zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Revised multi-choice goal programming for multi-period, multi-stage inventory controlled supply chain model with popup stores in guerrilla marketing. (English) Zbl 1201.90015
Summary: We consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0-1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works.

90B05Inventory, storage, reservoirs
90B06Transportation, logistics
90C11Mixed integer programming
90C29Multi-objective programming; goal programming
Full Text: DOI
[1] Wikipedia, ”Guerrilla Marketing”, The Free Encyclopedia [Online], 2007, available: <http://en.wikipedia.org/wiki/Guerrilla_marketing>, (accessed 04.12.2007).
[2] Levinson, J. C.: Guerrilla marketing: secrets for making big profits from your small business, (1998)
[3] Gogoi, P., ”Popup Stores: All the Rage”, Business Week [Online], 2007, available: <http://www.businessweek.com/bwdaily/dnflash/content/feb2007/db20070206_949107.htm> (accessed 13.12.2007).
[4] C. T. Chang, Revised multi-choice goal programming, Appl. Math. Modell., 2007, doi: 10.1016/j.apm.2007.09.008 (available on line). · Zbl 1167.90637
[5] Syarif, A.; Yun, Y.; Gen, M.: Study on multi-stage logistics chain network: a spanning tree-based genetic algorithm approach, Comput. ind. Eng. 43, No. 1 -- 2, 299-314 (2002)
[6] Yan, H.; Yu, Z.; Cheng, T. C. E.: A strategic model for supply chain design with logical constraints: formulation and solution, Comput. operat. Res. 30, No. 14, 2135-2155 (2003) · Zbl 1039.90047 · doi:10.1016/S0305-0548(02)00127-2
[7] Min, H.; Zhou, G.: Supply chain modeling: past, present and future, Comput. ind. Eng. 43, No. 1 -- 2, 231-249 (2002)
[8] Cohen, M. A.; Lee, H. L.: Resource deployment analysis of global manufacturing and distribution networks, J. manuf. Operat. manage. 2, 81-104 (1989)
[9] Pyke, D. F.; Cohen, M. A.: Performance characteristics of stochastic integrated production-distribution systems, Euro. J. Operat. res. 68, No. 1, 23-48 (1993) · Zbl 0800.90508 · doi:10.1016/0377-2217(93)90075-X
[10] Petrovic, D.; Roy, R.; Petrovic, R.: Supply chain modeling using fuzzy sets, Int. J. Prod. econ. 59, 443-453 (1999)
[11] Ganeshan, R.; Boone, T.; Stenger, A. J.: The impact of inventory and flow planning parameters on supply chain performance: an exploratory study, Int. J. Prod. econ. 71, 111-118 (2001)
[12] Sakawa, M.; Nishizaki, I.; Uemura, Y.: A decentralized two-level transportation problem in a housing material manufacturer: interactive fuzzy programming approach, Euro. J. Operat. res. 141, 167-185 (2002) · Zbl 0998.90091 · doi:10.1016/S0377-2217(01)00273-9
[13] Jayaraman, V.; Ross, A.: A simulated annealing methodology to distribution network design and management, Euro. J. Operat. res. 144, No. 3, 629-645 (2003) · Zbl 1012.90531 · doi:10.1016/S0377-2217(02)00153-4
[14] Santoso, T.; Ahmed, S.; Goetschalckx, M.; Shapiro, A.: A stochastic programming approach for supply chain network design under uncertainty, Euro. J. Operat. res. 167, 96-115 (2005) · Zbl 1075.90010 · doi:10.1016/j.ejor.2004.01.046
[15] Ko, H. J.; Ko, C. S.; Kim, T.: A hybrid optimization/simulation approach for a distribution network design of 3PLs, Comput. ind. Eng. 50, 440-449 (2006)
[16] Paksoy, T.; Gules, H. K.; Bayraktar, D.: Design and optimization of a strategic production-distribution model for supply chain management: case study of a plastic profile manufacturer in Turkey, Selçuk J. Appl. math. 8, No. 2, 83-99 (2007) · Zbl 1149.90357
[17] Lin, L.; Gen, M.; Wang, X.: A hybrid genetic algorithm for logistics network design with flexible multi-stage model, Int. J. Inform. syst. Log. manage. 3, No. 1, 1-12 (2007)
[18] U.R. Tuzkaya, S. Önüt, A holonic approach based integration methodology for transportation and warehousing functions of the supply network, Comput. Ind. Eng., 2007, doi: 10.1016/j.cie.2007.09.003.
[19] F. Altiparmak, M. Gen, L. Lin, I. Karaoglan, A steady-state genetic algorithm for multi-product supply chain network design, Comput. Ind. Eng., 2007, doi: 10.1016/j.cie.2007.05.012.
[20] Pokharel, S.: A two objective model for decision making in a supply chain, Int. J. Prod. econ. 111, 378-388 (2008)
[21] Tsai, K. -M.; You, S. -Y.; Lin, Y. -H.; Tsai, C. -H.: A fuzzy goal programming approach with priority for channel allocation problem in steel industry, Expert syst. Appl. 34, 1870-1876 (2008)
[22] Xu, J.; Liu, Q.; Wang, R.: A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of chinese liquor, Inform. sci. 178, 2022-2043 (2008) · Zbl 1161.90016 · doi:10.1016/j.ins.2007.11.025
[23] T.-F. Liang, Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain, Comput. Ind. Eng., 2008, doi: 10.1016/j.cie.2008.02.008.
[24] Farahani, R. Z.; Elahipanah, M.: A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain, Int. J. Prod. econ. 111, 229-243 (2008)
[25] Charnes, A.; Cooper, W. W.: Manage. models ind. Appl. linear prog., Manage. models ind. Appl. linear prog. 1 (1961)
[26] Chang, C. T.: Multi-choice goal programming, Omega: int. J. manage. Sci. 35, 389-396 (2007)