The conveyor assembly line has been widely used in manufacturing industries to produce standard products with low costs. However, due to lack of flexibility, this production method has not been conducive to multivariety and small-batch production. In this situation, seru production formed by converting conveyor assembly lines has been a successful innovation in the Japanese manufacturing industry. Most of the existing literature has studied the benefits of this line-seru conversion from the perspective of the enterprises themselves, but this paper studies the effect of the line-seru conversion on the waiting time from the perspective of the customer. First, the change in the average waiting queue length caused by the line-seru conversion is proposed as an evaluation index. Second, with the consideration of the practical situation of random batch arrivals, the average waiting queue length formulas for the conveyor assembly line and seru production are established based on the assumption that the arrival is a Poisson process. Then, under two scenarios, we investigate the relationship between the average waiting queue length changed by the line-seru conversion and other parameters and find that the conversion can reduce the average waiting queue length in multivariety and small-batch production. Finally, under other potential scenarios, the equations for determining the average waiting queue length resulting from a change to line-seru conversion are derived.

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

In order to improve the efficiency of warehouse on picking, an optimal model for ware location assignment is presented, which is with multi-objective function. To solve the multi-objective optimization problem, a differential evolution algorithm combined with lexicographic sort algorithm is presented. In order to verify the above algorithm, an example is presented. The calculated results show that the algorithm is operable and versatile. The optimized solution can be found fast with better fitness than genetic algorithm which is used to be compared.