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.     

Towards An Intelligent Maintenance Optimization System

This paper is concerned with the evaluation and enhancement of the maintenance routines of large and complex technical systems. An ‘intelligent decision support system’ approach is suggested as a method for overcoming the difficulties associated with the scale, variability and changeability of such systems. The main features of the proposed intelligent maintenance optimization system (IMOS) are identified. A prototype system is then presented and its main mathematical models of maintenance are introduced. Some sample test data and the results produced from them are presented. Other aspects discussed include dealing with censored data, optimization criteria, the development of a maintenance model selection rule base, the recognition of data patterns and models' robustness. Results of IMOS system validation against expert advice have shown a high measure of consistency.     

Generalized stationary models for scheduling single and multiple preventive maintenance routines

This paper is concerned with the development and applicationof stationary models for scheduling single and multiple preventivemaintenance (PM) situations focusing on issues of model implementation.The first part of the paper deals with the practical implementationof the basic single PM scheduling model based on the renewalprocess. The main practical difficulty is lifetime-distributionselection for small data sets which is typical in PM situations.Thus a sensitivity analysis of optimal PM interval to selectedlife distributions following PM and failures (corrective maintenance)is carried out. It has been found that the selected pair ofdistributions using AIC criteria as well as the Weibull–Weibullfitted pair have the smallest availability loss in estimatingthe optimal PM interval. The second part of this paper is concernedwith modelling multi-PM situations—something which hasreceived very little attention in the literature despite itsfrequent implementation in real life. A multi-PM model basedon the renewal process is discussed. The model assumes a multi-PMinterval which is an integer multiple of the single PM intervalsand different renewal functions following each type of PM. Theprocedure of model implementation is discussed through numericalexample.     

Intelligent management systems in operations: a review

Operations management is an area that has recently started to benefit from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review ‘intelligent’ decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the field of artificial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most significant and more recent publications.     

The development of a hybrid intelligent maintenance optimisation system (HIMOS)

This paper reports on the development of a hybrid intelligent maintenance optimisation system (HIMOS) for decision support. It is a follow-up to an earlier paper published in the Journal of the Operational Research Society in 1995. Both papers refer to systems where there are very many components which may break down independently. When a component breaks down, corrective action (CO) is required. The problem is to determine the optimal maintenance policy, essentially the frequency of preventive maintenance (PM) which minimises the sum of down time due to PM and CO.HIMOS, like its predecessor IMOS, uses an ‘intelligent’ decision support system to carry out an automated analysis of the maintenance history data. Maintenance data are presented to the system and the most suitable mathematical model from a model-base is identified utilising a hybrid knowledge/case based system (KBS/CBR). Thus initially a rule base is applied to select a model, as in the case of IMOS. If no model is matched, the system reverts to its historical case-base to match the current case with a similar case that has been previously modelled. This double reasoning adds to the system's true learning capabilities (intelligence) and increases the rate of success of model selection. A prototype system is written in Visual Basic® for an IBM compatible PC. The study results include optimal PM intervals for a sample of industrial data sets. The results of the validation exercise of HIMOS against expert advice has shown that the system functions satisfactorily.     

Optimization of the Daily Production Rates for an Offshore Oilfield

This paper presents a model to assist in setting the daily production rates for an offshore oilfield to achieve a quarterly production target. The produced crude oil is frequently accompanied by the production of gas, which has much lower value. Since there are environmental limits to the amount of gas that can be flared to waste, problems on gas-processing can very quickly limit oil output. With the commencement of natural decline in many oilfields in the North Sea, the loss of crude output as a result of gas constraints cannot be compensated in the same planning period. This makes the ultimate output, and hence the reward, more sensitive to the problems associated with gas-processing. The model uses a stochastic dynamic programming algorithm to maximize the financial reward. Gas-related limiting factors and the oil and gas plant downtime are considered. The user is allowed to interrogate the model and interact with it when major unforeseen restrictions are imposed on gas capacities. The output includes the optimal production rate and probability and cost of not achieving the target.     

A Systems Model of Underwater Inspection Operations

The paper reports on the development of a systems model of the sub-sea inspection of an offshore structure in the North Sea. Diving practices, weather conditions, topside management procedures and inspection techniques are incorporated into the model, along with support-vessel capabilities. The model provides a research tool capable of indicating and quantifying ways of increasing the efficiency of inspection diving. Characteristic distributions of many of the variables involved are presented, along with an indication of the consequences of changes in decision and operational parameters. The study indicates that a significant increase in sub-sea inspection efficiency will come from synchronization of topside and sub-sea activities.     

Analysis of supply contracts from a supplier's perspective

Hexin Wang and Khairy A. H. Kobbacy Centre for Operational Research and Applied Statistics, University of Salford, Salford, M5 4WT, UK Email: w.wang{at}salford.ac.uk Received on 9 May 2006. Accepted on 22 December 2006. Incentive structure and demand uncertainty may cause supplychains to operate at a low efficiency. Therefore, many supplycontracts are employed in practice to improve the performanceof supply chains, i.e. to benefit all members involved in thechain. Supply chain contracts provide mechanisms to change theincentive structures of the supply chain members so that theirdecisions can improve the supply chain efficiency, while alsoprotect their own interests. It is important to understand theimpacts of supply contracts and their differences from a supplier'sperspective, since it is often the supplier who initiates asupply contract. This paper reports on a comprehensive analysisof supply contracts from a supplier's perspective. Six commonlyused supply contracts are analysed and the contract parametersare optimized to maximize the supplier's expected profit withconsideration to improve the retailer's profit. This case hasnot been thoroughly investigated in literature to date. Therisk-sharing mechanism and the division of the increased profitbetween the retailer and supplier for some of the contractsare also investigated in detail.     

On the evolution of an intelligent maintenance optimization system

In this paper the author reviews the development of an intelligent maintenance optimization system over the past 16 years. The paper starts with discussion of the initial motivation behind developing the system and the designs of the early versions of a computer program to access maintenance history data and provide an analysis. The concept behind this system was gradually developed to incorporate a rule base for the selection of a suitable model for preventive maintenance (PM) scheduling and then to a fully developed knowledge-based system for decision support. The need to incorporate case-based reasoning thus creating a hybrid system that can learn with use in addition to using elicited knowledge from experts is discussed. The experience with system validation with two versions of the system is analysed. The paper also reviews the extensive fundamental work on developing appropriate PM models that can deal with real data patterns. Finally, the scope for future development is presented.