Title: Modelling and analysis of intermodal freight cost and CO₂ emissions: application of mixed-integer linear programming and genetic algorithm

Authors: Rizwan Shoukat

Addresses: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, Sichuan, China

Abstract: This paper investigates the multi-objective problems of CO₂ emissions and cost minimisation in the transport of Pakistan. Mixed-integer linear programming is used to formulate the problems. The data is obtained from the transport and logistics department from one of the largest manufacturing industries in Asia. The genetic algorithm's Pareto front solutions support industrialists and practitioners to trade-off between cost and CO₂ emissions. Our results show a significant difference in the mode choice between road and intermodal. Intermodal transport is 68.9% less costly than road freight, whereas CO₂ emissions in road freight transport are more than 64% higher than intermodal freight. We provide a sensitivity analysis that shows the critical parameters influencing the increase of cost and CO₂ emissions in Pakistan's transportation operations. The study's findings are applicable to all other manufacturing sectors globally, including chemicals, textiles, machinery, and equipment.

Keywords: CO₂ emissions; genetic algorithm; Pareto fronts; multi-objective; manufacturing sectors.

DOI: 10.1504/WRITR.2021.119534

World Review of Intermodal Transportation Research, 2021 Vol.10 No.4, pp.378 - 399

Received: 28 Mar 2021
Accepted: 30 Aug 2021
Published online: 08 Dec 2021 *

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