ORIGINAL_ARTICLE
A hybrid ant colony optimization algorithm to optimize capacitated lot-sizing problem
The economical determination of lot size with capacity constraints is a frequently complex, problem in the real world. In this paper, a multi-level problem of lotsizing with capacity constraints in a finite planning horizon is investigated. A combination of ant colony algorithm and a heuristic method called shifting technique is proposed for solving the problem. The parameters, including the costs, demands and capacity of resources vary during the time. The goal is to determine the economical lot size value of each product in each period, so that besides fulfilling all the needs of customers, the total cost of the system is minimized. To evaluate the performance of the proposed algorithm, an example is used and the results are compared other algorithms such as: Tabu search (TS), simulated annealing (SA), and genetic algorithm (GA). The results are also compared with the exact solution obtained from the Lagrangian relaxation method. The computational results indicate that the efficiency of the proposed method in comparison to other meta-heuristics.
https://www.jise.ir/article_7407_43c3dc6956c53d393209ccb540aa10ba.pdf
2014-12-01
1
20
Production Planning
Capacitated lot-sizing
Ant Colony Algorithm
Shifting technique
Vahid
Hajipour
v.hajipour@basu.ac.ir
1
Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Parviz
Fattahi
fattahi@basu.ac.ir
2
Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
LEAD_AUTHOR
Arash
Nobari
arashnob@basu.ac.ir
3
Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Absi N., Detienne B., Dauzere S. (2013), Heuristics for the multi-item capacitated
1
lot-sizing problem with lost sales; Computers & Operations Research 40; 264-272.
2
Absi N., Kedad-Sidhoum S. (2008), The multi-item capacitated lot-sizing problem
3
with setup times and shortage costs; European Journal of Operational Research
4
185; 1351-1374.
5
Afentakis P., Gavish B., Karmarkar V. (1984), Optimal solutions to the lot-sizing
6
problem in multi-stage assembly systems; Management Science 30; 222-239.
7
Afentakis P., Gavish B. (1986), Optimal lot-sizing algorithms for complex product
8
structures; Operations Research 34; 237-249.
9
Aggarwal A., Park J.K. (1993), Improved algorithms for economic lot size
10
problems; Operations Research, 41; 549-571.
11
Aksen D., Altınkemer K., Chand S. (2003), The single-item lot-sizing problem with
12
immediate lost sales; European Journal of Operational Research 147(3); 558-566.
13
Almeder C. (2010), A hybrid optimization approach for multi-level capacitated lotsizing
14
problems; European Journal of Operational Research 200; 599-606.
15
Barang I., VanRoy T.J., Wolsey L.A. (1984), Strong formulations for multi item
16
capacitated lot-sizing; Management Science 30; 1255-1261.
17
Belvaux G., Wolsey L.A. (2000), A specialized branch-and-cut system for lotsizing
18
problems; Management Science 46 (5); 724-738.
19
Billington P.J., McClain J.O., Thomas L.J. (1986), Heuristic for multi-level lotsizing
20
with a bottleneck; Management Science 32; 989-1006.
21
Brahimi N., Dauzere S. (2006), Review Single item lot-sizing problems; European
22
Journal of Operational Research 168; 1-16.
23
Choi S., Enns S.T. (2004), Multi-product capacity-constrained lot sizing with
24
economic objectives; International Journal of Production Economics 91(1); 47-62.
25
Dellaert N., Jeunet J., Jonard N. (2000), A genetic algorithm to solve the general
26
multi-level lot-sizing problem with time varying costs; International Journal of
27
Production Economics 68; 241-257.
28
Friedman M., Winter J.L. (1978), A Study of the infinite horizon ‘solution’ of
29
inventory lot size models with a linear demand function; Computers & Operations Research 5(2); 157-160.
30
Gallego G., Shaw D.X. (1997), Complexity of the ELSP with general cyclic
31
schedules; IEEE Transactions 29; 109–13.
32
Ganas I., Papachristos S. (2005), The single-product lot-sizing problem with
33
constant parameters and backlogging; Exact results, a new solution, and all
34
parameter stability regions; Operations Research 53 (1); 170–176.
35
Guan Y., Ahmed S., Miller AJ, Nemhauser G.L. (2006), On formulations of the
36
stochastic uncapacitated lot-sizing problem; Operations Research Letters 34(3);
37
Gupta Y.P., Keung Y. (1990), A review of multi-stage lot-sizing models;
38
International Journal of Operations and Production Management 10; 57-73.
39
Gupta D., Magnusson T. (2005), The capacitated lot-sizing and scheduling problem
40
with sequence-dependent setup costs and setup times; Computers & Operations
41
Research 32 (4); 727-747.
42
Huang K., Küçükyavuz S. (2008), On stochastic lot-sizing problems with random
43
lead times; Operations Research Letters 36(3); 303-308
44
Jans R., Degraeve Z. (2004), Improved lower bounds for the capacitated lot-sizing
45
problem with setup times; Operations Research Letters 32; 185-195.
46
Jans R., Degraeve Z. (2007), Meta-heuristics for dynamic lot-sizing; A review and
47
comparison of solution approaches; European Journal of Operational Research
48
177; 1855-1875.
49
Karimi B., Fatemi Ghomi S.M.T., Wilson JM. (2003), The capacitated lot-sizing
50
problem; a review of models and algorithms; Omega 31; 365-378.
51
Kovacs A., Brown K.N., Tarim S.A. (2009), An efficient MIP model for the
52
capacitated lot-sizing and scheduling problem with sequence-dependent setups;
53
International Journal of Production Economics 118(1); 282-291.
54
Kuik R., Salomon M., Van Wassenhove L.N., Maes J. (1993), Linear programming,
55
simulated annealing and tabu search heuristic for lot-sizing in bottleneck Assembly
56
systems; IIE Transactions 25; 62-72.
57
Lambrecht M.R., VanderEchen J., VanderVeken H. (1981), Review of optimal and
58
heuristic models for a class of facilities in series dynamic lot size problems, In
59
Multi-Level Production-Inventory Control Systems; Theory and Practice, North-
60
Holland, Amsterdam, 69-94.
61
Maes J., McClain J., VanWassenhove N .( 1991), Multilevel capacitated lot-sizing
62
complexity and LP-based heuristics; European Journal of Operational Research 53
63
(2); 131-148.
64
MATLAB Version 7.10.0.499 (R2010a). The MathWorks, Inc. Protected by U.S.
65
and international patents, 2010.
66
Ozdamar L., Barbarosoglu G. (2000), An integrated Lagrangian relaxationsimulated
67
annealing approach to the multilevel multi-item capacitated lot-sizing
68
problem; International Journal of Production Economics 68 (3); 319-331.
69
Pitakaso R., Almeder C., Doerner K., Hartl R. (2006), Combining population-based
70
and exact methods for multi-level capacitated lot-sizing problems; International
71
Journal of Production Research 44 (22); 4755-4771.
72
Pitakaso R., Almederb C., Doernerb K.F., Hartlb R.F. (2007), A MAX-MIN ant
73
system for unconstrained multi-level lot-sizing problems; Computers & Operations
74
Research 34; 2533-2552.
75
Rezaei J., Davoodi M. (2011), Multi-objective models for lot-sizing with supplier
76
selection, International Journal of Production Economics 130(1); 77-86.
77
Robinson P., Narayanan A., Sahin F. (2009), Coordinated deterministic dynamic
78
demand lot-sizing problem; A review of models and algorithms; Omega 37(1); 3-
79
Senyigit E., Düğenci M., Aydin M.E., Zeydan M. (2013), Heuristic-based neural
80
networks for stochastic dynamic lot sizing problem; Applied Soft Computing 13( 3);
81
1332-1339.
82
Shim I.S., Kim H.C., Doh H.H., Lee D.H. (2011), A two-stage heuristic for single
83
machine capacitated lot-sizing and scheduling with sequence-dependent setup costs;
84
Computers & Industrial Engineering 61(4); 920-929.
85
Tempelmeier H., Derstroff M. (1996), A Lagrangean-based heuristic for dynamic
86
multilevel multi item constrained lotsizing with setup times; Management Science
87
42 (5); 738-757.
88
Teng J.T., Chern M.S., Yang H.L., Wang Y.J. (1999), Deterministic lot-size
89
inventory models with shortages and deterioration for fluctuating demand;
90
Operations Research Letters 24; 65-72.
91
Toledo C.F.M., Ribeirode Oliveira R.R.R., Franca P.M. (2013), A hybrid multipopulation
92
genetic algorithm applied to solve the multi-level capacitated lot-sizing
93
problem with backlogging; Computers & Operations Research 40; 910-919.
94
Ustun O., Demırtas E.A. (2008), An integrated multi-objective decision-making
95
process for multi-period lot-sizing with supplier selection; Omega, 36(4); 509-521.
96
Wolsey L.A. (1995), Progress with single-item lot-sizing; European Journal of
97
Operational Research 86; 395-401.
98
Wua T., Shi L., Geunes J., Akartunalı K. (2011), An optimization framework for
99
solving capacitated multi-level lot-sizing problems with backlogging; European Journal of Operational Research 214; 428-441.
100
Xiao Y., Kaku I., Zhao Q., Zhang R. (2011), A reduced variable neighborhood
101
search algorithm for uncapacitated multilevel lot-sizing problems; European
102
Journal of Operational Research 214; 223-231.
103
Xie J., Dong J. (2002), Heuristic Genetic Algorithms for General Capacitated Lot-
104
Sizing; An International journal computers & mathematics 44; 263-276.
105
Zhou Y.W., Lau H.S., Yang S.L. (2004), A finite horizon lot-sizing problem with
106
time-varying deterministic demand and waiting-time-dependent partial
107
backlogging; International Journal of Production Economics 91(2); 109-119.
108
ORIGINAL_ARTICLE
Developing EOQ model with instantaneous deteriorating items for a Vendor-Managed Inventory (VMI) system
This paper studies the economic-order-quantity model (EOQ) for deteriorating items in two cases (with and without shortages) to evaluate how vendor managed inventory (VMI) affects supply chain. We consider two-level supply chain (single supplier and a single retailer) with one instantaneous deteriorating item. A numerical example and sensitivity analysis are provided to illustrate the effect of related parameters on total cost and optimal order quantity of two systems. The results show that VMI works better and delivers lower cost in all conditions than traditional supply chain (the system before implementation of VMI).
https://www.jise.ir/article_7408_199976d9f5917c9087a823e09f2072a3.pdf
2014-12-01
21
42
Vendor-managed inventory
Supply chain
Economic order quantity
model (EOQ)
Deterioration
Roya
Tat
roya.tat@yahoo.com
1
Department of Industrial Engineering, Alzahra University, Tehran, Iran
AUTHOR
Maryam
Esmaeili
esmaeili_m@alzahra.ac.ir
2
Department of Industrial Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Ataollah
Taleizadeh
taleizadeh@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran
AUTHOR
Bahari-Kashani, H. (1989). Replenishment schedule for deteriorating items with timeproportional
1
demand. Journal of the Operational Research Society, 75-81.
2
Bhunia, A. K., & Maiti, M. (1998). Deterministic inventory model for deteriorating items
3
with finite rate of replenishment dependent on inventory level. Computers & operations
4
research, 25(11), 997-1006.
5
Cárdenas-Barrón, L. E., Treviño-Garza, G., & Wee, H. M. (2012). A simple and better
6
algorithm to solve the vendor managed inventory control system of multi-product multiconstraint
7
economic order quantity model. Expert Systems with Applications, 39(3), 3888-
8
Covert, R. P., & Philip, G. C. (1973). An EOQ model for items with Weibull distribution
9
deterioration. AIIE transactions, 5(4), 323-326.
10
Darwish, M. A., & Odah, O. M. (2010). Vendor managed inventory model for single-vendor
11
multi-retailer supply chains. European Journal of Operational Research, 204(3), 473-484.
12
Dong, Y., & Xu, K. (2002). A supply chain model of vendor managed inventory.
13
Transportation Research Part E: Logistics and Transportation Review, 38(2), 75-95.
14
Ghare PM, Schrader GH., (1963). A model for exponentially decaying inventory system, Journal of
15
Industrial Engineering (14). 238-43.
16
Ghosh, S. K., & Chaudhuri, K. S. (2006). An EOQ model with a quadratic demand, timeproportional
17
deterioration and shortages in all cycles. International journal of systems
18
science, 37(10), 663-672.
19
Goswami, A., & Chaudhuri, K. S. (1992). Variations of order-level inventory models for
20
deteriorating items. International Journal of Production Economics, 27(2), 111-117.
21
Goyal, S. K., & Giri, B. C. (2001). Recent trends in modeling of deteriorating inventory.
22
European Journal of operational research, 134(1), 1-16.
23
Guchhait, P., Maiti, M. K., & Maiti, M. (2014). Inventory policy of a deteriorating item with
24
variable demand under trade credit period. Computers & Industrial Engineering, 76, 75-88.
25
Khanra, S., Ghosh, S. K., & Chaudhuri, K. S. (2011). An EOQ model for a deteriorating
26
item with time dependent quadratic demand under permissible delay in payment. Applied
27
Mathematics and Computation, 218(1), 1-9.
28
Kim DH., (1995).A heuristic for replenishment of deteriorating items with linear trend in demand.
29
International Journal of Production Economics (39). 265-70.
30
Lin, C., & Lin, Y. (2004). A joint EOQ model for supplier and retailer with deteriorating
31
items. Asia-Pacific Journal of Operational Research, 21(02), 163-178.
32
Lin, C., Tan, B., & Lee, W. C. (2000). An EOQ model for deteriorating items with timevarying
33
demand and shortages. International Journal of Systems Science, 31(3), 391-400.
34
Magee, John F., (1995) .Production planning and inventory control., McGraw Hill, New York.
35
Pasandideh, S. H. R., Niaki, S. T. A., & Nia, A. R. (2011). A genetic algorithm for vendor
36
managed inventory control system of multi-product multi-constraint economic order quantity
37
model. Expert Systems with Applications, 38(3), 2708-2716.
38
Pasandideh, S. H. R., Niaki, S. T. A., & Nia, A. R. (2010). An investigation of vendormanaged
39
inventory application in supply chain: the EOQ model with shortage. The
40
International Journal of Advanced Manufacturing Technology, 49(1-4), 329-339.
41
Pentico, D. W., & Drake, M. J. (2009). The deterministic EOQ with partial backordering: a
42
new approach. European Journal of Operational Research, 194(1), 102-113.
43
Philip, G. C. (1974). A generalized EOQ model for items with Weibull distribution
44
deterioration. AIIE Transactions, 6(2), 159-162.
45
Raafat, F. (1991). Survey of literature on continuously deteriorating inventory models.
46
Journal of the Operational Research society, 27-37.
47
Sana, S. S. (2010). Optimal selling price and lotsize with time varying deterioration and
48
partial backlogging. Applied Mathematics and Computation, 217(1), 185-194.
49
Sicilia, J., González-De-la-Rosa, M., Febles-Acosta, J., & Alcaide-López-de-Pablo, D.
50
(2014). An inventory model for deteriorating items with shortages and time-varying demand.
51
International Journal of Production Economics.
52
Simchi-Livi, D., Kaminsky, P., Simchi-Livi, E., (2008). Designing and Managing the Supply Chain-
53
Concepts, Strategies and Case Studies. 3ed, McGraw-Hill, New York.
54
Yao, Y., Evers, P. T., & Dresner, M. E. (2007). Supply chain integration in vendor-managed
55
inventory. Decision support systems, 43(2), 663-674.
56
Wang, C., Ji, S., Shen, J., & Wei, W. (2008, October). Supply chain model in vendor
57
managed inventory. In Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI
58
2008. IEEE International Conference on (Vol. 2, pp. 2110-2113). IEEE.
59
ORIGINAL_ARTICLE
A two stage model for Cell Formation Problem (CFP) considering the inter-cellular movements by AGVs
This paper addresses to the Cell Formation Problem (CFP) in which Automated Guided Vehicles (AGVs) have been employed to transfer the jobs which may need to visit one or more cells. Because of added constraints to problem such as AGVs’ conflict and excessive cessation on one place, it is possible that AGVs select the different paths from one cell to another over the time. This means that the times and costs between cells are dynamic. The proposed model consists of 2 stages that stage (1) is related to a basic CFP, with a set of machine cells and their corresponding job families, while stage (2) is related to finding AGVs’ routing, to determine the dynamic costs. For solving this problem, a two-stage heuristic algorithm based on an exact method has been proposed. A computational experiment has been solved to show efficiency of proposed heuristic.
https://www.jise.ir/article_7409_6386c195f9c46495102e77cf4a748df4.pdf
2014-12-01
43
55
cell formation problem
Routing problem
Automated guided vehicle
Twostage
model
Two-stage heuristic
Saeed
Arani
1
Department of Industrial Enginering, Iran University of Science and Technology
LEAD_AUTHOR
Mohammad
Mehrabad
mehrabad@iust.ac.ir
2
Department of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Aljaber, N., Baek, W., & Chen, C. L. (1997). A tabu search approach to the cell formation
1
Andrés, C., & Lozano, S. (2006). A particle swarm optimization algorithm for part–machine
2
grouping. Robotics and Computer-Integrated Manufacturing, 22(5), 468-474.
3
Arkat, J., Hosseini, L., & Farahani, M. H. (2011). Minimization of exceptional elements and
4
voids in the cell formation problem using a multi-objective genetic algorithm. Expert Systems
5
with Applications, 38(8), 9597-9602.
6
Chung, S. H., Wu, T. H., & Chang, C. C. (2011). An efficient tabu search algorithm to the
7
cell formation problem with alternative routings and machine reliability considerations.
8
Computers & Industrial Engineering, 60(1), 7-15.
9
Elbenani, B., Ferland, J. A., & Bellemare, J. (2012). Genetic algorithm and large
10
neighbourhood search to solve the cell formation problem. Expert Systems with Applications,
11
39(3), 2408-2414.
12
Ghezavati, V., & Saidi-Mehrabad, M. (2010). Designing integrated cellular manufacturing
13
systems with scheduling considering stochastic processing time. The International Journal of
14
Advanced Manufacturing Technology, 48(5-8), 701-717.
15
Gravel, M., Luntala Nsakanda, A., & Price, W. (1998). Efficient solutions to the cellformation
16
problem with multiple routings via a double-loop genetic algorithm. European
17
Journal of Operational Research, 109(2), 286-298.
18
Guerrero, F., Lozano, S., Smith, K. A., Canca, D., & Kwok, T. (2002). Manufacturing cell
19
formation using a new self-organizing neural network. Computers & Industrial Engineering,
20
42(2), 377-382.
21
James, T. L., Brown, E. C., & Keeling, K. B. (2007). A hybrid grouping genetic algorithm
22
for the cell formation problem. Computers & Operations Research, 34(7), 2059-2079.
23
Jolai, F., Tavakkoli-Moghaddam, R., Golmohammadi, A., & Javadi, B. (2012). An
24
electromagnetism-like algorithm for cell formation and layout problem. Expert Systems with
25
Applications, 39(2), 2172-2182.
26
Liang, M., & Zolfaghari, S. (1999). Machine cell formation considering processing times and
27
machine capacities: an ortho-synapse Hopfield neural network approach. Journal of
28
Intelligent Manufacturing, 10(5), 437-447.
29
Li, X., Baki, M. F., & Aneja, Y. P. (2010). An ant colony optimization metaheuristic for
30
machine–part cell formation problems. Computers & Operations Research, 37(12), 2071-
31
Lozano, S., Canca, D., Guerrero, F., & Garcı́a, J. M. (2001). Machine grouping using
32
sequence-based similarity coefficients and neural networks. Robotics and Computer-
33
Integrated Manufacturing, 17(5), 399-404.
34
Mahdavi, I., Paydar, M. M., Solimanpur, M., & Heidarzade, A. (2009). Genetic algorithm
35
approach for solving a cell formation problem in cellular manufacturing. Expert Systems with
36
Applications, 36(3), 6598-6604.
37
Mak, K. L., Wong, Y. S., & Wang, X. X. (2000). An adaptive genetic algorithm for
38
manufacturing cell formation. The International Journal of Advanced Manufacturing
39
Technology, 16(7), 491-497.
40
Pailla, A., Trindade, A. R., Parada, V., & Ochi, L. S. (2010). A numerical comparison
41
between simulated annealing and evolutionary approaches to the cell formation problem.
42
Expert Systems with Applications, 37(7), 5476-5483.
43
Pasupuleti, V. (2012). Schaduling in cellular manufacturing system. Iberoamerican Journal
44
of Industrial Engineering, 4(7), 231-243.
45
Prabhaharan, G., Asokan, P., Girish, B. S., & Muruganandam, A. (2005). Machine cell
46
formation for cellular manufacturing systems using an ant colony system approach. The
47
International Journal of Advanced Manufacturing Technology, 25(9-10), 1013-1019.
48
Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, R., & Sassani, F. (2008). A fuzzy
49
programming approach for a cell formation problem with dynamic and uncertain conditions.
50
Fuzzy Sets and Systems, 159(2), 215-236.
51
Solimanpur, M., Saeedi, S., & Mahdavi, I. (2010). Solving cell formation problem in cellular
52
manufacturing using ant-colony-based optimization. The International Journal of Advanced
53
Manufacturing Technology, 50(9-12), 1135-1144.
54
Spiliopoulos, K., & Sofianopoulou, S. (2008). An efficient ant colony optimization system
55
for the manufacturing cells formation problem. The International Journal of Advanced
56
Manufacturing Technology, 36(5-6), 589-597.
57
Tavakkoli-Moghaddam, R., Gholipour-Kanani, Y., & Cheraghalizadeh, R. (2008). A genetic
58
algorithm and memetic algorithm to sequencing and scheduling of cellular manufacturing
59
systems. International Journal of Management Science and Engineering Management, 3(2),
60
Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G. R., & Siadat, A. (2012). A cell
61
formation problem considering machine utilization and alternative process routes by scatter
62
search. Journal of Intelligent Manufacturing, 23(4), 1127-1139.
63
Wu, T. H., Chung, S. H., & Chang, C. C. (2009). Hybrid simulated annealing algorithm with
64
mutation operator to the cell formation problem with alternative process routings. Expert
65
Systems with Applications, 36(2), 3652-3661.
66
Wu, T. H., Low, C., & Wu, W. T. (2004). A tabu search approach to the cell formation
67
problem. The International Journal of Advanced Manufacturing Technology, 23(11-12), 916-
68
Wu, Tai-Hsi, Shu-Hsing Chung, and Chin-Chih Chang. "A water flow-like algorithm for
69
manufacturing cell formation problems." European Journal of Operational Research 205.2
70
(2010): 346-360.
71
ORIGINAL_ARTICLE
Robust approach to DEA technique for supplier selection problem: A case study at Supplying Automative Parts Company (SAPCO)
In many industries such as automotive industry, there are a lot of suppliers dealing with the final products manufacturer. With growing numbers of suppliers, the suppliers’ efficiency measurement often becomes the most significant concern for manufacturers. Therefore, various performance measurement models such as DEA, AHP, TOPSIS, are developed to support supplier selection decisions. After an exhaustive review of the supplier selection methods, we employ data envelopment analysis (DEA) for computing the relative efficiency of the suppliers and introducing the most efficient supplier as a benchmark. In reality, there are large amounts of uncertainty regarding the suppliers’ measurements; therefore, we propose the robust optimization approach to the real application of DEA (RDEA). In this approach, uncertainties about incomes and outcomes of decision making units (DMUs) are involved in the relative suppliers’ efficiencies. The proposed RDEA approach is utilized for the selection of suppliers which manufacture the automotive safety components in Supplying Automotive Parts Company (SAPCO), an Iranian leading automotive enterprise. Numerical example will illustrate how our proposed approach can be used in the real supplier selection problem when considerable uncertainty exists regarding the suppliers’ input and output data.
https://www.jise.ir/article_7410_65c0a8e05b073adbe37412d573de079e.pdf
2014-12-01
56
79
robust optimization
Supplier selection
Data Envelopment Analysis
Supply
chain management
Ashkan
Hafezalkotob
a_hafez@azad.ac.ir
1
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Mohammad-Hadi
Banihashemi
samim@iust.ac.ir
2
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Elnaz
Rezaee
3
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Hamid
Tavakoli
4
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Albino V, Garavelli AC (1998). A Neural Network Application to Subcontractor Rating in
1
Construction Firms. International Journal of Project Management, 16: 9-14.
2
Arunkumar N, Karunamoorthy, L, Anand, S, Babu, TR (2006). Linear Approach for Solving a
3
Piecewise Linear Vendor Selection Problem of Quantity Discounts Using Lexicographic Method. The
4
International Journal of Advanced Manufacturing Technology, 28: 1254-1260.
5
Banker RD (1993). Maximum Likelihood, Consistency and Data Envelopment Analysis: a Statistical
6
Foundation. Management science, 39: 1265-1273.
7
Bayazit O (2006). Use of Analytic Network Process in Vendor Selection Decisions. Benchmarking:
8
An International Journal, 13: 566-579.
9
Bertsimas D, Sim M (2004). The Price of Robustness. Operations research, 52: 35-53.
10
Bertsimas D, Sim, M (2003). Robust Discrete Optimization and Network Flows. Mathematical
11
Programming, 98: 49-71.
12
Bertsimas D, Sim M (2006). Tractable Approximations to Robust Conic Optimization
13
Problems. Mathematical Programming, 107: 5-36.
14
Ben-Tal A, Nemirovski A (2000). Robust Solutions of Linear Programming Problems Contaminated
15
with Uncertain Data. Mathematical Programming, 88: 411-424.
16
Bevilacqua M, Petroni A (2002). From Traditional Purchasing to Supplier Management: a Auzzy
17
Logic-based Approach to Supplier Selection. International Journal of Logistics, 5: 235-255.
18
Bhutta KS, Huq F (2002). Supplier Selection Problem: a Comparison of the Total Cost of Ownership
19
and Analytic Hierarchy Process Approaches. Supply Chain Management: An International Journal, 7:
20
Bradley E, Tibshirani R (1993). An Introduction to the Bootstrap. CRC press, Boca Raton, Florida,
21
Cakravastia A, Takahashi K (2004). Integrated Model for Supplier Selection and Negotiation in a
22
Make-to-order Environment. International Journal of Production Research, 42: 4457-4474.
23
Cebi F, Bayraktar D (2003). An Integrated Approach for Supplier Selection. Logistics Information
24
Management, 16: 395-400.
25
Chan, FS (2003). Interactive Selection Model for Supplier Selection Process: an Analytical Hierarchy
26
Process Approach. International Journal of Production Research, 41: 3549-3579.
27
Chang SL, Wang RC, Wang SY (2006). Applying Fuzzy Linguistic Quantifier to Select Supply
28
Chain Partners at Different Phases of Product Life Cycle. International Journal of Production
29
Economics, 100: 348-359.
30
Charnes A, Cooper WW, Rhodes E (1978). Measuring the Efficiency of Decision Making
31
Units. European Journal of Operational Research, 2: 429-444.
32
Supply Chain Management. International Journal of Production Economics, 102: 289-301.
33
Choy KL, Lee WB, Lo V (2002). An Intelligent Supplier Management Tool for Benchmarking
34
Suppliers in Outsource Manufacturing. Expert Systems with Applications, 22: 213-224.
35
Choy KL, Lee WB (2003). A Generic Supplier Management Tool for Outsourcing
36
Manufacturing. Supply Chain Management: An International Journal, 8: 140-154.
37
Choy KL, Lee WB, Lo V (2004). Development of a Case Based Intelligent Supplier Relationship
38
Management System–Linking Supplier Rating System and Product Coding System. Supply Chain
39
Management: An International Journal, 9: 86-101.
40
Cooper WW, Seiford LM, Zhu J (Eds.) (2011). Handbook on Data Envelopment Analysis. Springer
41
Science+ Business Media.
42
Dahel NE (2003). Vendor Selection and Order Quantity Allocation in Volume Discount
43
Environments. Supply Chain Management: An International Journal,8: 335-342.
44
Degraeve Z, Roodhooft F (1998). Determining Sourcing Strategies: a Decision Model Based on
45
Activity and Cost Driver Information. Journal of the Operational Research Society: 781-789.
46
Degraeve Z, Roodhooft F (1999). Improving the Efficiency of the Purchasing Process Using Total
47
Cost of Ownership Information: The case of heating electrodes at Cockerill Sambre SA. European
48
Journal of Operational Research,112: 42-53.
49
Degraeve Z, Roodhooft F (2000). A Mathematical Programming Approach for Procurement Using
50
Activity Based Costing. Journal of Business Finance & Accounting, 27: 69-98.
51
Dickson GW (1966). An Analysis of Vendor Selection Systems and Decisions. Journal of
52
Purchasing, 2: 5-17.
53
Dulmin R, Mininno V (2003). Supplier Selection Using a Multi-criteria Decision Aid
54
Method. Journal of Purchasing and Supply Management, 9: 177-187.
55
Feng DZ, Chen LL, Jiang MX (2005, June). Vendor Selection in Supply Chain System: An Approach
56
Using Fuzzy Decision and AHP. In Services Systems and Services Management, 2005. Proceedings
57
of ICSSSM'05. p. 721-5.
58
Figueira J, Greco S, Ehrgott M (Eds.). (2005). Multiple Criteria Decision Analysis: State of the Art
59
Surveys. Springer, New York, USA.
60
Chamodrakas I, Batis D, Martakos D (2010). Supplier Selection in Electronic Marketplaces Using
61
Satisficing and Fuzzy AHP. Expert Systems with Applications, 37: 490-498.
62
Ghodsypour S H, O'brien C (1998). A Decision Support System for Supplier Selection Using an
63
Integrated Analytic Hierarchy Process and Linear Programming. International journal of production
64
economics, 56: 199-212.
65
Ghodsypour S H, O'brien C (2001). The Total Cost of Logistics in Supplier Selection, under
66
Conditions of Multiple Sourcing, Multiple Criteria and Capacity Constraint. International Journal of
67
Production Economics, 73: 15-27.
68
Giokas DI, Pentzaropoulos GC (2008). Efficiency Ranking of the OECD Member States in the Area
69
of Telecommunications: A composite AHP/DEA study. Telecommunications Policy, 32: 672-685.
70
Guo P, Tanaka H (2001). Fuzzy DEA: a Perceptual Evaluation Method. Fuzzy sets and systems, 119:149-160.
71
Hajidimitriou YA, Georgiou AC (2002). A Goal Programming Model for Partner Selection Decisions
72
in International Joint Ventures. European Journal of Operational Research, 138: 649-662.
73
Ho W, Xu X, Dey PK (2010). Multi-criteria Decision Making Approaches for Supplier Evaluation
74
and Selection: A Literature Review. European Journal of Operational Research, 202: 16-24.
75
Holand SM (2008). Principal Components Analysis (PCA), Department of Geology, University of
76
Georgia, Athens, GA.
77
Ip WH, Yung KL, Wang D (2004). A Branch and Bound Algorithm for Sub-Contractor Sselection in
78
Agile Manufacturing Environment. International Journal of Production Economics, 87: 195-205.
79
Kahraman C, Cebeci U, Ulukan Z (2003). Multi-criteria Supplier Selection Using Fuzzy
80
AHP. Logistics Information Management, 16: 382-394.
81
Kameshwaran S, Narahari Y, Rosa CH, Kulkarni DM, Tew JD (2007). Multiattribute Electronic
82
Procurement Using Goal Programming. European Journal of Operational Research, 179: 518-536.
83
Kao C, Liu ST (2000). Fuzzy Efficiency Measures in Data Envelopment Analysis. Fuzzy sets and
84
systems, 113: 427-437.
85
Karpak B, Kumcu E, Kasuganti RR (2001). Purchasing Materials in the Supply Chain: Managing a
86
Multi-Objective Task. European Journal of Purchasing & Supply Management, 7: 209-216.
87
Keeney RL, Raiffa H (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs.
88
Cambridge University Press, Cambridge, UK.
89
Kumar J, Roy N (2010). A Hybrid Method for Vendor Selection Using Neural
90
Network. International Journal of Computer Applications, 11: 35-40.
91
Kumar M, Vrat P, Shankar R (2004). A Fuzzy Goal Programming Approach for Vendor Selection
92
Problem in a Supply Chain. Computers & Industrial Engineering, 46: 69-85.
93
Kwong CK, Ip, WH, Chan, JWK (2002). Combining Scoring Method and Fuzzy Expert Systems
94
Approach to Supplier Assessment: a Case Study.Integrated manufacturing systems, 13: 512-519.
95
Land KC, Lovell CA, Thore S (1993). Chance‐constrained Data Envelopment Analysis. Managerial
96
and Decision Economics, 14: 541-554.
97
Lau HC, Lau PK, Fung RY, Chan FT, Ip, RW (2005). A Virtual Case Benchmarking Scheme for
98
Vendors' Performance Assessment. Benchmarking: An International Journal, 12: 61-80.
99
León T, Liern V, Ruiz JL, Sirvent I (2003). A Fuzzy Mathematical Programming Approach to the
100
Assessment of Efficiency with DEA Models. Fuzzy Sets and Systems, 139: 407-419.
101
Li CC, Fun, YP, Hung JS (1997). A New Measure for Supplier Performance Evaluation. IIE
102
transactions, 29: 753-758.
103
Lin CWR., Chen HYS (2004). A Fuzzy Strategic Alliance Selection Framework for Supply Chain
104
Partnering under Limited Evaluation Resources. Computers in Industry, 55: 159-179.
105
Liu FHF, Hai HL (2005). The Voting Analytic Hierarchy Process Method for Selecting
106
Supplier. International Journal of Production Economics, 97: 308-317.
107
Mendoza A, Ventura JA (2008). An Effective Method to Supplier Selection and Order Quantity
108
Allocation. International Journal of Business and Systems Research, 2: 1-15.
109
Min H (1994). International Supplier Selection: A Multi-attribute Utility Approach. International Journal of Physical Distribution & Logistics Management, 24: 24-33.
110
Morlacchi P (1999, March). Vendor Evaluation and Selection: the Design Process and a Fuzzyhierarchical
111
Model. In Proceedings of the 8th IPSERA Conference, Dublin.
112
Noorul Haq A, Kannan G (2006). Design of an Integrated Supplier Selection and Multi-echelon
113
Distribution Inventory Model in a Built-to-order Supply Chain Environment. International Journal of
114
Production Research, 44: 1963-1985.
115
Nydick, RL, Hill, RP (1992). Using the Analytic Hierarchy Process to Structure the Supplier
116
Selection Procedure. International Journal of Purchasing and Material Management, 28: 31-36.
117
Ohdar R, Ray PK (2004). Performance Measurement and Evaluation of Suppliers in Supply Chain: an
118
Evolutionary Fuzzy-based Approach. Journal of Manufacturing Technology Management, 15: 723-
119
Olesen OB, Petersen, NC (1995). Chance Constrained Efficiency Evaluation. Management
120
Science, 41: 442-457.
121
Petroni A, Braglia M (2000). Vendor Selection Using Principal Component Analysis. Journal of
122
supply chain management, 36: 63-69.
123
Pi WN, Low C (2006). Supplier Evaluation and Selection via Taguchi Loss Functions and an
124
AHP. The International Journal of Advanced Manufacturing Technology, 27: 625-630.
125
Premachandra IM (2001). A Note on DEA vs Principal Component Analysis: An Improvement to Joe
126
Zhu's Approach. European Journal of Operational Research, 132: 553-560.
127
Saaty TL (1990). Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority
128
Setting Resource Allocation. RWS publications, Pittsburgh, PA.
129
Saen RF (2007). A New Mathematical Approach for Suppliers Selection: Accounting for Nonhomogeneity
130
is Important. Applied Mathematics and Computation, 185: 84-95.
131
Sadjadi SJ, Omrani H (2008). Data Envelopment Analysis with Uncertain Data: An Application for
132
Iranian Electricity Distribution Companies. Energy Policy, 36: 4247-4254.
133
Sadjadi SJ, Omrani H (2010). A Bootstrapped Robust Data Envelopment Analysis Model for
134
Efficiency Estimating of Telecommunication Companies in Iran. Telecommunications Policy, 34:
135
Saen RF (2007). A New Mathematical Approach for Suppliers Selection: Accounting for Nonhomogeneity
136
is Important. Applied Mathematics and Computation, 185: 84-95.
137
Simar L, Wilson PW (1998). Sensitivity Analysis of Efficiency Scores: How to Bootstrap in
138
Nonparametric Frontier Models. Management science, 44: 49-61.
139
Simar L, Wilson PW (2000). A General Methodology for Bootstrapping in Non-parametric Frontier
140
Models. Journal of applied statistics, 27: 779-802.
141
Sha DY, Che ZH (2005). Supply Chain Network Design: Partner Selection and
142
Production/Distribution Planning Using a Systematic Model. Journal of the Operational Research
143
Society, 57: 52-62.
144
Shyur HJ, Shih HS (2006). A Hybrid MCDM Model for Strategic Vendor Selection. Mathematical
145
and Computer Modelling, 44: 749-761.
146
Talluri S, Baker RC (2002). A Multi-phase Mathematical Programming Approach for Effective
147
Supply Chain Design. European Journal of Operational Research, 14: 544-558.
148
Tahriri F, Osman MR, Ali A, Yusuff RM (2008). A Review of Supplier Selection Methods in Manufacturing Industries. Suranaree Journal of Science and Technology, 15: 201-208.
149
Talluri S, Narasimhan R (2003). Vendor Evaluation with Performance Variability: a Max–Min
150
Approach. European Journal of Operational Research,146: 543-552.
151
Teixeira de Almeida A (2001). Multicriteria Decision Making on Maintenance: Spares and Contracts
152
Planning. European Journal of Operational Research, 129: 235-241.
153
Teixeira de Almeida A (2007). Multicriteria Decision Model for Outsourcing Contracts Selection
154
Based on Utility Function and ELECTRE Method. Computers & Operations Research, 34: 3569-
155
Timmerman E (1987). An Approach to Vendor Performance Evaluation. Engineering Management
156
Review, IEEE, 15: 14-20.
157
Von Neumann J, Morgenstern O (1946). Theory of Games and Economic behavior (commemorative
158
edition). Princeton university press, Princeton, USA.
159
Wang G, Huang SH, Dismukes JP (2004). Product-driven Supply Chain Selection Using Integrated
160
Multi-Criteria Decision-Making Methodology. International journal of production economics, 91: 1-
161
Wang G, Huang SH, Dismukes JP (2005). Manufacturing Supply Chain Design and Evaluation. The
162
International Journal of Advanced Manufacturing Technology, 25: 93-100.
163
Wang J, Zhao R, Tang W (2008). Fuzzy Programming Models for Vendor Selection Problem in a
164
Supply Chain. Tsinghua Science & Technology, 13: 106-111.
165
Weber CA, Current JR, Desai A (2000). An Optimization Approach to Determining the Number of
166
Vendors to Employ. Supply Chain Management: An International Journal, 5: 90-98.
167
Weber CA, Current JR, Benton WC (1991). Vendor Selection Criteria and Methods. European
168
journal of operational research, 50: 2-18.
169
Wen M, Li H (2009). Fuzzy Data Envelopment Analysis (DEA): Model and Ranking
170
Method. Journal of Computational and Applied Mathematics, 223: 872-878.
171
Wong YH, Beasley JE (1990). Restricting Weight Flexibility in Data Envelopment Analysis. Journal
172
of the Operational Research Society: 829-835.
173
Xia W, Wu Z (2007). Supplier Selection with Multiple Criteria in Volume Discount
174
Environments. Omega, 35: 494-504.
175
Yusuff RM, Yee KP, Hashmi MSJ (2001). A Preliminary Study on the Potential Use of the
176
Analytical Hierarchical Process (AHP) to Predict Advanced Manufacturing Technology (AMT)
177
Implementation. Robotics and Computer-Integrated Manufacturing, 17: 421-427.
178
Zadeh LA (1965). Fuzzy Sets. Information and control, 8: 338-353.
179
Zeng ZB, Li Y, Zhu W (2006). Partner Selection with a Due Date Constraint in Virtual
180
Enterprises. Applied Mathematics and Computation, 175: 1353-1365.
181
Zenz G J, Thompson, GH (1994). Purchasing and the Management of Materials, Wiley, New Jersey,
182
Zhang Y, Bartels R (1998). The Effect of Sample Size on the Mean Efficiency in DEA with an
183
Application to Electricity Distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis, 9: 187-204.
184
Zhu J (1998). Data Envelopment Analysis vs. Principal Component Analysis: An Illustrative Study of
185
Economic Performance of Chinese Cities. European Journal of Operational Research, 111: 50-61.
186
Zhu J (2004). Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile
187
Telecommunication Company. Operations Research, 52: 323-329.
188
ORIGINAL_ARTICLE
Introducing a mathematical model in supply chain with adding trust flow
These day, supply chains (SCs) have become more and more complicated and have extensively expanded and due to these complexities, the supply chain management (SCM) has encountered several uncertainties, and, as a result, trust and assurance between members in SCs has become essential for a successful SCM. Although trust is an inevitable component in nearly all fields in SCs, like cooperation, coordination and management. As Trust increases the sense of security among members and cuts back on the losses, this research attempts to introduce a mathematical model that is able to utilize trust as a main element in a two-echelon SC. Defining trust is difficult since it is analyzed from different perspectives, and it is used in a wide range of situations. Therefore, the aim of this study is to propose an appropriate definition for this concept according to SCs, and to present a two-echelon SC, including a retailer and a supplier. The supplier and the retailer play Stackelberg game in newsvendor framework. The order quantity and stock, as the best sections for proposing the definition of trust, is developed for retailer and supplier. In addition, Beta model is presented for calculating trust and finally in order to verify the quality and efficiency of the proposed model, a numerical example is also offered.
https://www.jise.ir/article_7411_8d4b3eead74ab4dbbba9a5ecef426564.pdf
2014-12-01
80
103
Newsvendor problem
Computational Trust model
Stackelberg game
Trust
Supply chain
Somayeh
Esmaeili
1
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
2
Department of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Ashkan
Hafezalkotob
ashkan.hafez@gmail.com
3
Industrial Engineering department, South branch of Azad University, Iran
AUTHOR
Akkermans, H., Bogerd, P., & Van Doremalen, J. (2004). Travail, transparency and trust: A
1
case study of computer-supported collaborative supply chain planning in high-tech
2
electronics. European Journal of Operational Research, 153(2), 445-456.
3
Binmore, K. G. (1998). Game theory and the social contract: Playing fair (Vol. 2). The MIT
4
Casella, R. (2003). Zero tolerance policy in schools: Rationale, consequences, and
5
alternatives. The Teachers College Record, 105(5), 872-892
6
Castaldo, S., Premazzi, K., & Zerbini, F. (2010). The meaning (s) of trust. A content analysis
7
on the diverse conceptualizations of trust in scholarly research on business relationships.
8
Journal of Business Ethics, 96(4), 657-668.
9
Day, M., Fawcett, S. E., Fawcett, A. M., & Magnan, G. M. (2013). Trust and relational
10
embeddedness: Exploring a paradox of trust pattern development in key supplier
11
relationships Industrial Marketing Management.
12
Devangan, L., Amit, R. K., Mehta, P., Swami, S., & Shanker, K. (2012). Individually rational
13
buyback contracts with inventory level dependent demand. International Journal of
14
Production Economics.
15
Di, X., Wei, D., & Yun-hong, H. (2012). Buy Back Contract Based on Quality Effort in
16
Supply chain. In Frontiers in Computer Education (pp. 313-323). Springer Berlin Heidelberg
17
Esfandiari, B., & Chandrasekharan, S. (2001, May). On how agents make friends:
18
Mechanisms for trust acquisition. In Proceedings of the Fourth Workshop on Deception,
19
Fraud and Trust in Agent Societies, Montreal, Canada (pp. 27-34).
20
Gambetta, D. (1990). Trust: Making and breaking cooperative relations
21
Gelman, D., & Buchwald, S. L. (2003). Efficient palladium‐catalyzed coupling of aryl
22
chlorides and tosylates with terminal alkynes: Use of a copper cocatalyst inhibits the reaction
23
Angewandte Chemie International Edition, 42(48), 5993-5996.
24
Hassan, J., Sirisena, H., & Landfeldt, B. (2008). Trust-based fast authentication for multiwireless
25
networks. Mobile Computing, IEEE Transactions on, 7(2), 247-261.
26
Hung, H. C., & Wang, T. W. (2011). Determinants and mapping of collective perceptions of
27
technological risk: the case of the second nuclear power plant in Taiwan. Risk analysis,
28
31(4), 668-683.
29
Huynh, T. D., Jennings, N. R., & Shadbolt, N. R. (2006, May). Certified reputation: how an
30
Agent can trust a stranger. In Proceedings of the fifth international joint conference on
31
Autonomous agents and multiagent systems (pp. 1217-1224). ACM.
32
Jøsang, A., & Sanderud, G. (2003, January). Security in mobile communications: challenges
33
and opportunities. In Proceedings of the Australasian information security workshop
34
conference on ACSW frontiers 2003-Volume 21 (pp. 43-48). Australian Computer
35
Society,Inc.
36
Kamvar, S. D., Schlosser, M. T., & Garcia-Molina, H. (2003, May). The eigentrust algorithm
37
for reputation management in p2p networks. In Proceedings of the 12th international
38
conference on World Wide Web (pp. 640-651). ACM
39
Keren, B. and Pliskin, J. S. (2006). A benchmark solution for the risk-averse vendorproblem.
40
European Journal of Operational Research, 174(3):1643–1650.
41
Khouja, M. (1999). The single-period (news-vendor) problem: literature review and
42
suggestions for future research. Omega, 27(5), 537-553.
43
Kwon, I. W. G., & Suh, T. (2004). Factors affecting the level of trust and commitment in
44
supply chain relationships. Journal of Supply Chain Management, 40(1), 4-14.
45
Lau, N., Hasija, S., & Bearden, J. N. (2014). Newsvendor pull-to-center reconsidered.
46
Decision Support Systems, 58, 68-73.
47
Lewis, J. D., & Weigert, A. (1985). Trust as a social reality. Social forces, 63(4), 967-985.
48
Li, X., Li, Y., & Cai, X. (2012). Quantity decisions in a supply chain with early returns
49
remanufacturing. International Journal of Production Research, 50(8), 2161-2173.
50
Lin, Y. H., Chen, J. M., & Chiang, T. C. (2010). Channel coordination for a newsvendor
51
problem with return and quantity discount. Journal of Information and Optimization Sciences
52
31(4), 857-873.
53
Mcknight, D. H. and Chervany, N. L. “The meanings of trust: University of Minnesota,
54
Technical reports”.http://misrc.umn.edu/wpaper/WorkingPapers/9604.pdf, 1996.
55
Marsh, S. (1994). Trust in distributed artificial intelligence. In Artificial Social Systems (pp.
56
94-112). Springer Berlin Heidelberg.
57
Mejia, M., ChaparroVargas, R. (2013). Distributed Trust and Reputation Mechanisms for
58
Vehicular Ad-Hoc Networks.
59
Nguyen, D. Q., Lamont, L., & Mason, P. C. (2009). On trust evaluation in mobile ad-hoc
60
networks. In Security and Privacy in Mobile Information and Communication Systems (pp.
61
1-13). Springer Berlin Heidelberg
62
Nguyen, H. T., Zhao, W., & Yang, J. (2010, July). A trust and reputation model based on
63
bayesian network for Web services. In Web Services (ICWS), 2010 IEEE International
64
Conference on (pp. 251-258). IEEE.
65
Panayides, P. M., & Venus Lun, Y. H. (2009). The impact of trust on innovativeness and
66
supply chain performance. International Journal of Production Economics, 122(1), 35-46.
67
Petruzzi, N. C., & Dada, M. (1999). Pricing and the newsvendor problem: A review with
68
extensions. Operations Research, 47(2), 183-194.
69
Qin, Y., Wang, R., Vakharia, A. J., Chen, Y., & Seref, M. M. (2011). The newsvendor
70
problem: Review and directions for future research. European Journal of Operational
71
Research, 213(2), 361-374.
72
Romp, G. (1997). Game theory: introduction and applications. OUP Catalogue.
73
Shakouri, H., Menhaj, M.B. (2008). “A single fuzzy rule to smooth the sharpness of mixed
74
Data: Time and frequency domains analysis”, Fuzzy Sets & Systems (FSS), No. 159, pp.
75
2446 -2465.
76
Shubik, M. (2006). Game theory in the social sciences: Concepts and solutions.
77
Staples, D. S., & Webster, J. (2008). Exploring the effects of trust, task interdependence and
78
virtualness on knowledge sharing in teams. Information Systems Journal, 18(6), 617-640.
79
Vivekananth, P. (2011, May). Enhanced reliable trust model for grid computing based on
80
reputation. In Proceedings of the 2011 international conference on Computers and computing
81
(pp. 39-45). World Scientific and Engineering Academy and Society (WSEAS).
82
Wang, C., & Chen, X. (2013). Fresh Produce Supply Chain Management Decisions with
83
Circulation Loss and Options Contracts. In LISS 2012 (pp. 643-647). Springer Berlin
84
Heidelberg.
85
Wang, Y., & Singh, M. P. (2007, January). Formal Trust Model for Multiagent Systems. In
86
IJCAI (Vol. 7, pp. 1551-1556).
87
Williamson, O. E. (1993). Calculativeness, trust, and economic organization. Journal of law
88
and economics, 453-486.
89
Xu, J., Wei, J., & Jun, T. (2012). Comparing improvement strategies for inventory
90
inaccuracy in a two-echelon supply chain. European Journal of Operational Research, 221(1),
91
ORIGINAL_ARTICLE
Balanced clusters and diffusion process in signed networks
In this paper we study the topology effects on diffusion process in signed networks. Considering a simple threshold model for diffusion process, it is extended to signed networks and some appropriate definitions are proposed. This model is a basic model that could be extended and applied in analyzing dynamics of many real phenomena such as opinion forming or innovation diffusion in social networks. Studying the model declares that highly balanced dense clusters act as obstacles to diffusion process. This fact is verified by numerical simulations and it is declared that balanced dense clusters limit perturbation diffusion and the rest time. In other words the systems with more compatible communities and balanced clusters act more robust against perturbations. Moreover, the final state majority would be the same of more balanced cluster initially. These structural properties could be useful in analyzing and controlling diffusion process in systems.
https://www.jise.ir/article_7412_7a386c6e709c0625cc5ad0332850a02f.pdf
2014-12-01
104
117
Social Networks
Innovation Diffusion
Balanced clusters
Signed
network
Maryam
Ehsani
ehsaani.maryam@gmail.com
1
Department of Industrial Engineering, Tarbiat-Modares University
AUTHOR
Mohammad
Sepehri
mehdi.sepehri@modares.ac.ir
2
Department of Industrial Engineering, Tarbiat-Modares University
LEAD_AUTHOR
Alcántara, J. M., & Rey, P. J. (2012). Linking topological structure and dynamics in ecological
1
networks. The American Naturalist, 180(2), 186-199.
2
Balankin, A. S., Martínez Cruz, M. Á., & Martínez, A. T. (2011). Effect of initial concentration and
3
spatial heterogeneity of active agent distribution on opinion dynamics. Physica A: Statistical
4
Mechanics and its Applications, 390(21), 3876-3887.
5
Cartwright, D., & Harary, F. (1956). Structural balance: a generalization of Heider's theory.
6
Psychological review, 63(5), 277.
7
Delre, S. A., Jager, W., & Janssen, M. A. (2007). Diffusion dynamics in small-world networks with
8
heterogeneous consumers. Computational and Mathematical Organization Theory, 13(2), 185-202.
9
Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of
10
modern physics, 81(2), 591.
11
Deroı̈an, F. (2002). Formation of social networks and diffusion of innovations. Research policy,
12
31(5), 835-846.
13
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly
14
connected world. Cambridge University Press.
15
Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.
16
Heider, F. (1946). Attitudes and cognitive organization. The Journal of psychology, 21(1), 107-112.
17
J.A. Davis, Clustering and structural balance in graphs, Human Relations 20 (1967) 181–187.
18
Karsai, M., Perra, N., & Vespignani, A. (2014). Time varying networks and the weakness of strong
19
ties. Scientific reports, 4.
20
Li-Sheng, Z., Wei-Feng, G., Gang, H., & Yuan-Yuan, M. (2014). Network dynamics and its
21
relationships to topology and coupling structure in excitable complex networks. Chinese Physics B,
22
23(10), 108902.
23
Lyst, J. A. H. O., Kacperski, K., & Schweitzer, F. (2002). Social impact models of opinion dynamics.
24
Annual reviews of computational physics, 9, 253-273.
25
Malandrino, F., Kurant, M., Markopoulou, A., Westphal, C., & Kozat, U. C. (2012, March). Proactive
26
seeding for information cascades in cellular networks. In INFOCOM, 2012 Proceedings IEEE (pp.
27
1719-1727). IEEE.
28
Mustafa, N. H., & Pekeč, A. (2001). Majority consensus and the local majority rule. In Automata,
29
Languages and Programming (pp. 530-542). Springer Berlin Heidelberg.
30
Nematzadeh, A., Ferrara, E., Flammini, A., & Ahn, Y. Y. (2014). Optimal Network Modularity for
31
Information Diffusion. Physical review letters, 113(8), 088701.
32
Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A
33
critical review and research directions. International Journal of Research in Marketing, 27(2), 91-
34
Rahmandad, H., & Sterman, J. (2008). Heterogeneity and network structure in the dynamics of
35
diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998-
36
Sontag, E. D. (2007). Monotone and near-monotone biochemical networks. Systems and Synthetic
37
Biology, 1(2), 59-87.
38
Srinivasan, A. R., & Chakraborty, S. (2014, June). Effect of network topology on the controllability
39
of voter model dynamics using biased nodes. In American Control Conference (ACC), 2014 (pp.
40
2096-2101). IEEE.
41
Valente, T. W. (1995). Network models of the diffusion of innovations (Vol. 2, No. 2). Cresskill, NJ:
42
Hampton Press.
43
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. nature,
44
393(6684), 440-442.
45
Zhou, H., & Lipowsky, R. (2007). Activity patterns on random scale-free networks: Global dynamics
46
arising from local majority rules. Journal of Statistical Mechanics: Theory and Experiment, 2007(01),
47
ORIGINAL_ARTICLE
Approximating the step change point of the process fraction non conforming using genetic algorithm to optimize the likelihood function
Control charts are standard statistical process control (SPC) tools for detecting assignable causes. These charts trigger a signal when a process gets out of control but they do not indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change. Knowing when a process has begun to change simplifies the identification of the special cause and consequently saves time and expenditure. This study uses genetic algorithms (GA) with optimum search features for approximately optimizing the likelihood function of the process fraction nonconforming. Extensive simulation results show that the proposed estimator outperforms the Maximum Likelihood Estimator (MLE) designed for step change regarding to speed and variance.
https://www.jise.ir/article_7413_419b00d741f39f075bb3a319e041e92f.pdf
2014-12-01
118
128
Quality Control
Statistical process control
Change point
Genetic
algorithm
np chart
Raziyeh
Hosseini
1
Department of Statistic, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Vahid
Amirzadeh
2
Department of Statistic, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Mohammad
Yaghoobi
yaghoobi@uk.ac.ir
3
Department of Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Hojjat
Mirzaie
4
Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Alaeddini, A., Ghazanfari, M., and Nayeri, M. A. (2009). A hybrid fuzzy-statistical clustering
1
approach for estimating the time of changes in fixed and variable sampling control charts.
2
Information sciences, 179(11), 1769-1784.
3
Amiri, A., and Allahyari, S. (2012). Change point estimation methods for control chart postsignal
4
diagnostics: a literature review. Quality and Reliability Engineering International, 28(7), 673-685.
5
Casella, G. and Berger, R. L. (2002), Statistical Inference; Second Edition; Duxbury; California.
6
Ghazanfari, M., A. Alaeddini, S. T.A. Niaki, and M.-B.Aryanezhad. (2008), A Clustering Approach
7
to Identify the Time of a Step Change in Shewhart Control Charts; Quality and Reliability
8
Engineering International 24 (7); 765–778.
9
Holland J. H. (1975), Adaptation in Natural and Artificial Systems; University of Michigan Press;
10
Kazemi, M. S., Bazargan, H. and Yaghoobi, M. A. (2013), Estimating the drift time for processes
11
subject to linear trend disturbance using fuzzy statistical clustering; International Journal of
12
Production Research 52(11); 3317-3330.
13
Noorossana, R., and Shademan, A. (2009), Estimating the change point of a normal process mean
14
with a monotonic change; Quality and Reliability Engineering International 25 (1); 79–90.
15
Perry, M. B. and Pignatiello. J.J. (2006), Estimation of the change point of a normal process mean
16
with a linear trend disturbance; Quality Technology and Quantitative Management; 3 (3); 325–334.
17
Perry, M. B. Pignatiello. J.J., and Simpson, J.R. (2007), Estimating of the change point of the
18
process fraction nonconforming with a monotonic change disturbance in SPC; Quality and
19
Reliability Engineering International 23(3); 327–339.
20
Perry, M.B., Pignatiello Jr, J.J. and Simpson, J.R. (2006), Estimating the change-point of a poisson
21
rate parameter with a linear trend disturbance; Quality and Reliability Engineering International 22
22
(4); 371–384.
23
Pignatiello. J.J., and Samuel, T.R. (2001), Estimation of the change point of a normal process
24
mean in SPC applications; Journal of Quality Technology 33 (1); 82–95.
25
Samuel, T. R., and Pignatjello Jr, J. J. (1998). Identifying the time of a change in a Poisson rate
26
parameter. Quality Engineering, 10(4), 673-681.
27
Samuel, T.R. and Pignatiello. J.J. (2001), Identifying the time of a step change in the process
28
fraction nonconforming; Quality Engineering 13; 357–365.
29
Samuel, T.R., Pignatiello. J.J., and Calvin, J.A. (1998b). Identifying the time of a step change in a
30
normal process variance; Quality Engineering 10 (3); 529–538.
31
Samuel, T.R., Pignatiello. J.J., and Calvin, J.A. (1998a). Identifying the time of a step change with
32
control charts; Quality Engineering 10 (3); 521–527.
33
Zandi, F., Niaki, S.T.A., Nayeri, M.A. and Fathi, M. (2011). Change-point estimation of the process
34
fraction nonconforming with a linear trend in statistical process control; International Journal of
35
Computer Integrated Manufacturing 24 (10); 939–947.
36
Zarandi, M. H. F., and A. Alaeddini. (2010). AGeneral Fuzzy-statistical Clustering Approach for
37
Estimating the Time of Change invariable Sampling Control Charts; Information Sciences 180 (16);
38
3033–3044.
39
Zarandi, M. F., A. Alaeddini, and I. Turksen. (2008). A Hybrid Fuzzy Adaptive Sampling Run
40
Rules for Shewhart Control Charts; Information Sciences 178 (4); 1152–1170.
41
ORIGINAL_ARTICLE
Robust scheduling for three-machine robotic cell using interval data
In reality, due to the lack of adequate environmental information, uncertainty is a common practice. In order to provide good and acceptable solutions, development of systematic methods for solving problems of uncertainty is important. One of these methods is based on robust optimization. This type of planning is to find a solution that is not sensitive to parameter fluctuations. In this article, a new way is represented to solve a three-machine robotic cell problem. An intervallic processing time is concerned as the problem being discussed. Different scenarios are defined by using robust optimization; afterwards, applying min-max regret method, robust counterpart of original problem is specified. Since the problem is NP-hard, a metaheuristic is applied to solve it. Genetic Algorithm (GA) as a population-based metaheuristic is employed. Cycle time and program operating time are calculated for different number of parts. It is demonstrated that by increasing the part numbers, gap between the robust and original cycle time increases. It is observed that both the cycle time and algorithm operating time increase.
https://www.jise.ir/article_96814_8f18a139a8529190eed6b18c620fa4d1.pdf
2014-11-29
129
140
robust optimization
min-max regret
Cycle Time
Genetic Algorithm
Saeedeh
Gholami
s_gholami@kntu.ac.ir
1
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Faezeh
Deymeh
faezeh.daymeh@gmail.com
2
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
Agnetis A. (2000), Scheduling No-Wait Robotic Cells with Two and Three Machines; European Journal of Operational Research 123;303–314.
1
Aytug H., Lawley M.A., McKay K., Mohan S., Uzsoy R. (2005), Executing production schedules in the face of uncertainties: A review and some future directions; European Journal of Operational Research161;86–110.
2
Bedini R., Lisini G.G., Sterpos P. (1979), Optimal Programming of Working Cycles for Industrial Robots; Journal of Mechanical Design. Transactions of the ASME 101; 250–257.
3
Che A., Chu C., Chu F. (2002), Multicyclic Hoist Scheduling with Constant Processing Times; IEEE Transactions on Robotics and Automation18; 69–80.
4
Che A., Chu C., Levner E.(2003), A Polynomial Algorithm for 2-degree Cyclic Robot Scheduling; European Journal of Operational Research 145;31–44.
5
Chen H., Chu C., Proth J. (1998), Cyclic Scheduling with Time Window Constraints; IEEE Transactions on Robotics and Automation 14;144–152.
6
Claybourne B.H. (1983), Scheduling Robots in Flexible Manufacturing Cells; CME Automation 30;36–40.
7
Kondoleon A.S. (1979), Cycle Time Analysis of Robot Assembly Systems; Proceedings of the Ninth Symposium on Industrial Robots;575–587.
8
Lei L., Wang T.J. (1994), Determining Optimal Cyclic Hoist Schedules in a Single- Hoist Electroplating Line; IIE Transactions 26;25–33.
9
Levner E., Kats V., Levit V. (1997), An Improved Algorithm for Cyclic Flowshop Scheduling in a Robotic Cell; European Journal of Operational Research 97;500–508.
10
Luce R.D., Raiffa H. (1957), Games and Decisions: Introduction and Critical Survey; Dover Publications Inc.
11
Maimon O.Z., Nof S.Y. (1985), Coordination of Robots Sharing Assembly Tasks; Journal of Dynamic Systems Measurement and Control. Transactions of the ASME 107; 299–307.
12
Roy B. (2010), Robustness in operational research and decision aiding: A multi-faceted issue; European Journal of Operational Research200; 629–638.
13
Sabuncuoglu I., Goren S. (2009), Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing22; 138–57.
14
Wilhelm W.E. (1987), Complexity of Sequencing Tasks in Assembly Cells Attended by One or Two Robots; Naval Research Logistics 34; 721–738.
15