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

**Volume :** **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.

213

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.

References

Anandakumar, H.,
Umamaheswari, K., 2018. A Bio-inspired Swarm Intelligence Technique for Social
Aware Cognitive Radio Handovers. *Computers and Electrical Engineering*,
Volume 71, pp. 925–937

Andersen, J., Crainic,
T.G., Christiansen, M., 2009. Service Network Design with Asset Management:
Formulations and Comparative Analyses. *Transportation Research Part C:
Emerging Technologies*, Volume 17(2), pp. 197–207

Baskoro, A.S., Masuda,
R., Suga, Y., 2011. Comparison of Particle Swarm Optimization and Genetic
Algorithm for Molten Pool Detection in Fixed Aluminum Pipe Welding. *International
Journal of Technology*, Volume 2(1), pp. 74–83

Budiyanto, M.A.,
Huzaifi, M.H., Sirait, S.J., 2019. Estimating of CO_{2} Emissions in a
Container Port based on Modality Movement in the Terminal Area*.
International Journal of Technology*, Volume 10(8), pp. 1618–1625

Burhani, J.T.,
Zukhruf, F., Frazila, R.B., 2014. Port Performance Evaluation Tool based on
Microsimulation Model. *MATEC Web of Conferences*, Volume 101, pp. 1–5

Cartenì, A., Luca, S.
De., 2012. Tactical and Strategic Planning for a Container Terminal: Modelling Issues
within a Discrete Event Simulation Approach. *Simulation Modelling Practice
and Theory*, Volume 21(1), pp. 123–145

Cimpeanu, R., Devine,
M.T., O’Brien, C., 2017. A Simulation Model for the Management and Expansion of
Extended Port Terminal Operations. *Transportation Research Part E: Logistics
and Transportation Review*, Volume 98, pp. 105–131

Chen, J., Shi, J.,
2019. A Multi-compartment Vehicle Routing Problem with Time Windows for Urban Distribution
– A Comparison Study on Particle Swarm Optimization Algorithms. *Computers
and Industrial Engineering*, Volume 133, pp. 95–106

Chen, R.M., Shen,
Y.M., Hong, W.Z., 2019. Neural-like Encoding Particle Swarm Optimization for Periodic
Vehicle Routing Problems. *Expert Systems
with Applications*, Volume 138, pp. 112833

Di, X., He, X., Guo,
X., Liu, H.X., 2014. Braess Paradox under the Boundedly Rational user Equilibria.
*Transportation Research Part B: Methodological*,
Volume 67, pp. 86–108

Dragovi?, B.,
Tzannatos, E., Park, N.K., 2017. Simulation Modelling in Ports and Container
Terminals: Literature Overview and Analysis by Research Field, Application Area
and Tool. *Flexible Services and
Manufacturing Journal*, Volume 29(1), pp. 4–34

Frazila, R.B.,
Zukhruf, F., 2017. A Stochastic Discrete Optimization Model for Multimodal Freight
Transportation Network Design. *International Journal of Operations Research*,
Volume 14(3), pp. 107–120

Govindan, K.,
Jafarian, A., Nourbakhsh, V., 2019. Designing a Sustainable Supply Chain
Network Integrated with Vehicle Routing: A Comparison of Hybrid Swarm
Intelligence Metaheuristics. *Computers and Operations Research*, Volume
110, pp. 220–235

Hoff, A., Lium, A.G.,
Løkketangen, A., Crainic, T.G., 2010. A Metaheuristic for Stochastic Service
Network Design. *Journal of Heuristics*, Volume 16(5), pp. 653–679

Kennedy, J., Eberhart,
R.C., 1995. Particle Swarm Optimization. *In*:
Proceedings of IEEE International Conference on Neural Networks, Volume 4,
Perth, Australia, pp. 1942–1948

Kennedy, J., Eberhart,
R.C., 1997. A Discrete Binary Version of the Particle Swarm Algorithm. *In*: Proceedings of the IEEE
International Conference on Systems, Man and Cybernetics 5, pp. 4104–4108

Krishnanand, K.N.,
Ghose, D., 2005. Detection of Multiple Source Locations using a Glowworm
Metaphor with Applications to Collective Robotics. Swarm Intelligence symposium.
*In*: Proceedings 2005 IEEE Swarm
Intelligence Symposium, 2005, pp. 84–91

Krishnanand, K.N.,
Ghose, D., 2008. Theoretical Foundations for Rendezvous of Glowworm-Inspired
Agent Swarms at Multiple Locations. *Robotics
and Autonomous Systems, *Volume 56(7), pp. 549–569

Krishnanand, K.N.,
Ghose, D., 2009. Glowworm Swarm Optimization for Simultaneous Capture of Multiple
Local Optima of Multimodal Functions. *Swarm
Intelligence*, Volume 3(2), pp. 87–124

Li, M., Wang, X.,
Gong, Y, Liu, Y., Jiang, C., 2014. Binary Glowworm Swarm Optimization for Unit
Commitment. *Journal of Modern Power
Systems and Clean Energy*, Volume 2(4), pp. 357–365

Liu, Z., Guo, S.,
Wang, L., 2019. Integrated Green Scheduling Optimization of Flexible Job Shop
and Crane Transportation Considering Comprehensive Energy Consumption. *Journal
of Cleaner Production*, Volume 211, pp. 765–786

Marinaki, M.,
Marinakis, Y., 2016. A Glowworm Swarm Optimization Algorithm for the Vehicle
Routing Problem with Stochastic Demands. *Expert
Systems with Applications*, Volume 46, pp. 145–163

Menhas, M.I., Wang,
L., Fei, M., Pan, H., 2012. Comparative Performance Analysis of Various Binary
Coded PSO Algorithms in Multivariable PID Controller Design. *Expert Systems with Applications,* Volume
39(4), pp. 4390–4401

Mishra, N., Roy, D.,
Van Ommeren, J.-K., 2017. A Stochastic Model for Interterminal Container
Transportation. *Transportation Science*,
Volume 51(1), pp. 67–87

Özgün-Kibiro?lu, Ç.,
Serarslan, M.N., Topcu, Y.?., 2019. Particle Swarm Optimization for
Uncapacitated Multiple Allocation Hub Location Problem under Congestion. *Expert
Systems with Applications*, Volume 119, pp. 1–19

Shen, Q., Jiang, J.H., Jiao, C.X., Shen, G.L., Yu,
R.Q., 2004. Modified Particle Swarm Optimization Algorithm for Variable Selection
in MLR and PLS Modeling: QSAR Studies of Antagonism of Angiotensin II Antagonists.
*European Journal of Pharmaceutical
Sciences*, Volume 22(2–3), pp. 145–152

Sim, T., Lowe, T.J.,
Thomas, B.W., 2009. The Stochastic P-Hub Center Problem with Service-Level Constraints.
*Computers and Operations Research*, Volume 36(12), pp. 3166–3177

Summakieh, M.A., Tan,
C.K., El-saleh, A.A., Chuah, T.C., 2019. Improved Load Balancing for LTE-A
Heterogeneous Networks using Particle Swarm Optimization*. International
Journal of Technology*, Volume 10(7), pp. 1407–1415

Watling, D.P.,
Rasmussen, T.K., Prato, C.G., Nielsen, O.A., 2018. Stochastic user Equilibrium
with a Bounded Choice Model. *Transportation Research Part B: Methodological*,
Volume 114, pp. 254–280

Xiuwu, Y., Qin, L.,
Yong, L., Mufang, H., Ke, Z., Renrong, X., 2019. Uneven Clustering Routing
Algorithm based on Glowworm Swarm Optimization. *Ad Hoc Networks*, Volume
93, pp. 101923

Yamada, T., Zukhruf,
F., 2015. Freight Transport Network Design using Particle Swarm Optimisation in
Supply Chain–Transport Supernetwork Equilibrium. *Transportation Research Part E: Logistics and Transportation Review*
75, pp. 164–187

Yamada, T., Russ,
B.F., Castro, J., Taniguchi, E., 2009. Designing Multimodal Freight Transport
Networks: A Heuristic Approach and Applications. *Transportation Science*,
Volume 43(2), pp. 129–143

Yun, W.Y., Choi, Y.S.,
1999. Simulation Model for Container-Terminal Operation Analysis using an Object-Oriented
Approach. *International Journal of Production Economics*, Volume 59(1),
pp. 221–230

Zhao, X., Wang, C.,
Su, J., Wang, J., 2019. Research and Application based on the Swarm
Intelligence Algorithm and Artificial Intelligence for Wind Farm Decision System.
*Renewable Energy*, Volume 134, pp. 681–697

Zhou, Y., Lou, Q.,
Liu, J., 2014. Glowworm Swarm Optimization for Dispatching System of Public
Transit Vehicles. *Neural Processing
Letters*, Volume 40, pp. 25–33

Zukhruf, F., Yamada,
T., Taniguchi, E., 2014. Designing Cocoa Transport Networks using a Supply
Chain Network Equilibrium Model with the Behaviour of Freight Carriers. *Journal of Japan Society of Civil Engineers,
*Volume 70(5), pp. 709–722