieved by the comparison algorithms. However, its stability needs
to be carefully considered in future works. Another essential issue of
stochastic optimization is computational time because MC simulation requires
massive repetitions, albeit the proposed algorithm can reduce this requirement
significantly. Further efforts may be needed to inquire into the development of
a more efficient algorithm.

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