AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Adaptive decision support for suggesting a machine tool maintenance strategy: from reactive to preventative

Purpose -- To produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach -- A maintenance cost estimation model is utilised within the research and development of this decision support system. An empirical-based methodology is pursued and validated through case study analysis. Findings -- A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case-study, a 28% reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications -- The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value -- The paper presents an adaptive decision support system to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique

Feasibility analysis of using special purpose machines for drilling-related operations

This work focuses on special purpose machine tools (SPMs), providing a modular platform for performing drilling-related operations. One of the main challenges in using SPMs is selecting the most appropriate machine tool among many alternatives. This thesis introduces a feasibility analysis procedure developed to support decision-making through the assessment of the strengths and limitations of SPMs. To achieve this, technical and economic feasibility analyses, a sensitivity analysis, and an optimisation model were developed and a case study was provided for each analysis. The results indicated that although technical feasibility analysis leads decision-makers to select a feasible machine tool, complementary analyses are required for making an informed decision and improving profitability. Accordingly, a mathematical cost model was developed to perform economic and sensitivity analyses and investigate the profitability of any selected SPM configuration. In addition, an optimisation procedure was applied to the cost model in order to investigate the effect of process parameters and the SPM configuration on the decision-making. Finally, the developed analyses were then integrated into a model in a proper sequence that can evaluate whether the SPM is appropriate for producing the given part and achieving higher productivity. To validate this integrated model three different case studies were presented and results were discussed. The results showed that the developed model is a very useful tool in assisting manufacturers to evaluate the performance of SPMs in comparison with other alternatives considered from different perspectives

Modelling flexible manufacturing systems through discrete event simulation

As customisation and product diversification are becoming standard, industry is looking for strategies to become more adaptable in responding to customer’s needs. Flexible manufacturing systems (FMS) provide a unique capability where there is a need to provide efficiency through production flexibility. Full potential of FMS development is difficult to achieve due to the variability of components within this complex manufacturing system. It has been recognised that there is a requirement for decision support tools to address different aspects of FMS development. Discrete event simulation (DES) is the most common tool used in manufacturing sector for solving complex problems. Through systematic literature review, the need for a conceptual framework for decision support in FMS using DES has been identified. Within this thesis, the conceptual framework (CF) for decision support for FMS using DES has been proposed. The CF is designed based on decision-making areas identified for FMS development in literature and through industry stakeholder feedback: set-up, flexibility and schedule configuration. The CF has been validated through four industrial simulation case studies developed as a part of implementation of a new FMS plant in automotive sector. The research focuses on: (1) a method for primary data collection for simulation validated through a case study of material handling robot behaviour in FMS; (2) an approach for evaluation of optimal production set-up for industrial FMS with DES; (3) a DES based approach for testing FMS flexibility levels; (4) an approach for testing scheduling in FMS with the use of DES. The study has supported the development of systematic approach for decision making in FMS development using DES. The approach provided tools for evidence based decision making in FMS

An integrated framework for developing generic modular reconfigurable platforms for micro manufacturing and its implementation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The continuing trends of miniaturisation, mass customisation, globalisation and wide use of the Internet have great impacts upon manufacturing in the 21st century. Micro manufacturing will play an increasingly important role in bridging the gap between the traditional precision manufacturing and the emerging technologies like MEMS/NEMS. The key requirements for micro manufacturing in this context are hybrid manufacturing capability, modularity, reconfigurability, adaptability and energy/resource efficiency. The existing design approaches tend to have narrow scope and are largely limited to individual manufacturing processes and applications. The above requirements demand a fundamentally new approach to the future applications of micro manufacturing so as to obtain producibility, predictability and productivity covering the full process chains and value chains. A novel generic modular reconfigurable platform (GMRP) is proposed in such a context. The proposed GMRP is able to offer hybrid manufacturing capabilities, modularity, reconfigurablity and adaptivity as both an individual machine tool and a micro manufacturing system, and provides a cost effective solution to high value micro manufacturing in an agile, responsive and mass customisation manner. An integrated framework has been developed to assist the design of GMRPs due to their complexity. The framework incorporates theoretical GMRP model, design support system and extension interfaces. The GMRP model covers various relevant micro manufacturing processes and machine tool elements. The design support system includes a user-friendly interface, a design engine for design process and design evaluation, together with scalable design knowledge base and database. The functionalities of the framework can also be extended through the design support system interface, the GMRP interface and the application interface, i.e. linking to external hardware and/or software modules. The design support system provides a number of tools for the analysis and evaluation of the design solutions. The kinematic simulation of machine tools can be performed using the Virtual Reality toolbox in Matlab. A module has also been developed for the multiscale modelling, simulation and results analysis in Matlab. A number of different cutting parameters can be studied and the machining performance can be subsequently evaluated using this module. The mathematical models for a non-traditional micro manufacturing process, micro EDM, have been developed with the simulation performed using FEA. Various design theories and methodologies have been studied, and the axiomatic design theory has been selected because of its great power and simplicity. It has been applied in the conceptual design of GMRP and its design support system. The implementation of the design support system is carried out using Matlab, Java and XML technologies. The proposed GMRP and framework have been evaluated through case studies and experimental results

Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

Integrated inpection of sculptured surface products using machine vision and a coordinate measuring machine

In modem manufacturing technology with increasing automation of manufacturing processes and operations, the need for automated measurement has become much more apparent. Computer measuring machines are one of the essential instruments for quality control and measurement of complex products, performing measurements that were previously laborious and time consuming. Inspection of sculptured surfaces can be time consuming since, for exact specification, an almost infinite number of points would be required. Automated measurement with a significant reduction of inspected points can be attempted if prior knowledge of the part shape is available. The use of a vision system can help to identify product shape and features but, unfortunately, the accuracy required is often insufficient. In this work a vision system used with a Coordinate Measuring Machine (CMM), incorporating probing, has enabled fast and accurate measurements to be obtained. The part features have been enhanced by surface marking and a simple 2-D vision system has been utilised to identify part features. In order to accurately identify all parts of the product using the 2-D vision system, a multiple image superposition method has been developed which enables 100 per cent identification of surface features. A method has been developed to generate approximate 3-D surface position from prior knowledge of the product shape. A probing strategy has been developed which selects correct probe angle for optimum accuracy and access, together with methods and software for automated CMM code generation. This has enabled accurate measurement of product features with considerable reductions in inspection time. Several strategies for the determination and assessment of feature position errors have been investigated and a method using a 3-D least squares assessment has been found to be satisfactory. A graphical representation of the product model and errors has been developed using a 3-D solid modelling CAD system. The work has used golf balls and tooling as the product example

Machining conditions optimization, tool allocation, and tool magazine arrangement on a CNC turning center

Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references.In the view of the high investment and tooling cost of a CNC machining center, the cutting and idle times should be optimized by considering the tool consumption and the non-machining time components for an effective utilization. Therefore, it is necessary to develop a new module as a part of the overall computer-aided process planning system, which will improve both the system effectiveness and provide consistent process plans. In this thesis, it is proposed to build a detailed mathematical model for the operation of a CNC lathe which will include the system characterization, the cutting conditions and tool life relationship, and related constraints. Then an algorithm is presented to find tool-operation assignments, machining conditions, appropriate tool magazine organization, and an operations sequence which will result in a minimum production cost.Avcı, SelçukM.S