Mathematical programming representation of pull controlled single-product serial manufacturing systems

Pull policies may perform quite differently depending on the particular manufacturing system they must control. Hence, it is clear the necessity of having efficient performance evaluation models to select the best control policy in a specific context. This paper proposes a mathematical programming representation of the main pull control policies applied to single-product serial manufacturing systems. The proposed models simulate the pull controlled system in the sense that, if instantiated with the same parameter values as in a simulation model, their solution gives the same event sequence of the simulation. The proposed mathematical representation is also used for a formal comparison of the considered pull control policies. The new models presented in this paper can represent a base to build new efficient optimization algorithms for the design of pull controlled production systems.     

动态多阶段强波动型表情识别模型

考虑到问卷调查顾客的满意度评价时存在样本获取困难、主观误差等问题,利用表情识别技术分析视频中顾客的情感表达,但实际应用存在人物表情变化波动过大的强波动型表情识别问题.为更好地挖掘强波动型表情变化的信息,提出一种动态多阶段强波动型表情识别模型(LNsCo),该模型包括长期情绪波动状态生成器(LEF)、近短期表情特征生成器(NsSE)、平行共同注意力网络(co-attention network).将图像序列预处理后,分别送到LEF和NsSE提取表情的隐藏特征,利用共同注意网络生成二者相互依赖的表征,用分类器进行表情分类.在公共数据集和真实应用场景下的结果表明,所述方法具有良好的性能.     

A single-product parallel-facilities production-planning model

A single-product parallel-facilities production-planning model, in which known demands must be satisfied, is analysed. Concave production and inventory costs are considered. No backlogging is assumed. The structure of an optimal solution is characterized, upon which only one facility in each period is shown optimal to supply exact requirements. This is then used in a simple dynamic-programming algorithm. A numerical example for the algorithm is discussed     

Solution of single-product nonlinear programming problem with joint constraints

Single product, single factor optimal planning problems with a decreasing cost function and joint constraints are considered. Existence conditions are obtained and effective algorithms for the construction of optimal programs are proposed.Translated from Kibernetika, No. 1, pp. 76–80, January–February, 1990.     

基于Java的串口通信应用编程

介绍了Java实现串口通信编程的技术处理。着重就串口通信的连接、数据缓冲区资源的多线程访问控制以及数据读取中的超时控制等问题进行了详细讨论,有效地实现了主机与下位单片机之间的数据传递。该通信方式已用于基于工控机的绝缘电阻检测应用中。     

Enhanced mixed integer programming model for a transfer line design problem

Modular machining lines with multi-spindle workstations are considered. A multi-spindle head executes a set of operations. The problem of optimal design or reconfiguration of such lines is considered here. The set of all available spindle heads, operations executed by each spindle head, spindle head times and costs are assumed to be known. There are operations which can be executed by one of several candidate spindle heads, i.e., in different configuration with other operations. The problem consists in the choice of spindle heads from the given set and their assignment to workstations. The goal is to minimize the line cost while satisfying the precedence, inclusion and exclusion constraints. This problem is an extension of well known assembly line balancing and equipment selection problem. In our previous work, we proposed a MIP model which was significantly limited as to the size of the problems treated. In this paper, quite a few original approaches are suggested to improve the previous MIP model. The numerical tests reported show that the calculation time is drastically decreased, thereby expanding the model to larger and more realistic industrial problems.     

A fuzzy linear programming model for production planning in coal mines

This article shows how fuzzy linear programming may be used to solve production scheduling problem in coal industry. First, a fuzzy linear programming model is presented. The proposed model is then tested on a hypothetical problem developed by using production cost estimates from independent coal mines in the states of Virginia, Illinois and Pennsylvania. The results of the model indicate that the model has potential for solving production scheduling problems in the coal industry.     

Dynamic programming for noncausal problems

The theory of dynamic programming as used in most control applications relies heavily on the causal structure of the underlying dynamics. In this survey paper, we will show noncausal problems, such as those arising in partial differential equations, multidimensional smoothing and filtering, and digital image processing can often be converted into causal problems by imposing a causal vector structure on the process. The resulting problem can then be handled as a vector multistage decision process using dynamic programming. We will present this approach for some simple two-dimensional deterministic problems. Applications to the numerical solution of partial differential equations and image restoration will be shown. Although this paper covers a body of theory and applications which have been developed over the past decade, the approach presented here is considerably more unified than appears elsewhere.     

Dynamic programming for impulse controls

This paper describes the theory of feedback control in the class of inputs which allow delta-functions and their derivatives. It indicates a modification of dynamic programming techniques appropriate for such problems. Introduced are physically realizable bang-bang-type approximations of the “ideal” impulse-type solutions. These may also serve as “fast” feedback controls which solve the terminal control problem in arbitrary small time. Examples of damping high-order oscillations in finite time are presented.     

A bimodal scheme for multi-stage production and inventory control

This study considers a multi-stage multi-item production plant with its supply chain and customer environment. The production, supply and inventory plan is optimized on a dual-mode basis, under two different information patterns. The short-term plan relies on firm orders received from customers. On the contrary, the long-term plan is based on predicted demands represented by random sequences. In this study, the role of the long-term plan is mainly to impose a final condition set to the short-term plan.     

A goal programming model for aggregate production planning with resource utilization constraint

This paper addresses the aggregate production planning problem with different operational constraints, including production capacity, workforce level, factory locations, machine utilization, storage space and other resource limitations. Three production plants in North America and one in China are considered simultaneously. A pre-emptive goal programming model is developed to maximize profit, minimize repairing cost and maximize machine utilization of the Chinese production plant hierarchically. A set of data from a surface and materials science company is used to test the effectiveness and the efficiency of the proposed model. Results illustrate the flexibility and the robustness of the proposed model by adjusting goal priorities with respect to importance of each objective and the aspiration level with respect to desired target values.     

多Agent多阶段一对多协商模型

为解决多Agent一对多、多议题协商问题,提出了具有议题属性协商阶段的多阶段协商模型,设计了一种根据Agent让步幅度变化所形成的曲线来判定Agent类型和使用何种协商方法的协商策略.详细地分析了多Agent、多阶段一对多协商的协商过程.将三角模糊数多属性决策方法引入到多Agent协商过程中降低了决策者评估对方所提出方案的难度,能更自然地对不同方案的优劣进行排序.模拟算例表明,该模型有效且可行,为多Agent协商提供可参考的模型和求解算法.     

Dynamic programming for computer register allocation

A procedure for optimal index register allocation in loops is described. The procedure is a result of the dynamic programming formulation of the index register allocation problem for other than straightline code. An example involving a simple loop is solved.     

A vector space model approach to production planning for a multi-product,multi-cell and multi-stage manufacturing system

This paper develops a formal mathematical approach to aggregate production planning for a multi-product, multi-cell and multi-stage manufacturing system. The model, based upon a vector space approach, includes all the important variables relating to the demand for individual items, inventory levels, the availability of machines taking into account any breakdowns, subcontracting of orders and overtime working. The computational procedure for determining the production planning strategies, in terms of overtime/undertime working and increase/decrease in the number of orders subcontracted, are presented. Three numerical examples are presented showing the use of the model developed. This approach makes it possible to develop realistic models of practical manufacturing systems. It is particularly applicable to flexible manufacturing systems.     

On serial and parallel information flows,incorporated in a multi-stage production-inventory-system

We consider a chain of production operations separated by inventories, where two different systems-for replenishing the inventories at each stage may be adopted. In the first, the serial information-flow system, each stage works against orders placed only by the following stage. In the second system, called the parallel information-flow system, every stage in the network works against the actual customer demand, materializing at the finished-goods stage.

The basic objective is to show that in the former system the levels of base stock to be maintained at the different stages are more sensitive to changes in the customer demand than in the second system. In fact, in the former case, base-stock fluctuations become larger and larger, progressively back in the chain. Consequently, the production rates also behave in the same way and their variations got multiplied successively back in the chain.     

A new pricing constrained single-product inventory-production model in perishable food for maximizing the total profit

In this paper, a new constrained single-product pricing and inventory model is presented. The goal of the proposed model is to decide on the prices as well as on the inventory and production decisions in order to maximize the total profit. The developed model is a nonlinear programming model which is solved by using hybrid genetic algorithm (HGA) and simulated annealing. Comparison of results obtained from the two algorithms shows that HGA is better. To enhance the performance of our algorithms, we apply the Taguchi experimental design method to tune their parameters. Finally, some recommendations for future developments are presented.     

Enhanced dynamic programming algorithms for series line optimization

Dynamic programming value iteration is made more efficient on a five-machine unreliable series line by characterizing the transient and "insensitive" states. Holding costs are minimized subject to a service level constraint in a make-to-stock system with backordering. State-space truncations are chosen by checking the recurrent class in previous runs. An approximate model is developed that reduces the number of machine states. Monotone control theory is used to restrict the search for a control switching surface. Numerical optimal policies are compared with the heuristic control point policy and several characteristics of optimal policies are identified.     

An autonomous decentralized supply chain planning system for multi-stage production processes

In this paper, we propose an autonomous decentralized optimization system for multi-stage production processes. The proposed system consists of a Material Requirement Planning subsystem, a Distribution Planning subsystem and Decentralized Scheduling subsystems belonging to each production stage that constitute the entire plant. In the proposed system, each subsystem repeats both optimization of the schedule at each subsystem and data exchange among the subsystems. Computational results demonstrate that the results of the proposed planning system are superior to those of the hierarchical planning system, despite the fact that the proposed system has wide flexibility for adding the constraints and modifying the criterion of performance evaluation.This revised version was published in June 2005 with corrected page numbers.