ORIGINAL_ARTICLE
Extending Two-Dimensional Bin Packing Problem: Consideration of Priority for Items
In this paper a two-dimensional non-oriented guillotine bin packing problem is studied when items have different priorities. Our objective is to maximize the total profit which is total revenues minus costs of used bins and wasted area. A genetic algorithm is developed to solve this problem where a new coding scheme is introduced. To evaluate the performance of the proposed GA, first an upper bound is presented. Then, a series of computational experiments are conducted to evaluate the quality of GA solutions comparing with upper bound values. From the computational analysis, it appears that the GA algorithm is able to give good solutions.
https://www.jise.ir/article_4001_95bbbdfb578ed5dbc6d6ad162a5cecb0.pdf
2009-07-01
72
84
Two Dimensional Bin Packing Problem
priority
Genetic algorithm
Continuous
Lower Bound
Majid
Shakhsi-Niyaei
1
Industrial Engineering Department, Faculty of Engineering, University of Tehran, P.O. Box: 11155/4563, Tehran, Iran.
AUTHOR
Fariborz
Jolai
2
Industrial Engineering Department, Faculty of Engineering, University of Tehran, P.O. Box: 11155/4563, Tehran, Iran.
AUTHOR
Jafar
Razmi
3
Industrial Engineering Department, Faculty of Engineering, University of Tehran, P.O. Box: 11155/4563, Tehran, Iran.
AUTHOR
[1] Berkey J.O., Wang P.Y. (1987), Two-dimensional finite bin packing algorithms; Journal of the
1
Operational Research Society 38; 423-429.
2
[2] Correa J.R. (2004), Near-optimal solutions to two-dimensional bin packing with 90° rotations;
3
Electronic Notes in Discrete Mathematics 18; 89–95.
4
[3] Correa J.R. (2006), Resource augmentation in two-dimensional packing with orthogonal rotations;
5
Operations Research Letters 34; 85–93.
6
[4] Davis L. (1985), Applying adaptive search algorithms to epistatic domains; Proceedings of the 9th Int.
7
Joint Conference on Artificial Intelligence; 162-164.
8
[5] Dyckhoff H. (1990), A typology of cutting and packing problems; European Journal of Operational
9
Research 44; 145–159.
10
[6] Fritsch A., Vornberger O. (1995), Cutting Stock by Iterated Matching; Operations Research
11
Proceedings, Selected Papers of the Int. Conf. on OR 94, U. Derigs, A. Bachem, A. Drexl (eds);
12
Springer Verlag; 92-97.
13
[7] Hopper E., Turton B. (1997), Application of Genetic Algorithms to Packing Problems: A Review;
14
Proceedings of the 2nd On-line World Conference on Soft Computing in Engineering Design and
15
Manufacturing; Springer Verlag, London; 279-288.
16
[8] Kröger B. (1995), Guillotineable bin packing: A genetic approach; European Journal of Operational
17
Research 84; 645–661.
18
[9] Lodi A., Martello S., Vigo D. (1998), Neighborhood search algorithm for the guillotine non-oriented
19
two-dimensional bin packing problem; In S. Voss, S. Martello, L.H Osman, and C. Roucairol, editors,
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Meta-Heuristics: Advances and Trends in Local search Paradigms for optimization; Kluwer Academic
21
Publishers, Boston; 125-139.
22
[10] Lodi A., Martello S., Vigo D. (1999), Heuristic and metaheuristic approaches for a class of two
23
Dimensional Bin Packing Problems; INFORMS J. Comput 11; 345–357.
24
[11] Lodi A., Martello S., Vigo D. (2002), Recent advances on two-dimensional bin packing problems;
25
Discrete Applied Mathematics 123; 379-396.
26
[12] Lodi A., Martello S., Vigo D. (2004), TSpack: A Unified Tabu Search Code for Multi-Dimensional
27
Bin Packing Problems; Annals of Operations Research 131; 203-213.
28
[13] Martello S., Vigo D. (1998), Exact solution of the two-dimensional finite bin packing problem;
29
Management Science 44; 388-399.
30
[14] Polyakovsky S., M’Hallah R. (2009), An agent-based approach to the two-dimensional guillotine bin
31
packing problem; European Journal of Operational Research 192; 767–781.
32
[15] Puchinger J., Raidl G.R. (2006), Models and algorithms for three-stage two-dimensional bin packing;
33
European Journal of Operational Research 183; 1304-1327.
34
[16] Sevaux M., Dauzère-Pérès S. (2003), Genetic algorithms to minimize the weighted number of late jobs
35
on a single machine; European Journal of Operational Research 151; 296–306.
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[17] Smith D. (1985), Bin-packing with adaptive search; in Grefenstette (ed.). Proceedings of International
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Conference on Genetic Algorithms and their Applications; Lawrence Erlbaum; 202-206.
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[18] Vasko F.J., Wolf F.E., Stott K.L. (1989), A practical solution to a fuzzy two dimensional cutting stock
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problem; Fuzzy Sets and Systems 29; 259-275.
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[19] Wäscher G., Haußn er H., Schumann H. (2007), An improved typology of cutting and packing
41
problems; European Journal of Operational Research 183(3); 1109-1130.
42
ORIGINAL_ARTICLE
A Markov Model for Performance Evaluation of Coal Handling Unit of a Thermal Power Plant
The present paper discusses the development of a Markov model for performance evaluation of coal handling unit of a thermal power plant using probabilistic approach. Coal handling unit ensures proper supply of coal for sound functioning of thermal Power Plant. In present paper, the coal handling unit consists of two subsystems with two possible states i.e. working and failed. Failure and repair rates of both subsystems are taken to be constant. After drawing transition diagram, differential equations have been generated. After that, steady state probabilities are determined. Besides, some decision matrices are also developed, which provide various performance levels for different combinations of failure and repair rates of all subsystems. Based upon various performance values obtained in decision matrices and plots of failure rates/ repair rates of various subsystems, performance of each subsystem is analyzed and then maintenance decisions are made for all subsystems. The developed model helps in comparative evaluation of alternative maintenance strategies.
https://www.jise.ir/article_4002_7bbf0d2f3ed030e808e493963eeaaaa5.pdf
2009-07-01
85
96
Probabilistic approach
Steady state probabilities
Decision matrices
Sorabh
Gupta
1
Department of Mechanical Engineering, Haryana College of Technology and Management, Kaithal (Haryana) India
AUTHOR
P.C.
Tewari
2
Mechanical Engineering, NIT, Kurukshetra (Haryana), India
AUTHOR
Avdhesh Kr.
Sharma
3
Department of Mechanical Engineering, D.C.R. University of Science and Technology, Murthal (Sonipat-Haryana), India
AUTHOR
[1] Arora N., Kumar D. (1997), Availability analysis of steam and power generation system in thermal
1
power plant; Micro electron Reliability 37(5); 95-99.
2
[2] Balaguruswamy E. (1984), Reliability Engineering, Tata McGraw Hill; New Delhi.
3
[3] Barabady J., Kumar U. (2007), Availability allocation through importance measures; International
4
journal of quality & reliability management 24(6); 643-657.
5
[4] Beounes C., et al. (1993), SURF-2 A program for dependability evaluation of complex hardware and
6
software systems; In 23rd Int. Symp. On Fault-Tolerant Computing, Toulouse, France; 668–673.
7
[5] Bernson S., de Souza e Silva E., Muntz R. (1991), A Methodology for the specification of Markov
8
models; Numerical solution to Markov chains, W. Stewart ed.; 11-37.
9
[6] Butler R. (1986), The SURE reliability analysis program; AIAA Guidance, Navigation, and Control
10
Conference; Williamsburg, Virginia.
11
[7] Butler R.W. (1986A), An abstract language for specifying Markov reliability models; IEEE Trans on
12
Reliability R35; 595–602.
13
[8] Ciardo G., Muppala J., Trivedi K. (1989), Stochastic petri net package; In: Proc. 3rd Int. workshop on
14
Petri nets and performance models; Kyoto, Japan; 142–151.
15
[9] Deming W. (1982), Quality productivity and competitive position; Centre for Advanced Engineering
16
Study, MIT Press, Cambridge; MA.
17
[10] Dhillon B.S. (1983), Reliability engineering in systems design and operation; Van Nostrand-Reinhold;
18
[11] Goyal A., Carter W.C., de Souza e Silva E., Lavenberg S.S., Trivedi K.S. (1986), The system
19
availability estimator; In: Proc. of the Sixteenth International Symposium on Fault-Tolerant
20
Computing; Vienna, Austria; 84–89.
21
[12] Gupta S., Tewari P.C., Sharma A.K. (2008), Performance modeling and decision support system of
22
feed water unit of a thermal power plant; South African Journal of Industrial Engineering 19(2); 125-
23
[13] Gupta S., Tewari P.C., Sharma A.K. (2009), Development of simulation model for performance
24
evaluation of feed water system in a typical thermal power plant; Journal of Industrial Engineering
25
International (JIEI); Accepted for publication on 01.03.2009.
26
[14] Gupta S., Tewari P.C., Sharma A.K. (2009A), Reliability and availability analysis of the ash handling
27
unit of a steam thermal power plant; South African Journal of Industrial Engineering (SAJIE) 20(1);
28
[15] Gupta S., Tewari P.C., Sharma A.K. (2009B), A probabilistic model for performance evaluation of
29
steam and water system of a thermal power plant; International Journal of Management Science and
30
Engineering Management (MSEM), UK. 1(1); 03-13.
31
[16] Høyland A., Rausand M. (1994), System reliability theory; Wiley, New York.
32
[17] Joseph R.S., Ian Douglas (2006), A conceptual integration of performance analysis, knowledge
33
management, and technology: from concept to prototype; Journal of knowledge management 10(6);
34
[18] Khanduja R., Tewari P.C., Kumar D. (2008), Availability analysis of bleaching system of paper plant;
35
Journal of Industrial Engineering, Udyog Pragati, N.I.T.I.E. Mumbai (India) 32(1); 24-29.
36
[19] Koren J.M., Gaertner J. (1987), CAFTA: a fault tree analysis tool designed for PSA; Proc. of
37
Probabilistic Safety Assessment and Risk Management, PSA, Zurich, Switzerland; 588–592.
38
[20] Krishnamurthi G., Gupta A. and Somani, A.K. (1996), The HIMAP modeling environment;
39
Proceedings of the 9th International Conference on Parallel and Distributed Computing Systems,
40
Dijon, France; 254–259.
41
[21] Kumar D., Pandey P.C. (1993), Maintenance planning and resource allocation in urea fertilizer plant;
42
Quality and reliability Engineering international journal 9; 411-423.
43
[22] Kumar S., Tewari P.C., Sharma R. (2007), Simulated availability of CO2 cooling system in a fertilizer
44
plant; Industrial Engineering Journal (Indian Institution of Industrial Engineering, Mumbai) 36(10);
45
[23] Kumar S., Kumar D., Mehta N.P. (1999), Maintenance management for ammonia synthesis system in
46
a urea fertilizer plant; International Journal of Management and System (IJOMAS) 15(3); 211-214.
47
[24] Lim T.J., Chang H.K. (2000), Analysis of system reliability with dependent repair models; IEEE Trans
48
Reliab 49(2); 153–62.
49
[25] Misra K.B. (1992), Reliability analysis and prediction; Elsevier, Amsterdam.
50
[26] Sahner R.A., Trivedi K.S. (1987), Reliability modeling using SHARPE; IEEE Trans Reliability
51
R36(2); 186–193.
52
[27] Samrout M., Yalaoui F., Chatelet E., Chebbo N. (2005), Few methods to minimize the preventive
53
maintenance cost of series-parallel systems using ant colony optimization; Reliability Engineering and
54
system safety 89(3); 346-54.
55
[28] Sanders W.H., Obal W.D. (1993), Dependability evaluation using UltraSAN; In Proc. of The Twenty-
56
Third International Symposium on Fault- Tolerant Computing; 674–679.
57
[29] Sharma A.K. (1994), Reliability analysis of various complex systems in thermal power station; M.Tech
58
Thesis; Kurukshetra University (REC Kurukshetra), Haryana, INDIA.
59
[30] Smotherman M.K., Dugan J.B., Trivedi K.S., Geist R.M. (1986), The hybrid automated reliability
60
predictor; AIAA Journal of Guidance, Control and Dynamics 5(May/June); 319–331.
61
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62
analysis for systems with variable configuration; IEEE Transactions on Reliability 41(4); 504–511.
63
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64
and monitoring; In Proc. of Annual Reliability and Maintainability Symposium; 195–200.
65
[33] Srinath L.S. (1994), Reliability engineering; 3rd edition, East-West Press Pvt. Ltd.; New Delhi, India.
66
ORIGINAL_ARTICLE
Integrative Cell Formation and Layout Design in Cellular Manufacturing Systems
This paper proposes a new integrative view of manufacturing cell formation and both inter-cell and intra-cell layout problems. Cells formation and their popular bi-directional linear layout are determined simultaneously through a Dynamic Programming algorithm (with the objective of minimizing the inter-cell flow cost under a cell size constraint). This Dynamic Programming algorithm is implemented in a Simulated Annealing approach with Genetic operators to reach near optimal solutions. Moreover, within this approach and by using an Ant Colony Optimization technique, we also solve the intra-cell layout problem, i.e., we also determine how to lay out machines within relative cells. In contrast with most of the available approaches in the literature, we consider: (1) An integrated objective function to minimize overall inter-cell and intra-cell flow costs instead of merely minimizing the number of inter-cell movements/costs. (2) The integrative and simultaneous determination of cell formation and their layout instead of using sequential approaches. (3) All three phases of cell formation, intercell and intra-cell layout design problems, which are all important for overall performance of the system, and (4) An easy to code and solve integrated procedure through implementing metaheuristic approaches. Our computational results show that by incorporating intra-cell decisions in cell formation and inter-cell design process through implementing our proposed integrated approach, a manufacturer can largely reduce her total material flow cost. Particularly, our computational tests show good quality solutions in comparison with the most similar available approach in the literature with an average improvement of 24.97% in total flow cost for a set of randomly generated test problems.
https://www.jise.ir/article_4003_b56d51780836f790c0e990b8966a2770.pdf
2009-07-01
97
115
Cell formation
Intra-cell Layout
Inter-cell Layout
Simulated Annealing
Dynamic
Programming
ant colony optimization
Graph theory
Soroush
Saghafian
1
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
AUTHOR
M. Reza
Akbari Jokar
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Akturk M.S., Turkcan A. (2000), Cellular manufacturing system design using a holonistic approach;
1
International Journal of Production Research 38(10); 2327-2347.
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] Alfa A.S., Chen M., Heragu S.S. (1992), Integrating the grouping and layout problems in cellular
5
manufacturing systems; Computers & Industrial Engineering 23; 55-58.
6
[4] Arvindh B., Irani S.A. (1994), Cell formation: the need for an integrated solution of the sub problems.
7
International Journal of Production Research 32; 1197-1218.
8
[5] Askin R.G., Standridge C.R. (1994), Modeling and analyzing manufacturing systems; Wiley, New
9
[6] Askin R., Subramanian S. (1987), A cost-based heuristic for group technology configuration;
10
International Journal of Production Research 25(1); 101–113.
11
[7] Bazargan-Lari M., Kaebernick H., Harraf A. (2000), Cell formation and layout design in a cellular
12
manufacturing environment-a case study; International Journal of Production Research 38(7); 1689-
13
[8] Burbidge J.L. (1975), The Introduction of Group Technology; Wiley, New York.
14
[9] Chen W-H, Srivastava B. (1994), Simulated annealing procedures for forming machine cells in group
15
technology; European Journal of Operational Research 75; 100–11.
16
[10] Chiang C.-P., Lee S.-D. (2004), Joint determination of machine cells and linear intercell layout;
17
Computer and Operations Research 31(10); 1603-1619.
18
[11] Colorni A., Dorigo M., Maniezzo V., Trubian M. (1994), Ant system for job-shop scheduling.
19
JORBEL; Belgian Journal of Operations Research Statistics and Computer Science 34 (1); 39–53.
20
[12] Dorigo M., Gambardella L.M. (1997), Ant colony system: a cooperative learning approach to the
21
traveling salesman problem; IEEE Transactions on Evolutionary Computation (1); 53–66.
22
[13] Gambardella L.M., Taillard E.D., Dorigo M. (1999), Ant colonies for the QAP; Journal of Operational
23
Research Society 50; 167–176.
24
[14] Garey M.R., Johnson D.S. (1979), Computers and intractability: a guide to the theory of NPcompleteness;
25
San Francisco, Freeman.
26
[15] Ham I., Hitomi K., Yoshida T. (1985), Layout planning for group technology in group technology;
27
Applications to Production Managements; 153-169.
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[16] Harhalakis G., Nagi R., Proth J. (1990), An efficient heuristic in manufacturing cell formation of group
29
technology application; International Journal of Production Research 28; 185-198.
30
[17] Irani S.A., Cavalier T.M., Cohen P.H. (1993), Virtual manufacturing cells: exploiting layout design
31
and intercell flows for the machine sharing problem; International Journal of Production Research 31;
32
[18] Irani S. A. (1999), Handbook of Cellular Manufacturing Systems; Wiley, New York.
33
[19] Bazargan-Lari M., Kaebernick H. (1996), Intra-cell and inter-cell layout designs for cellular
34
manufacturing; International Journal of Industrial Engineering-Applications and Practice 3; 139-150.
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[21] Laarhoven V.P.J.M., Aarts E.H.L (1988), Simulated Annealing: Theory and Applications; Kluwer,
37
[22] Lee S-D, Chen Y-L. (1997), A weighted approach for cellular manufacturing design: minimizing
38
intercell movement and balancing workload among duplicated machines; International Journal of
39
Production Research 35; 1125–46.
40
[23] Lee S.-D., Chiang C.-P. (2001), A cut-tree-based approach for clustering approach for clustering
41
machine cells in the bi-directional linear flow layout; International Journal of Production Research
42
39; 3491-3512.
43
[24] Lee S.-D., Chiang C.-P. (2002), Cell formation in the uni-directional loop material handling
44
environment; European Journal of Operational Research 137; 401-420.
45
[25] Maniezzo V. (1999), Exact and approximate nondeterministic tree-search procedures for the quadratic
46
assignment problem; INFORMS Journal on Computing 11; 358–369.
47
[26] Maniezzo V., Colorni A. (1999), The ant system applied to the quadratic assignment problem; IEEE
48
Transactions on Knowledge and Data Engineering 11(5); 769–778.
49
[27] Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., Teller E. (1953), Equation of state
50
calculations by fast computing machines; Journal of Chemical Physics 21; 1087-1092.
51
[28] Mitrofanov S.P. (1966), The scientific principles of group technology; National Lending Library of
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Science and Technology, Boston Spa, Yorkshire; England.
53
[29] Olorunniwo F. (1996), Changes in production planning and control systems with implementation of
54
cellular manufacturing; Production and Inventory Management, First Quarter; 65-69.
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[30] Salum L. (2000), The cellular manufacturing layout problem; International Journal of Production
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Research 38(5); 1053-1069.
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[31] Shunk D.L. (1987), Computer integrated manufacturing in Manufacturing High Technology Handook;
58
New York; 83-100.
59
[32] Solimanpur M., Vrat P., Shankar R. (2004), Ant colony optimization algorithm to the inter-cell layout
60
problem in cellular manufacturing; European Journal of Operational Research 157(3); 592-606.
61
[33] Vakharia A.J. (1986), Methods of cell formation in group technology: A frame work for evaluation;
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Journal of Operations Management 6(3); 257-272.
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64
approach based on operation sequence; IIE Transactions 22; 84-97.
65
[35] Verma P., Ding F.Y. (1995), A sequence-based materials flow procedure for designing materials flow
66
procedure for designing manufacturing cells; International Journal of Production Research 33; 3267-
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[36] Wang T.Y., Lin H.C., Wu K.B. (1998), An improved simulated annealing for facility layout problems
68
in cellular manufacturing systems; Computers & Industrial Engineering 34; 309-319.
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Production Research 25(3); 413-431.
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International Journal of Production Research 27(9); 1511-1530.
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[39] Wemmerlov U., Hyer N.L. (2002), “Reorganizing The Factory” competing through cellular
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manufacturing; Productivity Press.
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[40] Wemmerlov U., Johnson D.J. (1997), Cellular manufacturing at 46 user plants: Implementation
76
experiences and performance improvements; International Journal of Production Research 35; 29–49.
77
ORIGINAL_ARTICLE
Software Implementation and Experimentation with a New Genetic Algorithm for Layout Design
This paper discusses the development of a new GA for layout design. The GA was already designed and reported. However the implementation used in the earlier work was rudimentary and cumbersome, having no suitable Graphical User Interface, GUI. This paper discusses the intricacies of the algorithm and the GA operators used in previous work. It also reports on implementation of a new GA operator which was not included in earlier reports. The software was then used to conduct a series of experimentations to establish the best configuration of the operators for better results. The paper is also demonstrating a comparison of the new GA results and results from the literature. In addition the results show the solution of two new problems by various methods from the author’s own layout developments in industry. The results demonstrate that in most cases the new GA is superior to the existing methods. In particular the speed of the new GA is achieving a reasonable solution is significantly low.
https://www.jise.ir/article_4004_748ce9bb872844edec13d97639159a83.pdf
2009-07-01
116
124
Layout design
Genetic algorithm
Graphical user interface
E.
Shayan
1
Department of Operations Management, Kazakhstan Institute f Management, Economics and Strategic Research (KIMEP), 4 Abai St , Almaty, Kazakhstan
AUTHOR
1] Bazaraa M.S. (1975), Computational Layout Design: a branch-and-bound approach, AIIE Transactions
1
7(4); 432-438.
2
[2] Bazaraa M.S., Kirca O. (1983), A Branch-and-bound heuristic to solve QAP; Naval research logistics
3
quarterly 30; 287-304.
4
[3] Bazaraa M.S., Sherali M.D. (1980), Benders' partitioning scheme applied to a new formulation of
5
Quadratic Assignment Problem; Naval Research logistic quarterly 27(1); 29-41.
6
[4] Chittallappilly A. (2003), A Genetic Algorithm with two-dimensional chromosomes for Facility
7
Layout; Swinburne University of Technology.
8
[5] Francis R.L., McGinnis L.F, White J.A. (1992), Facility Layout and Location: An Analytical
9
Approach; Prentice-Hall, Englewood Cliffs, NJ.
10
[6] Gero J.S., Kazakov V. (1997), Learning and reusing information in space layout planning problems
11
using genetic engineering; Artificial Intelligence Engineering 11(4); 329–334.
12
[7] Gorey M.R., Johnson D.S. (1979), Computers and intractability: a guide to the theory of NPcompleteness;
13
W. H. Freeman, New York.
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[8] Hanafi R. (2000), Design of a Genetic Algorithm to solve the Facility Layout problem; Swinburne
15
University of Technology, Melbourne, unpublished dissertation.
16
[9] Holland J. (1975), Adaptation in Natural and Artificial Systems; Ann Arbor, The University of
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Michigan Press.
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[10] Imam M.H., Mir M. (1993), Automated layout of facilities of unequal areas; Computers and Industrial
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Engineering 24(3); 355-366.
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[11] Kusiak A., Heragu S.S. (1987), The facility layout problem; European Journal of Operations Research
21
29; 229–251.
22
[12] Moghaddam R.T. (1997), Design of a genetic algorithm to solve manufacturing facilities layout
23
problem; Swinburne University of Technology, Melbourne, dissertation.
24
[13] Moghaddam R.T., Shayan E. (1998), Facility layout design by genetic algorithm; International
25
Journal of computers and Industrial Engineering 35(3-4); 527-530.
26
[14] Pinto Wilsten J. , Shayan E. (2007), Layout Design of a Furniture Production Line Using Formal
27
Methods, Journal of Industrial and Systems Engineering 1(1); 81-96.
28
[15] Shayan E., Al-Hakim L. (1999), Cloning in layout design problem: A genetic algorithm approach;
29
Proceedings of the 15th International Conference on Production Research (Hillery M., Lewis, H.
30
Eds.); University of Limerick, Ireland; 787-792.
31
[16] Shayan E., Chittilappilly A. (2004), Genetic algorithm for facilities layout problems based on slicing
32
tree structure; International Journal of Production Research 42(19); 4055-4067.
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Production Research 33; 3411-23.
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[18] Tam K.Y. (1992), A simulated annealing algorithm for allocating space to manufacturing cells;
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International Journal of Production Research 30(1); 63–87.
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[19] Tam K.Y. (1992), Genetic algorithm, function optimization, and facility layout design; European
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Journal of Operational Research 63; 322–346.
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[20] Tam K.Y., Chan D.K. (1998), Solving facility layout problems with geometric constraints using
40
parallel genetic algorithms: experimentation and findings; International Journal of Production
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Research 36(12); 3253–3272.
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[21] Tate David M., Smith Alice E. (1995), A Genetic Approach to the Quadratic Assignment Problem;
43
Computers and Operations Research 22(1); 73-83.
44
[22] Tompkins James A., White John A., Bozer Yavuz A. (1996), Facilities Planning, 2nd Edition; John
45
Wiley & Sons Canada, Ltd
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[23] Wang Rong-Long, Okazaki Kozo (2004), Solving Facility Layout Problem Using an Improved
47
Genetic Algorithm; ICICE transactions.
48
[24] Zhang L., Zhang B. (1999), A Geometrical Representation of McCulloch-Pitts Neural Model and Its
49
Applications; IEEE Transactions on Neural Networks 10(4); 291-295.
50
ORIGINAL_ARTICLE
A Model for Sharing the Costs of Uncontrollable Risks among Contracting Parties
The allocation of risks among the contracting parties in a contract is an important decision affecting the project success. Some risks in a project are uncontrollable; these are imposed to a project by external factors. Since contracting parties can neither control nor affect the occurrence of such risks, their allocation to a party would be inequitable. Therefore the cost overrun related to uncontrollable risks should be shared between contracting parties with a ratio which makes a win-win relationship between them in contract. This paper presents a mathematical model to achieve an equitable cost sharing ratio for uncontrollable risks between an owner and a contractor before contract conclusion using multi attribute utility theory.
https://www.jise.ir/article_4005_aefae59fdbe34d7e332a2016cfa8f99b.pdf
2009-07-01
125
139
Cost sharing ratio
Uncontrollable risks
Contract
Contracting parties
Utility value
Win-win relationship
Ahmad
Makui
1
Iran University of Science and Technology, Tehran, Iran
AUTHOR
Iraj
Mahdavi
2
Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
Fatemeh
Farrokhian
3
Mazandaran University of Science and Technology, Babol, Iran
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ORIGINAL_ARTICLE
Performance Analysis of Screening Unit in a Paper Plant Using Genetic Algorithm
This paper deals with the performance analysis of the screening unit in a paper plant using Genetic Algorithm. The screening unit in the paper plant has four main subsystems. These subsystems are arranged in series and parallel configurations. Considering exponential distribution for the probable failures and repairs, the mathematical formulation of the problem is done by Markov birth-death process. Using probabilistic approach, the differential equations are developed. These equations are then solved using normalizing conditions to determine the steady state availability of the screening unit. The performance behavior of each subsystem of the screening unit has also been analyzed using Genetic Algorithm. So, the findings of the present paper will be highly useful to the plant management for the timely execution of proper maintenance decisions and hence to enhance the system performance.
https://www.jise.ir/article_4006_37f637cb2de6aaae0de9d72a0fa9120a.pdf
2009-07-01
140
151
Performance Modeling
Screening Unit
Genetic algorithm
Rajiv
Khanduja
1
Department of Mechanical Engineering, SKIET, Kurukshetra-136118, Haryana, India.
AUTHOR
P. C.
Tewari
2
Department of Mechanical Engineering, NIT, Kurukshetra-136119, Haryana, India.
AUTHOR
R.S.
Chauhan
3
Department of Electronic and Communication Engineering, JMIT, Radaur, Yamuna Nagar- 135133, Haryana, India
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