Multi-objective mixed mode sugarcane transportation model using fuzzy NSGA

Sandesh Kurade (), Raosaheb Latpate () and Vinayak Gedam ()
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Sandesh Kurade: MES Abasaheb Garware College (Autonomous)
Raosaheb Latpate: Savitribai Phule Pune University
Vinayak Gedam: Savitribai Phule Pune University

OPSEARCH, 2025, vol. 62, issue 1, No 7, 149-177

Abstract: Abstract The sugar factory business plays a significant role as a source of employment for the developed countries like India, Brazil, South Africa etc. Sugar factory business in India impacts the livelihood of almost each and every sugarcane farmers and workers employed in these factories. Hence in this paper, a multi-objective mixed mode transportation model for sugarcane industry is formulated. To make the model realistic, uncertainty in costs, time, supply and demand are considered. Uncertainty in these key parameters of the sugarcane industry are occur due to competitive nature of the sugar market. The sugar industries located to nearby places to the market always have huge impact on the sugar market. In the formulated model, parameters are expressed by means of triangular fuzzy number due to uncertainty in the market. Various modes of transportation with their restricted capacities like bullock carts, trucks, tractors, tractor trolleys etc. are considered in the developed model. For solving the model, a new optimization algorithm using fuzzy set theory, convex set theory and evolutionary algorithm is developed. For applicability of the model, a real world case study of supply chain of the co-operative sugarcane factory located in Maharashtra state for season 2018 is considered. Finally, Pareto decision space of the model is discussed at different levels of uncertainty. The developed model has several industrial applications. It will be helpful to sugar factories to optimize their supply chains.

Keywords: Mixed mode; Multi-objective; Non-dominated sorting genetic algorithm; Supply chain; Transportation; Triangular fuzzy number (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12597-024-00800-1

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