ORIGINAL_ARTICLE
A Customized Bi-Objective Location-Routing Problem for Locating Post Offices and Delivery of Post Parcels
One of the most important problems for distribution companies is to find the best locations for depots and to find proper routes for transportation vehicles and to optimize supply network. This study intends to develop a model for the problem of location-routing in post offices. So, a new Bi-Objective Location-Routing Problem for Locating Town Post Office and Routing Parcels is defined. This problem is modeled through mixed-integer mathematical programming. The aim of proposed model is to select potential post offices and to find optimal routes for transportation vehicles while time constraints are taken into account. The proposed model is applied in a real case study including eight main post area and 21 regional offices in Tehran, Iran. A goal programming approach is proposed to solve this bi-objective optimization model. The GAMS Software is used to code and solve the associated mathematical model. Some required parameters of the model such as demands are estimated using Geographical Information System (GIS) and simulation methods. The results of proposed model including the objective functions, decision variables, and proposed routing of vehicles have been compared with the existing practical solutions. Sensitivity analysis on main parameters of proposed models is accomplished and the results are analyzed. This comparison illustrate the efficacy and applicability of proposed approach.
https://www.jise.ir/article_33654_85cfeb983b3201f639aae5a06b5e24c9.pdf
2017-04-10
1
17
Location-routing problem
Post Office Location-Routing
Bi-objective Optimization
Goal Programming
Tehran Post Office
Mohammad
Tahmasebi
m.hossein_tahmasebi@yahoo.com
1
Department of Industrial Engineering Faculty of Industrial Engineering South Tehran Branch Islamic Azad University Tehran, Iran
AUTHOR
Kaveh
Khalili-Damghani
k_khalili@azad.ac.ir
2
Department of Industrial Engineering Faculty of Industrial Engineering South Tehran Branch Islamic Azad University Tehran, Iran
LEAD_AUTHOR
Vahid
Ghezavati
3
Department of Industrial Engineering Faculty of Industrial Engineering South Tehran Branch Islamic Azad University Tehran, Iran
AUTHOR
Bruns, A., Klose, A., Staghly, P., 2000. Restructuring of Swiss parcel delivery services. OR Spektrum 22, 285–302.
1
Chan, Y ., Baker, S., 2005.The multiple depot, multiple traveling salesmen facility-location problem: Vehicle range, service frequency and heuristic implementations. Mathematical and Computer Modelling 41,1035-1053.
2
Charnes, A., Cooper, WW., Ferguson, R. (1955) Optimal estimation of executive compensation by linear programming, Management Science, 1, 138-151.
3
Charnes, A., Cooper, WW., (1961) Management models and industrial applications of linear programming, Wiley, New York.
4
De Camargo, R., de Miranda, G., kketangen,A.,2013.A new formulation and an exact approach for the many-to-many hub location-routing problem. Applied Mathematical Modelling, 37,7468-7480.
5
Govindan, K., Jafarian, A., Khodaverdi, R., Devika,K.,2014. Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food . International Journal of Production Economics, 152,9-28.
6
Hashemi Doulabi, S., Seifi,A.,2013.Lower and upper bounds for location-arc routing problems with vehicle capacity constraints. European Journal of Operational Research, 224,189-208.
7
Hosseininezhad, S.J., Jabalameli, M.S. (2016). A multi-objective continuous covering location model. Journal of Industrial and Systems Engineering, 9(2), Article in press.
8
Ignizio, JP (1976) Goal programming and extensions, Lexington Books, Lexington, MA.
9
Ignizio, JP., Cavalier, TM (1994) Linear programming, Prentice Hall.
10
Jarboui, B., Derbel,H., Hana,S., Mladenovi,N.,2013.Variable neighborhood search for location routing.Computers& Operations Research,40,47-55.
11
Jones DF, Tamiz M (2010) Practical Goal Programming, Springer Books.
12
Kordjazi, M., Kazemi, A. (2016). Presenting a three-objective model in location-allocation problems using combinational interval full-ranking and maximal covering with backup model, Journal of Industrial and Systems Engineering, 9(2), Article in press.
13
Laporte, G., Nobert, Y., 1981. An exact algorithm for minimizing routing and operating costs in depot location. European Journal of Operational Research 6, 224–226.
14
Laporte, G., Nobert, Y., Pelletier, P., 1983. Hamiltonian location problems. European Journal of Operational Research 12, 82–89.
15
Laumanns,M., Thiele,L., Zitzler,E.,2006. An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method.European Journal of Operational Research, 169,932-942.
16
Lee, SM (1972) Goal programming for decision analysis, Auerback, Philadelphia.
17
Lischak, C., Triesch, E., 2008. Location planning for a parcel delivery service.
18
Mohamadi, A., Yaghoubi, S. (2016). A new stochastic location-allocation emergency medical services healthcare system model during major disaster, Journal of Industrial and Systems Engineering, 9(2), Article in press.
19
Maranzana, F.E., 1964.On the location of supply points to minimize transport costs.Operational Research Quarterly 15,261-270.
20
Nadizadeh,A.,2009.Using greedy clustering method to solve capacitated location-routing problem.Direcci_on y Organizaci_on, 39,79-85.
21
Pareto, V. (1896).Course of Political Economy.
22
Rath,S.,Gutjahr,W.,2014. A math-heuristic for the warehouse location-routing problem in disaster relief.Computers& Operations Research, 42,25-39.
23
Rawls, J. (1973). Some ordinalist-utilitarian notes on Rawls's theory of justice. The Journal of Philosophy, 70(9), 245-263.
24
Rieck,J., Ehrenberg,C., Zimmermann,J.,2014. Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery. European Journal of Operational Research,263,863-878.
25
Romero, C (1991) Handbook of critical issues in goal programming, Pergamon Press, Oxford.
26
Stenger,A., Schneider,M., Schwind,M., Vigo,D.,2012.Location routing for small package shippers with subcontracting options.International Journal of Production Economics, 140,702-712.
27
Ting, C, J., Chen,C,A,2013 .A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141,34-44.
28
Wasner, M., Zapfel, G., 2004.An integrated multi-depot hub location vehicle routing model for network planning of parcel service. International Journal of Production Economics 90,403–419.
29
Zare Mehrjerdi,Y., Nadizadeh,A.,2013.Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229,75-84.
30
ORIGINAL_ARTICLE
A multi-objective optimization model for process targeting
Customers and consumers are the necessities for the survival of industries and organizations. Trying to improve the process in order to increase consumer satisfaction is the most important aim. The survival of an organization depends on its ability to continue the activities in compliance with the demands of customers to meet their legitimate needs. An organization is successful when it exactly knows these needs and provides the right products. The selection of the optimal process target is an important problem in production planning and quality control. For complex manufacturing systems, process or product optimization can be instrumental in achieving a significant economic advantage. To reduce costs associated with product non-conformance or excessive waste, engineers often identify the most critical quality characteristics and then use methods to obtain their ideal parameter settings. The purpose of this study is to find the optimum targeting value. a product with two quality characteristics with independent distributions is considered. To determine the market of product sales, random sample size of lot size selected. based on the quality of products, the lot placed in primary market, secondary market, reworked and scraped. To obtain the optimum targeting value, use NSGA II algorithm with Maximize expected profit and minimize expected loss.
https://www.jise.ir/article_33662_eed09027921c0d08de2b418e38730d8c.pdf
2017-04-12
18
34
Quality Control
Process Adjustment
Loss function
Profit
Mohammad Saber
Fallah Nezhad
fallahnezhad@yazd.ac.ir
1
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Mehdi
Abbasi
mehdi.a6730@yahoo.com
2
epartment of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Ehsan
Shahin
ehsan.shahin09@gmail.com
3
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Chen, C.-H. and M.-T. Lai, Economic manufacturing quantity, optimum process mean, and economic specification limits setting under the rectifying inspection plan. European Journal of Operational Research, 2007. 183(1): p. 336-344.
1
Chen, C.-H. and H.-S. Kao, The determination of optimum process mean and screening limits based on quality loss function. Expert Systems with Applications, 2009. 36(3): p. 7332-7335.
2
Darwish, M., F. Abdulmalek, and M. Alkhedher, Optimal selection of process mean for a stochastic inventory model. European Journal of Operational Research, 2013. 226(3): p. 481-490.
3
Das, C., Selection and evaluation of most profitable process targets for the control of canning quality. Computers & industrial engineering, 1995. 28(2): p. 259-266.
4
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
5
Deep, K. and M. Thakur, A new mutation operator for real coded genetic algorithms. Applied mathematics and Computation, 2007. 193(1): p. 211-230.
6
Duffuaa, S.O. and A. El-Ga’aly, A multi-objective optimization model for process targeting using sampling plans. Computers & Industrial Engineering, 2013. 64(1): p. 309-317.
7
Ebrahimizade, A. The problem of locating the hub maximum coverage under uncertainty. Master thesis of yazd University , [Persian] 2013.
8
Fallah Nezhad M.S., Niaki S.T.A. and Shahin E, A Markov Model to Determine Optimal Equipment Adjustment in Multi-stage Production Systems Considering Variable Cost. Iranian Journal of Operations Research, 2013. 4(2): pp. 146-160.
9
Fallah Nezhad M.S. and Ahmadi E, Optimal Process Adjustment with Considering Variable Costs for Uni-variate and Multi-variate Production Process. International Journal of Engineering, 2014. 27(4): p. 561-572.
10
Friedricha D. and Luibleba A., Assessment of standard compliance of Central Europeanplastics-based wall cladding using multi-criteria decision making (MCDM). Case Studies in Structural Engineering, 2016. 5: p. 27–37
11
Goethals, P.L. and B. Cho, The optimal process mean problem: Integrating predictability and profitability into an experimental factor space. Computers & Industrial Engineering, 2012. 62(4): p. 851-869.
12
Gong, W., Cai, Z., Ling, C. X., & Li, H. (2010). A real-coded biogeography-based optimization with mutation. Applied Mathematics and Computation, 216(9), 2749-2758.
13
Hunter, W. G., & Kartha, C. P. (1977). Determining the most profitable target value for a production process. Journal of Quality Technology, 9(4), 176-181.
14
Ishikawa, K. (1986). Guide to quality control. Quality Resources.
15
Juran, J., & Godfrey, A. B. (1999). Quality handbook. Republished McGraw-Hill.
16
Lee, M.K., et al., Determination of the optimum target value for a production process with multiple products. International Journal of Production Economics, 2007. 107(1): p. 173-178.
17
Noorossana. R. Statistical Quality Control. Iran University of Science and Technology. Third Edition, [Persian],2006.
18
Park, T., Kwon, H. M., Hong, S. H., & Lee, M. K. (2011). The optimum common process mean and screening limits for a production process with multiple products. Computers & Industrial Engineering, 60(1), 158-163.
19
Pulak, M. and K. Al-Sultan, "The optimum targeting for a single filling operation with rectifying inspection". Omega, 1996. 24(6): p. 727-733.
20
Sadeghie, A. Decision-making based on genetic algorithm optimization. New Science Publications, [Persian] ,2006.
21
Shao, Y.E., J.W. Fowler, and G.C. Runger, Determining the optimal target for a process with multiple markets and variable holding costs. International Journal of Production Economics, 2000. 65(3): p. 229-242.
22
Springer, C. H. (1951). A method for determining the most economic position of a process mean. Industrial Quality Control, 8(1), 36-39.
23
Srinivas, N. and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 1994. 2(3): p. 221-248.
24
Tareghian H. R., Bozorgnia A., the application of the quality control systems using the statistical methods. Publishers of Ferdowsi Mashhad University,[Persian] 1998 .219.
25
ORIGINAL_ARTICLE
A novel bi-objective reliable location routing model considering impedance function under demand-side and supply-side uncertainty (A Case study)
Reliable location routing problem considers a location problem and a vehicle routing problem in order to select the optimal location of facilities and at the same time the optimal routes for vehicles considering the unexpected failure for facilities in which, all facilities may fail with a probability. In this paper, a bi-objective mathematical model has been developed to minimize the total costs and minimize expected value of total impedance value-weighted travel distance. To approach the model to real world, two types of uncertainty in model have been considered: 1) demand-side uncertainty and 2) supply side uncertainty and also, impedance function has been utilized to operationalize the concept of accessibility in transport planning research. To solve the model, first Ɛ-constraint method has been used for multi objective solution and then we implemented a small-sized case study in an urban district in Iran. The findings offer managerial insights into how various system parameters affect the optimal solution.
https://www.jise.ir/article_33677_4b7bc81b7d9acb74b2c34ea9e9c0cbbd.pdf
2017-04-12
35
49
Reliable location routing problem
Demand-side uncertainty
Supply-side uncertainty
Impedance function
case study
Ahmad
Mohamadi
mohamadi_a@ind.iust.ac.ir
1
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
AUTHOR
Saeed
Yaghoubi
yaghoubi@iust.ac.ir
2
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
LEAD_AUTHOR
Aboolian, R., Cui, T., & Shen, Z. J. M. (2012). An efficient approach for solving reliable facility location models. INFORMS Journal on Computing,25(4), 720-729.
1
Ahmadi-Javid, A., & Seddighi, A. H. (2013). A location-routing problem with disruption risk. Transportation Research Part E: Logistics and Transportation Review, 53, 63-82.
2
Albareda-Sambola, M., Fernández, E., & Laporte, G. (2007). Heuristic and lower bound for a stochastic location-routing problem. European Journal of Operational Research, 179(3), 940-955.
3
Avriel, M. (1980). A geometric programming approach to the solution of location problems. Journal of Regional Science, 20(2), 239-246.
4
Chen, Q., Li, X., & Ouyang, Y. (2011). Joint inventory-location problem under the risk of probabilistic facility disruptions. Transportation Research Part B: Methodological, 45(7), 991-1003.
5
Cui, T., Ouyang, Y., & Shen, Z. J. M. (2010). Reliable facility location design under the risk of disruptions. Operations Research, 58(4-part-1), 998-1011.
6
Etemadnia, H., Goetz, S. J., Canning, P., & Tavallali, M. S. (2015). Optimal wholesale facilities location within the fruit and vegetables supply chain with bimodal transportation options: An LP-MIP heuristic approach. European Journal of Operational Research, 244(2), 648-661.
7
Haimes, Y. Y., Ladson, L. S., & Wismer, D. A. (1971). Bicriterion formulation of problems of integrated system identification and system optimization. IEEE Transactions on Systems Man and Cybernetics, (3), 296.
8
Hassan-Pour, H. A., Mosadegh-Khah, M., & Tavakkoli-Moghaddam, R. (2009). Solving a multi-objective multi-depot stochastic location-routing problem by a hybrid simulated annealing algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(8), 1045-1054.
9
Herazo-Padilla, N., Montoya-Torres, J. R., Munoz-Villamizar, A., Nieto Isaza, S., & Ramirez Polo, L. (2013, December). Coupling ant colony optimization and discrete-event simulation to solve a stochastic location-routing problem. In Simulation Conference (WSC), 2013 Winter (pp. 3352-3362). IEEE.
10
Horner, M. W., & O'Kelly, M. E. (2001). Embedding economies of scale concepts for hub network design. Journal of Transport Geography, 9(4), 255-265.
11
http://map.tehran.ir/
12
Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582-597.
13
Klibi, W., Lasalle, F., Martel, A., & Ichoua, S. (2010). The stochastic multiperiod location transportation problem. Transportation Science, 44(2), 221-237.
14
Mohamadi, A., Yaghoubi, S., & Derikvand, H. (2015). A credibility-based chance-constrained transfer point location model for the relief logistics design (Case Study: earthquake disaster on region 1 of Tehran city).International Journal of Supply and Operations Management, 1(4), 466-488.
15
Norouzi, K. K., Omidvar, B., Malek, M. B., & Ganjehi, S. (2013). Multi-Hazards risk analysis of damage in urban residential areas (Case study: earthquake and flood hazards in tehran-iran). Journal of Geography and Environmental Hazards, 2(7), 566-608.
16
Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45(8), 1190-1211.
17
Snyder, L. V. (2003). Supply chain robustness and reliability: Models and algorithms (Doctoral dissertation, Northwestern University).
18
Snyder, L.V., Daskin, M.S., (2005). Reliability models for facility location: the expected failure cost case. Transp. Sci. 39 (3), 400–416
19
Wei-long, Y. E., & Qing, L. I. (2007, August). Solving the Stochastic Location-Routing Problem with Genetic Algorithm. In Management Science and Engineering, 2007. ICMSE 2007. International Conference on (pp. 429-434). IEEE.
20
Xie, W., Ouyang, Y., & Wong, S. C. (2015). Reliable Location-Routing Design Under Probabilistic Facility Disruptions. Transportation Science.
21
Yeager, C. D., & Gatrell, J. D. (2014). Rural food accessibility: An analysis of travel impedance and the risk of potential grocery closures. Applied Geography, 53, 1-10.
22
Zhang, B., Ma, Z., & Jiang, S. (2008, October). Location-routing-inventory problem with stochastic demand in logistics distribution systems. In Wireless Communications, Networking and Mobile Computing, 2008. WiCOM'08. 4th International Conference on (pp. 1-4). IEEE.
23
Zhang, Y., Qi, M., Lin, W. H., & Miao, L. (2015). A metaheuristic approach to the reliable location routing problem under disruptions. Transportation Research Part E: Logistics and Transportation Review, 83, 90-110.
24
ORIGINAL_ARTICLE
A branch and bound algorithm to minimize the total weighted number of tardy jobs and delivery costs with late deliveries for a supply chain scheduling problem
In this paper, we study a supply chain scheduling problem that simultaneously considers production scheduling and product delivery. jobs have to be scheduled on a single machine and delivered to customers for further processing in batches. The objective is to minimize the sum of the total weighted number of tardy jobs and the delivery costs. In this paper, we present a heuristic algorithm (HA) and a branch and bound (B&B) method for the restricted case, where the tardy jobs are delivered separately, and compare these procedures with an existing dynamic programming (DP) algorithm by computational tests. The results of computational tests show significant improvement of the B&B over the dynamic programming algorithm.
https://www.jise.ir/article_33679_27ca14243e0408444b538419bcd3ab5a.pdf
2017-04-19
50
60
Supply chain scheduling
Batch delivery and tardy job
Branch and bound
Morteza
Barzoki
rasti@cc.iut.ac.ir
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
LEAD_AUTHOR
Seyed Reza
Hejazi
rehejazi@cc.iut.ac.ir
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
AUTHOR
Brucker P. and Kovalyov M.Y., 1996. Single Machine Batch Scheduling to Minimize The Weighted Number of Late Jobs, Mathematical Methods of Operation Research 43, 1-8.
1
Chen Z-L., 2010. Integrated Production and Outbound Distribution Scheduling: Review and Extensions. Operations Research 58 (1), 130-148.
2
Gens G.V. and Levner E.V., 1979. Discrete optimization problems and efficient approximate algorithms, Engineering Cybernetics 17 (6), 1-11.
3
Gens G.V. and Levner E.V., 1981. Fast Approximation Algorithm for Job Sequencing With Deadlines. Discrete Applied Mathematics 3 (4), 313-318.
4
Hall N.G., Potts C.N., 2003. Supply Chain Scheduling: Batching And Delivery. Operations Research 51 (4), 566-584.
5
Hallah R.M., Bulfin R.L., 2003. Minimizing the Weighted Number of Tardy Jobs on a Single Machine, European Journal of Operational Research 145, 45-56.
6
Hallah R.M., Bulfin R.L., 2007. Minimizing the Weighted Number of Tardy Jobs on a single machine with release dates, European Journal of Operational Research 176, 727-744.
7
Hamidinia A., Khakabimamaghani S., Mahdavi Mazdeh M., Jafari M., 2012, A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system, Computers & Industrial Engineering 62, 29–38
8
Hochbaum D.S., Landy D., 1994. Scheduling with batching: minimizing the weighted number of tardy jobs. Operations Research Letters, 16:79-86.
9
Ji M., He Y., Cheng T.C.E., 2007, Batch delivery scheduling with batch delivery cost on a single machine, European Journal of Operational Research 176 745–755.
10
Kalantari, M., Rabbani, M. and Ebadian, M. (2011) ‘A decision support system for order acceptance/rejection in hybridMTS/MTO production systems’, Applied Mathematical Modelling, Vol. 35, No. 3, pp.1363–1377.
11
Karp, R. M., 1972. Reducibility among Combinatorial Problems. R. E. Miller, J. W. Thatcher, Eds. Complexity of Computer Computations. Plenum Press, New York, 85-103.
12
Mazdeh M.M., Sarhadi M., Ashouri A., Hindi K.S., 2011. Single-Machine Batch Scheduling Minimizing Weighted Flow Times and Delivery costs. Applied Mathematical Modelling 35, 563-570.
13
Mazdeh M.M., Sarhadi M., Hindi K.S., 2007. A Branch-And-Bound Algorithm For Single-Machine Scheduling With Batch Delivery Minimizing Flow Times And Delivery costs, European Journal of Operational Research 183, 74-86.
14
Mazdeh M.M., Sarhadi M., Hindi K.S., 2008. A Branch-And-Bound Algorithm For Single-Machine Scheduling With Batch Delivery And Job Release Times, Computers and Operations Research 35, 1099-1111.
15
Moore J. M., 1968. An n job, one machine sequencing algorithm for minimizing the number of late jobs, Management Science 15, 102-109.
16
Rasti-Barzoki, M., & Hejazi, S. R. (2013). Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains. European Journal of Operational Research, 228, 345-357.
17
Rasti-Barzoki, M., & Hejazi, S. R. (2015). Pseudo-polynomial dynamic programming for an integrated due date assignment, resource allocation, production, and distribution scheduling model in supply chain scheduling. Applied Mathematical Modelling.
18
Rasti-Barzoki, M., Hejazi, S. R., & Mazdeh, M. M. (2013). A Branch and Bound Algorithm to Minimize the Total Weighed Number of Tardy Jobs and Delivery Costs. Applied Mathematical Modelling, 37, 4924-4937.
19
Ramayah, T., Roy, M.H., Li, K.B., Jantan, M., Zbib, I. and Ahmed, Z.U. (2007) ‘Type of procurement and operational performance: comparing e-procurement and offline purchasing’. International Journal of Services and Operations Management, Vol. 3, No. 3, pp.279–296.
20
Reisi–Nafchi, M., & Moslehi, G. (2015). Integrating two–agent scheduling and order acceptance problems to maximise total revenue by bounding each agent penalty function. International Journal of Services and Operations Management, 20, 358-384.
21
Renna, P. (2009) ‘A multi-agent system architecture for business-to-business applications’. International Journal of Services and Operations Management, Vol. 5, No. 3, pp.375–401.
22
Renna, P. (2012) ‘Simulation-based tool to analyse the effect oforder acceptance policy in a make-to-order manufacturing system’, International Journal of Services and Operations Management, Vol. 11, No. 1, pp.70–86.
23
Sahni S.K., 1976. Algorithms for Scheduling Independent Tasks. Journal of the ACM 23 (1), 116-127.
24
Steiner G., Zhang R., 2007. Minimizing the Weighted Number of Late Jobs with Batch Setup Times and Delivery costs on a Single Machine , Multiprocessor Scheduling: Theory and Applications, Book edited by Eugene Levner, Itech Education and Publishing, Vienna, Austria.
25
Steiner G., Zhang R., 2009. Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains, journal of scheduling 12, 565–574.
26
Yin, Y., Cheng, S.R., Cheng, T.C.E., Wu, W.H.and Wu, C.C. (2010) ‘Two-agent single-machine scheduling with release times and deadlines’. International Journal of Shipping and Transport Logistics, Vol. 5, No. 1, pp.75–94.
27
Zegordi S.H. Kamal Abadi I.N., Beheshti Nia, M.A., 2010. A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain, Computers & Industrial Engineering 58, 373-381.
28
ORIGINAL_ARTICLE
Real-Time Building Information Modeling (BIM) Synchronization Using Radio Frequency Identification Technology and Cloud Computing System
The online observation of a construction site and processes bears significant advantage to all business sector. BIM is the combination of a 3D model of the project and a project-planning program which improves the project planning model by up to 6D (Adding Time, Cost and Material Information dimensions to the model). RFID technology is an appropriate information synchronization tool between the real and virtual world of BIM. Also, the use of cloud storage and computing system, would bring about outstanding data access capabilities to the constructors as well as other project participants. The accurate prediction power that BIM brings to construction management and real-time status update, while utilizing the RFID technology, would greatly influence the efficiency of automated project management and post construction life-cycle maintenance. Meanwhile, cloud system will facilitate broad data transaction capabilities with the project. Hence, these integrations would enhance construction management by improving the data communication speed, real-time construction site control process and Human Resource Management as well. This paper introduces and evaluates a framework, which integrates Radio Frequency Identification (RFID) technology for real-time, mobile construction-site monitoring through a virtual model (BIM) and cloud data sharing as well as for processing activities. A case study has been carried out in the construction site of a new dormitory in Eastern Mediterranean University to examine different feasibilities and limitations of the framework.
https://www.jise.ir/article_33707_79a1a021fcfbaa70f2a34cbc01054297.pdf
2017-04-19
61
68
BIM
cloud computing
RFID technology
Construction management
Ali
Vatankhah Barenji
ali.vatankhah@cc.emu.edu.tr
1
Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta, KKTC, Via 10 Mersin, Turkey
LEAD_AUTHOR
Majid
Hashemipour
majid.hashemipoor@cc.emu.edu.tr
2
Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta, KKTC, Via 10 Mersin, Turkey
AUTHOR
Adetunla, A. O., Barenji, A. V., & Barenji, R. V. Developing manufacturing execution software as a service for small and medium size enterprise.
1
Barenji, A. V., Barenji, R. V., & Hashemipour, M. (2013, June). Structural modeling of a RFID-enabled reconfigurable architecture for a flexible manufacturing system. In Smart Objects, Systems and Technologies (SmartSysTech), Proceedings of 2013 European Conference on (pp. 1-10). VDE.
2
Barenji, A. V. (2013). An RFID-based distributed control system for flexible manufacturing system (Doctoral dissertation, Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)).
3
Barenji, A. V., & Degirmenci, C. (2015, January). Robot Control System based on Web Application and RFID Technology. In MATEC Web of Conferences (Vol. 28). EDP Sciences.
4
Barenji, A. V., Barenji, R. V., & Hashemipour, M. (2016). Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Advances in Engineering Software, 91, 1-11.
5
Barenji, R. V., Barenji, A. V., & Hashemipour, M. (2014). A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. The International Journal of Advanced Manufacturing Technology, 71(9-12), 1773-1791.
6
Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud computing: Distributed internet computing for IT and scientific research. IEEE Internet computing, 13(5), 10-13.
7
Eadie, R., Browne, M., Odeyinka, H., McKeown, C., & McNiff, S. (2013). BIM implementation throughout the UK construction project lifecycle: An analysis. Automation in Construction, 36, 145-151.
8
Eastman, C., Eastman, C. M., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. John Wiley & Sons.
9
Goedert, J. D., & Meadati, P. (2008). Integrating construction process documentation into building information modeling. Journal of construction engineering and management, 134(7), 509-516.
10
Howell, I., & Batcheler, B. (2005). Building information modeling two years later–huge potential, some success and several limitations. The Laiserin Letter, 22, 4.
11
Meadati, P., Irizarry, J., & Akhnoukh, A. K. (2010). BIM and RFID integration: a pilot study. Advancing and Integrating Construction Education, Research and Practice, 570-78.
12
Ren, Z., Sha, L., & Hassan, T. M. (2007). RFID facilitated construction material management-a case study of water supply project. In Proceedings of the 24th CIB W78 Conference Information Technology in Construction, Maribor, Slovenia (pp. 401-406).
13
Jardim-Goncalves, R., & Grilo, A. (2010). SOA4BIM: Putting the building and construction industry in the Single European Information Space. Automation in Construction, 19(4), 388-397.
14
Penttilä, H. (2006). Describing the changes in architectural information technology to understand design complexity and free-form architectural expression.
15
Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), 7-18.
16
Varkonyi, V. (2011). Debunking the Myths about BIM in the ‘Cloud’. AEC bytes, 1-4.
17
ORIGINAL_ARTICLE
A Novel Two-Stage Mathematical Model for Green Supplier Development
Nowadays, numerous processes of any supply chain are done by suppliers and consequently they cause a massive amount of pollution released to the nature. Hence, greening the suppliers has become a necessity. Although most of green supplier development programs need high investment, formal optimization models that address this issue are very rare. This paper mainly aims to address this problem by introducing a two-stage mathematical model which can help managers allocate optimal investment in their suppliers. In the first stage, suitable green supplier development programs are selected. Then, a multi-objective optimization model is presented for investing in an appropriate set of green programs, concurrently. Moreover, the conceptual framework presented in this paper provides managerial insight in every step of this process. Also, a comprehensive analysis is done under two scenarios of budget estimation and it has been found that these programs highly influence their required investment, and therefore, they must be considered, simultaneously.
https://www.jise.ir/article_33708_d068c9bcf6393def43d3f52398269363.pdf
2017-04-20
69
90
Green Supply Chain Management
green supplier development programs
non-linear multi-objective optimization model
Atoosa
Teymouri
atoosa.teymouri@aut.ac.ir
1
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, 424 Hafez Ave, 15875-4413, Iran
AUTHOR
Abbas
Ahmadi
abbas.ahmadi@aut.ac.ir
2
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, 424 Hafez Ave, 15875-4413, Iran
LEAD_AUTHOR
Saeid
Mansour
s.mansour@aut.ac.ir
3
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, 424 Hafez Ave, 15875-4413, Iran
AUTHOR
Ackermann,T., Garner, K.,Gardiner, A., 1999, Wind power generation in weak grids economic optimisation and power quality simulation, Proceedings of the World Renewable Energy Congress, Perth, Australia, Murdoch University, 18(2), 527–532.
1
Akman, G., 2014, Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods, Computers and Industrial Engineering, 2(233), 420-431.
2
Ameli, M., Mansour, S., Ahmadi-Javid, A., 2016, A multi-objective model for selecting design alternatives and end-of-life options under uncertainty: A sustainable approach,Resources, Conservation & Recycling, 109, 123-136.
3
Awasthi, A., Chauhan,S. S., Goyal, S.K., 2010, A fuzzy multi criteria approach for evaluating environmental performance of suppliers, International Journal of Production Economics, 126(2), 370–378.
4
Bai, C., Sarkis, J., 2010, Green supplier development: analytical evaluation using rough set theory, Journal of Cleaner Production, 18(12), 1200-1210
5
Cao, J. and Yao, Q., 2013, Incentive mechanism of green supply chain to promote supplier’s technology R&D, Research Journal of Applied Sciences, Engineering and Technology, 5(10), 3032-3036
6
Chiou, T.Y., Chan, H.K., 2011, The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan, Transportation Research, 47(6), 822-836
7
Dou, Y., Zhu, Q., 2013, Evaluating green supplier development programs with a grey-analytical network process-based methodology, European Journal of Operational Research, 233(2), 420-431.
8
Ettehadieh. D., 2011, Cost-benefit analysis of recycling in the United States: Is recycling worth it?, University of Maryland, http://www.english.umd.edu/interpolations/2601.
9
Frondel, M., Horbach, J., Rennings, K., 2007, End-of-pipe or cleaner production? an empirical comparison of environmental innovation decisions across OECD countries, Business Strategy and the Environment, 16(8), 571-584.
10
Fu, X., Zhu, Q., Sarkis, J., 2012, Evaluating green supplier development programs at a telecommunications systems provider, International Journal of Production Economics, 140(1), 357-367.
11
Gerlagh, R., Zwaan, B.V.D., 2006, Options and instruments for a deep cut in CO2 emissions: Carbon Dioxide capture or renewables, taxes or subsidies?, The Energy Journal, 27(3), 25-48.
12
Hammar, H., Lofgren, A.S., 2010, Explaining adoption of end of pipe solutions and clean technologies: Determinants of firms investments for reducing emissions to air in four sectors in Sweden, Energy Policy, 38(7), 3644-3651.
13
Hatcher, G.D., Ijomah, W.L., Windmill, J.F.C., 2011, Design for remanufacturing: a literature review and future research needs, Journal of Cleaner Production, 19(17-18), 2004-2014.
14
Hickle, G.T., 2013, “Moving beyond the patchwork: a review of strategies to promote consistency for extended producer responsibility policy in the U.S., Journal of Cleaner Production, 64, 266-276.
15
Ho, W., Xu, X., Dey, P.K., 2010, Multi-Criteria decision making approaches for supplier evaluation and selection: A literature review, European Journal of Operational Research, 202(1), 16-24.
16
Hosseininasab, A., Ahmadi, A., 2015, Selecting a supplier portfolio with value, development, and risk consideration, European Journal of Operational Research, 245 (1), 146-156.
17
Ignizio, J. P. and Romero, C., 2003, Goal programming, in: Encyclopedia of Information Systems, Vol. 2, Academic Press, San Diego, CA.
18
Jofre, S., Morioka, V., 2005, Waste management of electric and electronic equipment: comparative analysis of end-of-life strategies, Journal of Material Cycles and Waste Management, 7(1), 24-32.
19
Kannan, G., Rajendran, S., Sarkis, J., Murugesan, P., 2015, Multi Criteria Decision Making approaches for Green supplier evaluation and selection: A literature review, Journal of Cleaner Production, 98, 66-83.
20
Kerr, W., Ryan, C., 2001, Eco-efficiency gains from remanufacturing: A case study of photocopier remanufacturing at Fuji Xerox Australia, Journal of Cleaner Production, 9(1), 75–81.
21
Kim, K., Song, I., Kim, J., Jeong, B., 2006, Supply planning model for remanufacturing system in reverse logistics environment, Computers & Industrial Engineering, 51(2), 279–287
22
Krause, D.R., Ellram, L.M., 1997, Success factors in supplier development, International Journal of Physical Distribution & Logistics Management, 27(1), 39-52
23
Krause, D.R., Handfield, R.B., 1998, An empirical investigation of supplier development: reactive and strategic processes, Journal of Operations Management, 17(1), 39-58.
24
Makarieva, A.M., Gorshkov, V.G., Li, B., 2008, Energy budget of the biosphere and civilization: Rethinking environmental security of global renewable and non-renewable resources, Ecological Complexity, 5(4), 281-288.
25
Mani V., Gunasekaran, A., Papadopoulos, T., Hazen, B., Dubey R., 2016, Supply chain social sustainability for developing nations: Evidence from India, Resources, Conservation & Recycling, 111, 42-52.
26
Noci, G., 1997, Designing ‘Green’ vendor rating systems for the assessment of a supplier’s environmental performance, European Journal of Purchasing & Supply Management, 3(2), 103-114.
27
Saaty, T. L., 1980, The Analytic Hierarchy Process, McGraw-Hill, New York.
28
Saavedra, Y.M.B., Barquet, A.P.B., 2013, Remanufacturing in Brazil: case studies on the automotive sector, Journal of Cleaner Production, 53, 267-276.
29
Sims, E.H.S., Rogner, H.H., Gregory, K., 2003, Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation, Energy Policy, 31(13), 1315-1326.
30
Sutherland, W.J., Adler, P.D., Haapala, R.K., Kumar, V., 2008, A comparison of manufacturing and remanufacturing energy intensities with application to diesel engine production, CIRP Annals, 57(1), 5-8.
31
Talluri, S., Narasimhan, R., Chung, W., 2010, Manufacturer cooperation in supplier development under risk, European Journal of Operational Research, 207(1), 165- 173.
32
ORIGINAL_ARTICLE
A new classification method based on pairwise SVM for facial age estimation
This paper presents a practical algorithm for facial age estimation from frontal face image. Facial age estimation generally comprises two key steps including age image representation and age estimation. The anthropometric model used in this study includes computation of eighteen craniofacial ratios and a new accurate skin wrinkles analysis in the first step and a pairwise binary support vector machine (SVM) in the second one. Anthropometric model is the first model that has been provided; however, it hasn't been much considered and even hasn't been applied on any large database so far. Therefore, the algorithm is applied on FG-Net database and the average of the absolute errors (MAE) and cumulative score (CS) measures are provided to make comparison with other approaches much easier. Experimental results show that the proposed method can give MAE=6.34 and CS (<=10) =81.14 using a pairwise binary tree support vector machine (SVM).
https://www.jise.ir/article_33725_0be02dd637a166879449f82328cdb05f.pdf
2017-04-27
91
107
Data mining
Classification
Support Vector Machine
SVM
facial age estimation
Mohammad Ali
Beheshti-Nia
beheshtinia@semnan.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, Iran
LEAD_AUTHOR
Zahra
Mousavi
zahramousavi001@yahoo.com
2
Faculty of Computer engineering, Amir Kabir University, Tehran, Iran
AUTHOR
Chao, W.-L., Liu, J.-Z. &Ding, J.-J. (2013). 'Facial age estimation based on label-sensitive learning and age-oriented regression'. Pattern Recognition, 46(3), 628-641.
1
Cootes, T. F., Edwards, G. J. &Taylor, C. J. (2001). 'Active Appearance Models'. IEEE Trans. Pattern Anal. Mach. Intell., 23(6), 681-685.
2
Dehshibi, M. M. and Bastanfard, A. (2010). 'A new algorithm for age recognition from facial images'. Signal Processing, 90(8), 2431-2444.
3
El Dib, M. Y. and Onsi, H. M. (2011). 'Human age estimation framework using different facial parts'. Egyptian Informatics Journal, 12(1), 53-59.
4
'The FG-NET Aging Database'. (2010).
5
Fu, Y., Guo, G. &Huang, T. S. (2010). 'Age Synthesis and Estimation via Faces: A Survey'. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955-1976.
6
Chao, W.-L., Liu, J.-Z. &Ding, J.-J. (2013). 'Facial age estimation based on label-sensitive learning and age-oriented regression'. Pattern Recognition, 46(3), 628-641.
7
Cootes, T. F., Edwards, G. J. &Taylor, C. J. (2001). 'Active Appearance Models'. IEEE Trans. Pattern Anal. Mach. Intell., 23(6), 681-685.
8
Dehshibi, M. M. and Bastanfard, A. (2010). 'A new algorithm for age recognition from facial images'. Signal Processing, 90(8), 2431-2444.
9
El Dib, M. Y. and Onsi, H. M. (2011). 'Human age estimation framework using different facial parts'. Egyptian Informatics Journal, 12(1), 53-59.
10
'The FG-NET Aging Database'. (2010).
11
Fu, Y., Guo, G. &Huang, T. S. (2010). 'Age Synthesis and Estimation via Faces: A Survey'. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955-1976.
12
Fu, Y. and Huang, T. S. (2008). 'Human Age Estimation With Regression on Discriminative Aging Manifold'. IEEE Transactions on Multimedia, 10(4), 578-584.
13
Fu, Y., Xu, Y. &Huang, T. S. (2007). 'Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features'. 2007 IEEE International Conference on Multimedia and Expo, 1383-1386.
14
Geng, X., Zhou, Z.-H. &Smith-Miles, K. (2007). 'Automatic Age Estimation Based on Facial Aging Patterns'. IEEE Trans. Pattern Anal. Mach. Intell., 29(12), 2234-2240.
15
Geng, X., Zhou, Z.-H., Zhang, Y., Li, G. &Dai, H. (2006). 'Learning from facial aging patterns for automatic age estimation'. Proceedings of the 14th ACM international conference on Multimedia, 307-316.
16
Guo, G., Fu, Y., Dyer, C. R. &Huang, T. S. (2008). 'Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression'. IEEE Transactions on Image Processing, 17(7), 1178-1188.
17
Guo, G., Fu, Y., Huang, T. S. &Dyer, C. R. (2008). 'Locally Adjusted Robust Regression for Human Age Estimation'. Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on, 1-6.
18
Guodong, G., Yun, F., Dyer, C. R. &Huang, T. S. (2008). 'A Probabilistic Fusion Approach to human age prediction'. Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on, 1-6.
19
Hayashi, J., Yasumoto, M., Ito, H., Niwa, Y. &Koshimizu, H. (2002). 'Age and gender estimation from facial image processing'. SICE 2002. Proceedings of the 41st SICE Annual Conference, 1(13-18.
20
Hironobu Fukai, H. T., Yasue Mitsukura, Minoru Fukumi. (2007). 'Apparent age estimation system based on age perception'. Proceedings of the SICE Annual Conference, 2808-2812.
21
Koruga, P., Ba, M., x010D, J, x &eva. (2011). 'Application of modified anthropometric model in facial age estimation'. ELMAR, 2011 Proceedings, 17-20.
22
Kwon, Y. H. and Lobo, N. d. V. (1999). 'Age Classification from Facial Images'. Computer Vision and Image Understanding, 74(1), 1-21.
23
Lanitis, A., Draganova, C. &Christodoulou, C. (2004). 'Comparing different classifiers for automatic age estimation'. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 621-628.
24
Lanitis, A., Taylor, C. J. &Cootes, T. F. (2002). 'Toward automatic simulation of aging effects on face images'. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 442-455.
25
Liu, J., Ma, Y., Duan, L., Wang, F. &Liu, Y. (2014). 'Hybrid constraint SVR for facial age estimation'. Signal Processing, 94(576-582.
26
Shuicheng, Y., Xi, Z., Ming, L., Hasegawa-Johnson, M. &Huang, T. S. (2008). 'Regression from patch-kernel'. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 1-8.
27
Ueki, K., Hayashida, T. &Kobayashi, T. (2006). 'Subspace-based age-group classification using facial images under various lighting conditions'. 7th International Conference on Automatic Face and Gesture Recognition (FGR06), 6 pp.-48.
28
Yan, S., Wang, H., Tang, X. &Huang, T. S. (2007). 'Learning Auto-Structured Regressor from Uncertain Nonnegative Labels'. 2007 IEEE 11th International Conference on Computer Vision, 1-8.
29
ORIGINAL_ARTICLE
Genetic Algorithm-Based Optimization Approach for an Uncapacitated Single Allocation P-hub Center Problem with more realistic cost structure
A p-hub center network design problem is definition of some nodes as hubs and allocation of non-hub nodes to them wherein the maximum travel times between any pair of nodes is minimized. The distinctive feature of this study is proposing a new mathematical formulation for modeling costs in a p-hub center problem. Here, instead of considering costs as a linear function of distance, for the first time, we formulate costs as a summation of different parts: fixed cost, Health, Safety and Environment (HSE) cost, energy cost and personnel cost. Such integrated model results in a hard-to-solve nonlinear formulation. To validate the proposed model, a small scale problem instance of CAB dataset solved by LINGO software. Because of inability to solve bigger problems, we prepared a Genetic Algorithm (GA) by MATLAB software to solve complete problems of CAB and AP datasets.
https://www.jise.ir/article_33726_ced69eabc6c1864d041f8c6fcf62d3e3.pdf
2017-05-03
108
124
Hub Location Problem
Uncapacitated single allocation p-hub center problem
Stepwise cost function
Genetic algorithm
Masoud
Rabbani
mrabani@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Hamed
Farrokhi-Asl
hamed.farrokhi@ut.ac.ir
2
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Razieh
Heidari
raziyeh.heidari@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran
AUTHOR
ABYAZI-SANI, R. & GHANBARI, R. 2016. An efficient tabu search for solving the uncapacitated single allocation hub location problem. Computers & Industrial Engineering, 93, 99-109.
1
ALIZADEH, Y., TAVAKKOLI-MOGHADDAM, R. & EBRAHIMNEJAD, S. 2016. A new multi-objective model for a capacitated hub covering problem solving by two multi-objective evolutionary algorithms. International Journal of Mathematics in Operational Research, 9, 99-124.
2
AVERSA, R., BOTTER, R., HARALAMBIDES, H. & YOSHIZAKI, H. 2005. A mixed integer programming model on the location of a hub port in the east coast of South America. Maritime Economics & Logistics, 7, 1-18.
3
BASHIRI, M., MIRZAEI, M. & RANDALL, M. 2013. Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution. Applied Mathematical Modelling, 37, 3513-3525.
4
CAMPBELL, J. F. 1994. Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72, 387-405.
5
CAMPBELL, J. F. 1996. Hub location and the p-hub median problem. Operations Research, 44, 923-935.
6
CORREIA, I., NICKEL, S. & SALDANHA-DA-GAMA, F. 2014. Multi-product capacitated single-allocation hub location problems: formulations and inequalities. Networks and Spatial Economics, 14, 1-25.
7
DAMGACIOGLU, H., DINLER, D., OZDEMIREL, N. E. & IYIGUN, C. 2015. A genetic algorithm for the uncapacitated single allocation planar hub location problem. Computers & Operations Research, 62, 224-236.
8
EISELT, H. A. & MARIANOV, V. 2009. A conditional p-hub location problem with attraction functions. Computers & Operations Research, 36, 3128-3135.
9
ERNST, A., HAMACHER, H., JIANG, H., KRISHNAMOORTHY, M. & WOEGINGER, G. 2002. Heuristic algorithms for the uncapacitated hub center single allocation problem. Unpublished Report, CSIRO Mathematical and Information Sciences, Australia.
10
ERNST, A. T., HAMACHER, H., JIANG, H., KRISHNAMOORTHY, M. & WOEGINGER, G. 2009. Uncapacitated single and multiple allocation p-hub center problems. Computers & Operations Research, 36, 2230-2241.
11
FARAHANI, R. Z., HEKMATFAR, M., ARABANI, A. B. & NIKBAKHSH, E. 2013. Hub location problems: A review of models, classification, solution techniques, and applications. Computers & Industrial Engineering, 64, 1096-1109.
12
GHADERI, A. & RAHMANIANI, R. 2016. Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times. The International Journal of Advanced Manufacturing Technology, 82, 1627-1647.
13
HOLLAND, J. H. 1975. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, U Michigan Press.
14
ABYAZI-SANI, R. & GHANBARI, R. 2016. An efficient tabu search for solving the uncapacitated single allocation hub location problem. Computers & Industrial Engineering, 93, 99-109.
15
ALIZADEH, Y., TAVAKKOLI-MOGHADDAM, R. & EBRAHIMNEJAD, S. 2016. A new multi-objective model for a capacitated hub covering problem solving by two multi-objective evolutionary algorithms. International Journal of Mathematics in Operational Research, 9, 99-124.
16
AVERSA, R., BOTTER, R., HARALAMBIDES, H. & YOSHIZAKI, H. 2005. A mixed integer programming model on the location of a hub port in the east coast of South America. Maritime Economics & Logistics, 7, 1-18.
17
BASHIRI, M., MIRZAEI, M. & RANDALL, M. 2013. Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution. Applied Mathematical Modelling, 37, 3513-3525.
18
CAMPBELL, J. F. 1994. Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72, 387-405.
19
CAMPBELL, J. F. 1996. Hub location and the p-hub median problem. Operations Research, 44, 923-935.
20
CORREIA, I., NICKEL, S. & SALDANHA-DA-GAMA, F. 2014. Multi-product capacitated single-allocation hub location problems: formulations and inequalities. Networks and Spatial Economics, 14, 1-25.
21
DAMGACIOGLU, H., DINLER, D., OZDEMIREL, N. E. & IYIGUN, C. 2015. A genetic algorithm for the uncapacitated single allocation planar hub location problem. Computers & Operations Research, 62, 224-236.
22
EISELT, H. A. & MARIANOV, V. 2009. A conditional p-hub location problem with attraction functions. Computers & Operations Research, 36, 3128-3135.
23
ERNST, A., HAMACHER, H., JIANG, H., KRISHNAMOORTHY, M. & WOEGINGER, G. 2002. Heuristic algorithms for the uncapacitated hub center single allocation problem. Unpublished Report, CSIRO Mathematical and Information Sciences, Australia.
24
ERNST, A. T., HAMACHER, H., JIANG, H., KRISHNAMOORTHY, M. & WOEGINGER, G. 2009. Uncapacitated single and multiple allocation p-hub center problems. Computers & Operations Research, 36, 2230-2241.
25
FARAHANI, R. Z., HEKMATFAR, M., ARABANI, A. B. & NIKBAKHSH, E. 2013. Hub location problems: A review of models, classification, solution techniques, and applications. Computers & Industrial Engineering, 64, 1096-1109.
26
GHADERI, A. & RAHMANIANI, R. 2016. Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times. The International Journal of Advanced Manufacturing Technology, 82, 1627-1647.
27
HOLLAND, J. H. 1975. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, U Michigan Press.
28
HWANG, Y. H. & LEE, Y. H. 2012. Uncapacitated single allocation p-hub maximal covering problem. Computers & Industrial Engineering, 63, 382-389.
29
KARA, B. & TANSEL, B. 2003. The single-assignment hub covering problem: Models and linearizations. Journal of the Operational Research Society, 54, 59-64.
30
KARA, B. Y. & TANSEL, B. C. 2000. On the single-assignment p-hub center problem. European Journal of Operational Research, 125, 648-655.
31
KRATICA, J. & STANIMIROVIĆ, Z. 2006. Solving the uncapacitated multiple allocation p-hub center problem by genetic algorithm. Asia-Pacific Journal of Operational Research, 23, 425-437.
32
MEYER, T., ERNST, A. T. & KRISHNAMOORTHY, M. 2009. A 2-phase algorithm for solving the single allocation p-hub center problem. Computers & Operations Research, 36, 3143-3151.
33
O’KELLY, M. E. 2012. Fuel burn and environmental implications of airline hub networks. Transportation Research Part D: Transport and Environment, 17, 555-567.
34
RABBANI, M. & KAZEMI, S. 2015. Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing. International Journal of Industrial Engineering Computations, 6, 405-418.
35
RABBANI, M., ZAMENI, S. & KAZEMI, S. M. Proposing a new mathematical formulation for modeling costs in a p-hub center problem. Modeling, Simulation and Applied Optimization (ICMSAO), 2013 5th International Conference on, 2013. IEEE, 1-4.
36
RAHIMI, Y., TAVAKKOLI-MOGHADDAM, R., MOHAMMADI, M. & SADEGHI, M. 2016. Multi-objective hub network design under uncertainty considering congestion: An M/M/c/K queue system. Applied Mathematical Modelling, 40, 4179-4198.
37
ROSTAMI, B., MEIER, J., BUCHHEIM, C. & CLAUSEN, U. 2015. The uncapacitated single allocation p-hub median problem with stepwise cost function. Tech. rep., Optimization Online.
38
SEDEHZADEH, S., TAVAKKOLI-MOGHADDAM, R., BABOLI, A. & MOHAMMADI, M. 2015. Optimization of a multi-modal tree hub location network with transportation energy consumption: A fuzzy approach. Journal of Intelligent & Fuzzy Systems, 30, 43-60.
39
STANIMIROVIĆ, Z. 2012. A genetic algorithm approach for the capacitated single allocation p-hub median problem. Computing and Informatics, 29, 117-132.
40
TOPCUOGLU, H., CORUT, F., ERMIS, M. & YILMAZ, G. 2005. Solving the uncapacitated hub location problem using genetic algorithms. Computers & Operations Research, 32, 967-984.
41
YAMAN, H., KARA, B. Y. & TANSEL, B. Ç. 2007. The latest arrival hub location problem for cargo delivery systems with stopovers. Transportation Research Part B: Methodological, 41, 906-919.
42
ZADE, A. E., SADEGHEIH, A. & LOTFI, M. M. 2014. A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments. Journal of Industrial Engineering International, 10, 185-197.
43
ORIGINAL_ARTICLE
A Practical Self-Assessment Framework for Evaluation of Maintenance Management System based on RAMS Model and Maintenance Standards
A set of technical, administrative and management activities are done in the life cycle of equipment, to be located in good condition and have proper and expected functioning. This is refers to be, maintenance management system (MMS). The framework and models of assessment in order to enhance effectiveness of a MMS could be proposed in two categories: qualitative and quantitative. In this research, the self-assessment’s dimensions of MMS have been established and examined that affect the successful implementation of this system in companies. This research uses a case study and review methodology (second hand material such as TPM, ISO 14224 and IEC 60300-3-14 and RAMS model) to extract dimensions, related indices and issues about how MMS to be measured and self-assessed these dimensions and indices. According to surveys and studies done by the authors the common dimensions of frameworks and models for evaluating MMS have been determined which includes; maintenance organization, training programs in maintenance, maintenance reporting, reliability engineering, maintenance – general practices, financial optimization, asset care continuous improvement, maintenance contracting, document management, the safety level of work environment, maintenance inventory and purchasing, predictive maintenance (PdM), maintenance work orders, maintenance planning and scheduling, maintenance automation(CMMS), operations/ facilities involvement on running of maintenance programs, preventive maintenance (PM) and maintenance quality management (MQM). Additionally, this paper will be presented a self-assessment framework based on the above mentioned topics for evaluation and placement of the MMS according to the total score earned in a Wireman pyramid. Then, according to the review of the literature, the appropriate indicators for each dimension of evaluation have been extracted and the framework has been tested in a petrochemical company.
https://www.jise.ir/article_33678_b56998ae91f6f135646b76af7f2e4bc5.pdf
2017-05-09
125
143
maintenance management system
self-assessment framework
evaluation, total preventive maintenance (TPM)
IEC 60300-3-14 standard
RAMS model
Bakhtiar
Ostadi
bostadi@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Hooshang
Saifpanahi
hsaifpanahy@yahoo.com
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR