An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry

Due to increased attention to coal mining, the industrial transport in most countries including India necessitated search for sustainable transportation leading to environmental protection, maximum speed of delivery, minimum cost of transportation, and enhanced traffic safety. In this paper, we formulate an integrated multi-objective optimization model for an extended capacitated sustainable transportation problem in a coal mining industry using the analytic hierarchy process (AHP) and data envelopment analysis (DEA) techniques. The AHP technique is used to estimate the weights of different types of vehicles available for transportation on the basis of all three parameters of sustainability, namely economic, environmental, and corporate social responsibility. The DEA technique is used for calculating efficiency scores of vehicles on various routes of the given transportation network using inputs and outputs considered critical in the industrial sector particularly the mining industry. Furthermore, we reduce dependency on carbon based fuels for transportation leading to reduction in greenhouse gas emissions. A fuzzy interactive optimization approach is presented to get preferred compromise transportation solutions including the optimal number of vehicles employed for sustainable transportation. A real-world case of a mining industry in India is discussed to demonstrate applicability of the proposed optimization model and solution method. Moreover, some comparisons are done with existing transportation models in order to present advantages of our approach.

Automated summary

An integrated multi-objective optimization model is proposed in this paper for sustainable transportation in the coal mining industry, utilizing AHP and DEA techniques for weight estimation and efficiency score calculation. By reducing dependency on carbon based fuels, the model leads to a decrease in greenhouse gas emissions. A fuzzy interactive optimization approach is used to determine the optimal transportation solutions.