Hierarchical production planning for complex manufacturing systems

A hierarchical approach to production planning for complex manufacturing systems is presented. A single facility comprising a number of work-centers that produce multiple part types is considered. The planning horizon includes a sequence of time periods, and the demand for all part types is assumed known. The production planning problem consists of minimizing the holding costs for all part types, as well as the work-in-process and the backlogging costs for the end items. We present a two-level hierarchy that is based on aggregating parts to part families, work-centers to manufacturing cells and time periods to aggregate time periods. The solution at the aggregate level is imposed as a constraint to the detailed level problems which are formulated for each manufacturing cell separately. This architecture uses a rolling horizon strategy to perform the production management function. We have employed perturbation analysis techniques to adjust certain parameters of the optimization problems at the detailed level to reach a near-optimal detailed production plan. Numerical results for several realistic example problems are presented and the solutions obtained from the hierarchical and monolithic approaches are compared. The results indicate that the hierarchical approach offers major advantages in computational efficiency, while the loss of optimality is acceptable.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

An efficient planning technique for robotic assemblies and intelligent robotic systems

An efficient planning algorithm for the organization and formulation of complete plans applicable to both robotic assemblies and intelligent robotic systems is proposed. The constraint of task precedence and the concepts of the criticality of tasks-events and valid repetitive orderings are introduced to facilitate and optimize the formulation of every complete plan capable of executing a user-requested job. Two examples demonstrate the applicability of the proposed algorithm to both robotic assemblies and intelligent robotic systems.     

An integral theorem prover and the role of proof planning

An investigation is made into the ways proof planning can enhance the capability of a rule based prover for the theory of integration. The integrals are of the Riemann type and are defined in a way to maximize the theorem proving methods of predicate calculus. Approximately fifty theorems have been proved and several examples are discussed. A major shortcoming was found to be the inability of the system to work with or produce a proof plan. As a result, a planning scheme based on the idea of subgoals or milestones was considered. With user defined plans, there was a substantial increase in performance and capability of the system and, in some cases, proofs which were previously unsuccessful were completed.     

集合论等式型定理机器证明系统的研究与开发

集合论定理机器证明,至今在国内外尚无相关研究。虽然集合论在数学领域中所处的基础地位显得在这一领域实现机械化极其重要,但是多年来尚无进展。到目前为止,还没有发现能产生可读证明的系统。通过对人工智能搜索算法的研究,提出了集合论等式型定理证明的机械化方法。实现的系统能自动生成定理的可读证明以及相关的说明。     

LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica

This paper descibes an explanation-based learning (EBL) system based on a version of Newell, Shaw, and Simon's LOGIC-THEORIST (LT). Results of applying this system to propositional calculus problems from Principia Mathematica are compared with results of applying several other versions of the same performance element to these problems. The primary goal of this study is to characterize and analyze differences between non-learning, rote learning (LT's original learning method), and EBL. Another aim is to provide a characterization of the performance of a simple problem solver in the context of the Principia problems, in the hope that these problems can be used as a benchmark for testing improved learning methods, just as problems like chess and the eight puzzle have been used as benchmarks in research on search methods.     

A contingency model for estimating success of strategic information systems planning

Strategic information system planning (SISP) has been identified as a critical management issue. It is considered by many as the best mechanism for assuring that IT activities are congruent with those of the rest of the organization and its evolving needs.     

On the receding horizon hierarchical optimal control of manufacturing systems

This paper concerns the development of a hierarchical framework for the integrated planning and scheduling of a class of manufacturing systems. In this framework, dynamic optimization plays an important role in order to define control strategies that, by taking into account the dynamic nature of these systems, minimize customized cost functionals subject to state and control constraints. The proposed architecture is composed of a set of hierarchical levels where a two-way information flow, assuming the form of a state feedback control, is obtained through a receding horizon control scheme. The averaging effect of the receding horizon control scheme enables this deterministic approach to handle random and unexpected events at all levels of the hierarchy. At a given level, production targets to the subsystems immediately below are defined by solving appropriate optimal control problems. Efficient iterative algorithms based on optimality conditions are used to yield control strategies in the form of production rates for the various subsystems. At the lower level, this control strategy is further refined in such a way that all sequences of operations are fully specified. The minimum cost sensitivity information provided in the optimal control formulation supports a mechanism, based on the notion of a critical machine, which plays an important role in the exploitation of the available flexibility. Finally, an important point to note is that our approach is particularly suited to further integration of the production system into a larger supply chain management framework, which is well supported by recent developments in hybrid systems theory.     

A three-level knowledge-based system for the generation of live and safe Petri nets for manufacturing systems

New generation manufacturing systems are involved in a transformation process which aims for more reliable production processes and with a lower response time to the demand of the market. This work presents an application of artificial intelligence planning techniques for the automatic generation of the control program for a manufacturing system expressed as a safe and live Petri net. The advantage of the system presented here is straightforward: it allows for a fast generation of sound results free of human errors, reducing the cost and duration of the development phase of control programs.     

A hierarchic approach to production planning and scheduling of a flexible manufacturing system

The paper deals with the problem of improving the machine utilization of a flexible manufacturing cell. Limited tool magazine space of the machines turns out to be a relevant bottleneck. A hierarchic approach for this problem is proposed. At the upper level, sets of parts that can be concurrently processed (batches) are determined. At the lower levels, batches are sequenced, linked, and scheduled. Methods taken from the literature are used for the solution of the latter subproblems, and an original mixed integer programming model is formulated to determine batches. The proposed methods are discussed on the basis of computational experience carried out on real instances.     

An automated coding and classification system with supporting database for effective design of manufacturing systems

The philosophy of group technology (GT) is an important concept in the design of flexible manufacturing systems and manufacturing cells. Group technology is a manufacturing philosophy that identifies similar parts and groups them into families. Beside assigning unique codes to these parts, group technology developers intend to take advantage of part similarities during design and manufacturing processes. GT is not the answer to all manufacturing problems, but it is a good management technique with which to standardize efforts and eliminate duplication. Group technology classifies parts by assigning them to different families based on their similarities in: (1) design attributes (physical shape and size), and/or (2) manufacturing attributes (processing sequence). The manufacturing industry today is process focused; departments and sub units are no longer independent but are interdependent. If the product development process is to be optimized, engineering and manufacturing cannot remain independent any more: they must be coordinated. Each sub-system is a critical component within an integrated manufacturing framework. The coding and classification system is the basis of CAPP and the functioning and reliability of CAPP depends on the robustness of the coding system. The proposed coding system is considered superior to the previously proposed coding systems, in that it has the capability to migrate into multiple manufacturing environments. This article presents the design of a coding and classification system and the supporting database for manufacturing processes based on both design and manufacturing attributes of parts. An interface with the spreadsheet will calculate the machine operation costs for various processes. This menu-driven interactive package is implemented using dBASE-IV. Part Family formation is achieved using a KAMCELL package developed in TURBO Pascal.     

Hierarchical stochastic production planning for the highest business benefit

This paper addresses the hierarchical stochastic production planning (HSPP) problem of flexible automated workshops (FAWs), each with a number of flexible manufacturing systems (FMSs) the part-transfer between which is a delay of a time period. The problem not only includes uncertainties in the demand, capacities, material supply, processing times, necessity for rework, and scrap, but also considers multiple products and multiple time periods. The objective is to develop a production plan which tells each FMS how many parts to produce and when to produce them so as to obtain the highest business benefit. Herein, the HSPP problem is formulated by a stochastic nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the stochastic nonlinear programming model above, it is approximately transformed into a deterministic nonlinear programming model and further into a linear programming model. Because the scale of the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar's algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for the medium or small scale problems and the latter for the large scale problems. By the implementation of the above-mentioned algorithms and through many HSPP examples, Karmarkar's algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, the result of which shows that the proposed approaches are very effective and suitable for not only “push” production but also “pull” production.     

Solution to the Generalized Champagne Problem on simultaneous stabilization of linear systems

The well-known Generalized Champagne Problem on simultaneous stabilization of linear systems is solved by using complex analysis and Blondel’s technique. We give a complete answer to the open problem proposed by Patel et al., which auto-matically includes the solution to the original Champagne Problem. Based on the recent development in automated inequality-type theorem proving, a new stabiliz-ing controller design method is established. Our numerical examples significantly improve the relevant results in the literature.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Strategies for planning and implementation of flexible fixturing systems in a computer integrated manufacturing environment

In general, for any manufacturing operation to be successful, parts must be located and held to remain in position and orientation when subjected to external forces during the manufacturing operations. The traditional approach to fixturing involves designing and manufacturing special-purpose fixtures. These fixtures are generally dedicated devices designed and manufactured for specific parts and manufacturing operations. Flexible fixturing technology involves employing a single fixturing system to hold workpieces of various shapes and sizes. Such systems have reached the embryonic stage of implementation into manufacturing environment. Therefore the integration of current and future flexible fixturing systems into the manufacturing processes becomes an important issue for future work in computer-integrated manufacturing (CIM) environment.

The approach to CIM is dependent on the functional model of the company. Therefore, the implementation of any sub-systems within it must be studied on an individual basis. However, in order to plan and implement new technologies, such as flexible fixturing systems, within CIM environment, certain requirements must be met and guidelines must be followed. The strategies for planning and implementation of flexible fixturing systems within the larger frame work of CIM environment are presented. Some guidelines for designing and developing the interconnection of the data bases are also discussed.     

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

An organism-oriented modeling approach to support the analysis and design of intelligent manufacturing systems

In this paper a generic macroscopic object, termed an organism, is introduced. An organism defines a high-level modeling object that has the capabilities for organizational networking, standardization or characteristics specifications, decoupling of editing and visualization as well as temporal management. Organism-oriented models inherit from their parent object-oriented and object/agent-oriented models a simplified representation of the manufacturing entities as well as a capacity for many levels of abstraction. Moreover, the organism-oriented modeling approach enriches these models by not only considering basic manufacturing objects and agents, but also the fact that each of these objects and agents may itself be an organization and also part of one or several organizations. The paper first surveys some of the current approaches used for modeling and analysing manufacturing systems: structured analysis, Petri nets, object and object/ agent models. The object model behind the organism-oriented modeling approach is then presented and its application to a manufacturing case is illustrated.     

A decision support system for the selection of computer-integrated manufacturing technologies

The adoption of computer-integrated manufacturing (CIM) offers manufacturing organizations many tangible and intangible benefits which enable them to produce products of high quality at low costs. However, the selection and evaluation of CIM is a complex process as it involves the consideration of many parameters to ensure that the selected technology meets the requirements of individual companies. This paper describes the development of a quantitative/qualitative decision support system for the evaluation of CIM which takes into consideration the objectives and operating characteristics of a company, thus ensuring that the selected technology matches the individual needs of that company. The methodology used in the decision support system is based on a combination of the analytical hierarchy process (AHP) and database technology. The AHP provides a means to consider both the tangible and the intangible benefits of CIM while databases are used to store the knowledge about the various benefits that CIM may offer. The system has been implemented in EXCEL, which fully automates the evaluation process. A case study is also presented to illustrate the capability of the proposed decision support system.     

Model and control holonic manufacturing systems based on fusion of contract nets and Petri nets

Holonic manufacturing systems (HMS) can be modeled as multi-agent systems to which contract net protocol can be effectively and robustly applied. However, the lack of analysis capability of contract nets makes it difficult to avoid undesirable states such as deadlocks in HMS. This paper presents a framework to model and control HMS based on fusion of Petri net and multi-agent system theory. The main results include: (1) a multi-agent model and a collaboration process to form commitment graphs in HMS based on contract net protocol, (2) a procedure to convert commitment graph to collaborative Petri net (CPN), and (3) feasible conditions and collaborative algorithms to award contracts in HMS based on CPNs.