Determining the number of kanbans for dynamic production systems: An integrated methodology

<span>Just-in-time (JIT) is a management philosophy that reduces the inventory levels and eliminates manufacturing wastes by producing only the right quantity at the right time. A kanban system is one of the key elements of JIT philosophy. Kanbans are used to authorize production and to control movement of materials in JIT systems. In Kanban systems, the efficiency of the manufacturing system depends on several factors such as number of kanbans, container size etc. Hence, determining the number of kanbans is a critical decision in Kanban systems. The aim of this study is to develop a methodology that can be used in order to determine the number of kanbans in a dynamic production environment. In this methodology, the changes in system state is monitored in real time manner, and the number of the kanbans are dynamically re-arranged. The proposed methodology integrates simulation, neural networks and Mamdani type fuzzy inference system. The methodology is modelled in simulation environment and applied on a hypothetic production system. We also performed several comparisons for different control policies to show the effectiveness of the proposed methodology.</span

A multi-criteria decision making procedure based on neural networks for kanban allocation

In this study, we proposed a methodology for determining optimal number of kanbans for each station in a JIT manufacturing system. In this methodology, a backpropagation neural network is used in order to generate simulation meta-models, and a multi-criteria decision making technique (TOPSIS) is employed in order to evaluate kanban combinations with respect to the relative importance of the performance measures. The proposed methodology is applied to a case problem and the results are presented. The results show that the methodology can solve this type of problems effectively and efficiently. © Springer-Verlag Berlin Heidelberg 2006

Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology

In this study, we proposed a methodology for determining the design parameters of kanban systems. In this methodology, a backpropagation neural network is used in order to generate simulation meta-models, and a multi-criteria decision making technique (TOPSIS) is employed to evaluate kanban combinations. In order to reflect the decision maker's point of view, different weight structures are used to find the optimum design parameters. The proposed methodology is applied to a case problem and the results are presented. We also performed several experiments on different types of problems to show the effectiveness of the methodology

Portfolio selection problem: a comparison of fuzzy goal programming and linear physical programming

Investors have limited budget and they try to maximize their return with minimum risk. Therefore this study aims to deal with the portfolio selection problem. In the study two criteria are considered which are expected return, and risk. In this respect, linear physical programming (LPP) technique is applied on Bist 100 stocks to be able to find out the optimum portfolio. The analysis covers the period April 2009- March 2015. This period is divided into two; April 2009-March 2014 and April 2014 – March 2015. April 2009-March 2014 period is used as data to find an optimal solution. April 2014-March 2015 period is used to test the real performance of portfolios. The performance of the obtained portfolio is compared with that obtained from fuzzy goal programming (FGP). Then the performances of both method, LPP and FGP are compared with BIST 100 in terms of their Sharpe Indexes. The findings reveal that LPP for portfolio selection problem is a good alternative to FGP

Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure

Supplier selection and evaluation is increasingly seen as a strategic issue for companies. Unlike the past, an emerging trend is to select suppliers where a long-term relationship is desired and supplier involvement in product development is required and to sort suppliers into categories based oil performances. This paper describes a supplier evaluation and management methodology for strategic sourcing, in which suppliers are assessed considering supplier's co-design capabilities and categorized based oil overall performances, potential reasons for differences in performance of supplier groups are identified, and performances of the suppliers are improved by applying supplier development programs. A new multicriteria sorting method based oil the PROMETHEE methodology is also introduced. By means of a strategic supplier selection example, we demonstrate that out, methodology is a flexible and responsive decision-making tool for assessing strategic suppliers. (c) 2006 Elsevier B.V. All rights reserved

A multicriteria sorting procedure for financial classification problems: The case of business failure risk assessment

This paper presents a new multicriteria sorting procedure in financial classification problems, based on the methodological framework of PROMETHEE method. The proposed procedure, called as PROMSORT, is applied to the business failure risk problem and compared to PROMETHEE TRI and ELECTRE TRI. The proposed methodology also identifies the differences in performances across risk groups, and assists in monitoring the firms' financial performances. The results showed that the proposed procedure can be considered as an effective alternative to existing methods in financial classification problems. © Springer-Verlag Berlin Heidelberg 2005