A Pseudo-Parallel Genetic Algorithm Integrating Simulated Annealing for Stochastic Location-Inventory-Routing Problem with Consideration of Returns in E-Commerce

Facility location, inventory control, and vehicle routes scheduling are three key issues to be settled in the design of logistics system for e-commerce. Due to the online shopping features of e-commerce, customer returns are becoming much more than traditional commerce. This paper studies a three-phase supply chain distribution system consisting of one supplier, a set of retailers, and a single type of product with continuous review (Q, r) inventory policy. We formulate a stochastic location-inventory-routing problem (LIRP) model with no quality defects returns. To solve the NP-hand problem, a pseudo-parallel genetic algorithm integrating simulated annealing (PPGASA) is proposed. The computational results show that PPGASA outperforms GA on optimal solution, computing time, and computing stability.

The Optimization Design of Multistage Hybrid Planetary Transmission Based on Improved Genetic Algorithm

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.