Shipping optimisation systems (SOS): liner optimisation perspective

Noshokaty, Said El

doi:10.1504/ijstl.2013.054189

Cited by 4 publications

(1 citation statement)

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“…There are optimised models which helps effective cargo management softwares [ 5 , 6 ]. The model works using integer linear programming based approach for effective load balancing.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

Hareendran²,

S³

2020

Lecture Notes in Computer Science

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Cargo management in all mode of transports like airlines, ships and trucks is a challenging task. The way in which an optimal allocation of packages in different containers are done using a software controlled method. An agent based software module is enabled as a service for the optimum allocation of cargo packages in the container terminals. There are multiple factors that will affect this allocation - size, shape, weight of the cargo packets and the container. When we design an optimal allocation module in a software these components need to be addressed along with capacity of the container. Hence, a multi-objective optimization algorithm will improve the performance of cargo management software. In this paper we suggest a Mixed Species Particle Swarm Optimisation (MSPSO) procedure for optimal allocation of cargo packages in containers of different size and capacity. The redesigned version of cargo management software performs well with search space on normal time complexity. The simulated results gives an improved optimised allocation than normalised allocation of cargo packets. The improved implementation performed better in terms of efficient cargo package allocation.

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“…There are optimised models which helps effective cargo management softwares [ 5 , 6 ]. The model works using integer linear programming based approach for effective load balancing.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

Hareendran²,

S³

2020

Lecture Notes in Computer Science

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Statistical modelling of ship operational performance monitoring problem

2018

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The implication of the stochastic gross-profit-per-day objective on the cargo ship profitability, capacity, and speed

Noshokaty¹

2019

MABR

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Purpose This paper aims to study the implication of the stochastic gross-profit-per-day objective on the ship profitability and the ship capacity and speed. Design/methodology/approach The paper has used the mathematical model and the solution methodology given by El Noshokaty, 2013, 2014, 2017a, 2017b, and SOS, 2019. Findings The paper finds that if the ship owner follows the rate concept and the cargo demand forecast, he can improve the profitability of his company and be able to select the proper capacities and speeds for the ships used. Research limitations/implications The findings are not only useful for the shipping or other cargo transport companies but also for businesses like gas reservoir development, car assembly lines in the industry, cooperative farming and crop harvesting in agriculture, port cargo handling in trade and road paving in construction. Originality/value The contribution of this paper lies in notifying the ship owners of the possible profitability improvement and the consequences of building ships of larger capacities and slower speeds.

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Shipping optimisation systems (SOS): liner optimisation perspective

Cited by 4 publications

References 0 publications

Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

Statistical modelling of ship operational performance monitoring problem

The implication of the stochastic gross-profit-per-day objective on the cargo ship profitability, capacity, and speed

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