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.     

Process vs resource‐oriented Petri net modeling of automated manufacturing systems

Since the 1980s, Petri nets (PN) have been widely used to model automated manufacturing systems (AMS) for analysis, performance evaluation, simulation, and control. They are mostly based on process‐oriented modeling methods and thus termed as process‐oriented PN (POPN) in this paper. The recent study of deadlock avoidance problems in AMS led to another type of PN called resource‐oriented PN (ROPN). This paper, for the first time, compares these two modeling methods and resultant models in terms of modeling power, model complexity for analysis and control, and some critical properties. POPN models the part production processes straightforwardly, while ROPN is more compact and effective for deadlock resolution. The relations between these two models are investigated. Several examples are used to illustrate them. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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.     

自动制造系统的稳健控制方法的综述

随着科学技术的快速发展,制造自动化在制造工厂已经成为一个主流方向.在过去的几十年中,研究人员已经对自动制造系统的死锁问题做了大量的研究.但是大多数解决方案总是假设分配的资源不会故障.然而,任何一个制造研究者都知道,资源故障来自各种各样的原因,包括工件破损、传感器故障、零件缺失和电器失灵等.显然,一旦资源发生故障,后续加工路径中需要使用这个故障资源的进程将停滞,不能完成其加工生产,直到故障资源被修复.那些不使用故障资源的支路也会被发生停滞的进程所阻塞.最坏的情况就是一个简单的资源故障可能会导致整个系统的崩溃.因此,制造系统中的资源故障问题急需解决.通过分析大量的文献资料,本文对解决死锁和阻塞问题的控制方法做了系统的总结研究.同时,对本文提出的稳健无死锁控制策略以及亟待开展的研究工作做了详细的介绍.     

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.     

A strategic investment model for phased implementation of flexible manufacturing systems

This paper describes a set of strategic, tactical, and operational models that can be used to analyze the phased implementation of flexible technology in a manufacturing system. The strategic model represents capital investment decisions, the tactical model represents aggregate production decisions, and the operational model represents the functional form of the production costs. Learning effects and other nonlinearities (such as setup costs, economies of scale, and congestion effects) can be quantified in the models. An efficient solution procedure (using dynamic programming and minimum cost network flow optimization) is described. The efficiency of the solution method permits a host of 'what-if' scenarios to be examined by the decision maker.     

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.     

Soft computing based procurement planning of time-variable demand in manufacturing systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning (ERP).It can be described using the non-analytic mathematical programming model proposed in this paper.To solve the model we propose to use a fuzzy decision embedded genetic algorithm.The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones.Then,a fuzzy decision quantification method is used to quantify experience from planning experts.Thus,decision rules can easily be embedded in the computation of genetic operations.This approach is applied to purchase planning problem in a practical machine tool works,where satisfactory results have been achieved.     

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.     

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.     

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 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.     

Advancing the assessment of automated deception detection systems: Incorporating base rate and cost into system evaluation

In the last two decades, there has been an increased interest in automated deception detection systems (ADDs) for use in screening, although little attention has been paid to the usefulness of these systems. ADDs use various means, both invasive and non‐invasive, to ascertain individual intent to deceive or engage in malicious behaviour. Many papers introducing ADDs use signal detection theory to compare a technique's ability to detect malicious intent with other techniques, but in doing so, they do not include contextual information such as base rate and cost. In this paper, we aim to improve future research by showing how the inclusion of contextual information provides a more realistic picture of the research. Through both theoretical arguments and a real‐data example, we show that especially for those contexts where malicious intent is infrequent (ie, with low base rates of deception) that not factoring in the base rate overestimates the accuracy and therefore usefulness. We conclude with recommendations for how future research should provide a fuller picture of the accuracy and usefulness of ADDs.     

New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems

This paper proposes a new integration method for cell formation, group scheduling, production, and preventive maintenance (PM) planning problems in a dynamic cellular manufacturing system (CMS). The cell formation sub-problem aims to form part families and machine groups, which minimizes the inter-cell material handling, under-utilization, and relocation costs. The production planning aspect is a multi-item capacitated lot-sizing problem accompanied by sub-contracting decisions, while the group scheduling problem deals with the decisions on the sequential order of the parts and their corresponding completion times. The purpose of the maintenance sub-problem is to determine the availability of the system and the time when the noncyclical perfect PM must be implemented to reduce the number of corrective actions. Numerical examples are generated and solved by Bender’s decomposition pack in GAMS to evaluate the interactions of the proposed model. Statistical analysis, based on a nonparametric method, is also used to study the behavior of the model’s cost components in two different situations. It is shown that by adding the PM planning decisions to the tactical decisions of the dynamic CMS, the optimal configuration and production plans of the system are heavily affected. The results indicate that omitting the PM actions increases the number of sudden failures, which leads to a higher total cost. Finally, it is concluded that the boost in the total availability of the dynamic CMS is one of the main advantages of the proposed integrated method.     

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.