ORIGINAL_ARTICLE
The optimal warehouse capacity: A queuing-based fuzzy programming approach
Among the various existing models for the warehousing management, the simultaneous use of private and public warehouses is as the most well-known one. The purpose of this article is to develop a queuing theory-based model for determining the optimal capacity of private warehouse in order to minimize the total corresponding costs. In the proposed model, the available space and budget to create a private warehouse are limited. Due to the ambiguity, some parameters are naturally simulated by expert-based triangular fuzzy numbers and two well-known methods are applied to solve the queuing-based fuzzy programming model and optimize the private warehouse capacity. The numerical results for three cases confirm that unlike the previous approaches, the proposed one may easily and efficiently be matched with various lines of manufacturing environments and conditions.
http://www.jise.ir/article_8740_0c83bab990b19e209eba7f049d794912.pdf
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12
Optimal warehouse capacity
Queuing Theory
fuzzy programming
multi-objective
Saeed
Khalili
s.khalili1367@yahoo.com
true
1
Department of Industrial Engineering, Yazd University, Yazd, IRAN
Department of Industrial Engineering, Yazd University, Yazd, IRAN
Department of Industrial Engineering, Yazd University, Yazd, IRAN
AUTHOR
Mohammad
Lotfi
lotfi@yazd.ac.ir
true
2
Department of Industrial Engineering, Yazd University, Yazd, IRAN
Department of Industrial Engineering, Yazd University, Yazd, IRAN
Department of Industrial Engineering, Yazd University, Yazd, IRAN
LEAD_AUTHOR
Ashayeri, J., and Gelders, L.,(1985),Warehouse design optimization; European Journal of Operational
1
Research, 21(3), 285-294.
2
Ballou, R. H.,(1985), Business Logistics Management: planning and control: Prentice-Hall.
3
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4
Journal of Operational Research, 193(2), 425-436.
5
Barak, S., and Fallahnezhad, M., (2012), Cost Analysis of Fuzzy Queuing Systems, International
6
Journal of Applied Operational Research, 2(2), 25-36.
7
Cormier, G., and Gunn, E. A., “A review of warehouse models,” European Journal of
8
Operational Research, vol. 58, no. 1, pp. 3-13, 4/10/, 1992.
9
Cormier, G., and Gunn, E. A., (1996), On coordinating warehouse sizing, leasing and inventory
10
policy, IIE transactions, 28(2), 149-154.
11
Friemann, F., Rippel, M., & Schönsleben, P. (2014). Warehouse Capacities in the
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Pharmaceutical Industry–Plan or Outsource?. In Advances in Production Management Systems.
13
Innovative and Knowledge-Based Production Management in a Global-Local World (pp. 427-
14
434). Springer Berlin Heidelberg.
15
Gill, A., (2009), A Warehousing Option Model in Supply Chain Management; Administrative
16
Sciences Association of Canada (ASAC), canada; 30 (2).
17
Goh, M., Jihong, O., and Chung‐Piaw, T., (2001), Warehouse sizing to minimize inventory and
18
storage costs, Naval Research Logistics (NRL), 48(4), 299-312.
19
Gu, J., Goetschalck, M., and McGinnis, L. F., (2010), Research on warehouse design and
20
performance evaluation: A comprehensive review; European Journal of Operational Research,
21
203( 3), 539-549.
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Hung, M. S., and Fisk, J. C.,(1984), Economic sizing of warehouses: A linear programming
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approach, Computers & Operations Research, 11(1), 13-18.
24
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25
problem; a review of models and algorithms; Omega 31; 365-378.
26
Ke, J.-C., and Lin, C.-H., (2006), Fuzzy analysis of queueing systems with an unreliable server:
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A nonlinear programming approach, Applied mathematics and computation, 175(1), 330-346.
28
Kumar, V. A., (2012), A Nonlinear Programming Approach For a Fuzzy queue with an
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unreliable server, Bulletin of Society for mathematical services & standards (B SO MA SS), 1(2),
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Petinis, V., Tarantilis*, C. D., and Kiranoudis, C. T., (2000), Warehouse sizing and inventory
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of Operational Research, 108(1), 16-25.
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685). Springer US.
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46
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1870214846, http://books.google.com/books?id=xNFdAAAACAAJ.
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50
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51
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52
ORIGINAL_ARTICLE
A new mathematical model for intensity matrix decomposition using multileaf collimator
Cancer is one of the major causes of death all over the globe and radiotherapy is considered one of its most effective treatment methods. Designing a radiotherapy treatment plan was done entirely manually in the past. RecentlyIntensity Modulated Radiation Therapy (IMRT) was introduced as a new technology with advanced medical equipmentin the recent years. IMRT provides the opportunity to deliver complex dose distributions to cancer cells while sparing the vital tissues and cells from the harmful effects of the radiations. Designing an IMRT treatment plan is a very complex matter due to the numerous calculations and parameters which must be decided for. Such treatment plan is designed in three separate phases: 1) selecting the number and the angle of the beams 2) extracting the intensity matrix or the corresponding dose map of each beam and 3) realizing each intensity matrix. The third phase has been studied in this research and a nonlinear mathematical model has been proposed for multileaf collimators. The proposed model has been linearized through two methods and an algorithm has been developed on its basis in order to solve the model with cardinality objective function. Obtained results are then compared with similar studies in the literature which reveals the capability of proposed method.
http://www.jise.ir/article_8741_e4ed262ab297a95dd1ba3ea5671a95af.pdf
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Intensity modulated radiation therapy (IMRT)
Decomposing intensity matrix
Multileaf collimator
Benders Decomposition
Integer programming
Vahid
Mahmoodian
vahid_mahmoodian@ind.iust.ac.ir
true
1
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
LEAD_AUTHOR
ahmad
Makui
amakui@iust.ac.ir
true
2
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
AUTHOR
Mohammad
Gholamian
gholamian@iust.ac.ir
true
3
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
AUTHOR
Ahuja, R. K. and Hamacher, H. W., (2005), A network flow algorithm to minimize beam‐on time for
1
unconstrained multileaf collimator problems in cancer radiation therapy,. Networks, 45, 36-41.
2
Baatar, D., Boland, N., Brand, S. and Stuckey, P. J., (2007), Minimum cardinality matrix decomposition
3
into consecutive-ones matrices: CP and IP approaches, Integration of AI and OR Techniques in
4
Constraint Programming for Combinatorial Optimization Problems. Springer.
5
Baatar, D., Hamacher, H. W., Ehrgott, M. & Woeginger, G. J.,( 2005), Decomposition of integer matrices
6
and multileaf collimator sequencing., Discrete Applied Mathematics, 152, 6-34.
7
Bai, L. & Rubin, P. A.,(2009), Combinatorial benders cuts for the minimum tollbooth problem,
8
Operations Research, 57, 1510-1522.
9
Benders, J. F., (2005), Partitioning procedures for solving mixed-variables programming problems,
10
Computational Management Science, 2, 3-19.
11
Boland, N., Hamacher, H. W. & Lenzen, F., (2004), Minimizing beam‐on time in cancer radiation
12
treatment using multileaf collimators, Networks, 43, 226-240.
13
Cambrazard, H., O’mahony, E. & O’Sullivan, B.,(2010), Hybrid methods for the multileaf collimator
14
sequencing problem, Integration of AI and OR Techniques in Constraint Programming for Combinatorial
15
Optimization Problems. Springer.
16
Cambazard, H., O’mahony, E. & O’Sullivan, B.,( 2012), A shortest path-based approach to the multileaf
17
collimator sequencing problem., Discrete Applied Mathematics, 160, 81-99.
18
Collins, M. J., Kempe, D., Saia, J. & Young, M.,( 2007), Nonnegative integral subset representations of
19
integer sets, Information Processing Letters, 101, 129-133.
20
Ehrgott, M., Guller, Ç., Hamacher, H. W. & Shao, L., (2008), Mathematical optimization in intensity
21
modulated radiation therapy. 4OR, 6, 199-262.
22
Engel, K.,(2005), A new algorithm for optimal multileaf collimator field segmentation, Discrete Applied
23
Mathematics, 152, 35-51.
24
Ernst, A. T., Mak, V. H. & Mason, L. R., (2009), An exact method for the minimum cardinality problem
25
in the treatment planning of intensity-modulated radiotherapy,. INFORMS Journal on Computing, 21,
26
Gleeson, J. & Ryan, J., (1990), Identifying minimally infeasible subsystems of inequalities,. ORSA
27
Journal on Computing, 2, 61-63.
28
Kalinowski, T.,(2009), The complexity of minimizing the number of shape matrices subject to minimal
29
beam-on time in multileaf collimator field decomposition with bounded fluence, Discrete Applied
30
Mathematics, 157, 2089-2104.
31
Mak, V., (2007), Iterative variable aggregation and disaggregation in IP: An application, Operations
32
research letters, 35, 36-44.
33
Mason, L. R., Mak-Hau, V. H. & Ernst, A. T., (2012), An exact method for minimizing the total
34
treatment time in intensity-modulated radiotherapy, Journal of the Operational Research Society, 63,
35
1447-1456.
36
Meyer, J. L., Verhey, L., Pia, L. & Wong, J., (2006), IMRT· IGRT· SBRT.
37
Pishvaee, M. S., Razmi, J. & Torabi, S. A., (2014), An accelerated Benders decomposition algorithm for
38
sustainable supply chain network design under uncertainty: A case study of medical needle and syringe
39
supply chain, Transportation Research Part E: Logistics and Transportation Review, 67, 14-38.
40
Schlegel, W. & Mahr, A., (2007), 3D conformal radiation therapy: Multimedia introduction to methods
41
and techniques, Springer Publishing Company, Incorporated.
42
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43
paradigm, International Journal of Radiation Oncology* Biology* Physics, 43, 671-680.
44
Taskin, Z. C. & Cevik, M.,(2013), Combinatorial Benders cuts for decomposing IMRT fluence maps
45
using rectangular apertures, Computers & Operations Research, 40, 2178-2186.
46
Taskin, Z. C., Smith, J. C. & Romeijn, H. E., (2012), Mixed-integer programming techniques for
47
decomposing IMRT fluence maps using rectangular apertures,. Annals of Operations Research, 196, 799-
48
Taskin, Z. C., Smith, J. C., Romeijn, H. E. & Dempsey, J. F.,(2010), Optimal multileaf collimator leaf
49
sequencing in IMRT treatment planning, Operations Research, 58, 674-690.
50
ORIGINAL_ARTICLE
An Improved DPSO Algorithm for Cell Formation Problem
Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell formation problem effectively. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optimum becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called discrete particle swarm optimization-simulated annealing (DPSO-SA), based on the idea that PSO ensures fast convergence, while SA brings search out of local optimum. To illustrate the behavior of the proposed model and verify the performance of the algorithm, we introduce a number of numerical examples. The performance evaluation shows the effectiveness of the DPSO-SA.
http://www.jise.ir/article_8742_85808f67343cdd5bd0c5e0005689c387.pdf
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Particle Swarm Optimization
Simulated Annealing
Cellular manufacturing problem
meta-heuristic algorithms
Ashkan
Hafezalkotob
a_hafez@azad.ac.ir
true
1
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad university, South Tehran Branch
LEAD_AUTHOR
Maryam
Tehranizadeh
m.amiri.tehrani@gmail.com
true
2
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
AUTHOR
Fatemeh
Sarani Rad
f.sarani.rad@gmail.com
true
3
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Department of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
AUTHOR
Mohammad
Sayadi
mksayadi@iust.ac.ir
true
4
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad university, South Tehran Branch
AUTHOR
Albadawi, Z., Bashir, H. A., & Chen, M. (2005).A mathematical approach for the formation of
1
manufacturing cells. Computers & Industrial Engineering,48(1), 3-21.
2
Aljaber, N., Baek, W., & Chen, C. L. (1997).A tabu search approach to the cell formation
3
problem. Computers & industrial engineering, 32(1), 169-185.
4
Banks, A., Vincent, J., &Anyakoha, C. (2008).A review of particle swarm optimization. Part II:
5
hybridisation, combinatorial, multicriteria and constrained optimization, and indicative
6
applications. Natural Computing, 7(1), 109-124.
7
Bertsimas, D., &Tsitsiklis, J. (1993).Simulated annealing. Statistical science,8(1), 10-15.
8
Boulif, M., &Atif, K. (2006).A new branch-&-bound-enhanced genetic algorithm for the
9
manufacturing cell formation problem. Computers & operations research,33(8), 2219-2245.
10
Burbidge, J. L.(1989). Production Flow Analysis. Oxford: Clarendon Press.
11
Caux, C., Bruniaux, R., &Pierreval, H. (2000). Cell formation with alternative process plans and
12
machine capacity constraints: A new combined approach. International Journal of Production
13
Economics, 64(1), 279-284.
14
Chu, C. H., &Hayya, J. C. (1991).A fuzzy clustering approach to manufacturing cell
15
formation. The international journal of production research, 29(7), 1475-1487.
16
Dimopoulos, C., &Zalzala, A. M. (2000). Recent developments in evolutionary computation for
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manufacturing optimization: problems, solutions, and comparisons. Evolutionary Computation,
18
IEEE Transactions on, 4(2), 93-113.
19
Gonçalves, J. F., &Resende, M. G. (2004).An evolutionary algorithm for manufacturing cell
20
formation. Computers & Industrial Engineering, 47(2), 247-273.
21
Hachicha, W., Masmoudi, F., &Haddar, M. (2008). Principal component analysis model for
22
machine-part cell formation problem in group technology. arXiv preprint arXiv:0803.3343.
23
Hwang, H., &Ree, P. (1996).Routes selection for the cell formation problem with alternative part
24
process plans. Computers & industrial engineering, 30(3), 423-431.
25
Kao, Y., & Lin, C. H. (2012).A PSO-based approach to cell formation problems with alternative
26
process routings. International Journal of Production Research,50(15), 4075-4089.
27
Kennedy, J., &Eberhart, R. (1995).Particle swarm optimization.Proceeding of IEEE
28
International Conference on Neural Networks, 4, 1942-1948.
29
Kirkpatrick, S., Gelatt, C. D., &Vecchi, M. P. (1983).Optimization by simmulated
30
annealing. science, 220(4598), 671-680.
31
Kusiak, A. (1987). The generalized group technology concept. International journal of
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production research, 25(4), 561-569.
33
Li, J., Chu, C. H., Wang, Y., & Yan, W. (2007).An improved fuzzy clustering method for
34
cellular manufacturing. International journal of production research,45(5), 1049-1062.
35
Li, X., Baki, M. F., &Aneja, Y. P. (2010).An ant colony optimization metaheuristic for machine–
36
part cell formation problems. Computers & Operations Research, 37(12), 2071-2081.
37
Nagi, R., Harhalakis, G., &Proth, J. M. (1990). Multiple Routings and Capacity Consideration in
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Group Technology Applications. International Journal of Production Research,28, pp. 2243-
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Nair, G. J., &Narendran, T. T. (1998). CASE: A clustering algorithm for cell formation with
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sequence data. International Journal of Production Research, 36(1), 157-180.
41
Niknam, T. (2006, November). An approach based on particle swarm optimization for optimal
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operation of distribution network considering distributed generators. In IEEE Industrial
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Electronics, IECON 2006-32nd Annual Conference on (pp. 633-637).IEEE.
44
Niknam, T., Amiri, B., Olamaei, J., &Arefi, A. (2009). An efficient hybrid evolutionary
45
optimization algorithm based on PSO and SA for clustering.Journal of Zhejiang University
46
Science A, 10(4), 512-519.
47
Noktehdan, A., Karimi, B., &HusseinzadehKashan, A. (2010). A differential evolution algorithm
48
for the manufacturing cell formation problem using group based operators. Expert Systems with
49
Applications, 37(7), 4822-4829.
50
Nouri, H., & Hong, T. S. (2013). Development of bacteria foraging optimization algorithm for
51
cell formation in cellular manufacturing system considering cell load variations. Journal of
52
Manufacturing Systems, 32(1), 20-31.
53
Mahdavi, I., Teymourian, E., Baher, N. T., &Kayvanfar, V. (2013).An integrated model for
54
solving cell formation and cell layout problem simultaneously considering new
55
situations. Journal of Manufacturing Systems,32(4), 655-663.
56
Olamaei, J., Niknam, T., &Gharehpetian, G. (2008). Application of particle swarm optimization
57
for distribution feeder reconfiguration considering distributed generators. Applied Mathematics
58
and computation, 201(1), 575-586.
59
Pailla, A., Trindade, A. R., Parada, V., & Ochi, L. S. (2010). A numerical comparison between
60
simulated annealing and evolutionary approaches to the cell formation problem. Expert Systems
61
with Applications, 37(7), 5476-5483.
62
Paydar, M. M., &Sahebjamnia, N. (2009).Designing a mathematical model for cell formation
63
problem using operation sequence. Journal of Applied Operational Research, 1(1), 30-38.
64
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization.Swarm
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intelligence, 1(1), 33-57.
66
Saeidi, S., Solimanpur, M., Mahdavi, I., &Javadian, N. (2014).A multi-objective genetic
67
algorithm for solving cell formation problem using a fuzzy goal programming approach. The
68
International Journal of Advanced Manufacturing Technology, 70(9-12), 1635-1652.
69
Sankaran, S., &Kasilngam, R. G. (1990).An integrated approach to cell formation and part
70
routing in group technology manufacturing systems.Engineering Optimization+ A35, 16(3), 235-
71
Sayadi, M. K., Hafezalkotob, A., &Naini, S. G. J. (2013). Firefly-inspired algorithm for discrete
72
optimization problems: An application to manufacturing cell formation. Journal of
73
Manufacturing Systems, 32(1), 78-84.
74
Shafer, S. M., & Meredith, J. R. (1990).A comparison of selected manufacturing cell formation
75
techniques. The international journal of production research, 28(4), 661-673.
76
Shiyas, C. R., &MadhusudananPillai, V. (2014).A mathematical programming model for
77
manufacturing cell formation to develop multiple configurations. Journal of Manufacturing
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Systems, 33(1), 149-158.
79
Shunk, D. L. (1985). Group Technology Provides Organized Approach to Realizing Benefits of
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CIMS.Industrial Engineering, 17, 74-81.
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Sofianopoulou, S.(1999). Manufacturing cells design with alternative process plans and/or
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replicate machines. International Journal of Production Research,37(3), 707-720.
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Sun, D., Lin, L., &Batta, R. (1995).Cell formation using tabusearch.Computers& industrial
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engineering, 28(3), 485-494.
85
Tavakkoli-Moghaddam, R., Javadian, N., Khorrami, A., &Gholipour-Kanani, Y. (2010).Design
86
of a scatter search method for a novel multi-criteria group scheduling problem in a cellular
87
manufacturing system. Expert Systems with Applications, 37(3), 2661-2669.
88
Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G. R., &Siadat, A. (2012).A cell
89
formation problem considering machine utilization and alternative process routes by scatter
90
search. Journal of Intelligent Manufacturing, 23(4), 1127-1139.
91
Won, Y. (2000).Two-phase approach to GT cell formation using efficient p-median
92
formulations. International Journal of Production Research, 38(7), 1601-1613.
93
Wu, T. H., Chang, C. C., &Yeh, J. Y. (2009).A hybrid heuristic algorithm adopting both
94
Boltzmann function and mutation operator for manufacturing cell formation
95
problems. International Journal of Production Economics, 120(2), 669-688.
96
Wu, T. H., Chung, S. H., & Chang, C. C. (2009). Hybrid simulated annealing algorithm with
97
mutation operator to the cell formation problem with alternative process routings. Expert Systems
98
with Applications, 36(2), 3652-3661.
99
Wu, T. H., Low, C., & Wu, W. T. (2004). A tabu search approach to the cell formation
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problem. The International Journal of Advanced Manufacturing Technology, 23(11-12), 916-
101
Yang, M. S., & Yang, J. H. (2008). Machine-part cell formation in group technology using a
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modified ART1 method. European Journal of Operational Research, 188(1), 140-152.
103
Zhao, C., & Wu, Z. (2000).A genetic algorithm for manufacturing cell formation with multiple
104
routes and multiple objectives. International Journal of Production Research, 38(2), 385-395.
105
ORIGINAL_ARTICLE
A new last aggregation compromise solution approach based on TOPSIS method with hesitant fuzzy setting to energy policy evaluation
Utilizing renewable energies is identified as one of significant issues for economical and social significance in future human life. Thus, choosing the best renewable energy among renewable energy candidates is more important. To address the issue, multi-criteria group decision making (MCGDM) methods with imprecise information could be employed to solve these problems. The aim of this paper is to propose a new compromise solution approach based on technique for order preference by similarity to ideal solution (TOPSIS) method under evaluations of a group of experts with hesitant fuzzy information. The hesitant fuzzy set (HFS) is a modern fuzzy set which could help the experts by providing some membership degrees for renewable energy candidates under the evaluation criteria to margin of errors. Also, weights of each expert and criterion are determined by proposing extended hesitant fuzzy entropy and maximizing deviation methods based on hesitant fuzzy Euclidean-Hausdorff distance measure. In addition, the judgments (preferences) of experts are aggregated in the final step to prevent the loss of data. Finally, an illustrative example about the energy policy selection is presented to demonstrate the procedure of the proposed decision approach. Also, a comparative analysis is provided with the recent decision method of the literature to show the capability of the proposed approach.
http://www.jise.ir/article_8744_00a3d5bbf6a2cc7b5b4c3f2522bf0f08.pdf
2015-05-01T11:23:20
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66
Energy policy evaluation
Compromise solution approach
Hesitant fuzzy sets
Last aggregation
Individual regret
Weighting Methods
Masome
Mousavi
mousavi.m89@gmail.com
true
1
Department of Energy Economics, Economics Faculty, University of Tehran, Tehran, Iran
Department of Energy Economics, Economics Faculty, University of Tehran, Tehran, Iran
Department of Energy Economics, Economics Faculty, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Reza
Moghaddam
tavakoli@ut.ac.ir
true
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Afgan, Naim H and Maria G Carvalho. (2002). Multi-criteria assessment of new and renewable
1
energy power plants. Energy, 27: 739-755.
2
Akash, Bilal A, Rustom Mamlook and Mousa S Mohsen. (1999). Multi-criteria selection of electric
3
power plants using analytical hierarchy process. Electric power systems research, 52: 29-35.
4
Ansari, Asif Jamil and Imtiaz Ashraf. (2012). Best Energy Option Selection using Fuzzy Multicriteria
5
Decision Making Approach. International Journal of Advanced Renewable Energy
6
Researches (IJARER), 1.
7
Cavallaro, Fausto. (2013). Assessment of Nuclear Energy Competiveness Using a Multi-Criteria
8
Fuzzy Approach. International Journal of Energy Optimization and Engineering (IJEOE), 2: 21-36.
9
Doukas, Haris, Anastasia Tsiousi, Vangelis Marinakis and John Psarras. (2014). Linguistic multicriteria
10
decision making for energy and environmental corporate policy. Information Sciences, 258:
11
Erol, Özgür and Birol Kılkıs. (2012). An energy source policy assessment using analytical hierarchy
12
process. Energy Conversion and management, 63: 245-252.
13
Georgiou, Dimitris, Essam Sh Mohammed and Stelios Rozakis. (2015). Multi-criteria decision
14
making on the energy supply configuration of autonomous desalination units. Renewable Energy, 75:
15
Goumas, M and V Lygerou. (2000). An extension of the PROMETHEE method for decision making
16
in fuzzy environment: Ranking of alternative energy exploitation projects. European Journal of
17
Operational Research, 123: 606-613.
18
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ORIGINAL_ARTICLE
A comparison of algorithms for minimizing the sum of earliness and tardiness in hybrid flow-shop scheduling problem with unrelated parallel machines and sequence-dependent setup times
In this paper, the flow-shop scheduling problem with unrelated parallel machines at each stage as well as sequence-dependent setup times under minimization of the sum of earliness and tardiness are studied. The processing times, setup times and due-dates are known in advance. To solve the problem, we introduce a hybrid memetic algorithm as well as a particle swarm optimization algorithm combined with genetic operators. Also, an application of simulated annealing is presented for the evaluation of the algorithms. A Taguchi design is conducted to set their parameters. Finally, a comparison is made via 16 small size and 24 large size test problems and each problem is run 10times. The results of one-way ANOVA demonstrate that the proposed memetic algorithm performs as efficient as the HSA qualitatively and with 63.77% decline in elapsed time.
http://www.jise.ir/article_9797_36ccc21ed206710db1d68deaea1329f7.pdf
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67
85
scheduling
hybrid flow-shop
unrelated machines
sequence-dependent setup time
earliness-tardiness
Farzaneh
Abyaneh
true
1
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
LEAD_AUTHOR
Saeedeh
Gholami
s_gholami@kntu.ac.ir
true
2
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.ToosiUniversity of Technology, Tehran, Iran
AUTHOR
Acosta, J. H. T., González, V. A. P., & Bello, C. A. L. (2013). A Genetic Algorithm for
1
Simultaneous Scheduling Problem in Flexible Flow Shop Environments with Unrelated Parallel
2
Machines, Setup Time and Multiple Criteria. International Conference on Advanced
3
Manufacturing Engineering and Technologies. 203-210.
4
Attar, S.F., Mohammadi, M., & Tavakkoli-Moghaddam, R. (2009). Hybrid flexible flow-shop
5
scheduling problem with unrelated parallel machines and limited waiting times.Int J Adv Manuf
6
Technol, 68, 1583-1599.
7
Behnamian, j., Ghomi, S. M. T. F., & Zandieh, M. (2009). A multi-phase covering Paretooptimal
8
front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid
9
metaheuristic. Expert Syst Appl, 35, 11057-11069.
10
Behnamian, J., & Zandie, M. (2011). A discrete colonial competitive algorithm for hybrid
11
flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Syst Appl,
12
38, 14490-14498.
13
Behnamian, J., & Zandie, M. (2012).Earliness and Tardiness minimizing on a realistic hybrid
14
flowshop scheduling with learning effect by advanced metaheuristic.Arab J Sci Eng, 38, 1229-
15
Crowder, B. (2006). Minimizing the Makespan in a Flexible Flowshop with Sequence
16
Dependent Setup Times, Uniform Machines, and Limited BuffersMinimizing the Makespan in a
17
Flexible Flowshop with Sequence Dependent Setup Times, Uniform Machines, and Limited
18
Buffers. West Virginia University, virginia.
19
Dai, M., Tang, D., Zheng, K. & Cai, Q. (2013). An improved Genetic-Simulated Annealing
20
Algorithm based on a Hormone Modulation Mechanism for a flexible flow-shop scheduling
21
problem. Advances in Mechanical Engineering, 2013, 13 pages, doi:10.1155/2013/124903.
22
Davoudpour, H., & Ashrafi, M. (2009). Solving multi -objective SDST flexible flow shop using
23
GRASP algorithm. Int J Adv Manuf Tech, 44, 737-747.
24
Farkhzad, M. B., & Heydari, M. (2008). A heuristic algorithm for hybrid flow-shop production
25
scheduling to minimize the sum of the earliness and tardiness costs. JCIIE, 25, 105-115.
26
Gupta, J. N. D. (1988). Two-stage, hybrid flowshop scheduling problem. J Oper Res Soc, 39,
27
Haddad, M.N., Cota, L.P., Souza, M.J.F. & Maculan, N. (2014). A Heuristic Algorithm based
28
on Iterated Local Search and Variable Neigbourhood Descent for solving the unrelated parallel
29
machine scheduling problem with setup times. Proceedings of the 16th International Conference
30
on Enterprise Information Systems, 376-383.
31
Janiak, A., Kozan, E., Lichtenstein, M., & Eguz, C. (2007). Metaheuristic approaches to the
32
hybrid flow shop scheduling problem with a cost-related criterion. Int J Prod Econ, 105(2), 407-
33
Jolai, F., Rabiee, M. & Asefi, H. (2012). A novel hybrid meta-heuristic algorithm for a no-wait
34
flexible flow shop scheduling problem with sequence dependent setup times. International
35
Journal of Production Research, 50(24), 7447-7466.
36
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2005). An Evaluation of
37
Sequencing Heuristics for Flexible Flowshop Scheduling Problems with Unrelated Parallel
38
Machines and Dual Criteria. Otto-von-Guericke-Universitat Magdeburg, 28(5), 1-23.
39
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2008). Algorithms for
40
flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Int
41
J Adv Manuf Tech, 37, 354-370.
42
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2009). A comparison of
43
scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup
44
times, and dual criteria. Comput Oper Res, 36(2), 358-378.
45
Kurz, M. E., & Askin, R. G. (2004). Scheduling flexible flow lines with sequence-dependent
46
setup times. Euro JOper Res, 159, 66-82.
47
Liao, C.-J., Tsengb, C.-T., & Luarn, P. (2007). A discrete version of particle swarm
48
optimization for flowshop scheduling problems. Comput Oper Res, 34, 3099-3111.
49
Naderi, B., M. Z., , A. K. G. B., & , V. R. (2009). An improved simulated annealing for hybrid
50
flowshops with sequence-dependent setup and transportation times to minimize total completion
51
time and total tardiness. Expert Syst Appl, 36(6), 9625–9633.
52
Nahavandi, N., & Gangraj, E. A. (2014). A New Lower Bound for Flexible Flow Shop Problem
53
with Unrelated Parallel Machines. International Journal of Industrial Engineering, 25(1), 65-
54
Niu, Q., Jiao, B., & Gu, X. (2008). Particle swarm optimization combined with genetic
55
operators for job shop scheduling problem with fuzzy processing time. Appl Math Comput, 205,
56
Rabiee, M., Rad, R. S., Mazinani, M., & Shafaei, R. (2014). An intelligent hybrid metaheuristic
57
for solving a case of no-wait two-stage flexible flow shop scheduling problem with
58
unrelated parallel machines. The International Journal of Advanced Manufacturing Technology,
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71(5-8), 1229-1245.
60
Rashidi, E., Jahandar, M., & Zandieh, M. (2010). An improved hybrid multi -objective parallel
61
genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. Int J Adv
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Manuf Tech, 49, 1129-1139.
63
Ruiz, R., & Maroto, C. (2006). A genetic algorithm for hybrid flowshops with sequence
64
dependent setup times and machine eligibility. Eur J Oper Res, 169, 781-800.
65
Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. Eur J
66
Oper Res, 205, 1-18.
67
Soltani, S. A., & Karimi, B. (2014). Cyclic hybrid flow shop scheduling problem with limited
68
buffers and machine eligibility constraints. The International Journal of Advanced
69
Manufacturing Technology, 1-17.
70
Tasgetiren, M. F., Liang, Y.-C., Sevlili, M., & Gencyilmaz, G. (2004). Particle Swarm
71
Optimization Algorithm for Single Machine Total Weighted Tardiness Problem. Paper presented
72
at the 2004 IEEE.
73
Urlings, T., Ruiz, R., & Serifoğlu, F. S. (2010). Genetic algorithms with different representation
74
schemes for complex hybrid flexible flow line problems. IJMHeur, 1(1), 30-54.
75
Yaurima, V., Burtseva, L., & Tchernykh, A. (2009). Hybrid flowshop with unrelated machines,
76
sequence-dependent setup time, availability constraints and limited buffers. Comput Oper Res,
77
56(4), 1452–1463.
78
Zandieh, M., FatemiGhomi, S. M. T., & MoattarHusseini, S. M. (2006). An immune algorithm
79
approach to hybrid flow shops scheduling with sequence-dependent setup times. Appl Math
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Comput, 180(1), 111-127.
81
Zandieh, M., Mozaffari, E., & Gholami, M. (2010). A robust genetic algorithm for scheduling
82
realistic hybrid flexible flow line problems. J Intell Manufa, 21, 731-743.
83
Zhang, C., Ning, J., & Ouyang, D. (2010). A hybrid alternate two phases particle swarm
84
optimization algorithm for flow shop scheduling problem. Comput. Ind. Eng., 58, 1-11
85
ORIGINAL_ARTICLE
Economic manufacturing model under partial backordering and sustainability considerations
Today, “Sustainable production” has attracted a great deal of interest by academic researchers and practitioners due to the raising environmental and social concerns. Sustainable EPQ model has been developed as a result of this interest and also necessity. This paper develops a novel sustainable EPQ (SEPQ) model under partial backordering consideration. The model converts all emission variations of inventory production lifecycle into economic tangible factors. A solution procedure to determine the optimal solutions of the problem is developed for this SEPQ-PBO model. In order to demonstrate validity of the proposed model and applicability of the developed solution procedure, numerical examples accompanied by comprehensive sensitivity analysis of key parameters of the model are provided.
http://www.jise.ir/article_9799_8b4e2ea056d31fde24c1c334da5c34ea.pdf
2015-05-01T11:23:20
2020-07-09T11:23:20
86
96
Economic manufacturing model
Sustainability
Inventory
Shortage
Vahid
Soleymanfar
vrsoleymanfar@yahoo.com
true
1
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
AUTHOR
Ata Allah
Taleizadeh
taleizadeh@ut.ac.ir
true
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
Nadia
Pourmohammad Zia
nadia.pmz@gmail.com
true
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
Absi, N., Dauzère-Pérès, S., Kedad-Sidhoum, S., Penz, B., and Rapine, C. “Lot sizing with carbon emission constraints.” European Journal of Operational Research, 227, (2013): 55-61.
1
Ambec, S. and Lanoie, P. "Does it pay to be green? A systematic overview." The Academy of Management Perspectives 22, (2008): 45-62.
2
Andriolo, A., Battini, D., Grubbström, R. W., Persona, A., and Sgarbossa, F. “A century of evolution
3
from Harris ׳s basic lot size model: Survey and research agenda.” International Journal of Production
4
Economics, 155, (2014): 16-38.
5
Battini, D., Persona, A., and Sgarbossa, F. “A sustainable EOQ model: Theoretical formulation and applications.” International Journal of Production Economics, 149, (2014):145-153.
6
Benjaafar, S., Li, Y., and Daskin, M. (2013). Carbon footprint and the management of supply chains: Insights from simple models. IEEE Transactions on Automation Science and Engineering, 10, no. 1 (2013): 99–116.
7
Bouchery, Y., Ghaffari, A., Jemai, Z., and Dallery, Y. “Including sustainability criteria into inventory models.” European Journal of Operational Research, 222, no. 2 (2012): 229-240.
8
Bonney, M., and Jaber, M. Y. “Environmentally responsible inventory models: Non-classical models for a non-classical era.” International Journal of Production Economics, 133, no. 1 (2011): 43-53.
9
Cheng, T. C. E. “An economic production quantity model with flexibility and reliability considerations.” European Journal of Operational Research, 39, no. 2 (1989): 174-179.
10
Despeisse, M., Oates, M. R., and Ball, P. D. Sustainable manufacturing tactics and cross-functional factory modelling. Journal of Cleaner Production, 42, (2013): 31-41.
11
El Saadany, A.M.A., Jaber, M.Y., and Bonney, M. “Environmental performance measures for supply chains.” Management Research Review 34, no. 11 (2011): 1202–1221.
12
Glock, C. H., Jaber, M. Y., and Searcy, C. “Sustainability strategies in an EPQ model with price-and quality-sensitive demand.” The International Journal of Logistics Management, 23, no. 3 (2012): 340-359.
13
Harris, F. “How many parts to make at once?” Factory: The Magazine of Management (Reprinted in Operations Research 1990; 38 no.6: 947–950) 10, no. 2 (1913): 135-136.
14
Jaber, M. Y., Glock, C. H., and El Saadany, A. M. (2013). “Supply chain coordination with emissions reduction incentives.” International Journal of Production Research, 51, no. 1 (2013) 69-82.
15
Katz, R. S. “Environmental pollution: Corporate crime and cancer mortality.” Contemporary Justice Review: Issues in Criminal, Social, and Restorative Justice, 15, no. 1, (2012): 97–125.
16
Mukhopadhyay, A., and Goswami, A. (2014). “Economic production quantity models for imperfect items with pollution costs.” Systems Science & Control Engineering: An Open Access Journal, 2, no. 1 (2014): 368-378.
17
Nouira, I., Frein, Y., and Hadj-Alouane, A. B. “Optimization of manufacturing systems under environmental considerations for a greenness-dependent demand.” International Journal of Production Economics, 150, (2014): 188-198.
18
Pentico, David W., and Matthew J. Drake. “The deterministic EOQ with partial backordering: A new approach.” European Journal of Operational Research, 2009: 102-113.
19
Rădulescu, M., Rădulescu, S., and Rădulescu, C. Z. “Sustainable production technologies which take into account environmental constraints.” European Journal of Operational Research, 193, no. 2, (2009): 730-740.
20
Tao, Z., Guiffrida, A. L., and Troutt, M. D. “A green cost based economic production/order quantity model.” In Proceedings of the 1st Annual Kent State International Symposium on Green Supply Chains, Canton, Ohio, US, 2010.
21
Wahab, M. I. M., Mamun, S. M. H., and Ongkunaruk, P. “EOQ models for a coordinated two-level international supply chain considering imperfect items and environmental impact.” International Journal of Production Economics, 134, no. 1 (2011), 151-158.
22
Wang, H. F., and Gupta, S. M. Green supply chain management: Product life cycle approach. McGraw Hill Professional. 2011.
23
Van der Veen, J.A., and Venugopal, V. “Economic and Environmental Performance of the Firm: Synergy or Trade-off? Insights from the EOQ model.” NYENRODE RESEARCH PAPER SERIES, 2011.
24
Van der Veen, J. A., and Venugopal, V. “Economic and environmental performance of the firm: Synergy or trade-off? Insights from the EOQ model.” Handbook of EOQ inventory problems, (2014): 121-137. Springer US.
25
Zhu, Q., Lujia, F., Mayyas, A., Omar, M. A., Al-Hammadi, Y., and Al Saleh, S. “Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing.” Journal of Cleaner Production. (2014).
26