Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Knowledge discOvery And daTa minINg inteGrated (KOATING) Moderators for collaborative projects

A major issue in any multidiscipline collaborative project is how to best share and simultaneously exploit different types of expertise, without duplicating efforts or inadvertently causing conflicts or loss of efficiency through misunderstanding of individual or shared goals. Moderators are knowledge based systems designed to support collaborative teams by raising awareness of potential problems or conflicts. However, the functioning of a Moderator is limited by the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update their knowledge about team members. This enables the reuse of discovered knowledge from operational databases within collaborative projects. The integration of knowledge discovery in database (KDD) techniques into the existing Knowledge Acquisition Module of a moderator enables hidden data dependencies and relationships to be utilised to facilitate the moderation process. The architecture for the Universal Knowledge Moderator (UKM) shows how Moderators can be extended to incorporate a learning element which enables them to provide better support for virtual enterprises. Unified Modelling Language diagrams were used to specify the ways to design and develop the proposed system. The functioning of a UKM is presented using an illustrative example

Designing multi-period supply chain network considering risk and emission: a multi-objective approach

This research formulates a multi-objective problem (MOP) for supply chain network (SCN) design by incorporating the issues of social relationship, carbon emissions, and supply chain risks such as disruption and opportunism. The proposed MOP includes three conflicting objectives: maximization of total profit, minimization of supply disruption and opportunism risks, and minimization of carbon emission considering a number of supply chain constraints. Furthermore, this research analyses the effect of social relationship levels between different tiers of SCN on the profitability, risk, and emission over the time. In this regard, we focus on responding to the following questions. (1) How does the evolving social relationship affect the objectives of the supply chain (SC)? (2) How do the upstream firms’ relationships affect the relationships of downstream firms, and how these relationships influence the objectives of the SC? (3) How does the supply disruption risk interact with the opportunism risk through supply chain relationships, and how these risks affect the objectives of the SC? (4) How do these three conflicting objectives trade-off? A Pareto-based multi-objective evolutionary algorithm–non-dominated sorting genetic algorithm-II (NSGA-II) has been employed to solve the presented problem. In order to improve the quality of solutions, tuning parameters of the NSGA-II are modulated using Taguchi approach. An illustrative example is presented to manifest the capability of the model and the algorithm. The results obtained evince the robust performance of the proposed MOP

Part selection and operation-machine assignment in FMS environment: A genetic algorithm with chromosome differentiation based methodology

Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, i.e. part type selection and operation allocation on machines. The combination of these problems is termed the machine-loading problem, which is a well-known complex puzzle and treated as a strongly NP-hard problem. In this research, a machine-loading problem has been modelled, taking into consideration several technological constraints related to the flexibility of machines, availability of machining time, tool slots, etc., while aiming to satisfy the objectives of minimizing the system unbalance, maximizing throughput, and achieving very good overall FMS utilization. The solution of such problems, even for moderate numbers of part types and machines, is marked by excessive computation complexities and therefore advanced random search and optimization techniques are needed to resolve them. In this paper, a new kind of genetic algorithm, termed a genetic algorithm with chromosome differentiation, has been used to address a well-known machine-loading problem. The proposed algorithm overcomes the drawbacks of the simple genetic algorithm and the methodology reported here is capable of achieving a better balance between exploration and exploitation and of escaping from local minima. The proposed algorithm has been tested on ten standard test problems adopted from literature and extensive computational experiments have revealed its superiority over earlier approaches

Enterprise competence organization schema: publishing the published competences

Competence is a standardized way to define the profile of an enterprise. Understanding and auditing competences acquired, required, and desired by a company and further representing them in a structured manner is a beneficial step for enhancing the company's performance. Ontology is emerging as an effective tool to structure competences for comprehensive and transportable machine understanding. In the present paper, ECOS (Enterprise Competence Organization Schema) is presented as a mechanism to capture enterprise competence in a manner understandable by computers. The objective behind this concept is to create a web of machine-readable pages describing basic information and competences of enterprises with sets of interconnected data and semantic models. The ECOS ontology captures enterprise competences using a consistent and comprehensive list of concepts and vocabulary and converts them into a semantic web resource using the Web Ontology Language (OWL). The novel concept of an ECOS-card and ECOS-form is proposed and used for developing and publishing enterprise competences. Examples from real-life enterprise applications of ECOS are also shown in the paper

Multi-objective approach for sustainable ship routing and scheduling with draft restrictions

We propose a multi-objective optimization model, which integrates different shipping operations to address the environmental sustainability and safety challenges associated with complex, practical and real-time maritime transportation problems. We formulate a mixed integer non-linear programming (MINLP) model, considering routing and scheduling of ships, time window concept regarding ports’ high tidal conditions, discrete planning horizon, loading/unloading operations, carbon emissions, and draft restrictions to maintain vessel’s safety in ports. The novelty of our research lies in (1) incorporating environmental sustainability in the optimization model by defining the relationship between fuel consumption and vessel speed to estimate the fuel consumption and carbon emissions from each vessel; (2) considering the time window to improve port’s service level by imposing penalty charges for early arrivals of vessels and for time window violation; (3) depicting the relationship between the number of containers carried by a ship with its maximum allowable draft restriction and tonnage of containerized cargo on a ship per centimetre of the draft; (4) applying two algorithms - Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) - to solve the mathematical model. Computational experiments are performed based on the practical problems of an international shipping company. Sensitivity analysis is carried out by varying the tonnage of containerized cargo loaded on the vessel per centimetre of the draft for different ports. Results associated with ship route, vessel speed, fuel consumption (tons per day), and carbon emissions rate are presented to provide an idea about the output of the mathematical model

A carbon market sensitive optimization model for integrated forward–reverse logistics

Globalized supply chains, volatile energy and material prices, increased carbon regulations and competitive marketing pressure for environmental sustainability are driving supply chain decision makers to reduce carbon emissions. Enterprises face the necessity and the challenge of implementing strategies to reduce their supply chain environmental impact in order to remain competitive. One of the most important strategic issues in this context is the configuration of the logistics network. The decision concerning the design of an optimal network of the supply chain plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. Therefore, the logistics network should be designed in a way that it could reduce both the cost and the carbon footprint across the supply chain. In this context, this research proposes a quantitative optimization model for integrated forward–reverse logistics with carbon-footprint considerations, by integrating the carbon emission into a quantitative operational decision-making model with regard to facility layout decisions. The proposed research incorporates carbon emission parameters with various decision variables and modifies traditional integrated forward/reverse logistics model into decision-making quantitative operational model, minimizing both the total cost and the carbon footprint. The proposed model investigates the extent to which carbon reduction requirements can be addressed under a particular set of parameters such as customer demand, rate of return of products etc., by selecting proper policy as an alternative to the costly investment in carbon-reducing technologies. To solve the quantitative model, this research implements a modified and efficient forest data structure to derive the optimal network configuration, minimizing both the cost and the total carbon footprint of the network. A comparative analysis shows the outperformance of the proposed approach over the conventional Genetic Algorithm (GA) for large problem sizes

Knowledge management based collaboration moderator services to support SMEs in virtual organisations

Recent decades have seen a move away from large-scale organisations and supply chains covering all stages of the value chain to smaller, more agile organisations focused on their core competencies and business areas. Whilst competitive advantage can be gained through efficient use of resources and optimisation of products and processes, challenges are also created as companies need to collaborate to provide customers with more complete solutions or greater product functionality. Specialist SMEs can offer vital skills and expertise in this context but may be vulnerable to the high costs of collaboration. Hence, knowledge management services to support collaborations are critical for the effective operation of such SMEs. This paper discusses the role of Collaboration Moderator Services (CMS) as a knowledge management service for SMEs operating in a virtual organisation. One of the key requirements for such SMEs at the pre-creation stage is to quickly identify potential business opportunities and corresponding collaborative partners to form a virtual organisation to capture business opportunities. CMS applies different data and text mining techniques to analyse calls for tender documents based on the competencies and areas of interest stored in the shared information of a collaboration pool. A case study of UK-based SMEs demonstrates the concept of the application of text mining as a knowledge discovery tool, supporting SMEs at the pre-creation stage of a virtual organisation. It has been shown that text mining for CMS can be used to (1) identify possible business opportunities for each SMEs in the collaborative network using text analysis (2) Indicate the possible collaboration between the two SMEs using link analysis and (3) Raise awareness in SMEs by indicating the possible business opportunities and possible collaborating SME partners for multi-enterprise collaborations using a dimensional matrix

Prioritising tendering activities for small to medium-sized enterprises (SMEs)

The tendering process involves high costs, in terms of time and effort and therefore it is not desirable or sustainable to tender for projects unless the chances of winning are good. Small to medium sized companies do not have enough human resources to enable staff to be dedicated to the job of tending and monitoring market opportunities, and hence company officials have to fit in this job around their usual duties. This paper proposes a knowledge discovery and mining approach to assist the tender offer selection process. Knowledge discovery and mining assures identification and matching of the areas of interest and other criteria of selection of the tender offers, while multi–criteria decision making supports the consideration of other relevant factors for selection