ORIGINAL_ARTICLE
An Efficient Algorithm to Solve Utilization-based Model for Cellular Manufacturing Systems
The design of cellular manufacturing system (CMS) involves many structural and operational issues. One of the important CMS design steps is the formation of part families and machine cells which is called cell formation. In this paper, we propose an efficient algorithm to solve a new mathematical model for cell formation in cellular manufacturing systems based on cell utilization concept. The proposed model is to minimize the number of voids in cells to achieve higher cell utilization. The proposed model is a non-linear model which cannot be optimally solved. Thus, a linearization approach is used and the linearized model is then solved by linear optimization software. Even after linearization, the large-sized problems are still difficult to solve, therefore, a Simulated Annealing method is developed. To verify the quality and efficiency of the SA algorithm, a number of test problems with different sizes are solved and the results are compared with solutions obtained by Lingo 8 in terms of objective function values and computational time.
http://www.jise.ir/article_4033_ca9b73afbf94b12ae3d4c31024bc5dc1.pdf
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209
223
Cell formation
mathematical model
Cell utilization
Simulated Annealing
Kaveh
Fallahalipour
true
1
MAPNA Turbine Engineering & Manufacturing Co. (TUGA), P.O.Box: 15875-5643, Tehran, Iran
MAPNA Turbine Engineering & Manufacturing Co. (TUGA), P.O.Box: 15875-5643, Tehran, Iran
MAPNA Turbine Engineering & Manufacturing Co. (TUGA), P.O.Box: 15875-5643, Tehran, Iran
AUTHOR
Iraj
Mahdavi
true
2
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
AUTHOR
Ramin
Shamsi
true
3
Department of Industrial and Management Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
Department of Industrial and Management Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
Department of Industrial and Management Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
AUTHOR
Mohammad Mahdi
Paydar
true
4
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Engineering, Babol, Iran
AUTHOR
[1] Adil G. K., Ragamani D., Strong D. (1993), A mathematical model for cell formation considering
1
investment and operational costs; European Journal of Operational Research 69; 330–341.
2
[2] Albadawi Z., Bashir H.A., Chen M. (2005), A mathematical approach for the formation of
3
manufacturing cells; Computers & Industrial Engineering 48; 3–21.
4
[3] Chen S.J., Cheng C.S. (1995), A neural network based cell formation algorithm in cellular
5
manufacturing; International Journal of Production Research 33(2); 293- 318.
6
[4] Collet S., Spicer R. (1995), Improving productivity through cellular manufacturing; Production and
7
Inventory Management Journal 36(1); 71–75.
8
[5] Fallahalipour K., Shamsi R., A mathematical model for cell formation in CMS using sequence data
9
(2008); Journal of Industrial and Systems Engineering 2(2); 144-153.
10
[6] Goncalves J., Resende M. (2004), An evolutionary algorithm for manufacturing cell formation;
11
Computers & Industrial Engineering 47; 247–273.
12
[7] Heragu S.S. (1994), Group technology and cellular manufacturing; IEEE Transactions on Systems,
13
Man and Cybernetics 24(2); 203–214.
14
[8] Hyer N. (1984), The Potential of Group Technology for U.S. Manufacturing; Journal of Operations
15
Management 4(3); 183-202.
16
[9] Hyer N., Wemmerlov U. (1984), Group technology and productivity; Harvard Business Review, July-
17
August; 140-149.
18
[10] Joines J.A., King R.E., Culbreth C.T. (1996), A comprehensive review of production oriented
19
manufacturing cell formation techniques; International Journal of Flexible Automation and Integrated
20
Manufacturing 3; 225–265.
21
[11] Kirkpatrick S., Gelatt C. D., Vecchi M. P. (1983), Optimization by simulated annealing; Science 220; 671-680.
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[12] Kusiak A. (1987), The generalized group technology concept; International Journal of Production Research 25(4); 561–569.
23
[13] Lozano S., Adenso-Diaz B., Eguia I., Onieva L. (1999), A one-step tabu search algorithm for manufacturing cell design; Journal of the Operational Research Society 50; 509–16.
24
[14] Majeed Ali A. A., Vrat P. (1999), Manufacturing cell formation: a neural network approach; Proceedings of the International Conference on Operations Management for Global Economy Challenges and Prospects; Indian Institute of Technology, New Delhi, India, 565-571.
25
[15] Mahdavi I., Kaushal O.P., Chandra M. (2001), Graph-neural network approach in cellular manufacturing on the basis of a binary system; International Journal of Production Research 39(13); 2913–2922.
26
[16] Mahdavi I., Javadi B., Fallah-Alipour K., Slomp J. (2007), Designing a new mathematical model for cellular manufacturing system based on cell utilization; Applied Mathematics and Computation 190; 662–670.
27
[17] Megala N., Rajendran C., Gopalan R. (2008), An ant colony algorithm for cell-formation in cellular manufacturing systems; European Journal of Industrial Engineering 2(3); 298-336.
28
[18] Nair G.J., Narendran T.T. (1998), CASE: A clustering algorithm for cell formation with sequence data; International Journal of Production Research 36; 157–179.
29
[19] Onwubolu G.C., Mutingi M. (2001), A genetic algorithm approach to cellular manufacturing systems; Computers & Industrial Engineering 39, 125–44.
30
[20] Papaioannou G., Wilson J.M. (2010), The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research; European Journal of Operational Research 206(3); 509-521.
31
[21] Ronald B.H. (1997), Forming minimum-cost machine cells with exceptional parts using zero-one integer programming; Journal of Manufacturing Systems 16(2); 79-90.
32
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33
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34
[24] Singh N. (1993), Design of cellular manufacturing systems: an invited review; European Journal of Operational Research 69; 284–291.
35
[25] Singh N., Rajamani D. (1996), Cellular manufacturing systems design, planning and control; Chapman and Hall Publishing; London, UK.
36
[26] Sofianopoulou S. (1997), Application of simulated annealing to a linear model for the formation of machine cells in group technology; International Journal of Production Research 35, 501–511.
37
[27] Soleymanpour M., Vrat P., Shanker R. (2002), A transiently chaotic neural network approach to the design of cellular manufacturing; International Journal of Production Research 40(10), 2225-2244.
38
[28] Wemmerlov U., Hyer N.L. (1989), Cellular manufacturing in the US industry: a survey of users; International Journal of Production Research 27(9); 1511–1530.
39
[29] Wemmerlov U., Johnson D.J. (1997), Cellular manufacturing at 46 user plants: implementation experiences and performance improvements; International Journal of Production Research 35(1); 29–49.
40
[30] Wu T., Low C., Wu W. (2004), A tabu search approach to the cell formation problem; International Journal ofAdvanced Manufacturing Technology 23; 916–924.
41
[31] Wu T.H., Chang C.C., Chung S.H. (2008), A simulated annealing algorithm for manufacturing cell formation problems; Expert Systems with Applications 34; 1609–1617.
42
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43
ORIGINAL_ARTICLE
A Comprehensive Fuzzy Multiobjective Supplier Selection Model under Price Brakes and Using Interval Comparison Matrices
The research on supplier selection is abundant and the works usually only consider the critical success factors in the buyer–supplier relationship. However, the negative aspects of the buyer–supplier relationship must also be considered simultaneously. In this paper we propose a comprehensive model for ranking an arbitrary number of suppliers, selecting a number of them and allocating a quota of an order to them considering three objective functions: minimizing the net cost, minimizing the net rejected items and minimizing the net late deliveries. The two-stage logarithmic goal programming method for generating weights from interval comparison matrices (Wang et al. 2005) is used for ranking and selecting the suppliers. It is assumed that the suppliers give price discounts. A fuzzy multiobjective model is formulated in such a way as to consider imprecision of information. A numerical example is given to explain how the model is applied.
http://www.jise.ir/article_4034_4a5a96a7db70d9782b94e5ed464c4d3e.pdf
2011-02-01T11:23:20
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244
Supplier selection
Interval Comparison Matrices
Fuzzy Multiobjective Model
Price Discounts
Supply chain
Mehdi
Seifbarghy
true
1
Technical and Engineering Department, Alzahra University, Tehran, Iran
Technical and Engineering Department, Alzahra University, Tehran, Iran
Technical and Engineering Department, Alzahra University, Tehran, Iran
AUTHOR
Ali
Pourebrahim Gilkalayeh
true
2
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
AUTHOR
Mehran
Alidoost
true
3
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
AUTHOR
[1] Amid A.,Ghodsypour S.H., O’Brien C.A. (2006), Fuzzy multiobjective linear model for supplier selection in a supply chain; International Journal of Production Economics 104; 394–407.
1
[2] Amid A., Ghodsypour S.H., O’Brien C.A. (2008), weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply chain; International Journal of Production Economics 121; 323-332.
2
[3] Amy H.I. Lee (2008), A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks; Expert Systems with Applications 36; 2879-2893.
3
[4] Arbel A. (1989), Approximate articulation of preference and priority derivation; European Journal of Operational Research; 43317–326.
4
[5] Arbel A. (1991), A linear programming approach for processing approximate articulation of preference, in: P. Korhonen, A. Lewandowski, J. Wallenius, (Eds.), Multiple Criteria Decision Support; Lecture Notes in Economics and Mathematical Systems 356, Springer, Berlin; 79–86.
5
[6] Arbel A., Vargas L.G. (1990), The analytic hierarchy process with interval judgments, in: A. Goicoechea, L. Duckstein, S. Zoints, (Eds.), 9th Internat. Conference on Multiple criteria decision making, Fairfax, Virginia, Springer, New York; 61–70.
6
[7] Arbel A., Vargas L.G. (1993), Preference simulation and preference programming: robustness issues in priority deviation; European Journal of Operational Research 69; 200–209.
7
[8] Bonder C.G. E., deGraan J.G., Lootsma. F.A. (1989), Multicriteria decision analysis with fuzzy pairwise comparisons; Fuzzy Sets and Systems 29; 133–143.
8
[9] Buckley J.J. (1985), Fuzzy hierarchical analysis; Fuzzy Sets and Systems 17; 233–247.
9
[10] Buckley J.J., Feuring T., Hayashi Y. (2001), Fuzzy hierarchical analysis revisited; European Journal of Operational Research 129; 48–64.
10
[11] Conde E., de la Paz Rivera Pérez M.(2010), A linear optimization problem to derive relative weights using an interval judgement matrix; European Journal of Operational Research 201(2); 537-544.
11
[12] Csutora R., Buckley J.J. (2001), Fuzzy hierarchical analysis: the Lamda–Max method; Fuzzy Sets and Systems 120; 181–195.
12
[13] Dempsey W.A. (1978), Vendor selection and buying process; Industrial Marketing Management 7; 257-267.
13
[14] Dickson G.W. (1966), An analysis of vendor selection systems and decisions; Journal of Purchasing 2(1); 5-17.
14
[15] Dopazo E., Ruiz-Tagle M.A. (2009), GP formulation for aggregating preferences with interval assessments; Lecture Notes in Economics and Mathematical Systems 618; 47-54.
15
[16] Dopazo E., Ruiz-Tagle M., Robles J. (2007), Preference learning from interval pairwise data. A distance-based approach; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4881 LNCS; 240-247.
16
[17] Geringer J.M. (1988), Joint venture partner selection: Strategies for develop countries; Westport, Quorum Books.
17
[18] Ghodsypour S.H, O’Brien C. (1998), A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming; International Journal of Production Economics 56; 199–212.
18
[19] Haines L.M. (1998), A statistical approach to the analytic hierarchy process with interval judgments.(I).Distributions on feasible regions; European Journal of Operational Research 110; 112–125.
19
[20] Hong G.H., Park S.C., Jang D.S., Rho H.M. (2005), An effective supplier selection method for constructing a competitive supply relationship; Expert Systems with Applications 28; 629–639.
20
[21] Islam R., Biswal M.P., Alam S.S. (1997), Preference programming and inconsistent interval judgments; European Journal of Operational Research 97; 53–62.
21
[22] Kaslingam R., Lee C. (1996), Selection of vendors – a mixed integer programming approach; Computers and Industrial Engineering 31; 347–350.
22
[23] Kress M. (1991), Approximate articulation of preference and priority derivation—A comment; European Journal of Operational Research 52; 382–383.
23
[24] Kumar M., Vrat P., Shankar R. (2004), A fuzzy goal programming approach for vendor selection problem in a supply chain; Computers and Industrial Engineering 46; 69–85.
24
[25] Kumar M., Vrat P., Shankar R. (2006), A fuzzy programming approach for vendor selection problem in a supply chain; International Journal of Production Economics 101; 273–285.
25
[26] Lee A.H.I. (2009), A fuzzy AHP evaluation model for buyer–supplier relationships with the consideration of benefits, opportunities, costs and risks; International Journal of Production Research 47; 4255-4280
26
[27] Leung L.C., Cao D. (2000), On consistency and ranking of alternatives in fuzzy AHP; European Journal of Operational Research 124; 102–113.
27
[28] Lewis J.D. (1990), Partnership for profit: structuring and managing strategic alliance; The Free Press, New York.
28
[29] Lin C.-W.R., Chen H.-Y. S. (2004), A fuzzy strategic alliance selection framework for supply chain partnering under limited evaluation resources; Computers in Industry 55; 159–179.
29
[30] Liu F.-H.F, Hai H.L. (2005), The voting analytic hierarchy process method for selecting supplier; International Journal of Production Economics 97; 308–317.
30
[31] Lorange P., Roos J., Bronn P.S. (1992), Building successful strategic alliances; Long Range Planning 25(6); 10–17.
31
[32] Mikhailov L. (2002), Fuzzy analytical approach to partnership selection in formation of virtual enterprises; Omega: International Journal of Management Science 30; 393–401.
32
[33] Mikhailov L. (2003), Deriving priorities from fuzzy pairwise comparison judgments; Fuzzy Sets and Systems 134; 365–385.
33
[34] Mikhailov L. (2004), Group prioritization in the AHP by fuzzy preference programming method; Comput. Oper. Res 31; 293–301.
34
[35] Moreno-Jiménez. J.M. (1993), A probabilistic study of preference structures in the analytic hierarchy process within terval judgments; Math. Comput. Modeling 17 (4/5); 73–81.
35
[36] Muralidharan C., Anantharaman N., Deshmukh S.G. (2002), A multi-criteria group decisionmaking model for supplier rating; Journal of Supply Chain Management 38(4); 22–33.
36
[37] Narasimahn R. (1983), An analytical approach to supplier selection; Journal of Purchasing and Materials Management 19(4); 27–32.
37
[38] Nydick R.L., Hill R.P. (1992), Using the analytic hierarchy process to structure the supplier selection procedure; Journal of Purchasing and Materials Management 25(2); 31–36.
38
[39] Partovi F.Y., Burton J., Banerjee A. (1989), Application of analytic hierarchy process in operations management; International Journal of Operations and Production Management 10(3); 5–19.
39
[40] Ravindran A.R., Bilsel R.U., Wadhwa V., Yang T. (2010), Risk adjusted multicriteria supplier selection models with applications; International Journal of Production Research 48(2); 405-424.
40
[41] Saaty R.W. (2003), Decision making in complex environment: The analytic hierarchy process (AHP) for decision making and the analytic network process (ANP) for decision making with dependence and feedback; Pittsburgh, Super Decisions.
41
[42] Saaty T.L., Vargas L.G. (1987), Uncertainty and rank order in the analytic hierarchy process; European Journal of Operational Research 32 ; 107–117.
42
[43] Saaty T.L. (2004), Fundamentals of the analytic network processmultiple networks with benefits, opportunities, costs and risks; Journal of Systems Science and Systems Engineering 13(3); 348–379.
43
[44] Salo A., Hämäläinen R.P. (1992), Processing interval judgments in the analytic hierarchy process, in: A. Goicoechea, L. Duckstein, S. Zoints, (Eds.); Proc. 9th Internat. Conference on Multiple Criteria Decision Making; Fairfax, Virginia, Springer, NewYork; 359–372.
44
[45] Salo A., Hämäläinen R.P. (1995), Preference programming through approximate ratio comparisons, European Journal of Operational Research 82; 458–475.
45
[46] Van Laarhoven P.J.M., Pedrycz W. (1983), A fuzzy extension of Saaty’s priority theory; Fuzzy Sets and Systems 11; 229–241.
46
[47] Wang Y.M., Chin K.S. (2006), An eigenvector method for generating normalized interval and fuzzy weights; Applied Mathematics and Computation 181(2); 1257-1275.
47
[48] Wang Y.M., Yang J.B.,Xu D.L. (2005), A two-stage logarithmic goal programming method for generating wehghts from interval comparison matrices; Fuzzy Sets and Systems 152; 475–498.
48
[49] Weber C.A., Current J.R. (1993), A multi-objective approach to vendor selection; European Journal of Operational Research 68(2); 173–184.
49
[50] Weber C.A., Current J.R., Benton W.C. (1991), Vendor selection criteria and methods; European Journal of Operational Research 50; 1-17.
50
[51] Weber C.A., Current J.R., Desai A. (1998), Non-cooperative negotiation strategies for vendor selection; European Journal of Operational Research 108; 208–223.
51
[52] Weber C.A, Desai A. (1996), Determination of path to vendor market efficiency using parallel coordinates representation: A negotiation tool for buyers; European Journal of Operational Research 90; 142–155.
52
[53] Wu D.D., Zhang Y., Wu D., Olson D.L. (2010), Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach; European Journal of Operational Research 200(3); 774-787.
53
[54] Xu R. (2000), Fuzzy least-squares priority method in the analytic hierarchy process; Fuzzy Sets and Systems 112; 359–404.
54
[55] Xu R., Zhai X. (1996), Fuzzy logarithmic least squares ranking method in analytic hierarchy process; Fuzzy Sets and Systems 77; 175–190.
55
[56] Zimmermann H.J. (1978), Fuzzy programming and linear programming with several objectives functions; Fuzzy Sets and Systems 1; 45-55.
56
ORIGINAL_ARTICLE
Development of a Genetic Algorithm for Advertising Time Allocation Problems
Commercial advertising is the main source of income for TV channels and allocation of advertising time slots for maximizing broadcasting revenues is the major problem faced by TV channel planners. In this paper, the problem of scheduling advertisements on prime-time of a TV channel is considered. The problem is formulated as a multi-unit combinatorial auction based mathematical model. This is an efficient mechanism for allocating the advertising time to advertisers in which the revenue of TV channel is maximized. However, still this problem is categorized as a NP-Complete problem. Therefore, a steady-state genetic algorithm is developed for finding a good or probably near-optimal solution, and is evaluated through a set of test problems for its robustness. Computational results reveal that the proposed algorithm is capable of obtaining high-quality solutions for the randomly generated real-sized test problems.
http://www.jise.ir/article_4035_449d296da1de8b504edd056ae34ecd08.pdf
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Combinatorial Optimization
TV Advertising Allocation Problem
Combinatorial Auctions
Genetic Algorithms
Reza
Alaei
true
1
PO.Box: 53816-14497, Tehran, Iran
PO.Box: 53816-14497, Tehran, Iran
PO.Box: 53816-14497, Tehran, Iran
AUTHOR
Farhad
Ghassemi-Tari
true
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Benoist T., Bourreau E., Rottembourg B. (2007), The TV-break packing problem; European Journal of Operational Research 176; 1371-1386.
1
[2] Bichler M., Davenport A., Hohner G., Kalagnanam J., In Cramton P., Shoham Y., Steinberg R. (2006), Combinatorial Auctions, Chapter 22; Industrial Procurement Auctions, MIT Press.
2
[3] Bollapragada S., Cheng H., Philips M., Garbiras M., Scholes M., Gibbs T., Humhreville M. (2002), NBCs optimization systems increase revenues and productivity; Interfaces 32(1); 47-60.
3
[4] Bollapragada S., Garbiras M. (2004), Scheduling commercials on broadcast television; Operations Research 52(3); 337-345.
4
[5] Bollapragada S., Bussieck M., Mallik S. (2004), Scheduling commercial videotapes in broadcast television; Operations Research 52(5); 679-689.
5
[6] Brown A.R. (1969), Selling television time: An optimization problem; Computer Journal 12; 201-206.
6
[7] Brusco M.J. (2008), Scheduling advertising slots for television; Journal of the Operational Research Society 59(10); 1373-1382.
7
[8] Cramton P., Shoham Y., Steinberg R. (2005), Combinatorial Auctions, MIT Press.
8
[9] Holland J. (1975), Adaptation in Natural and Artificial Systems; University of Michigan Press.
9
[10] Jones J.J. (2000), Incompletely Specified Combinatorial Auction: An Alternative Allocation Mechanism for Business to Business Negotiations; PhD Dissertation, University of Florida, FL.
10
[11] Kimms A., Muller-Bungart M. (2007), Revenue management for broadcasting commercials; International Journal of Revenue Management 1; 28-44.
11
[12] Leyard J.O., Olson M., Porter D., Swanson J.A., Torma D.P. (2002), The first use of combined-value auction for transportation services; Interfaces 32(5); 4-12.
12
[13] Mao J., Shi J., Wanitwattanakosol J., Watanabe Sh. (2011), An ACO-based algorithm for optimizing the revenue of TV advertisement using credit information; International Journal of Revenue Management 5(2); 109-120.
13
[14] Mihiotis A., Tsakiris I. (2004), A mathematical programming study of advertising allocation problem; Applied Mathematics and Computation 148; 373-379.
14
[15] Pereira P.A., Fontes F.A.C.C., Fontes D.B.M.M. (2007), A decision support system for planning promotion time slots; Operations Research Proceedings; 147-152.
15
[16] Rassenti S., Smith V., Bulfin R. (1982), A combinatorial auction mechanism for airport time slot allocation; Bell Journal of Economics 13(2); 402-417.
16
[17] Sandholm T., Suri S., Gilpin A., Levine D. (2002), Winner determination in combinatorial auction generalizations; Proceeding of Autonomous Agents and Multi-Agent Systems Conference; 69-76.
17
[18] Wuang M.S., Yang C.L., Huang R.H., Chuang S.P. (2010), Scheduling of television commercials; IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); Macao, 803-807.
18
[19] Zhang X. (2006), Mathematical models for the television advertising allocation problem; International Journal of Operational Research 1(3); 302-322.
19
ORIGINAL_ARTICLE
A New Acceptance Sampling Plan Based on Cumulative Sums of Conforming Run-Lengths
In this article, a novel acceptance-sampling plan is proposed to decide whether to accept or reject a receiving batch of items. In this plan, the items in the receiving batch are inspected until a nonconforming item is found. When the sum of two consecutive values of the number of conforming items between two successive nonconforming items falls underneath of a lower control threshold, the batch is rejected. If this number falls above an upper control threshold, the batch is accepted, and if it falls within the upper and the lower thresholds then the process of inspecting items continues. The aim is to determine proper threshold values and a Markovian approach is used in this regard. The model can be applied in group- acceptance sampling plans, where simultaneous testing is not possible. A numerical example along a comparison study are presented to illustrate the applicability of the proposed methodology and to evaluate its performances in real-world quality control environments.
http://www.jise.ir/article_4036_1f15901e89251874e58963f75bfdf1f4.pdf
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264
Acceptance Sampling
Quality Control
Inspection
Markov process
Mohammad Saber
Fallah Nezhad
true
1
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
AUTHOR
Seyed Taghi
Akhavan Niaki
niaki@sharif.edu
true
2
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran
AUTHOR
Mohammad Hossein
Abooie
true
3
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
AUTHOR
[1] Bowling S.R., Khasawneh M.T., Kaewkuekool S., Cho BR. (2004), A Markovian approach to
1
determining optimum process target levels for a multi-stage serial production system; European
2
Journal of Operational Research 159; 636–650.
3
[2] Bourke P.D. (2003), A continuous sampling plan using sums of conforming run-lengths; Quality and
4
Reliability Engineering International 19; 53–66.
5
[3] Bourke P.D. (2002), A continuous sampling plan using CUSUMs; Journal of Applied Statistics 29;
6
1121–1133.
7
[4] Bourke P.D. (1991), Detecting a shift in fraction nonconforming using run-length control charts with
8
100% inspection; Journal of Quality Technology 23; 225–238.
9
[5] Calvin T.W. (1983), Quality control techniques for ‘zero-defects; IEEE Transactions on Components,
10
Hybrids, and Manufacturing Technology 6; 323–328.
11
[6] Goh T.N. (1987), A charting technique for control of low-nonconformity production; International
12
Journal of Quality and Reliability Management 5; 53–62.
13
[7] Klassen C.A.J. (2001), Credit in acceptance sampling on attributes; Technometrics 43; 212–222.
14
[8] McWilliams T.P., Saniga E.M., Davis D.J. (2001), On the design of single sample acceptance
15
sampling plans; Economic Quality Control 16; 193–198.
16
[9] Niaki S.T.A., Fallahnezhad M.S. (2009), Designing an optimum acceptance plan using Bayesian
17
inference and stochastic dynamic programming; Scientia Iranica 16; 19-25.
18
ORIGINAL_ARTICLE
Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid models for time series forecasting. Several researches in the literature have been shown that these models can outperform single models. In this paper, the predictive capabilities of three different models in which the autoregressive integrated moving average (ARIMA) as linear model is combined to the multilayer perceptron (MLP) as nonlinear model, are compared together for time series forecasting. These models are including the Zhang’s hybrid ANNs/ARIMA, artificial neural network (p,d,q), and generalized hybrid ANNs/ARIMA models. The empirical results with three well-known real data sets indicate that all of these methodologies can be effective ways to improve forecasting accuracy achieved by either of components used separately. However, the generalized hybrid ANNs/ARIMA model is more accurate and performs significantly better than other aforementioned models.
http://www.jise.ir/article_4037_aa9ad9380cac2203195414d60c0b2da0.pdf
2011-02-01T11:23:20
2020-09-26T11:23:20
265
285
Artificial neural networks (ANNs)
Auto-Regressive Integrated Moving Average (ARIMA)
Time series forecasting
Hybrid linear/nonlinear models
Mehdi
Khashei
true
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Mehdi
Bijari
bijari@cc.iut.ac.ir
true
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
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