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

Generating Knowledge in Maintenance from Experience Feedback

Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

A Generic Library of Problem Solving Methods for Scheduling Applications

In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification. Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse. Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications

A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Investigating project management maturity in the ship repair industry of South Africa, a case study

Purpose Ship repair companies that employ a greater degree of the project management process functions enjoys greater business value and business success. Such certainty within a business' structure supports its longer-term sustainability and improves its growth potential. This research seeks to address the problem relating to the inability of ship repair companies to continually achieve targeted project estimates because of a lower levels of project management maturity. Research Design The research is exploratory in nature as the response in term of the selected maturity model used, seeks to understand the level of application of the ten PMI knowledge areas and how deeply engrained the function is adopted in the performance and within the organisation within the ship repair industry. The principle of communities of practise was adopted for this study which implies that the response and the data obtained will be based on the information shared by the respondents on their insights, experience, knowledge, and common interests within the industry. Findings - This study found an active, informal, and partially structured project management function present within the Western Cape's ship repair industry. The study further found the actual project maturity level at an average of 3.24, in line expectations for the industry and following the same direction, though at a lower level as similar research done on South Africa's IT, mining, engineering, and construction industries. Research Limitations - The study is limited to the Western Cape province's ship repair industry and based on the views of the industry's community of practise as indicator of its project management maturity

Reforming Policies for Small and Medium-Sized Enterprises in Indonesia

Including micro firms, SMEs are responsible for more than 97 percent of total employment in Indonesia and represent 99 percent of all firms. The Indonesian government has tried to assist SMEs through programmes, such as subsidised credit, one-stop shops to lower business registration costs and government-sponsored trade fairs. However, there is little evidence on how effective these programmes are or on ways to improve government policies aimed at helping SMEs. We conducted structured interviews with 192 firms across five provinces in Indonesia to investigate the constraints that firms face and how existing programmes do – or do not – help reduce these constraints. We conclude the report with policy recommendations targeted at the Indonesian government and other stakeholders, focusing on the importance of credit and on the need to remove information barriers

(Re)ordering and (dis)ordering of street trade:the case of Recife, Brazil

Informal urban street trade is a prevalent feature across the Global South where much of the production and/or buying and selling of goods and services is unregulated. For this reason, local authorities have historically seen it as backward, inefficient and detrimental to the development of urban areas and have thus developed formalisation programmes aimed to control and ultimately make it disappear. Critics argue that the design and implementation of these programmes can marginalise and disempower informal traders as it acts against the traders’ livelihoods and long-established practices they have developed for decades. This research speaks to these concerns and aims to investigate how informal urban street trade manages to continuously reproduce itself despite formalising efforts to make it vanish. The study follows a post-structuralist approach informed by post-development sensibilities (Escobar, 2011). The purpose is two-fold. First, to critically investigate the implications of imposed power-knowledge essentialism inherent to formalisation processes (Foucault, 1980). Second, to analyse the ways in which cultural and socioeconomic development is enacted through the daily assembling of informal urban street trade (Farías and Bender, 2012; McFarlane, 2011). The research offers a thick ethnographic inquiry, conducted over a one year-long period (2014-2015) in the urban centre of Recife, Northeast capital of Pernambuco state, Brazil. Recife is a particularly rich site to investigate these issues as informal urban street trade has historically been pervasive of its squares and streets and the municipally has in place a formalisation programme aimed to gather information about traders, license them and relocate them into purposefully-built facilities. The ethnographic inquiry focused on the practices, knowledges, materials and technologies associated with the daily work of both informal traders, selling on the streets, and governing officials implementing the formalisation programme, both on the streets and on the City Council office. Primary data collection was gathered through ethnographic observations and fieldnote diaries enriched with pictures and audio recordings of the day-to-day sensorial experience of informal urban street trade. This was enhanced with informal conversations as well as semi-structured and unstructured interviews with governing bodies’ officials, licenced and unlicensed street traders, formal shop owners, and a diversified set of urban citizens. The thesis highlights that formalisation, through the introduction of regulations, classification schemes and practices of classifying traders through an information system, seeks to establish and expand an individualistic developmentality among all actors. Through this, formalisation aims to shape and normalise their everyday practices to focus on the City Council’s agenda of rendering informal street trade as problematic and turning the solution of formalised trade not only unquestionable, but desirable by all. More problematically, the formalisation programme’s overdetermination of what a socioeconomic order is to be and its imposition of individualising subjectivities to assist in its implementation acts against the traders’ collective and community-based understanding of work and livelihoods which, contrary to the formalisation discourse, greatly benefit the cultural and socioeconomic development of these communities. This is achieved through the traders’ daily assembling of work, value and supply on the streets. The findings reveal that the collective organisation of traders’ work is strongly based on a ‘cooperative ethos’ that is not only efficient in taking advantage of and adapting to the challenging conditions of street markets, but also is key on the ongoing fostering and strengthening of the local community identity. The findings also show that traders, through their tacit knowledge of the best fits between products, services and sites, are key in shaping the valuation of both formal and informal enterprises as well as urban sites thus bolstering the local economy. Lastly, the findings also reveal that, through their interactions with formal and informal supply circuits, street traders are fundamental for the distribution and promotion of local artists and producers thus helping on the support and fostering of local culture. The main contribution of this research is it offers novel empirical and theoretical insights on the ways in which formalisation and informality are performed. It richly reveals the contested nature of development that is negotiated daily between the individualist developmentality imposed by formalisation and the communitarian- based development possibilities which are enacted through informal trading practices. These developmental possibilities are turned invisible by formalisation as classification enforces a strong reading of street trade which is ontologically distant and even contrary to the community-based values which make street trade not only resilient to formalising efforts but also adaptive to the challenging conditions and, more importantly, central to the cultural and socioeconomic development of these communities

Formalisation and use of competencies for industrial performance optimisation : a survey.

For many years, industrial performance has been implicitly considered as deriving from the optimisation of technological and material resources (machines, inventories,...), made possible by centralized organisations. The topical requirements for reactive and flexible industrial systems have progressively reintroduced the human workforce as the main source of industrial performance. Making this paradigm operational requires the identification and careful formalisation of the link between human resource and industrial performance, through concepts like skills, competencies or know-how. This paper provides a general survey of the formalisation and integration of competence-oriented concepts within enterprise information systems and decision systems, aiming at providing new methods and tools for performance management