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

Approach Tolerance in the Assemblies of Evolutionary Hybrid Prototypes

A new answer is proposed to replace the traditional “one shot” prototype (manufactured in one piece with one process): the hybrid rapid prototype. It is used to highly reduce time, cost and increase reactivity during the development times of new products. The part is decomposed in several components which can quickly be changed and can be manufactured with a process the most adapted. The main objective of the presented method is to propose an available technological assembly between the different components of the part in the respect of technological and topological function, and initial tolerance. Using a graph of representation, fuzzy logic and a tolerance point of view, some entities are associated with a CIA (Assembly Identity Card) in accordance with evolutionary and manufacturing analysis. This work will be illustrated by an industrial tooling for plastic injection.Mechanical Engineerin

Simulation based performance analysis of an end-of-Aisle automated storage and retrieval system

This paper presents and discusses simulation of an End-of-Aisle automated storage and retrieval system, using FLEXSIM 6. The objective of the simulation model is to analyze and compare results of different control policies and physical designs. The performance measures considered for the evaluation of each control policy and layout combination are the total travel time of the crane and the number of storage and retrieval operations performed. The experiments set up and the corresponding results are discussed

Review of research in feature-based design

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security