Optimal Level of Diversity and Price of Selected Cheese Product: Using Particle Swarm Algorithm (Case Study: Mashhad)

اقتصاد و توسعه کشاورزی

Article 3, Volume 31, Issue 1 - Serial Number 35, March 2017, Pages 50-59   

Document Type: Research Article

DOI: 10.22067/jead2.v31i1.54548

Authors

A. Dadrasmoghadam^{* 1}; M. Ghorbani²; K. Karbasi²; M.R. Kohansal³

¹Department of Agricultural Economics,

²Ferdowsi University of Mashhad

³Ferdowsi university of Mashhad

Abstract

Introduction: Product line design is a critical task that may determine a firm's survival. Producers need to constantly evolve in response to market and technology changes. As a result, the determining optimal diversity has attracted considerable attention in the marketing literature. So, all optimization approaches that have been applied to the optimal product line design problem aim at finding a better approximation of the global optimal solution that this paper solved the optimal diversity problem for brands with the use of a new population-based optimization algorithm called Particle Swarm Optimization (PSO). PSO is a nature-inspired intelligence technique, which has displayed high performance in providing a wide variety of good near-optimal solutions in optimization problems of high complexity.
Materials and Methods: In this article, diversity and prices of selected brands on the market Mashhad cheese product using Noshad project data and Pegah Milk Industry (including 435 Grocery Store) using seemingly unrelated regression model and particle swarm optimization algorithm were reviewed and analyzed and optimized in 2014. The objective function is the sum of market shares (Kalleh, Pegah and Sabah). Constrain is share total of available brands in the market which is equal to one. The parameters used in this study, with population size 50 and individual and social learning rate is 2
Results and Discussion: Results showed that the effect the price on share of Kalleh is positive. In addition, Kalleh brand diversity have been a significant positive impact on share brand of Kalleh but with the Pegah and Sabah brand diversity have been negative relationship (statistically meaningless). The impact Pegah price is negative on the share of Pegah brand so Pegah price has a positive relationship with the price of Kalleh. With rising price of Sabah increase Pegah brand share. And diversity of Kalleh is negative and significant. Diversity of sabah brand is negative and non-significant. The effect of price on share of Pegah brand is negative and non-significant. The coefficient of Sabah brand diversity have been positive and significant relationship with Sabah brand share in the market but Kalleh brand diversity on customers buying of Sabah has a significant and positive impact. The optimum level of diversification cheese brands of Kalleh, Pegah and Sabah respectively, 8, 5 and 3 obtained which it shows that the optimum level of Kalleh cheese brand diversity in the market is more than the other rival brands. The average price of cheese brand product diversity of Kalleh, Pegah and Sabah are 45696, 34626 and 30678 (rials) respectively and it suggests that the Kalleh brand price should be higher than the other competitors. After that, brand price have been Pegah and Sabah. Kalleh brand has maximum diversity, the optimum value diversity in this study still is 8 .Also, Pegah are optimized for these state and the optimum value is obtained 5 for Pegah in the market. In other words, the number required Kalleh and Pegah cheese brand is optimized in the market. The maximum of Sabah diversity is 4 which the optimal level of Sabah diversity should be reduced to 3. In the summery, results showed that the optimum level particle swarm optimization algorithm of cheese product diversity of Kalleh brand in the market is more than other rival brands. As well as, 1 type of cheese products Sabah brand should be removed in the market and Kalleh and Pegah brands are in optimal state from the terms of cheese product diversity.
Conclusion: According to the findings is suggested, Kalleh brand price is more than other competitors brands. The results also showed that grocery stores should have been more than Pegah and kalleh brand diversity to increase profits. Kalleh brand diversity lead to more profitability than other types of brands in the optimized state. One of the main reasons that the Kalleh brand has a special share in this market is its diversity. Optimum profit from their grocery stores showed that the optimal value of kalleh and Pegah diversity is caused to increase profitability in grocery stores. Kalleh cheese price is also more than other competitors because of the quality and products diversity could have been. Brands must be paid attention to the issue of diversity products of Cheese to increase their shares. Cheese product of brands must be investigated to packaging and processing, and other diversity of brands features to increase share and profit in the market.

Keywords

brand diversity; grocery store profit; optimal level of price; algorithms PSO

References

Alexouda G. and Paparrizos K. 2001. A Genetic Algorithm approach to the product line design problem using the Seller's Return criterion: An exhaustive comparative computational study. European Journal of Operational Research 134(1): 165−178.

2- Banks A., Vincent J. and Anyakoha C. 2008. A review of particle swarm optimization. Part II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing 7: 109–124.

3- Balakrishnan P., Gupta R. and Jacob V. 2004. Development of hybrid genetic algorithms for product line designs. IEEE Transactions on Systems, Man, and Cybernetics 34(1): 468−483.

4- Bayus B.L. and Putsis J.W. 1999. Product proliferation: An empirical analysis of product line determinants and market outcome.Marketing Science 18(2): 137-153.

5- Brander J.A. and Eaton J. 1984. Product line rivalry .American Economic Review 74: 323-334.

6-. Camm J. D., Cochran J. J., Curry D. J. and Kannan S. 2006. Conjoint optimization: An exact branch-and-bound algorithm for the share-of-choice problem. Management Science 52(3): 435−447.

7- Chen C., Gao X., Pei Q. and Li X. 2012. BNE-based concurrent transmission considering channel quality and its PSO searching strategy in Ad Hoc networks. Journal of Systems Engineering and Electronics 23(5):.649-660.

8-Davidson R., Mackinon J.G.1993. Estimation and inference in econometrics. Oxford University Press. ISBN 978-0-19-506011-9.

9-. Deneckere R. and Rothschild M. 1992. Monopolistic competition and preference diversity. Review of Economic Studies 59(2): 361-73.

10- Draganska M. and Dipak C.J. 2005. Product-line length as a competitive tool. Journal of Economics and Management Strategy 14(1):1-28.

11- Dixit A.K. and Stiglitz J.E. 1977. Monopolistic competition and optimum product diversity. American Economic Review 67(3): 297-308.

12- Edward F., Laura N. and John S. 2014.A dynamic model of shopping and consumption, Southern Methodist University.

13- Engelbrecht A. P. 2007. Computational intelligence: An introduction. England: John Wiley and Sons.

14- Foster G. and Ferguson S. 2013. Enhanced targeted initial populations for multi objective product line optimization. In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers.

15- Foster G., Turner C., Ferguson S. and Donndelinger J. 2014. Creating targeted initial populations for genetic product searches in heterogeneous markets. Engineering Optimization 46(12):.1729-1747.

16- Gilbert R.J. and Carmen M. 1993.Product line rivalry with brand differentiation. Journal of Industrial Economics 41(3): 223-240.

17- Gharibnavaz M. 1386. Effect of income distribution in urban and rural household on consumption pattern in Iran: An Application of Quadratic AIDS system. Master thesis, University of Shiraz. (in Persian with English abstract)

18- GreenW. H. 2002. Econometric analysis (5th ed).prentice Hall.

19- Grewal R., Chakravarty A., Ding M. and Liechty J. 2008. Counting chickens before the eggs hatch: Associating new product development portfolios with shareholder expectations in the pharmaceutical sector. International Journal of Research in Marketing 25(4): 261−272.

20- Hausman J. 1996.Valuation of new goods under perfect and imperfect competition.

21-Horrace R.H., William C. and Jeffrey M.P. 2009.Variety: Consumer choice and optimal diversity. Food Marketing Policy, Center Research Report, No. 115.

22- Israilevich G. 2004.Assessing supermarket product-line decisions: The impact of slotting fees. Quantitative Marketing and Economics 2:141-167.

23- Kadiyali V., Naufel V. and Pradeep C. 1999. Product line extensions and competitive market interactions: An empirical analysis. Journal of Econometrics 89(1-2): 339-363.

24- Kaul A. and Rao V. R. 1995. Research for product positioning and design decisions: An integrative view. International Journal of Research in Marketing 12(4): 293−320.

25- Kim J., Greg M.A. and Peter E.R. 2002.Modeling consumer demand for variety. Marketing Science 21(3): 229-250.

26- Kennedy J. and Eberhart R. C. 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks .Piscataway, NJ, USA: IEEE Press. 1942−1948.

27- Kohli R. and Sukumar R. 1990. Heuristics for product line design using conjointanalysis. Management Science 36(12): 1464−1478.

28- Michalek J.J., Ebbes P., Adigüzel F., Feinberg F.M. and Papalambros P.Y. 2011. Enhancing marketing with engineering: Optimal product line design for heterogeneous markets. International Journal of Research in Marketing, 28(1):1-12.

29- Nevo A. 2003. New products, quality changes, and welfare measures computed from estimated demand systems.Review of Economics and Statistics 85(2):266-275.

30- Papadimitriou C. H. and Steiglitz K. 1983. Combinatorial optimization — Algorithms and complexity. Upper Saddle River, NJ: Prentice-Hall.

31-Perloff J.M. and Steven C.S. 1985. Equilibrium with product differentiation. Review of Economic Studies 52(1): 107-20.

32- Poli R., Kennedy J. and Blackwell T. 2007. Particle swarm optimization. An overview. Swarm Intelligence 1: 33–57.

33- Shafaghi C., Farokhi F., Sabbagh Nodoshan R. 2013. Compare and intelligent evolutionary optimization algorithms to solve the problem of locating the FPGA. Eighth Symposium on science and technology, University of Mashhad. (in Persian with English abstract)

34- Shi Y. and Eberhart R. 1998. A modified particle swarm optimizer. Proceedings of the IEEE World Congress on Computational Intelligence Anchorage, AK, USA: IEEE Press. 69−73.

35- Spence A.M. 1996. Product selection, fixed costs, and monopolistic competition. Review of Economic Studies 43(2):217-36

36- Saridakis C., Tsafarakis S., Delias P., Baltas G. and Matsatsinis N. 2015. Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism. Expert Systems with Applications 42(5):.2323-2335.

37- Spence A.M. 1996. Product selection, fixed costs, and monopolistic competition. Review of Economic Studies, 43(2): 217-36.

38- Srinivasan R., Lilien G. L. and Rangaswamy A. 2008. Survival of high tech firms: The effects of diversity of product–market portfolios, patents, and trademarks. International Journal of Research in Marketing 25(2): 119−128.

39- Stelios T., Yannis M. and Nikolaos M. 2011. Particle swarm optimization for optimal product line design, International Journal of Research in Marketing 28: 13–22.

40- Stelios T., Charalampos S., George B. and Nikolaos M. 2013. Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables. Industrial Marketing Management 42: 496–506.

41- Tsafarakis S., Saridakis C., Baltas G., and Matsatsinis N. 2013. Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables. Industrial Marketing Management 42(4):.496-506.

42- Villas-Boas J. M. 2004. Communication strategies and product line design. Marketing Science 23(3): 304-316.

43- Wang S.C. and Yeh M.F. 2014. A modified particle swarm optimization for aggregate production planning. Expert Systems with Applications 41(6):.3069-3077.

44-Wu S. and Chen S. 2014, December. A Bi-level algorithm for product line design and pricing. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 14-18). IEEE.

45- Yakhkesh A. 1393. Factors affecting the market share of household purchases of fruits and vegetables from Mashhad municipality market. Master's Thesis, Ferdowsi University of Mashhad. (in Persian with English abstract)

46- Yang C.C. 2011. Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design. Computers & Industrial Engineering 60(4):.760-768.

47- Zellner A.1962. An efficient method of estimating seemingly unrelated regression and tests of regression bias. Journal of American Statistical Association 57:500-509.