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This paper studies the dual-source procurement problem that an assembler procures goods from one overseas supplier with low price but long lead-time and one local supplier with high price but short lead-time. We assume, the demand is normal distribution, a smoothing order strategy is proposed. With this order strategy, the new purchasing quantity is determined using the smoothing technique, i.e., the new purchasing quantity equals to the weighted value of the last period purchasing quantity and the current market demand. We establish optimization models for two situations, one considers warehouse space constraint and another one does not consider warehouse space constraint. Then, the optimal smoothing purchasing coefficient and the optimal storage space are obtained. In the last, numerical examples are conducted.

Erection mechanism adopts electro-hydraulic proportional valve to control telescopic cylinder. Electro-hydraulic system is a complicated system suffering from nonlinearities, uncertainties and disturbances. It is difficult to establish mathematical model and perform a high precision control using linear control methods. Sliding mode control (SMC) is robust to parameter variation but the accurate system function should be derived. In this study, adaptive fuzzy sliding mode control algorithm was designed to control the position of large erection mechanism. Fuzzy logic system was utilized to approximate the unknown system functions. The robust adaptive law was proposed to reduce the errors between true functions and fuzzy approximators. The stabilities of control schemes were proved in the sense of the Lyapunov stability theorem. Hydraulic model was established in AMESim software and controller was implemented in Simulink software. Union simulation results verify that the proposed control system can achieve favorable tracking performance for erection mechanism and robust with regard to parameter variations and external load disturbance.

This article develops a single-manufacturer single-retailer production-inventory model in which the manufacturer's production process is imperfect and it produces a random proportion of defective items in the production period. The manufacturer deliveries ordered items to the retailer with equal-sized shipments policy. The retailer performs a 100% screening process to identify the defective items immediately after receiving the order from manufacturer and handles defective items in multiple batches at the end of each shipment period. The objective is to minimize the expected average total joint annual cost of the integrated production-inventory system with optimal shipment quantity, shipment number and handling number of defective items. Finally, numerical examples are presented to illustrate the application of the proposed model.

A growing number of papers in the areas of both manufacturing and marketing have emphasized the integration of several decisions between the two functions. This paper proposes a structural equation modeling using AMOS to evaluate the relationship between manufacturing and marketing integration, combinative competitive capabilities and business performance. Data were collected from 199 manufacturing firms of various size, location, and industry to develop the instrument and test the relationships in the framework. The results show that two of the three hypothesized direct effects are significant at the 0.01 level. Manufacturing and marketing integration positively influences combinative competitive capabilities and combinative competitive capabilities positively influence business performance. Contrary to conventional wisdom, the influence of manufacturing and marketing integration on business performance is partially confirmed because of the insignificant statistics.

To solve the control problem of complex nonlinear system, a control strategy based on Data-drive is proposed. The method builds the local model on-line based on lazy learning algorithm, obtains the optimal control by solving the quadratic optimization problem formulated in a novel generalized predictive control framework, as a result, its adapting and anti-jamming are powerful. Further more, by using hierarchical searching strategy and simplify the solving of control, the searching efficiency is improved and the computation complexity is decreased, so it can realize the real-time control. Simulation results show that the proposed method is effective.

Ant colony algorithm (ACA) is an intelligent optimization algorithm based on the behavior of ants seeking a shortest path between their colony and a source of food guided by their pheromone trails. Preliminary study indicates that it has superiority in solving complicated optimization problems. Order sequencing problem is a typical combinatorial optimization problem. This paper presents a model of order sequencing on multi-stage processing in JIT manufacturing environment and employs ACA to find the optimal solution for this model with detail algorithm steps. Testing the algorithm on experimental data, the paper validates its effectiveness on finding optimal solutions and its computational efficient as well.