In this paper, a general methodology of agent- based manufacturing systems scheduling, incorporating game theoretic analysis of agent cooperation is presented to solve the n- job 3- stage. flexible. flowshop scheduling problem. The. flowshops are. flexible in the sense that a job can be processed by any of the identical machines at each stage. Our objective is to schedule a set of n jobs so as to minimize the makespan. We perform error bound analysis using the lower bound estimates developed in the literature as a datum for comparing the agent- based scheduling solutions with other heuristic solutions. The results of the evaluation show that the agent- based scheduling approach outperforms existing heuristics for the majority of the testing problems.

In this paper an attempt is taken to formalize the design and implementation of agentbased approach for agile manufacturing system scheduling. The general framework of an agentbased approach for agile manufacturing systems scheduling is presented. Some steps have been taken to construct a methodology for the development of negotiation mechanism in agent-based manufacturing systems scheduling to improve scheduling flexibility and robustness. A lower and upper bound for measuring the effectiveness of the scheduling system are also developed. The developed model integrates data and decisions associated with several entities within a scheduling system. This approach can be used to model and solve large-scale scheduling problems in an agile manufacturing environment.

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.

Producing customized products to respond to changing markets in a short time and at a low cost is one of the goals in agile manufacturing. To achieve this goal customized products can be produced using an assembly-driven product differentiation strategy. The successful implementation of this strategy lies in efficient scheduling of the system. However, little research has been done in addressing the scheduling issues related to assembly-driven product differentiation strategies in agile manufacturing. In this paper, scheduling problems associated with the assembly-driven product differentiation strategy in a general flexible manufacturing system are defined, formulated, and solved. The manufacturing system consists of two stages: machining and assembly. At the machining stage, multiple identical machines produce parts. These parts are then assembled at the assembly stage to form customized products. The products to be produced in the system are characterized by their assembly sequences that are represented by different digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of machines at the machining stage. The scheduling problems discussed in this paper have not been solved in the literature. The originality of the paper lies in defining and formulating the problems in the context of agile manufacturing and developing optimal and near-optimal for solving them. The heuristic algorithm solves the scheduling problem in two steps. First, an optimal aggregate schedule is determined by solving a two-machine flowshop problem. Next, the optimal aggregate schedule is decomposed by solving a simple integer programming formulation model. The computational experiment shows that the heuristics provide optimal and near-optimal solutions to the scheduling problems.

Multidisciplinary design selection considers simultaneously multiple design criteria across different disciplines. After identifying a set of design options, a designer will compare the design options and select the preferred design option from among the set. The scenario to be considered in this research assumes that design options are generated when designers intend to design a part or a product. They face the decision of selecting appropriate design options that minimize the design cost and improve performance of manufacturing system. In this paper, a framework for incorporating manufacturing system performance in optimal design selection is proposed. This general framework is then applied to a case where tradeoff between design cost and makespan of aggregate schedule in an automated manufacturing system has to be performed for optimal design selection. In particular, efficient algorithms for determining the makespan of optimal aggregate schedule are developed and the algorithms are incorporated into an optimal design selection scheme. The optimal design selection is formulated as a non-linear mixed integer programming model. An example of universal joint design selection is used to illustrate the methodology developed in the paper.Significance: Decision making in engineering design often requires considering multiple criteria across different disciplines. This paper presents a multi attribute utility based design selection scheme that can be used in design automation for manufacturing system performance improvement.

This paper aims to take stock of the recent research literature on application of wavelets toequipment health diagnosis. First, a brief review of wavelet basics is presented. This is followedby an examination and classification of the existing literature. Next, an extensive discussion ofthe research issues with reference to significant research papers is presented. Finally, aftersummarizing the survey, a set of general guidelines for future applications of wavelets toequipment health diagnosis is outlined.