Hybrid genetic algorithm for a type-Ⅱ robust mixed-model assembly line balancing problem with interval task times

The typemixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.

Core of the Reinsurance Market with Dependent Risks

Baton and Lemaire(Astin Bull 12:57–71,1981)proved the nonemptiness of the core of a reinsurance market in which the risks of companies are independent.However,cases involving dependent risks have received increasing concerns in modern actuarial science.In this paper,we investigate the nonemptiness of the core of a reinsurance market where the risks of different companies may be dependent.When the exponential utility function is employed,we find an important property on risk premium and show that the core of the market is always nonempty.

Body surface area:a new anthropometric measurement for diabetic retinopathy in Chinese adults with type 2 diabetes:a hospital registry case-control study

To the Editor:Diabetic retinopathy(DR)is a condition characterized by retinal microvascular damages of diabetes mellitus.As the primary cause of blindness in adults,DR has already become a public health problem worldwide and threatens the outcomes of diabetic patients and even healthy population.The correlation between DR and common anthropometric measurements represented by body mass index(BMI)was still controversial.

Just-in-time parts feeding optimization for assembly lines under travel time uncertainty

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.