Shunsheng Guo

Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy TOPSIS

E-alliance is the union of e-commerce and its success and efficiency is related to comprehensive quality of e-commerce. Thus, ranking e-commerce websites in e-alliance is of importance, which is a multi-criteria decision-making (MCDM) problem. This paper proposes an evaluation model based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to tackle the issue in fuzzy environment. The AHP is applied to analyze the structure of ranking problem and to determine weights of the criteria, fuzzy sets is utilized to present ambiguity and subjectivity with linguistic values parameterized by triangular fuzzy numbers, and TOPSIS method is used to obtain final ranking. Case analysis is conducted to illustrate the utilization of the model for the problem. It demonstrates the effectiveness and feasibility of the proposed model.

An extended support vector machine forecasting framework for customer churn in e-commerce

In order to accurately forecast and prevent customer churn in e-commerce, a customer churn forecasting framework is established through four steps. First, customer behavior data is collected and converted into data warehouse by extract transform load (ETL). Second, the subject of data warehouse is established and some samples are extracted as train objects. Third, alternative predication algorithms are chosen to train selected samples. Finally, selected predication algorithm with extension is used to forecast other customers. For the imbalance and nonlinear of customer churn, an extended support vector machine (ESVM) is proposed by introducing parameters to tell the impact of churner, non-churner and nonlinear. Artificial neural network (ANN), decision tree, SVM and ESVM are considered as alternative predication algorithms to forecast customer churn with the innovative framework. Result shows that ESVM performs best among them in the aspect of accuracy, hit rate, coverage rate, lift coefficient and treatment time. This novel ESVM can process large scale and imbalanced data effectively based on the framework.

A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system

Consider supplier selection for minimum the life cycle cost of complex product system.Develop Pareto solution approach for bi-objective optimization.Conduct a case study to test the performances of the approaches.Results show proposed approach outperforms for Pareto optimal solution searching. Supplier selection has significant impact on life cycle cost of complex product system (CoPS). In this paper, a new variant of supplier selection problem named life cycle supplier selection of CoPS (LSS&CoPS) problem is addressed. There are three kinds of choices for a manufacturer to complete a CoPS: self-made, purchasing the finished component and outsourcing. Different selection not only results in difference of procurement cost of CoPS, but also results in reliability changing after it delivered to customer which greatly influences the operating cost in CoPSs lifecycle. However, the minimizing of two objectives is mutually conflicted. This paper presents a bi-objective LSS&CoPS model which considering operating stage of CoPS to balance the procurement cost and operating cost. Moreover, a hybridization of Pareto genetic algorithm (PGA) with multi-intersection and similarity crossover (MSC) strategy is proposed to solve the bi-objective problem. Also, a dual-chromosome is used to represent the variable-length chromosome. Finally, a cement equipment supplier optimal in a cement equipment enterprise is provided. Example indicates that the procurement cost and operating cost have been optimized, yields a Pareto optimal solution of supplier schema for project managers to make-decision and decrease the life cycle cost of CoPS. Additionally, the results show that the proposed approach is more preferably in Pareto optimal solution searching.

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Textile Enterprise Internal Process Simulation and Optimization

The textile manufacture is a dynamic and complex process. Taking typical process in textile enterprise as a research object, a model is established by Petri net and ExSpect software is used to simulate the model. With the simulation result, optimization process is proposed. The simulation result shows that optimization process enhances efficiency and reduces cost.

Study of order lifecycle tracking systems in building material equipment manufacturing enterprises

Abstract The typical business processes in building material equipment manufacturing enterprises are analyzed, and the framework and functional models of the tracking system for building material equipment manufacturing enterprises in the order lifecycle are presented. Based on the model, using a mobile agent and XML technology, the order tracking system’s framework is built for building material equipment manufacturing enterprises based on a mobile agent in the Aglet platform. The key technologies in the process of tracking the order lifecycle are introduced in detail. Finally, a prototype system is developed and used in an enterprise successfully, verifying the feasibility and availability of the system.

An optimizing model for solving outsourcing supplier selecting problem based on particle swarm algorithm

ABSTRACT The characteristics of decentralization, diversification, and dynamics of outsourcing resources put forward higher requirements for the supplier selection in manufacturing enterprises. For building materials equipment manufacturing enterprises (BMEM), due to the complex product structure, the demand for outsourcing resources is very large. The traditional method is difficult to meet the needs of current outsourcer evaluation and selection. In order to solve the problem, this paper establishes an outsourcing supplier selection model based on the quality, price, delivery time, reliability, and availability of outsourced suppliers. And then, a hybrid supplier selection algorithm based on Analytic Hierarchy Process (AHP) and Particle Swarm Optimization (PSO) is designed in this paper. Finally, the model and algorithm are verified and analyzed in BMEM. The paper also compares the algorithm with genetic algorithm (GA) and ant colony optimization (ACO). The experimental results show that the model can effectively improve the effectiveness and reliability of outsourcing supplier selection.

A hybrid optimisation algorithm for production scheduling problem with random orders' arriving

Production scheduling problem is a very important functional optimisation problem in the modem manufacturing system and which greatly impacts the efficiency and capacity of the manufacturing system. It has been proved to be a NP-hard problem and cannot be well solved based on traditional algorithms. Therefore, it is very necessary to develop efficiency algorithms which can obtain a good production plan with approximate optimal solution in a reasonable time. In this paper, a new method based on integrated genetic algorithm and tabu search is proposed and which can feasibly balance the exploration ability and exploitation ability of calculation. Also, the solution encoding and decoding, crossover and mutation operator, tabu search strategies have been illustrated. In order to evaluate the performance of the proposed algorithm, one experiment has been carried out and the comparisons among them are also presented. The result shows that the proposed algorithm can obtain satisfactory solutions and also it is applied to solve the production scheduling problem in a practical textile machine manufacturing enterprise.

Research on Intelligent Process Quality Control System under Network Environment

In the networked manufacturing environment, how to monitor dynamic and variable quality fluctuation, diagnose the abnormal variation in real-time and adjust the process at the right moment, becomes a difficult problem for the modern networked manufacturing enterprise in process quality control. In order to overcome this difficulty, This paper presents an intelligent process quality control mode oriented to networked manufacturing by combining statistical process control (SPC), and artificial intelligent (AI) for quality prevention, online statistical analysis, intelligent diagnosis and adjustment, and corresponding functional modules and framework is put forward. Finally, the prototype system of intelligent process quality control is developed to demonstrate the rationality and validity of the method. This work confirms the potential synergies of hybrid AI techniques for realizing networking, intelligent and automatic process quality control.

Workflow-Based ERP Research in Automobile Refitting Enterprise

This article summarizes four types of workflow modeling. There are object-oriented, role-oriented, activity oriented and language-oriented. Order process model is extracted from automobile refitting enterprise and activity oriented approach is applied to establish workflow based on the model. Organization model is proposed in the enterprise. ERP system model is established based on order workflow, organization model and RBAC. The whole design process of ERP software is discussed in detail. Application shows that workflow-based ERP is totally suitable to the enterprise.