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A collaborative knowledge management framework

A collaborative knowledge management framework for supply chains: A UML-based model approach, information technologies imply a strong modelling approach to support the complexities involved in the supply chain management process. Since supply chains are made up of nodes, which need to reach common agreements in order to fulfil their own requirements, the information technologies-based model is an adequate tool to support the modelling of the knowledge management process, mainly in a collaborative context. In the most general cases, collaborative activities also imply a distributed decision-making process which involves several supply chain nodes. Because collaboration in supply chains implies information exchanges among the nodes, the framework proposal is oriented to support knowledge management by considering the information sharing process as one of the main aspects. Therefore by means of a literature review and by also considering the deficiencies of existing proposals, a collaborative knowledge management UML-based framework is herein proposed which encompasses both the conceptual and general perspectives of the supply chain management process. Finally, the proposed framework summarises existing knowledge by not only fulfilling but also enriching each of its components with the modeller’s own knowledge
The supply chain (SC) management process identifies goals, objectives and outlining policies, strategies and controls for its effective and efficient implementation (Smirnov & Chandra, 2000). In addition, Dubey, Veeramani and Gutierrez (2002) establish that the SC considers a set of approaches utilised to efficiently integrate companies, which compose the workgroups, in order to correctly produce and distribute customer requirements. In this context, it is possible to support knowledge management (KM) in a collaborative manner by sharing the right information among the players of these workgroups. Then in order to train people, by not only forming different workgroups, but also simultaneously working on different tasks, two types of sessions are used in the collaborative engine (which can also be understood as the collaborative process), these being user sessions to identify online users and collaboration sessions to represent a group of customers involved in the same collaboration (Ni, Lu, Yarlagadda, & Ming, 2006) which enhances KM. Then in order to fulfil the supply chain management requirements to support collaboration among its members (companies or nodes), this paper will consider the information flow (information sharing or transfer) among them in order to support and study the KM process in the SC. We refer readers to Aurum, Daneshgara, Warda (2008) in order to carry out an in-depth study on the knowledge management basis.
Recently, many studies have been done in the collaborative supply chain field and on its modelling implication. In this context, some suggested literature in this field are: Bafoutsou and Mentzas (2002); Saad, Jones, & James (2002); Gunasekaran and Ngai (2005); Meixell and Gargeya (2005); Fawcett, Osterhaus, Magnan, Brau and McCarter (2007); Fawcett, Magnan and McCarter (2008) and van der Vaart and van Donka (2008). In light of this, a literature review considering the most relevant papers on this matter has been done with a view to finding the main aspects and tools considered to support the KM in the SC under a collaborative approach. Thus, our review shows that the main aspects are related to the following subjects: new development software tools (ST) to support the KM process; decision-making processes; document management and SC management. Regarding the ST, Núñez and Núñez (2005) propose a classification to detect and understand their common applications. As a result, the most considered aspects found were those related to the decision-making process, which mainly uses information analysis tools. Conversely, the least considered aspect is that regarding the SC as a whole. This is related to the fact that KM, in a SC context, tends to be modelled as a centralised concept in relation to each workgroup element. Therefore, this paper proposes a framework which considers and supports collaboration among the SC nodes that is supported by considering a modelling tool approach.
The rest of the paper has been arranged as follows. Section 2 shows an overview related to the main topics to be considered in the framework. Section 3 presents an analysis of Section 2 in order to collect the main aspects and concepts that will support the framework achievement. Section 4 proposes a collaborative knowledge management UML-based framework to support collaboration in the supply chain management process. Finally, Section 5 provides the conclusions and further research

 

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Mathematical programming solver

Variations in the efficiency of a mathematical programming solver according to the order of the constraints in the model, It is well-known that the efficiency of mixed integer linear mathematical programming depends on the model (formulation) used. With the same mathematical programming solver, a given problem can be solved in a brief calculation time using one model but requires a long calculation time using another. In this paper a new, unexpected feature to be taken into account is presented: the order of the constraints in the model can change the calculation time of the solver considerably. For a test problem, the Response Time Variability Problem (RTVP), it is shown that the ILOG CPLEX 9.0 optimizer returns a ratio of 17.47 between the maximum and the minimum calculations time necessary to solve optimally 20 instances of the RTVP, according to the order of the constraints in the model. It is shown that the efficiency of the mixed integer linear mathematical programming depends not only on the model (formulation) used, but also on how the information is introduced into the solver.

Integer linear programming is a classical tool in practical operations research that can  be  applied  to  many  problems  (e.g.  Salkin  and  Mathur,  1989)  very  effectively (e.g.  in  Corominas et  al.  2008  it  was  applied  to  solve  a motorcycle assembly line and in Pastor et al. 2008 it was applied to solve the case of a woodturning company). The technique is well-known and reliable but it must be handled carefully. It is known that its efficiency depends on the model (formulation) used: with the same mathematical programming solver, a given problem can be solved in a brief calculation time using one model but requires a long calculation time using another. Therefore, as stated by Billionnet (1999, p. 105): “Given a problem with a few dozen of variables one cannot be confident that integer programming will work until it has been tried on realistic instances”.
Several techniques have been used to improve the efficiency of this tool. A standard technique is the elimination of symmetries: Margot (2007), for example, presents techniques for handling symmetries in integer linear programs in which variables can take integer values, which extends previous research that dealt exclusively with binary variables. Tightening the definition of the data and introducing redundant constraints have also provided good results: Corominas et al. (2006), for example, demonstrated the importance of modelling, as well as the huge impact that redundant constraints and the elimination of symmetries have on the effectiveness of MILPs for solving the Response Time Variability Problem (RTVP), an NP-hard scheduling problem (Corominas et al., 2007); the total computation time taken to solve 20 instances dropped from 38,603 to 398 seconds and its practical limit to obtaining optimal solutions was increased from 25 to around 40 units to be scheduled.
This paper argues that the order of the constraints in a model can have a considerable effect on the time that a mathematical programming solver takes to solve a problem optimally. Lets us, for example, take three sets of constraints (A, B and C) of a problem to be solved. To introduce the sets of constraints in the mathematical programming solver in the order A-B-C, A-C-B, B-A-C, B-C-A, C-A-B and C-B-A is not indifferent and can cause its efficiency to vary considerably. This new, unexpected feature that must be taken into account in mathematical programming has not been presented previously (to the best of the authors’ knowledge).For an integer programming formulation of a test problem, RTVP, it is shown that the ILOG CPLEX 9.0 optimizer returns a ratio of 17.47 between the maximum and minimum calculation times needed to solve optimally 20 instances of the RTVP, according to the order of the constraints in the model: with one permutation of the sets of constraints the solver takes only 335 seconds, whereas with another one it takes 5,851 seconds. It is shown that the efficiency of the mixed integer linear mathematical programming depends not only on the model (formulation) used, but also on how the information is introduced into the solver.
The rest of the paper is organized as follows. Section 2 presents the RTVP. Section 3 describes the computational experiment carried out. Finally, Section 4 is devoted to the conclusions.

 

#Fakultas Psikologi Medan #Fakultas Teknik Medan #Fakultas Pertanian Medan #Fakultas Sain dan Teknologi Medan #Fakultas Hukum Medan #Fakultas Fisipol Medan #Fakultas Ekonomi Medan #Pascasarjana Medan #Sipil Terbaik #Elektro Terbaik #Mesin Terbaik #Arsitektur Terbaik #Industri Terbaik #Informatika Terbaik