基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

Material requirement planning systems development

Material requirement planning systems are becoming an integral part of many manufacturing information systems. MRP is both a scheduling and control technique designed to minimize inventory investment, to maximize production efficiency, and to improve customer service. The purposes of this paper are to describe the inputs to an MRP system; discuss the logic of an MRP system; and provide examples of various exception reports generated by an MRP system. A listing of an MRP program with documentation is provided, also.     

Use of simulation in the analysis of shop floor operations

The use of computers in actual system applications is increasing with the availability of intelligent terminals on the shop floor. These terminals can be used by management as tools in the decision making process of planning shop floor operation. This paper discusses a pilot simulation study in the use of conventional Fortran-based simulation programs by shop floor management to:

1. 1. Participate in the evaluation of proposed FMS systems,

2. 2. Assess the impact of FMS acquisition on existing facilities,

3. 3. Assist in the identification of operational alternatives in “bottle neck” situations.

The pilot study employs a batch-oriented MRP system to provide daily updates of outstanding production center loadings on a monthly planning horizon. Two intelligent terminals are used to access a mini computer facility that executes the simulation models. The terminals have AT-compatible capabilities and are also used as data acquisition devices that support the numerically controlled operations within each work center.

The simulation models represent the 13 work centers of the firm and provide information about the average utilization of each work center, the number of parts in each queue and the average delay of parts in the queues. Future extensions of the models are planned to utilize the terminals' graphic animation capabilities to display the flow of production orders through the manufacturing facility.     

Experience-Based Approach to Scheduling Problems With the Learning Effect

The existence of the learning effect in many manufacturing systems is undoubted; thus, it is worthwhile that it be taken into consideration during production planning to increase production efficiency. Generally, it can be done by formulating the specified problem in the scheduling context and optimizing an order of jobs to minimize the given time criteria. To carry out a reliable study of the learning effect in scheduling fields, a comprehensive survey of the related results is presented first. It reveals that most of the learning models in scheduling are based on the learning curve introduced by Wright. However, further study about learning itself pointed out that the curve may be an “S”-shaped function, which has not been considered in the scheduling domain. To fill this gap, we analyze a scheduling problem with a new experience-based learning model, where job processing times are described by “S”-shaped functions that are dependent on the experience of the processor. Moreover, problems with other experience-based learning models are also taken into consideration. We prove that the makespan minimization problem on a single processor is NP-hard or strongly NP-hard with the most of the considered learning models. A number of polynomially solvable cases are also provided.      

Integration of neural networks and genetic algorithms for an intelligent manufacturing controller

This paper addresses the development and implementation of a “controller” for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This prediction capability is implemented by using neural networks, simulation, and genetic algorithms. Neural networks predict the behavior of different sequencing policies available in the system. The contribution of the genetic algorithms to the decision-making process is the development of a “new” scheduling rule based on a “building blocks” procedure initiated by the neural networks     

Industrial engineering spreadsheet applications from a manufacturing resource planning (MRP-II) system

This paper describes the MRP-II and distributed network initiatives undertaken at the Naval Air Rework Facility, Cherry Point, in the development of modular, integrated database management systems for material management, financial management, workload and work-in-process control, manufacturing scheduling and control, and inventory and kitting of parts back to production. The local area network and workstations using Zenith 248 PCs and bar coding data entry is described.

PC spreadsheet applications using the MRP-II database are illustrated. Production status, indirect cost management, and shop loading, using a special program called MAPPER to extract and format the data from the database, are just some of the examples to be described. “Lessons learned” are also related in the development of spreadsheet applications.     

Scheduling concurrent production over a finite planning horizon: polynomially solvable cases

The paper analyzes a manufacturing system made up of one workstation which is able to produce concurrently a number of product types with controllable production rates in response to time-dependent product demands. Given a finite planning horizon, the objective is to minimize production cost, which is incurred when the workstation is not idle and inventory and backlog costs, which are incurred when the meeting of demand results in inventory surpluses and shortages. With the aid of the maximum principle, optimal production regimes are derived and continuous-time scheduling is reduced to a combinatorial problem of sequencing and timing the regimes. The problem is proved to be polynomially solvable if demand does not exceed the capacity of the workstation or it is steadily pressing and the costs are “agreeable”.

Scope and purpose

Efficient utilization of modern flexible manufacturing systems is heavily dependent on proper scheduling of products throughout the available facilities. Scheduling of a workstation which produces concurrently a number of product types with controllable production rates in response to continuous, time-dependent demand is under consideration. Similar to the systems considered by many authors in recent years, a buffer with unlimited capacity is placed after the workstation for each product type. The objective is to minimize inventory storage, backlog and production costs over a finite planning horizon. Numerical approaches are commonly used to approximate the optimal solution for similar problems. The key contribution of this work is that the continuous-time scheduling problem is reduced to a combinatorial problem, exactly solvable in polynomial time if demand does not exceed the capacity of the workstation or the manufacturing system is organized such that the early production and storage of a product to reduce later backlogs are justified.     

Group technology applications in manufacturing operations through the computer system

The typical manufacturing facility is constantly developing new product designs and related manufacturing processes. The increased volume of new designs and processes causes rapid and inefficient construction of product designs and manufacturing processes. Many parts and manufacturing processes are developed over the life cycle of a production facility with no organized means of cataloging this past and present data. This procedure is extremely ineffective because there is no way to determine if a part or process has been previously developed. The constant “reinventing of the wheel” creates a tremendous waste of manpower and cost.

One approach to solving this problem is through the use of group technoogy. Group technology is the identification and grouping of similar parts and processes in order to take avantage of their similarities in the design and manufacturing process. Parts and processes can be grouped under a classification and implemented with a coding system. Concurrently, the number of parts and processes can be reduced by putting them in a “family.” This “family” has common characteristics such as shape, size, color, tolerance or production operations.

For handling and manipulation of this data, a computer system has been developed. The computer system would set up a reporting format that would classify, code and group the parts and processes, so the user can analyze if a previously designed process or part can be used in the current system and/or if a better layout can be feasible.

Many advantages such as reduced inventory cost, increased facility space and better utilization of manpower are but a few of the benefits from this system.     

Distributed short interval scheduling in a shop-floor network

The increasing need to reduce costs in the production process through responsive scheduling is feasible as a result of the availability of micro-computers. The transition from a batch MRP system to real time control is a long term evolutionary process, which classically begins with the development of a data collection network. This paper discusses a three phases approach to developing a distributed short interval scheduling system. The phases are: (1).

. Integration of the basic data dealing with the production process, i.e. availability of machines, tools, fixtures, and the selection of the production lot sizes. An MRP system allocates the load for specific work centers, assuming infinite capacity. Our distributed scheduling system uses the MRP schedule with the tools and fixtures inventory of individual work stations. (2).

. According to the local availability of tools and fixtures, a detailed run-time schedule is determined for every part and the number of parts that will be processed in each specific shift. (3).

. Involves the generation of individual operator and machine schedules that include general run-time instructions. The approach includes the development of a library of scheduling tools which can be customized to the unique characteristics and requirements of individual work stations found in a facility utilizing numerically controlled equipment.     

Production planning in semiconductor wafer fab considering variable cycle times

Recent research in the semiconductor manufacturing area has addressed some industrial engineering and operations research applications to solve the challenging production planning and scheduling problems. Relative to the other industries, the number of practical production management techniques and supporting software in the semiconductor industry is limited. The conventional models proposed use fixed or averaged cycle time and yield to get the results. However, in the actual wafer fabs, cycle time is spread widely and skewed due to the large variations in the operations. Therefore, the use of averaged cycle time and yield makes significant differences between the plan and the actual shop performance. Thus, we focus on the production planning problem in the wafer fabrication (“fab”), introducing a planning methodology that explicitly considers the variable cycle time and the shop status. We also evaluate some different types of input policies.     

A MIP based heuristic for capacitated MRP systems

Although Material Requirements Planning (MRP) is the most widely used production planning tool in today’s manufacturing companies, its inability to perform an exhaustive capacity planning, lack of a comprehensive and integrated shop floor extension and using constant and inflated lead times necessitate intelligent methods for developing cost effective production plans. A single optimization model might be employed to overcome these limitations, but it would be intractable to use it in large manufacturing systems. Hence, in this paper, we propose a heuristic method called Capacity Allocater and Scheduler, CAS, to eliminate drawbacks of MRP systems and provide solutions for large-scale instances. The CAS procedure, based on iteratively solving relaxed Mixed Integer Programming (MIP) models, is built on a lot sizing and scheduling framework, which considers both supply alternatives and lot size restrictions simultaneously. Finally, we give a detailed numerical example to demonstrate how CAS may be used in practice, and provide our concluding remarks.     

A hybrid push/pull system in assemble-to-order manufacturing environment

A hybrid push/pull system of an assemble-to-order manufacturing environment is investigated in this paper. In this environment, raw material can be transformed into common semi-finished products at a point where next downstream operations are triggered by customer orders. The production of the earlier upstream stations is controlled by push-type production, while the production of the later downstream stations is controlled by pull-type production. The hybrid system often compromises the conflicting performance characteristics of the push and the pull environments. In the push type, high inventory cost is anticipated in the return of low delivery leadtime. On the contrary, in the pull type, high delivery leadtime is expected in the return of low inventory cost. The objective function for the presented hybrid model is to minimize the sum of inventory holding cost and delivery leadtime cost, which is the cost of the time period since customers have placed an order until it is fulfilled. The model is applied to solve the inventory and late delivery problems in an assemble-to-order manufacturer. A genetic algorithm (GA) is used. A discrete event simulation model is used to evaluate the objective function for each chromosome in the GA. The pure push and pull systems are also simulated in order to compare their performance with the hybrid system. Sensitivity analysis on the coefficient of variation (CV) of time between actual customer order arrivals and on various cost ratios of delivery leadtime and inventory are carried out. In most cases, the hybrid performs the best. Results show that the hybrid production system would save the company significantly compared to the pure push or pure pull production systems.     

Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon

Although the lately evolved manufacturing technologies such as enterprise resource planning (ERP) provide a unified platform for managing and integrating core business processes within a firm, the decision-making between marketing and production planning still remains rather disjoint. It is due in large parts to the inherent weaknesses of ERP such as the fixed and static parameter settings and uncapacitated assumption. To rectify these drawbacks, we propose a decision model that solves optimally the production lot-size/scheduling problem taking into account the dynamic aspects of customer's demand as well as the restriction of finite capacity in a plant. More specifically, we consider a single product that is subject to continuous decay, faces a price-dependent and time-varying demand, and time-varying deteriorating rate, production rate, and variable production cost, with the objective of maximizing the profit stream over multi-period planning horizon. We propose both coordinated and decentralized decision-making policies that drive the solution of the multivariate maximization problem. Both policies are formulated as dynamic programming models and solved by numerical search techniques. In our numerical experiments, the solution procedure is demonstrated, comparative study is conducted, and sensitivity analysis is carried out with respect to major parameters. The numerical result shows that the solution generated by the coordinated policy outperforms that by the decentralized policy in maximizing net profit and many other quantifiable measures such as minimizing inventory investment and storage capacity.Scope and purposeWe consider a manufacturing firm who produces and sells a single product that is subjected to continuous decay over a lifetime, faces a price-dependent and time-varying demand function, shortages are allowed and a completely backlogged, and has the objective of determining price and production lot-size/scheduling so as to maximize the total profit stream over multi-period planning horizon. We develop a tactical-level decision model that solves the production scheduling problem taking into account the dynamic nature of customer's demand which is partially controllable through pricing schemes. As analogous to the sales and operations planning, the proposed scheme can be used as a coordination center of the APS system within a generic enterprise resource planning framework which integrates and coordinates distinct functions within a firm.This paper differs from the existing works in several ways. First, we propose a dynamic version of the joint pricing and lot-size/scheduling problem taking into account the capacitated constraint. Second, several key factors being considered in the model, such as the demand rate, deteriorating rate, production rate, and variable production cost are assumed time-varying that reflect the dynamic nature of the market and the learning effect of the production system. A third difference between the past research and ours is that the price can be adjusted upward or downward in our model, making the proposed pricing policy more responsive to the structural change in demand or supply.     

A strategic decision support system for supply network design and management in the semiconductor industry

This paper presents a strategic decision support system (DSS) which has been conceptualized and designed by SEMATECH^* to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. This DSS has been named “Manufacturing Enterprise Model” or “MEM”. MEM ties each factory and its primary metrics to the rest of the business enterprise to assess how changes in wafer fabrication affect other factories, the distribution system, and customer deliveries. The model is intended to be used to evaluate future factory concepts and to assist business planners in strategic decisions about product allocation and major resource/facility planning.     

General structure and characteristics of quick response production system

The diversification and shorter production cycles urge manufacturers to shift their production systems from “make to stock” to “make to order” or an intermediate production system between them. The present paper deals with a production system developed by some manufacturers to meet requirements in the last decade. The system, which we call “Quick response to orders production system” or in brief “quick response production system (QRPS)”, is characterized by two key factors, i.e., “acceptable response time” and “semifinished product.” An acceptable response time is decided through the explicit or implicit approval of customers beforehand and manufacturers guarantee to ship products within an acceptable response time in a stochastic sense by processing the semifinished product according to customer order. As a result, manufacturers are able to reduce the inventory of finished products, while customers are able to enjoy the diversification of products and a stable supply from manufacturers. The general structure and characteristics of QRPS are discussed primarily from a quantitative point of view and a numerical example is shown to assist in understanding the design and operation of QRPS.     

A MIP based heuristic for capacitated MRP systems

Although Material Requirements Planning (MRP) is the most widely used production planning tool in today’s manufacturing companies, its inability to perform an exhaustive capacity planning, lack of a comprehensive and integrated shop floor extension and using constant and inflated lead times necessitate intelligent methods for developing cost effective production plans. A single optimization model might be employed to overcome these limitations, but it would be intractable to use it in large manufacturing systems. Hence, in this paper, we propose a heuristic method called Capacity Allocater and Scheduler, CAS, to eliminate drawbacks of MRP systems and provide solutions for large-scale instances. The CAS procedure, based on iteratively solving relaxed Mixed Integer Programming (MIP) models, is built on a lot sizing and scheduling framework, which considers both supply alternatives and lot size restrictions simultaneously. Finally, we give a detailed numerical example to demonstrate how CAS may be used in practice, and provide our concluding remarks.     

Scheduling one-part-type serial manufacturing system under periodic demand: a solvable case

The paper studies one-part type, multiple-stage production system with periodic demands. A buffer of infinite capacity is placed after each machine. Inventory flow through buffers is controlled by machine production rates. The objective is to find a cyclic production rate, which minimizes all inventory-related expenses over an infinite planning horizon. With the aid of the maximum principle, optimal production policies are derived and the continuous-time scheduling problem is reduced to a discrete timing problem. As a result, a polynomial-time algorithm is suggested to calculate the optimal production rate. A numerical example is used to illustrate the algorithm.Scope and purposeNumerical and heuristic approaches have been suggested for production control of automated-serial-manufacturing systems. These approaches try to derive production control policies that would minimize overall costs related to inventory, backlog, and production. The quality of these approaches is often difficult to assess, and they can be time-consuming to implement. Therefore, increasing attention has been directed to optimal control policies of production systems that can be derived precisely and quickly. This paper addresses a special case of the production system manufacturing a single product type to meet a periodic demand. Given a certain assumption on cost relationship, we derive a fast and simple scheduling algorithm that calculates the optimal policy.     

A binary algorithm for solving n by n systems of integer equations

We discuss a “binary” algorithm for solving systems of linear equations with integer coefficients. So-called “binary” algorithms differ from ordinary ones in that there is no roundoff error, but only overflow, and the underlying analysis is p-adic analysis rather than conventional real analysis. The advantages of this algorithm are especially apparent when extremely large numbers are involved and no roundoff error can be tolerated.

VLSI implementation of this and other “binary” algorithms is very appealing because of the extreme regularity of the circuits involved.     

Enterprise Resource Planning as the Trojan Horse for New Rules of Operations Management

The control of operations, for example; the flow of materials, the scheduling of production, the planning of capacity – these are central problem in operations management. A substantial body of technique, with attendant technologies, has been developed to facilitate the problem of control of operations. One such technique is Manufacturing Resource Planning (MRP). This technique has developed in power and scope in concert with the development of power in computer based technology. From the perspective of the operations management literature, MRP has evolved into MRP II, and now into Enterprise Resource Planning (ERP). MRP, and MRP II are systems that are embedded within the operations function, and can be controlled legitimately by the operations function; ERP is fundamentally different. ERP systems in their model implementation are enterprise wide, integrated systems. As an enterprise wide system, ERP has created an opportunity to distance the power to influence operations actions from the location of the function. This change has significant implications for the companies adopting the technology, and more specifically, for the profession of operations management. This paper develops a framework of analysis for this change, presents a set of small cases, and discusses some implications that can be drawn from the analysis. The “three arenas of information use – sense making, knowledge creating, and decision making” (Choo 1998, p.3) must be allowed to energize each other and this can only happen if organization wide information systems, such as ERP, respect and empower situated action, enable ambiguity, and allow the use of multiple interpretive frames as managers interact with the situations of operations management.     

A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients

Much of the research work into artificial intelligence (AI) has been focusing on exploring various potential applications of intelligent systems with successful results in most cases. In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human learning and decision making: the emotional factor. While it currently sounds impossible to have “machines with emotions,” it is quite conceivable to artificially simulate some emotions in machine learning. This paper presents a modified backpropagation (BP) learning algorithm, namely, the emotional backpropagation (EmBP) learning algorithm. The new algorithm has additional emotional weights that are updated using two additional emotional parameters: anxiety and confidence. The proposed “emotional” neural network will be implemented to a facial recognition problem, and the results will be compared to a similar application using a conventional neural network. Experimental results show that the addition of the two novel emotional parameters improves the performance of the neural network yielding higher recognition rates and faster recognition time.