Research On Hazardous Materials Inventory Routing Problem Under The Environment Of Uncertainty

In recent years,with the development of economy,chemical industry has become an important pillar industry of national economy,and the demand for hazardous materials is continuously increasing.Chemical products have become an indispensable material in industry,agriculture,national defense and people's daily life,which are widely used in manufacturing,chemical,pharmaceutical and other industries.Generally speaking,chemical products are hazardous materials which are explosive,flammable,toxic,corrosive and radioactive.Once mismanaged,they will bring a series of harm to human health and environment.Therefore,hazardous materials safety management is vital to the sustainable development of the country and even the human society.Although hazardous materials safety management is improving continuously in China,the situation is still grim,and grave accidents frequently occur.According to statistics,the probability of hazardous materials accidents occurring in inventory and transportation is relatively high,and the consequences are serious.How to integrate and optimize the inventory and transportation links of hazardous chemicals under the premise of considering security risks is a hot topic for academic circles,governments,and enterprises in recent years.From the perspective of supply chain,an in-depth study for hazardous materials inventory routing problem is conducted in this study.Besides profit,risk is also taken as an important optimization objective.Considering the characteristics of this research and the uncertain factors such as accident risk,market demand and selling price,the corresponding models are established based on the credibility theory,and the algorithms are designed for the models.The research contents are constructed as follows.(1)We study a hazardous materials vehicle routing problem based on time-dependent fuzzy risk.By analyzing the influence of time variation on vehicle speed and transportation risk,a credibilistic integer programming model is constructed based on time-dependent fuzzy risk.The goal is to determine the optimal departure time and transportation routes.An improved genetic algorithm is designed to handle the proposed model.Numerical experiment illustrates that the model considering time-dependent factor can reduce the system risk by 41%.(2)The location routing problem for hazardous materials is studied under traffic restrictions.By analyzing the effect of actual factors on the problem,a credibilistic multi-objective chance-constrained programming model is established based on time-dependent fuzzy transportation risk.A hybrid particle swarm optimization algorithm integrating fuzzy simulation is proposed to solve the proposed model.Numerical experiments illustrate that implementing road restrictions can reduce the system risk by 15.7%?Considering the multi-path constraint can reduce the system risk by 30%,and time window constraints can reduce the system risk by 29.2%.(3)We study a hazardous materials inventory routing problem based on dynamic transportation risk and fuzzy demand.Considering that the variation of vehicle loading has a significant influence on the transportation risk,we formulate a loading-dependent transportation risk model.Considering the customer's fuzzy demand,we propose a credibilistic goal programming model to obtain the best balance between risk and cost.An improved genetic algorithm whose chromosomes contain two types of genes is designed to handle the goal programming model.Numerical examples illustrate the importance of risk factor for the hazardous materials inventory routing problem,and the model considering dynamic transportation risk reduces the risk of the whole supply chain system by 3.9%.(4)We study a hazardous material multi-period inventory routing problem under a limited production capacity,and formulate a dynamic transportation risk model for multiple vehicle transportation.Considering that hazardous material risk and sales price are fuzzy variables,we propose a credibilistic bi-objective integer programming model in order to increase the system profit and reduce the system risk,and design an improved genetic algorithm.Compared with the model only considering profit,the established model can effectively control the system risk in each period,and reduce the total risk of the entire periods by 20.79%.Under the premise that the system risk is controllable in each period,the multi-period decision-making can increase the profit by 40%.By comparing the results of the two risk models,the rationality of the established risk model is verified.Based on the characteristics of hazardous materials accidents and the lack of historical data,this study uses fuzzy variable to describe the uncertainties such as accident risk,market demand,and sales price of hazardous materials.Mathematical models are constructed using the credibility theory.Numerical experiments illustrate the efficiency of the proposed model.The research findings can be used to guide hazardous materials companies and management departments to develop effective strategies for reducing the supply chain system risk,which has important practical significance for maintaining the high-speed sustainable development of the chemicals industry.