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This study focuses on the post-harvest preprocessing of fruits and vegetables, aiming to provide an effective way to conduct preprocessing operations in short food supply chains. We consider both a heterogeneous fleet of mobile preprocessing units and the possibility to pick up products for centralized preprocessing. The resulting problem is a variant of the classic heterogeneous fleet vehicle routing problems with time windows (HFVRPTW), with the additional consideration of multi-depot and heterogeneous service types, which we refer to as HFVRPTW-MDHS. These additional considerations are important to include in the development of more efficient food supply chains, but lead to a challenging routing problem. In this paper, we formulate the HFVRPTW-MDHS using a mixed-integer linear programming model. Due to the complexity of the model, we propose a customized adaptive large neighborhood search (ALNS) metaheuristic. We design a multi-level struct-based solution representation to improve the efficiency of the ALNS and develop customized methods for solution evaluation, feasibility checks, and neighborhood search. Comparing our results with the results of an exact algorithm and solutions in the existing literature, we find that our ALNS algorithm can obtain high-quality solutions quickly when solving HFVRPTW-MDHS and related variants of the VRP. Finally, we study the application of our approach in the case of precooling, which is a commonly used preprocessing operation, to illustrate the effectiveness of our approach in a relevant practical context.

The process of choosing green suppliers is to choose and breed the enterprises which posses the awareness of environmental protection and pose great importance to environmental protection, and then publish strategic cooperation partnership with them to form a green supply chain. The paper firstly proposed a new construction method of the judgment matrix in Analytic Hierarchy Process (AHP) based on rough sets theory, and then built a supplier evaluation index system in green supply chain, and lastly applied the new method in the supplier comprehensive evaluation according to examples.

The demand forecasting of hospital stocks is a new management science with strong practicality, the basic principle of which is, on the foundation of keeping a high supply level of hospital clinical medicines, to apply mathematical and management methods to improve the accuracy of hospital stock demands forecasting, reduce the unreasonable stocks, accelerate cash flow, improve management measures of various aspects, and give full play to the hospital management functions in order to guarantee the safety and integrity of hospital medicines, and improve the hospital social and economic benefits. The paper firstly introduced the inventory control background of the Daping Hospital of Third Military Medical University (CQDP Hospital) and screened out the main influencing factors of the usage of 17GY scalp indwelling needles, and then applied data mining technologies including data cleaning, data integration and data transformation to pre-process the original data, obtaining the training samples for BP neural network, and finally built an inventory forecasting model based on BP neural networks, using the sample data for training to get the inventory forecasting network construction, and carried out the stock prediction in a certain period of the future, which provided basis for decision-making.

The paper combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.

This paper discusses a decision-making problem related to logistics base location. Selection of logistics base is a complex task as it involves both qualitative and quantitative factors. This calls for the use of multiple criteria decision-making methods. A new method of unstructured decision making-attribute hierarchical model (AHM) is different from the analytic hierarchy precess (AHP) by Saaty. There are not eigenvector computations and consistency test in AHM. Through AHM, we can get the importance weights of all alternative logistics bases. The results of illustrative example show that AHM is simple and efficient, compared with AHP.

The emergence and development of E-commerce has thoroughly transformed the interactive mode and operating means of materials flow, information flow and funds flow in the logistics, which has deep influence to the supply chain management (SCM). Therefore, studying the SCM based on E-commerce has great practical meaning. This paper will, according to Chinese auto industry, study and analyze the SCM under current e-commerce environment. Firstly, we will put forward the basic thought of SCM and it' s development stages; Then, according to the existing problems in different stages we discuss the E-commerce's influence upon the development modes of SCM; Finally, we summary the main problems which Chinese auto enterprises will face when implementing SCM under the electronic commerce environment and put forward corresponding countermeasures.