supply chain management analyzing/modelling
Amin Farahbakhsh; Amirsaman Kheirkhah
Abstract
Purpose: The production routing problem is created by combining the two problems of lot sizing and vehicle routing. Previous research has examined the effectiveness of this combination in reducing costs. In this paper, the production routing problem is considered with the aim of minimizing the risk of ...
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Purpose: The production routing problem is created by combining the two problems of lot sizing and vehicle routing. Previous research has examined the effectiveness of this combination in reducing costs. In this paper, the production routing problem is considered with the aim of minimizing the risk of production and distribution of hazardous materials. Attention to social and environmental criteria related to sustainability is being increased by researchers today. Hazardous materials are harmful to human health and the environment. Accidents related to these materials often have far-reaching adverse consequences. Risk is a danger measurement criterion for operations related to these materials.Methodology: The problem is modeled as a mixed integer program. The risk function in the proposed model is nonlinear and depends on the load of the machine, the population risk, and the type of hazardous material. Given that solving the mathematical model with the nonlinear objective function is more difficult, this function is approximated by a piecewise linear function.Findings: In this research, 8 standard instances have been used to evaluate and solve the model and compare the two nonlinear and linear models. The results show that by using the approximate model, a better answer can be achieved at the same time. Through sensitivity analysis, the effect of changes to production capacity and warehouses on risk has also been looked into.Originality/Value: This research proposed the production routing problem for hazardous materials according to sustainability criteria using a nonlinear mathematical model and uses a piecewise linear approximation to solve the model.