• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.24-31
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• 2009

Firms produce various products that differ by function, design, color, etc. Product proliferation occurs for three different reasons. When there exist economies of scope, the unit cost for a product is lower when it is produced in conjunction with another product than when it is produced separately. Second, consumers are heterogeneous in the sense that they have different tastes, preferences, or price elasticities. A firm can earn more profit by segmenting consumers into different groups with similar characteristics. For example, product proliferation helps a firm increase profits by satisfying various consumer needs more precisely. The third reason for product proliferation is based on strategy. Producing a number of products can not only deter entry by providing few niches, but can also cause a firm to react efficiently to a low-price entry. By producing various products, a firm can reduce niches so that potential entrants have less incentive to enter. Moreover, a firm can produce new products in response to entry, which is called fighting brands. That is, when an entrant tries to attract consumers with a low price, an incumbent introduces a new lower-quality product while maintaining the price of the existing product. The drawback of product proliferation, however, is cannibalization. Some consumers who would have bought a high-price product switch to a low-price product. Moreover, it is possible that proliferation can decrease profits when a new product is less differentiated from a rival’s than is the existing product because of more severe competition. Many studies have analyzed the effect of product line rivalry in the areas of economics and marketing. They show how a monopolist can solve the problem of cannibalization by adjusting quality in a market where consumers differ in their preferences for quality. They find that a consumer who prefers high-quality products will obtain his or her most preferred quality, but a consumer who has not such preference will obtain less than his or her preferred quality to reduce cannibalization. This study analyzed the effects of product line rivalry in a duopoly market with two types of consumers differentiated by quality preference. I assume that the two firms are asymmetric in the sense that an incumbent can produce both high- and low-quality products, while an entrant can produce only a low-quality product. The effects of product proliferation can be explained by comparing the market outcomes when an incumbent produces both products to those when it produces only one product. Compared to the case in which an incumbent produces only a high-quality product, the price of a low-quality product tends to decrease in a consumer segment that prefers low-quality products because of more severe competition. Prices, however, tend to increase in a segment with high preferences because of less severe competition. It is known that when firms compete over prices, it is optimal for a firm to increase its price when its rival increases its price, which is called a strategic complement. Since prices are strategic complements, we have two opposing effects. It turns out that the price of a high-quality product increases because the positive effect of reduced competition outweighs the negative effect of strategic complements. This implies that an incumbent needs to increase the price of a high-quality product when it is also introducing a low-quality product. However, the change in price of the entrant’s low-quality product is ambiguous. Second, compared to the case in which an incumbent produces only a low-quality product, prices tend to increase in a consumer segment with low preferences but decrease in a segment with high preferences. The prices of low-quality products decrease because the negative effect outweighs the positive effect. Moreover, when an incumbent produces both kinds of product, the price of an incumbent‘s low-quality product is higher, even though the quality of both firms’ low-quality products is the same. The reason for this is that the incumbent has less incentive to reduce the price of a low-quality product because of the negative impact on the price of its high-quality product. In fact, the effects of product line rivalry on profits depend not only on changes in price, but also on sales and cannibalization. If the difference in marginal cost is moderate compared to the difference in product quality, the positive effect of product proliferation outweighs the negative effect, thereby increasing the profit. Furthermore, if the cost difference is very large (small), an incumbent is better off producing only a low (high) quality product. Moreover, this study also analyzed the effect of product line rivalry when a firm can determine product characteristics by focusing on the issue of fighting brands. Recently, Korean air and Asiana airlines have established budget airlines called Jin air and Air Busan, respectively, to confront the launching of budget airlines such as Hansung airline and Jeju air, among others. In addition, as more online bookstores have entered the market, a leading off-line bookstore Kyobo began its own online bookstore. Through fighting brands, an incumbent with a high-quality product can increase profits by producing an additional low-quality product when its low-quality product is more differentiated from that of the entrant than is its high-quality product.

• Tuberculosis and Respiratory Diseases
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• v.57 no.6
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• pp.557-566
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• 2004

Background : To find out effectiveness of multimodality treatments based on induction chemotherapy(CTx) in patients with clinical stage IIIA NSCLC Methods : From 1997 to 2002, 74 patients with clinical stage IIIA NSCLC underwent induction CTx at the hospital of Chungnam National University. Induction CTx included above two cycles of cisplatin-based regimens(ectoposide, gemcitabine, vinorelbine, or taxol) followed by tumor evaluation. In 30 complete resection group, additional 4500-5000cGy radiotherapy(RTx) was delivered in 15 patients with pathologic nodal metastasis. 29 out of 44 patients who were unresectable disease, refusal of operation, and incomplete resection were followed by 60-70Gy RTx in local treatment. Additional 1-3 cycle CTx were done in case of induction CTx responders in both local treatment groups. Results : Induction CTx response rate were 44.6%(complete remission 1.4% & partial response 43.2%) and there was no difference of response rate by regimens(p=0.506). After induction chemotherapy, only 33 out of resectable 55 ones(including initial resectable 37 patients) were performed by surgical treatment because of 13 refusal of surgery by themselves and 9 poor predicted reserve lung function. There were 30(40.5%) patients with complete resection, 2(2.6%) persons with incomplete resection, and 1(1.3%) person with open & closure. Response rate in 27 ones with chest RTx out of non-operation group was 4.8% CR and 11.9% PR. In complete resection group, relapse free interval was 13.6 months and 2 year recur rate was 52%. In non-complete resection(incomplete resection or non-operation) group, disease progression free interval was 11.2 months and 2 year disease progression rate was 66.7%. Median survival time of induction CTx 74 patients with IIIA NSCLC was 25.1months. When compared complete resection group with non-complete resection group, the median survival time was 31.7 and 23.4months(p=0.024) and the 2-year overall survival rate was 80% and 41%. In the complete resection group, adjuvant postoperative RTx subgroup significantly improved the 2-year local control rate(0% vs. 40%, p= 0.007) but did not significantly improve overall survival(32.2months vs. 34.9months, p=0.48). Conculusion : Induction CTx is a possible method in the multimodality treatments, especially followed by complete resection, but overall survival by any local treatment(surgical resection or RTx) was low. Additional studies should be needed to analysis data for appropriate patient selection, new chemotherapy regimens and the time when should RTx be initiated.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.