Abstract
In practice, factories emit significant amounts of GHGs, which have been linked to health and environmental issues. These gases are produced during industrial processes and, in many cases, during transportation. Second, historically, generic costs such as production fixed and variable costs, transportation costs, and inventory holding costs have dominated the overall cost of the supply chain (SC). The objective of this research is to model a SC problem to minimize costs while also accounting for GHG emissions from facility to facility (Shanghai, China to Lahore, Pakistan). Initially, the total expenses and GHG emissions of the SC are modeled as a bi-objective mixed-integer linear programme (BOMILP). In comparison to other optimization models, the developed BOMILP model simultaneously optimizes transportation costs and GHG emissions while taking weight and distance constraints into account in the intermodal network. The real-world data comes from one of Asia's largest paper and board industries. To solve the BOMILP problem, a metaheuristic technique such as a multiobjective genetic algorithm (MOGA) and multiobjective bat algorithm (MOBA) is used. To improve the performance of the MOGA, we used multiple crossover operators. Random crossover operator selection and collision crossover are two strategies that have been implemented. The collision crossover is based on elastic collision, and the random crossover operator-selection strategy randomly selects the best operator. The Pareto optimum solution of the MOGA assists decision-makers in making the best trade-off between cost and emissions. Our findings show that the cost of producing (reeling, coating, rewinding) and storing white bleach board (WBB) accounts for 72% of the total cost of the SC, while GHG emissions are 83,484 kg CO2e in the delivery of 1 tonne of material from Shanghai, China to Lahore, Pakistan via intermodal transportation.
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Shoukat, R., Xiaoqiang, Z. The green and economical supply of coated board with intermodal distribution: an implementation of novel evolutionary algorithm. Oper Res Int J 24, 19 (2024). https://doi.org/10.1007/s12351-024-00825-w
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DOI: https://doi.org/10.1007/s12351-024-00825-w