A robust hierarchical production planning for a capacitated two-stage production system

In this paper, we propose a robust hierarchical production planning approach for a two-stage real world capacitated production system operating in an uncertain environment. The first stage of the system produces a set of semi-finished products having relatively stable annual demands, and the second finishing stage produces finished products having highly variable weekly demands. The fixed production setup costs incurred at the first stage are considerably high. Fixed production setup costs incurred at the second stage are fairly small compared to those of the first stage. We propose an integrated hierarchical planning model, where semi-finished products from the first stage (i.e. the aggregate level) are disaggregated into finished products to be produced in the second stage (i.e. the operational level). As a result of the relatively stable demands and the high setup costs experienced at the first stage, a cyclical aggregate planning model is proposed for production planning at the upper level of the hierarchical plan. Based on this aggregate plan, a modified periodic review policy is then proposed for production planning at the lower level. Finally, a coupling plan, linking the two planning levels, is proposed to ensure the feasibility of the disaggregation process at every period.     

Proplant: Multiagent system for production planning

The so-called ''tribase'' acquaintance model of the agent's behavior is presented in this paper. This represents an extension of the twin-base model (Cao et al., 1997). Based on practical experience, the new model tries to cope with parallel processing, precedence constraints, and sparse resources. The idea of substituting the interagent negotiation processes by the periodical internal planning activity of the agents is stressed. A multiagent system, ProPlanT, as an application of the tribase model for the project-oriented production planning developed for TESLA TV company is described in detail. Three types of agents production planning agent (PPA), production management agent (PMA), and production agent (PA) are distinguished. The corresponding tribase models and potential role of metaagents are discussed.     

An algorithm for production planning in a flexible production system

In this paper, we consider the problem of determining the production rate, production batch size, and production sequence when production rate, setup cost, and unit processing cost are sequence-dependent. Using a standard lot sizing model with backorder, a tabu search algorithm for solving this problem is proposed. The algorithm is tested on some random test problems and its performance is compared with random sequencing. Computational results show that the proposed algorithm is very efficient.     

A micro-model of internal supply forecasting for technical manpower planning

The manpower planning process includes forecasting the future demand for manpower and the future internal supply of manpower and then developing action plans which will balance supply and demand. Many of the models which exist for forecasting internal supply are for periods of one year or longer, which makes them inappropriate for many project planning and short term human resource management applications. This paper presents an easily implemented short-range (12-month horizon) model for forecasting internal supply. Time series analysis techniques are used to identify seasonal patterns and trends which exist in the determinants of internal supply. These are employed in the development of an internal supply forecast at both the aggregate firm level and at the individual skill group level. Feasibility of the model is demonstrated using empirical data. Output of the model is useful for further manpower planning.     

A fuzzy control system with application to production planning problems

A considerable part of the literature on fuzzy sets is devoted to the field of fuzzy control system. In this paper, an alternative control system is introduced to describe a dynamic system with fuzzy white noise. In order to find optimal ways to control such a system, fuzzy optimal control theory is further developed. Specifically, a linear quadratic model is formulated and solved as a fuzzy optimal control problem. The formulation and solution of this model provide an economic interpretation of a production planning model both in the finite horizon and in the infinite horizon.     

A decision support system for production planning and bid preparation for a small job shop manufacturer

With the advent of the inexpensive micro-computer, small manufacturers have been able to automate many applications in their day-to-day operations. Under the auspices of the State of Missouri “Productivity Improvement Program” - a program to aid small manufacturing businesses in improving their productivity, a Decision Support System (DSS) was developed to assist a small metals manufacturing company in production planning and bid preparation.

The DSS was developed at UMR by a graduate student as part of his master's thesis; the work was funded by both the Productivity Improvement Program and the small manufacturer. After development of the DSS, the graduate student was hired full time for one year to assist the manufacturer in implementing the DSS and to train on site personnel in its operation.

This paper discusses the development of the DSS, its implementation, and its utilization.     

A study on production planning for CIM

As for the conventional study of CIM system, the critical ratio rule has taken the remaining time until due date into consideration has been commonly utilized for a characteristic of the operational priority rule. In this paper, a characteristic of new priority rule was proposed in order to remove the defect of the critical ratio rule. To put it concretelt, both of backorders and outstandings were introduced into this priority rule as a substitution for considering the due date. The superiority of the proposed rule over the critical ratio rule was proved by the use of the model experiments.     

炼油企业生产计划和能量系统集成优化研究

炼油企业通常由生产过程和公用工程两个主要部分组成。将生产计划和公用工程集成优化,不仅获得真正的全局最优解,而且克服了传统生产计划人为制定生产过程与公用工程之间中间物流（各压力等级的蒸汽,瓦斯等）价格的缺陷,避免了蒸汽减温减压和放空,以及炼厂气放空的经济损失和环境破坏。首先采用IDEF0方法建立了生产计划与公用工程的功能模型,描述了两者之间的相互关系。接着建立了生产计划与公用工程集成的混合整数线性规划模型。模型描述了装置能耗不仅与加工量相关,而且与装置的生产方案相关,更真实地反映了生产过程与能量系统之间的定量关系。模型还在全厂范围内进行各压力等级蒸汽和燃料的平衡。最后,将建立的全厂集成优化模型应用于中国北方的某大型炼油厂,验证了模型的有效性。     

炼油企业全厂生产计划系统设计与开发

采用面向对象方法,使用统一建模语言进行炼油企业生产计划系统的需求分析和系统设计。首先描述了生产计划系统的功能需求,包括数据管理、模型管理、模型发生器和过程分析等,并且采用序列图初步分析了系统核心功能生产计划的实现过程。在此基础上,进行系统的数据模型设计,包括公用数据模型,装置数据模型和物流数据模型。上述数据模型描述了炼油企业生产流程的静态结构和生产计划活动的信息。最后开发了软件OpTechPlan,并且在中国北方某炼油企业得到了初步的应用。     

石化企业生产计划与能量系统集成研究

应用并行工程方法对生产计划与能量系统的集成进行了分析,提出了用于确定业务流程和信息流的业务过程模型,明确了系统的并行特性,采用并行工程方法建立了并行业务过程模型,并确定了用于数据建模的数据流,从而为集成效据库管理和集成软件系统的开发奠定了基础。     

油田产量预测系统的研制

如何有效地预测油田的未来产量一直是油藏工程研究中的一项重要内容,准确地预报油田开发过程中的动态产量,是合理调整油田规划和制定方案,实现优化开发和管理的重要依据。本文研究了几种新的预测方法(基于人工神经网络的稠油预测模型、灰色预测模型、CAR预测模型等),并首先将其应用于油田产量预测。本文开发的预测系统集成了20多种常规的和新型的预测方法,对油田产量的预测及管理都能够给出有价值的数据。在充分并准确采集油田各单位产量信息及建立切合实际的数据模型的基础上,将油田产量信息采集与处理、预测与管理等环节,有机地结合成为一个整体。实际预测结果表明,该方法可以取得较好的预测效果。     

多源定量遥感产品并行处理系统设计及实现*

针对遥感数据非结构化、有格式、单景数据量大、记录总量小、大部分处理过程后数据量不显著减少的特点和大规模遥感数据处理的迫切需求,基于已有硬件资源,设计和实现了集中式集群计算的多源定量遥感产品生产系统。系统为解决遥感科学工作者设计开发的算法进行并行计算的问题以及大规模数据管理的需求,设计了基于任务并行的处理系统和面向应用的并行文件系统。系统集成了遥感科学工作者开发的30多种原始数据的预处理算法和50多种多源定量遥感产品生产算法,解决了多源定量遥感产品规模化流程化按需生产的难题,并进行了产品生产,证明了系统在管理和处理大规模遥感数据时的可靠性和有效性。     

A real-time warehouse operations planning system for small batch replenishment problems in production environment

A factory consists of numerous production workstations, multiple production lines and many production floors. Due to the characteristics of just-in-time and make-to-order mode manufacturing, small batches of production materials are required for production lines within a short period of time in order to facilitate daily production operations. In this paper, a real-time warehouse operation planning system (R-WOPS) for solving small batch replenishment problems is described. Through using R-WOPS, real-time production and warehouse operations are monitored by radio frequency identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible small batch replenishment solutions. Simulation tests show that R-WOPS generates pick-up and delivery route plans for small batch replenishment orders very efficiently.     

A Web-based flood forecasting system for Shuangpai region

Traditional flood forecasting and operation of reservoirs in China are based on manual calculations by hydrologists or through standalone computer programs. The main drawbacks of these methods are long forecasting time due to time-consuming nature, individual knowledge, lack of communication, absence of experts, etc. A Web-based flood forecasting system (WFFS), which includes five main modules: real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, precipitation forecasting, and flood analysis, is presented in this paper. The WFFS brings significant convenience to personnel engaged in flood forecasting and control and allows real-time contribution of a wide range of experts at other spatial locations in times of emergency. The conceptual framework and detailed components of the proposed WFFS, which employs a multi-tiered architecture, are illustrated. Multi-tiered architecture offers great flexibility, portability, reusability and reliability. The prototype WFFS has been developed in Java programming language and applied in Shuangpai region with a satisfactory result.     

A long range production/resource planning system using linear programming

In January 1985, a project was initiated to optimize and update the entire Production Planning function, including long-range and short-range scheduling, Material Requirements Planning, and a Production/Resource Planning System for the Molding and Assembly area. This paper addresses the latter.

The Molding and Assembly area at Johnson Wax molds and assembles caps and their components for our products. We were looking for a long range strategic plan for the area. The natural solution was optimization in the form of linear programming.

The models were set up on Control Data's Cybernet Computer using PDS/Magen (for matrix generation) and APEX IV (for problem solving). This was accessed from an IBM-XT via a modem. (The models have since been moved to our inhouse IBM 3083 Mainframe and use Haverly Systems' OMNI and MPSX-370.) The project involved gathering information on the Molding and Assembly area, developing and testing a prototype, developing the data base and data tables for all caps concerned, expanding the prototype and developing the program to handle all caps, preparing output reports (using FOCUS Information Management System), and final implementation.     

A case-based system for process planning

Process planning is the phase of manufacturing that is concerned with the selection, and sequencing of manufacturing operations necessary to transform an initial stock material into a finished part. It is a tedious operation that requires highly skilled and experienced personnel of which there is currently great shortage. To disseminate the costly expertise and to reduce process planning times, which tend to add high costs to small batch production, many automated computer systems have been introduced and applied. All these systems, though, require the supervision of an experienced human being and fail to capture the knowledge and reasoning behind process planning decisions. The causes of the lack of an efficient automatic system for process planning are the unique characteristics of the domain. In contrast to other areas where knowledge-based and expert systems have been applied, process planning demands the solution of several different planning problems before a final solution is achieved. Every change introduced to the previous world model creates a completely new world model with its own, new constraints and preconditions that nedd to be satisfied. This, in turn, affects the applicability of the knowledge to the new model.

To provide solutions to the above problems we have designed a prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs (Memory Organization Packages), meta-MOPs and TOPs (Thematic Organization Packets) to rearrange its knowledge according to its experiences and to predict and avoid errors. To model the dynamic, sharable knowledge and the constantly changing world model a knowledge-representation language named TOLTEC has been designed and used in the system. Furthermore, we propose a novel planning method based on abstract plans and constraints that can solve planning problems in uncertain domains given incomplete information.     

A system for knowledge-based process planning

We describe the application of problem solving, knowledge based methods in creating process plans in manufacturing. The planner presented - called TOLTEC - is designed for experiential domains and bases its operation on the use of cases in a dynamic memory environment. We will describe the way TOLTEC creates process plans by utilizing previous experiences, dynamic clustering of its memories and dynamic constraint generation and by shifting its focus of attention to different features of the workpiece by using importance values. Also, we will present how TOLTEC learns by modifying its memories according to new experiences and how it helps bridge some of the gap between design and manufacturing by detecting design errors.The emphasis in this paper is more on the application aspects of our system and the examples presented will demonstrate the abilities of TOLTEC to design process plans, detect design errors, predict manufacturing errors, recover from planning errors, handle multiple branching solutions and improve its performance by utilizing learning techniques.     

A model for integrating process planning and production planning and control in machining processes

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.