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April 12, 2013 - April 13, 2026

Available Issues

April 12, 2013 - April 13, 2026

Growth Projections and Assortment Planning of Commodity Products Across Multiple Stores: A Data Mining and Optimization Approach

Published Online:27 Oct 2015https://doi.org/10.1287/ijoc.2015.0647

References

  • Agard B, Kusiak A (2004) Data mining based methodology for the design of product families. Internat. J. Production Res. 42(15):2955–2969.Crossref, Google Scholar
  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proc. 20th Internat. Conf. Very Large Data Bases, VLDB, Vol. 1215 (Morgan Kaufmann Publishers, San Francisco),487–499.Google Scholar
  • Agrawal R, Aggarwal CC, Prasad VVV (2000a) A tree projection algorithm for generation of frequent itemsets. J. Parallel Distributed Comput. 61(3):350–371.Crossref, Google Scholar
  • Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in massive databases. Proc. ACM/SIGMOD Internat. Conf. Management Data, Washington, DC, 207–216.Google Scholar
  • Agrawal R, Srikant R, Vu Q (2000b) Method and apparatus for mining association rules having item constraints. U.S. Patent 6,061,682.Google Scholar
  • Alptekinoğlu A, Corbett CJ (2008) Mass customization vs. mass production: Variety and price competition. Manufacturing Service Oper. Management 10(2):204–217.Link, Google Scholar
  • Anderson EE (1979) An analysis of retail display space: Theory and methods. J. Bus. 52(1):103–118.Crossref, Google Scholar
  • Borin N, Farris P (1995) A sensitivity analysis of retailer shelf management models. J. Retailing 71(2):153–171.Crossref, Google Scholar
  • Brijs T, Swinnen G, Vanhoof K, Wets G (1999) Using association rules for product assortment decisions: A case study. Proc. ACM SIGMOD Internat. Conf. Knowledge Discovery Data Mining (KDD-1999), San Diego, 254–260.Crossref, Google Scholar
  • Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Record 26(2):255–264.Crossref, Google Scholar
  • Bultez A, Naert P, Gijsbrechts E, Vanden Abeele P (1989) Asymmetric cannibalism in retail assortments. J. Retailing 65(2):153–192.Google Scholar
  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6):429–444.Crossref, Google Scholar
  • Cooper WW, Seiford LM, Zhu J (2011) Handbook on Data Envelopment Analysis, 2nd ed. (Springer, New York).Crossref, Google Scholar
  • Corstjens M, Doyle P (1981) A model for optimizing retail space allocations. Management Sci. 27(7):822–833.Link, Google Scholar
  • de Groote X (1994) Flexibility and marketing/manufacturing coordination. Internat. J. Production Econom. 36(2):153–167.Crossref, Google Scholar
  • Desai P, Kekre S, Radhakrishnan S, Srinivasan K (2001) Product differentiation and commonality in design: Balancing revenue and cost drivers. Management Sci. 47(1):37–51.Link, Google Scholar
  • Do TD, Hui SC, Fong A (2003) Mining frequent itemsets with category-based constraints. Discovery Science (Springer, Berlin), 76–86.Crossref, Google Scholar
  • Dobson G, Kalish S (1993) Heuristics for pricing and positioning a product-line using conjoint and cost data. Management Sci. 39(2):160–175.Link, Google Scholar
  • Eliashberg J, Steinberg R (1993) Marketing-production joint decision-making. Eliashberg J, Lilien GL, eds. Marketing, Handbooks in Operation Research and Management Science, Vol. 5 (Elsevier, Amsterdam), 827–880.Crossref, Google Scholar
  • Gaur V, Honhon D (2006) Assortment planning and inventory decisions under a locational choice model. Management Sci. 52(10):1528–1543.Link, Google Scholar
  • Han JA (2000) Mining frequent patterns by pattern growth: Methodology and implications. ACM SIGKDD Explorations Newsletter 2(2):14–20.Crossref, Google Scholar
  • Huffman C, Kahn BE (1998) Variety for sale: Mass customization or mass confusion? J. Retailing 74(4):491–513.Crossref, Google Scholar
  • ILOG (2009) ILOG CPLEX 12.1 User’s Manual (ILOG Inc., Gentilly, France).Google Scholar
  • Kahn BE (1995) Consumer variety-seeking among goods and services: An integrative review. J. Retailing Consumer Services 2(3):139–148.Crossref, Google Scholar
  • Kim K, Chhajed D (2000) Commonality in product design: Cost saving, valuation change and cannibalization. Eur. J. Oper. Res. 125(3):602–621.Crossref, Google Scholar
  • Kohli R, Sukumar R (1990) Heuristics for product-line design using conjoint analysis. Management Sci. 36(12):1464–1478.Link, Google Scholar
  • Kök AG, Fisher ML (2007) Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6):1001–1021.Link, Google Scholar
  • Kök AG, Fisher ML, Vaidyanathan R (2005) Assortment planning: Review of literature and industry practice. Agrawal N, Smith S, eds. Retail Supply Chain Management (Kluwer, Amsterdam), 99–103.Google Scholar
  • Malik HH, Kender JR (2006) Clustering web images using association rules, interestingness measures, and hypergraph partitions. Proc. 6th Internat. Conf. Web Engrg. (ICWE ’06), Palo Alto, CA, 48–55.Crossref, Google Scholar
  • McBride RD, Zufryden FS (1988) An integer programming approach to the optimal product line selection problem. Marketing Sci. 7(2):126–140.Link, Google Scholar
  • McGillivray AR, Silver EA (1978) Some concepts for inventory control under substitutable demand. INFOR. 16(1):47–63.Google Scholar
  • Mitchell T (1997) Machine Learning, 1st ed. (McGraw Hill, Columbus, OH).Google Scholar
  • Moorthy KS (1984) Market segmentation, self-selection, and product line design. Marketing Sci. 3(4):288–307.Link, Google Scholar
  • Mussa M, Rosen S (1978) Monopoly and product quality. J. Econom. Theory 18(2):301–317.Crossref, Google Scholar
  • Netessine S, Rudi N (2003) Centralized and competitive inventory models with demand substitution. Oper. Res. 51(2):329–335.Link, Google Scholar
  • Netessine S, Taylor TA (2007) Product line design and production technology. Marketing Sci. 26(1):101–117.Link, Google Scholar
  • Padmanabhan B, Tuzhilin A (2003) On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Management Sci. 49(10):1327–1343.Link, Google Scholar
  • Ray SC (1991) Resource-use efficiency in public schools: A study of Connecticut data. Management Sci. 37(12):1620–1628.Link, Google Scholar
  • SAS (2008) Getting Started with SAS Enterprise Miner 4.3 (SAS Institute, Inc., Cary, NC).Google Scholar
  • Simonson I (1999) The effect of product assortment on buyer preferences. J. Retailing 75(3):347–370.Crossref, Google Scholar
  • Smith SA, Agrawal N (2000) Management of multi-item retail inventory systems with demand substitution. Oper. Res. 48(1):50–64.Link, Google Scholar
  • Song JS (1998) On the order fill rate in a multi-item, base-stock inventory system. Oper. Res. 46(6):831–845.Link, Google Scholar
  • Tucker CS, Kim HM (2008) Optimal product portfolio formulation by merging predictive data mining with multilevel optimization. Trans. ASME: J. Mech. Design 130(4):041103.Crossref, Google Scholar
  • Tucker CS, Kim HM (2009) Data-driven decision tree classification for product portfolio design optimization. J. Comput. Inform. Sci. Engrg. 9(4):041004-1–041004-14.Google Scholar
  • Yuan H, Huang T (2005) A matrix algorithm for mining association rules. Huang D-S, Zhang X-P, Huang G-B, eds. Advances in Intelligent Computing, Lecture Notes in Computer Science, Vol. 3644 (Springer-Verlag, Berlin, Heidelberg),370–379.Crossref, Google Scholar
  • Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans. Knowledge Data Engrg. 12(3):372–390.Crossref, Google Scholar
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