A new multi-objective model for berth allocation and quay crane assignment problem with speed optimization and air emission considerations (A case study of Rajaee Port in Iran)

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Sadegh Sharifi Sadegh Sharifi; seyed farzad hosseini seyed farzad hosseini; mohammad kananizadeh mohammad kananizadeh; Hadi Gholami Hadi Gholami

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Journal Paper

Paper Information

Journal: International Journal of Maritime Technology Year:2023 | Volume:16 | Issue:1 Page(s): 25-48

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Title

A new multi-objective model for berth allocation and quay crane assignment problem with speed optimization and air emission considerations (A case study of Rajaee Port in Iran)

Author(s)

Sadegh Sharifi Sadegh Sharifi | seyed farzad hosseini seyed farzad hosseini | mohammad kananizadeh mohammad kananizadeh | Hadi Gholami Hadi Gholami | Issue Writer Certificate

Keywords

Container Terminals

Berth Allocation Problem

Quay Crane Assignment

Mathematical Modeling

Green Ports

Abstract

Over the past two decades, maritime transportation and container traffic worldwide has experienced rapid and continuous growth. With the increase in maritime transportation volume, the issue of greenhouse gas (GHG) emission has become one of the new concerns for port managers. Port managers and government agencies for sustainable development of maritime transportation considered "Green Ports" to balance between environmental impacts and economic interests. Therefore, this study aims to integrate the Berth Allocation and Quay Crane Assignment Problem (BACAP) with speed optimization and vessels emission considerations. Rajaee port, the most important port in Iran, was selected as the case study. A mathematical model is developed based on the main characteristics of this port and is solved by GAMS IDE/CPLEX software. Given the NP-hard complexity of the BACAP, exact solution approaches need huge time, even for small and medium problems. Hence, an adapted Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) and a Multi-Objective Simulated Annealing (MOSA) algorithm are adopted to deal with the complexity of the proposed model. Sensitivity analysis is used to assess the applicability of the proposed model and evaluate the efficiency of the solution algorithms.

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