Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts ...Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts required until the next arrival of the vehicle.However,if there are uncertain travel times,a shortage of materials in stations will be caused.In this paper,the JIT part feeding optimization problem under travel time uncertainty is studied.The uncertain travel time is represented by the interval number according to the actual situation.To minimize the largest possible vehicle trip time,the optimization model is developed based on robust optimization.In the model,a route-dependent uncertain parameter is introduced.Through this model,the route of each vehicle and the parts load needed to be delivered by the vehicle can be calculated.A hybrid simulated annealing algorithm is designed to solve this model.The parts feeding planning for an engine assembly line is taken as an example.By the Monte Carlo simulation,the relationship between the line stoppage probability and the uncertain parameter is studied to obtain the final solution.The effectiveness of the method is demonstrated by this case study.展开更多
基金This research received no external funding.This research is supported by the National Key Research and Development Program of China(Grant No.2017YFB1301600)。
文摘Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts required until the next arrival of the vehicle.However,if there are uncertain travel times,a shortage of materials in stations will be caused.In this paper,the JIT part feeding optimization problem under travel time uncertainty is studied.The uncertain travel time is represented by the interval number according to the actual situation.To minimize the largest possible vehicle trip time,the optimization model is developed based on robust optimization.In the model,a route-dependent uncertain parameter is introduced.Through this model,the route of each vehicle and the parts load needed to be delivered by the vehicle can be calculated.A hybrid simulated annealing algorithm is designed to solve this model.The parts feeding planning for an engine assembly line is taken as an example.By the Monte Carlo simulation,the relationship between the line stoppage probability and the uncertain parameter is studied to obtain the final solution.The effectiveness of the method is demonstrated by this case study.
