• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.13-25
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• 2004

In the online shopping environment, consumers suffer from the process of selecting an optimal supplier. Although comparison shopping agent-based web sites and consumers' online community sites support the selection process, they have limitations when considering diverse and dynamic purchase conditions as a whole, which is the cause of additional consumer effort for optimal supplier selection. This study provides a decision support model with a sensitivity analysis for selecting an optimal supplier considering purchase conditions as a whole. It screens suppliers with filtering factors and provides optimal suppliers through a sensitivity analysis from a Quadratic Programming model. We implemented a prototype system and showed that it could be an effective decision support system for selecting the optimal supplier in the online shopping environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.112-119
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• 2017

Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an "if-then" rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers' general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier's performance and eventually influence buyer's production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.155-164
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• 2012

As the global market becomes more competitive, manufacturing industries face relentless pressure caused by a growing tendency of greater varieties of products, shorter manufacturing cycles and more sophisticated customer requirements. Efficient and effective supplier selection and order allocation decisions are, therefore, important decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, delivery date) for economic reasons. This paper attempts to solve optimally the challenging problem of supplier selection and order allocation, taking into consideration the customer flexibility for a manufacturer producing multi-products to satisfy the customers' demands in a multi period planning horizon. A new mixed integer programming model is developed to describe the behavior of the supply chain. The objective is to maximize the manufacturer's total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to solve the problem optimally. This approach differs from a canonical genetic algorithm in three aspects: a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee feasibility of the solutions. The results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance. When compared with applying the canonical genetic algorithm to locate optimal solutions, the average improvement in the solution quality amounts to as high as ten percent.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.41-51
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• 1999

In sole-source procurement contraction for government goods and services, the buyer (government) needs to derive the optimal actions from the contractor so the buyer can obtain the maximum utility and the contractor, or single-source supplier, is guaranteed the equivalent of a minimum level of profit. Under the assumption of risk-neutrality for both the buyer and the contractor and the buyer's unobservability of the contractor's action, it is necessary for the buyer to design a (mathematical) model to achieve the above objective. This paper considers the mathematical formulation in which two problems - moral hazard and adverse selection - are present simultaneously; furthermore, from the formulation, a GAMS (General Algebraic Modeling System) program is used for a possible buyer to obtain the optimal actions.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.221-252
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• 2010

This study presents a concept of technology trade network and management, and proposes a procedural method for optimally selecting the technology transferor when a technology transferee needs to buy a specific technology. We develop a technology trade network where technology supplier, technology marketer, and technology transferee are informatively linked. And a technology trade management consists of three step of estimating technology, trading technology, and commercialization technology. Technology transferees could import the best appropriate technology which they want through these technology network method and cost optimization method. And we hope that these methodologies can be used in selecting new technology. A methodology can be classified into an estimating technology process and a choice of technology supplier process. In an estimating technology process, we calculate the technology similarity quantitatively through developing method of estimating technology which is focused on its technological characteristics. After defining the related cost of technology introduction, we suggest goal programming model to find a solution which can be acceptable both maximizing the technology similarity and minimizing the cost of technology. And suggested model is verified with a supplier selection problem of next generation tanks.

• IE interfaces
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• v.16 no.3
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• pp.291-299
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• 2003

The managerial environment of automobile industry has been rapidly changing, mainly with the tighter standard of legal and environmental regulations, the pressure of cost reduction from the end-user, the various functional improvements of vehicles, and the globalization management. In order to survive in this complicate circumstances, the automakers have strongly pursued the mutual collaboration with the suppliers, especially tier-1, for the optimal selection of parts/materials and the assembly manufacturing processes of the new model development. They prepare the early involvement program for the suppliers during the conceptual design stage, and implement the win-win strategy of technology development activity, in order to improve the value creation. This study considers the strategic technology management of automotive steel sheets makers on the aspects of strategy, processes, human & physical resources, and organization structure. It also suggests the corporate strategies of steel mills, in order to obtain the technology competence.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.136-145
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• 2007

The selection of suppliers and the determination of order quantities to be placed with those suppliers are important decisions in a supply chain. In this research, a non-linear mixed integer programming model is presented to select suppliers and determine the order quantities. The model considers the purchasing cost which takes into account quantity discount, the cost of transportation, the fixed cost for establishing suppliers, the cost for holding inventory, and the cost of receiving poor quality parts. The capacity constraints for suppliers, quality and lead-time requirements for the parts are also taken into account in the model. Since the purchasing cost, which is a decreasing step function of order quantities, introduces discontinuities to the non-linear objective function, it is not easy to employ traditional optimization methods. Thus, a heuristic algorithm, called particle swarm optimization (PSO), is used to find the (near) optimal solution. However, PSO usually generates initial solutions randomly. To improve the PSO solution quality, a heuristic procedure is proposed to find an initial solution based on the average unit cost including transportation, purchasing, inventory, and poor quality part cost. The results show that PSO with the proposed initial solution heuristic provides better solutions than those with PSO algorithm only.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.43-61
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• 2014

This research is an application of game theory to developing the supplier selection and demand allocation mechanism, which are the essential and major research areas of supply chain planning and operation. In this research, the most popular and widely accepted mechanism, the progressive reverse auction is analyzed and compared with the other game theoretic approach, Kalai-Smorodinsky Bargaining Solution in the viewpoint of holistic efficiency of supply chain operation. To logically and exquisitely compare the efficiencies, a heuristic algorithm based on Genetic Algorithm is devised to find the other optimal demand allocation plan. A well known metric, profit-cost ratio, as well as profit functions for both suppliers and buyer has been designed for evaluating the overall profitability of supply chain. The experimental results with synthesis data and supply chain model which were made to mimic practical supply chain are illustrated and analyzed to show how the proposed approach can enhance the profitability of supply chain planning. Based on the result, it can be said that the proposed mechanism using bargainging solution mayguarantee the better profitability for the whole supply chin including both suppliers and buyer, even though quite small portion of buyer's profitability should be sacrified.