ORIGINAL_ARTICLE
Flow shop Scheduling Problem with Maintenance Coordination: a New Approach
This study investigates the coordination of production scheduling and maintenance planning in theflow shop scheduling environment. The problem is considered in a bi-objective form, minimizing themakespan as the production scheduling criterion and minimizing the system unavailability as themaintenance planning criterion. The time interval between consecutive maintenance activities as well as thenumber of maintenance activities on each machine are assumed to be non-fixed. A mixed integerprogramming formulation of the problem is presented. A special case of the problem, named as single servermaintenance is also studied. Then, a bi-objective ant colony system algorithm is presented to solve theproblem in focus. To obtain the appropriate components of the proposed algorithm, two sets of experimentsare provided. Firstly, experiments are carried out to select the suitable heuristic method to build the heuristicinformation part of the algorithm between CDS and NEH. Secondly, experiments are reported to select thelocal search algorithm between iterated local search and adjacent pair-wise interchange. At last, experimentsare generated to evaluate the performance of the proposed algorithm, comparing it to the results of anexhaustive search algorithm.
http://jqepo.shahed.ac.ir/article_184_d19b88e9039fe0d19aa809128dfbb107.pdf
2015-02-26T11:23:20
2018-08-20T11:23:20
1
11
10.22070/jqepo.2015.184
Flow shop scheduling
Preventive maintenance
Coordination
Non-fixed time interval
Ant colony system
Mostafa
Khatami
true
1
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Hessameddin
Zegordi
zegordi@modares.ac.ir
true
2
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
1. Lee C.Y. (1997)."Minimizing the makespan in the two machine flowshop scheduling problem with an availability constraint",
1
Operations Research Letters, 20(3) pp. 129–139.
2
2. Ng C.T., & Kovalyov M.Y.( 2004). "An FPTAS for scheduling a two-machine flowshop with one unavailability interval",
3
Naval Research Logistics , 51(3) pp. 307-315.
4
3. Breit J.( 2004). "An improved approximation algorithm for two-machine flow shop scheduling with an availability constraint",Information Processing Letters, vol. 90(6) pp. 273-278.
5
4. Allaoui H., Artiba A., Elmaghraby S.E., Riane F. (2006)."Scheduling of a two-machine flowshop with availability constraints on the first machine", International Journal of Production Economics, 99(1-) pp. 16–27.
6
5. Blazewicz J., Breit J., Formanowicz P., Kubiak W., & Schmidt, G.( 2001). "Heuristic algorithms for the two-machine flowshop with limited machine availability". OMEGA, The International Journal of Management Science, 29(6) pp. 599-608.
7
6. Kubzin M.A., Potts C.N., and Strusevich V.A.( 2009). "Approximation results for flow shop scheduling problems with machine availability constraints", Computers & Operations Research, 36(2) pp. 379-390.
8
7. Yang D.-L, Hsu C.-J, and Kuo W.-H.( 2008). "A two-machine flowshop scheduling problem with a separated maintenance constraint", Computers & Operations Research, 35(3) pp. 876-883.
9
8. Lee C.-Y.( 1999). "Two-machine flowshop scheduling with availability constraints", European Journal of Operational Research,
10
114(2) pp. 420-429.
11
9. Aggoune R.( 2004). "Minimizing the makespan for the flow shop scheduling problem with availability constraints",
12
European Journal of Operational Research, 153(3) pp. 534-543.
13
10. Aggoune R., & Portmann M.-C.( 2006). "Flow shop scheduling problem with limited machine availability: A heuristic
14
approach", International Journal of Production Economics, 99(1-2) pp. 4-15.
15
11. Choi B.-C., Lee K., Leung J.Y.-T., Pinedo M.L.( 2010)."Flow shops with machine maintenance: Ordered and proportionate cases", European Journal of Operational Research, 207 (1) pp. 97-104.
16
12. Schmidt G.( 2000) "Scheduling with limited machine availability", European Journal of Operational Research, 121(1) pp. 1-15.
17
13. Ma Y., Chu C., and Zuo C.( 2010). "A survey of scheduling with deterministic machine availability constraints",
18
Computers & Industrial Engineering, 58(2) pp. 199–211.
19
14. Berrichi A., Amodeo L., Yalaoui F., Chatelet E., & Mezghiche M.( 2009). "Bi-objective optimization algorithms for joint
20
production and maintenance scheduling: application to the parallel machine problem", Journal of Intelligent Manufacturing, vol.
21
20, pp. 389–400.
22
7. Yang D.-L, Hsu C.-J, and Kuo W.-H.( 2008). "A two-machine flowshop scheduling problem with a separated maintenance constraint", Computers & Operations Research, 35(3) pp. 876-883.
23
8. Lee C.-Y.( 1999). "Two-machine flowshop scheduling with availability constraints", European Journal of Operational Research,
24
114(2) pp. 420-429.
25
9. Aggoune R.( 2004). "Minimizing the makespan for the flow shop scheduling problem with availability constraints",
26
European Journal of Operational Research , 153(3) pp. 534-543.
27
10. Aggoune R., & Portmann M.-C.( 2006). "Flow shop scheduling problem with limited machine availability: A heuristic
28
approach",International Journal of Production Economics, 99(1-2) pp. 4-15.
29
11. Choi B.-C., Lee K., Leung J.Y.-T., Pinedo M.L.( 2010)."Flow shops with machine maintenance: Ordered and proportionate cases", European Journal of Operational Research, 207 (1) pp. 97-104.
30
12. Schmidt G.( 2000) "Scheduling with limited machine availability", European Journal of Operational Research, 121(1) pp. 1-15.
31
13. Ma Y., Chu C., and Zuo C.( 2010). "A survey of scheduling with deterministic machine availability constraints",
32
Computers & Industrial Engineering, 58(2) pp. 199–211.
33
14. Berrichi A., Amodeo L., Yalaoui F., Chatelet E., & Mezghiche M.( 2009). "Bi-objective optimization algorithms for joint
34
production and maintenance scheduling: application to the parallel machine problem", Journal of Intelligent Manufacturing, vol.
35
20, pp. 389–400.
36
15. Moradi E., & Zandieh M.( 2010). "Minimizing the makespan and the system unavailability in parallel machine scheduling
37
problem: a similarity-based genetic algorithm",
38
Intelligent Journal of Advanced Manufacturing Technology, vol. 51, pp. 829–
39
16. Berrichi A., Yalaoui F., Amodeo L., & Mezghiche M.( 2010). "Bi-objective ant colony optimization approach to optimize
40
production and maintenance scheduling",
41
Computers & Operations Research, 37(9) pp. 1584–1596.
42
17. Moradi E., Fatemi Ghomi S.M.T., and Zandieh M.( 2011). "Bi-objective optimization research on integrated fixed time interval
43
preventive maintenance and production for scheduling flexible job-shop problem",
44
Expert Systems with Applications, 38(6) pp.
45
7169–7178.
46
18. Berrichi A. and Yalaoui F.( 2013). "Efficient bi-objective ant colony approach to minimize total tardiness and system
47
unavailability for a parallel machine scheduling problem",
48
International Journal of Advanced Manufacturing Technology, vol.
49
68, pp. 2295–2310.
50
19. Mokhtari H., Mozdgir A. & Nakhai Kamal Abadi I.( 2012). "A reliability/availability approach to joint production and
51
maintenance scheduling with multiple preventive maintenance services",
52
International Journal of Production Research, 50(20)
53
pp. 5906–5925.
54
20. Wang S. & Liu M.(2014). "Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabusearch method", International Journal of Production Research, 52(5) pp. 1495-1508.
55
21. Cui, W.-W., Lu, Z., and Pan, E.( 2014). "Integrated production scheduling and maintenance policy for robustness in a single machine", Computers & Operations Research, 47, pp. 81–91.
56
22. Ebeling C.E., An Introduction to Reliability and Maintainability Engineering, McGraw-Hill: USA, 1997.
57
23. Wilson J.M.( 1989). "Alternative formulations of a flow-shop scheduling problem", Journal of the Operational Research Society,
58
40(4) pp. 395–9.
59
24. Tseng F.T., Stafford E.F. Jr., & Gupta J.N.D.( 2004) "An empirical analysis of integer programming formulations for the permutation flowshop", Omega, 32(4) pp. 285–293.
60
25. Dorigo M., Optimization, Learning and Natural Algorithm. Ph.D. Thesis, DEI: Politecnico di Milano, 1992.
61
26. Dorigo M., and Gambardella L.M.( 1997). "Ant colony system: a cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation, 1(1) pp. 53–66.
62
27. Ying K.C., and Liao C.J.( 2003). "An ant colony system approach for scheduling problems", Production Planning & Control,
63
14(1), pp. 68–75.
64
28. Ying K., and Lin S., "Multi-heuristic desirability ant colony system heuristic for non-permutation flowshop scheduling
65
problems", International Journal of Advanced Manufacturing Technology, vol. 33, pp. 793–802, 2007.
66
29. Yagmahan B., and Yenisey M.M.( 2008). "Ant colony optimization for multi-objective flow shop scheduling problem",
67
Computers & Industrial Engineering, 54(3) pp. 411–420.
68
30. Tavares Neto R.F., and Godinho Filho M.( 2012). "Literature review regarding ant colony optimization applied to scheduling problems: guidelines for implementation and directions for future research", Engineering Applications of Artificial Intelligence, 26(1) pp. 150–161.
69
31. Campbell H.G., Dudek R.A., and Smith M.L. ( 1970). "A heuristic algorithm for the n job, m machine sequencing problem",
70
Management Science ,61(3) pp. B630–B637.
71
32. Nawaz M., Enscore Jr.E.E., and Ham I. ( 1983) ."A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem", Omega , 11(1) pp. 91–95.
72
33. Ruiz R., and Maroto C.( 2005). "A comprehensive review and evaluation of permutation flowshop heuristics", European Journal of Operational Research , 165(2) pp. 479–94.
73
34. Taillard E. (1993). "Benchmarks for basic scheduling problems", European Journal of Operational Research, 64(2) pp. 278–85.
74
35. Zitzler E., Evolutionary Algorithms for Multi-Objective Optimization: Methods and Applications. Ph.D. Thesis, Swiss Federal Institute of Technology, 1999.
75
ORIGINAL_ARTICLE
A New Optimization via Invasive Weeds Algorithm for Dynamic Facility Layout Problem
The dynamic facility layout problem (DFLP) is the problem of finding positions of departments onthe plant floor for multiple periods (material flows between departments change during the planning horizon)such that departments do not overlap, and the sum of the material handling and rearrangement costs isminimized. In this paper a new optimization algorithm inspired from colonizing weeds, Invasive WeedsOptimization (IWO) is utilized to solve the well-known DFLP. IWO is a simple algorithm which uses basiccharacteristics of a colony of weeds such as proliferation, growth and competition.A set of reference numerical problems is taken in order to evaluate the efficiency of the algorithm comparedwith the Dynamic Programming method which had been applied to solve the addressed problem. In order toverify the efficiency of the proposed algorithm a wide range of experiments are carried out to comparethe proposed algorithm. Computational results have indicated that the DIWO algorithm is capable ofobtaining optimal solutions for small and medium-scaled problems very efficiently.
http://jqepo.shahed.ac.ir/article_185_c3454f1b1a19429fba382986bb1ae3be.pdf
2015-04-01T11:23:20
2018-08-20T11:23:20
11
20
10.22070/jqepo.2015.185
Discrete Invasive Weed Optimization
Dynamic Facility layout Problem
Dynamic Programming
Farnaz
Barzinpour
true
1
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Mohammad
Mohammadpour Omran
true
2
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Seyed Farzad
Hoseini
true
3
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
LEAD_AUTHOR
Kaveh
Fahimi
m.bashiri@gmail.com
true
4
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Farshid
Samaei
f-hoseini@ind.iust.ac.ir
true
5
Department of Civil and Engineering, Ports & Maritime Organization, Bandar Abbas, Iran
Department of Civil and Engineering, Ports & Maritime Organization, Bandar Abbas, Iran
Department of Civil and Engineering, Ports & Maritime Organization, Bandar Abbas, Iran
AUTHOR
1. Rosenblatt, M.J. (1986). “The dynamics of plant layout”, Management Science, 32(1) pp. 76-86.
1
2. Lin, X., Floudas, C., & Kallrath, J. (2005). “Global Solution Approach for a Nonconvex MINLP Problem in Product Portfolio Optimization”, Journal of Global Optimization, 32(3) pp.417-431.
2
3. Urban, L. (1998). “Solution procedures for the dynamic facility layout problem,” Annals of Operations Research, 76 pp. 323–
3
4. Lacksonen, T.A., & Enscore E.E.( 1993). “Quadratic assignment algorithms for the dynamic layout problem”International
4
Journal of Production Research, 31(3) 503–517.
5
5. Balakrishnan, J., & Cheng, C.H.( 2000). “Genetic search and the dynamic layout problem,” Computers and Operations Research,
6
27(6) pp. 587–593.
7
6. Kaku, B. K., & Mazzola, J. B.( 1997).“A tabu-search heuristic for the dynamic plant layout problem,”INFORMS Journal on
8
Computing, 9(4) pp. 374–384.
9
7. Baykasoglu, A., & Gindy, N. N. Z. ( 2001). “A simulated annealing algorithm for dynamic facility layout problem,”
10
Computers and Operations Research, 28(14) pp. 1403–1426.
11
8. Balakrishnan, J., Cheng, C. H., Conway, D. G., & Lau, C. M.( 2003). “A hybrid genetic algorithm for the dynamic plant layoutproblem
12
,” International Journal of Production Economics, 86(2) pp. 107–120.
13
9. Erel, E., Ghosh, J. B, & Simon, J. T.( 2003). “New heuristic for the dynamic layout problem,”Journal of the Operational
14
Research Society, 56(8) p. 1001.
15
10. McKendall, A. R., & Shang, J.( 2006). “Hybrid ant systems for the dynamic facility layout problem,”Computers & Operations
16
Research, 33(3) pp. 790–803.
17
11. McKendall, A. R., Shang, J., & Kuppusamy, S.( 2006). “Simulated annealing heuristics for the dynamic facility layout problem,”Computers and Operations Research, 33(8) pp. 2431–2444.
18
12. Rodriguez, J. M., MacPhee, F. C., Bonham, D. J., & Bhavsar, V. C.( 2006). “Solving the dynamic plant layout problem using a new hybrid meta-heuristic algorithm,”International Journal of High Performance Computing and Networking, 4(5-6)pp. 286–294.
19
13. Krishnan, K. K., Cheraghi, S. H., & Nayak, C. N.( 2006). “Dynamic from-between charts: a new tool for solving dynamic facility layout problems,” International Journal of Industrial and Systems Engineering, 11(1-2) pp. 182–200.
20
14. Ripon, Kazi Shah Nawaz, et al (2011). “Dynamic facility layout problem under uncertainty: a Pareto-optimality based multiobjective evolutionary approach.”Central European Journal of Computer Science, 1.4 pp. 375-386.
21
15. Emami, S., & Nookabadi, A.S. (2013).“Managing a new multi-objective model for the dynamic facility layout problem”,
22
The International Journal of Advanced Manufacturing Technology”, 68.9-12 pp. 2215-2228.
23
16. Balakrishnan, J., & Cheng, C. H. (1998) .“Dynamic layout algorithms: a state-of-the-art survey”, 26(4) pp. 507–521.
24
17. Kulturel-Konak, S. ( 2007). “Approaches to uncertainties in facility layout problems: perspectives at the beginning of the 21st Century,”Journal of Intelligent Manufacturing, 18(2) pp. 273–284.
25
18. Mehrabian, A. R., & Lucas, C.( 2006) .“A novel numerical optimization algorithm inspired from weed colonization.”
26
Journal of Ecological Informatics , vol. 1 pp. 355–366.
27
19. Mehrabian, A., & Yousefi-Koma, A. ( 2007). “Optimal positioning of piezoelectric actuators on a smart fin using bio-inspired algorithms”Aerospace Science and Technology, vol. 11 pp. 174–182.
28
20. Rad, H., Lucas, C. ( 2008). “A recomniender system based on invasive weed optimization algorithm”, in: 2007
29
IEEE Congress on Evolutionary Computation, CEC 2007 pp. 4297–4304.
30
21. Ghalenoei, M., Hajimirsadeghi, H., Lucas, C. (2009).“Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs”, in: Proceedings of the IEEE Conference on Decision and Control, pp. 1665–1670.
31
ORIGINAL_ARTICLE
Bi-objective Optimization for Just in Time Scheduling: Application to the Two-Stage Assembly Flow Shop Problem
This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on the efficiency and performance of the proposed algorithm, we calculate the complexity of sub problems based on factors n and m and the computational results demonstrate that the computational time increases with increasing in n and m, in other words, complexity of the problem increases.
http://jqepo.shahed.ac.ir/article_186_84e9d0778cddba9ee10b2b155b4d1659.pdf
2015-02-26T11:23:20
2018-08-20T11:23:20
21
32
10.22070/jqepo.2015.186
Two-Stage Assembly flow shop problem
Just in time scheduling
-constraint method
Sahar
Tadayoni Rad
s.tadayonirad@yahoo.com
true
1
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Saiedeh
Gholami
true
2
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
Rasoul
Shafaei
true
3
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
AUTHOR
Hany
Seidgar
true
4
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
1. Arkat, J., Hosseini, L., & Hosseinabadi Farahani, M. (2011). Minimization of exceptional elements and voids in the cell
1
formation problem using a multi-objective genetic algorithm. Expert Systems with Applications, 38(8) 9597-9602.
2
2. Al-Anzi, F.S., & Allahverdi, A. (2006). A hybrid tabu search heuristic for the two-stage assembly scheduling problem,
3
International Journal of Operation Research, 3(2) 109–119.
4
3. Al-Anzi, F.S., & Allahverdi, A. (2007). A self-adaptive differential evolution heuristic for two- stage assembly scheduling
5
problem to minimize maximum lateness with setup times,European Journal of Operational Research, 182(1) 80–94.
6
4. Allahverdi, A., & Al-Anzi, F.S. (2006). A PSO and a tabu search heuristics for assembly scheduling problem of the two-stage distributed database application. Computer and Operation Research, 33(4) 1056–1080.
7
5. Allahverdi, A., & Al-Anzi, F.S. (2009). The two-stage assembly scheduling problem to minimize total completion time with setup times. Computer Operation Research, 36(10) 2740–2747.
8
6. Birgin Ernesto G., & Ronconi Debora P. (2012). Heuristic methods for the single machine scheduling problem with different ready times and a common due date. ngineering Optimization, 44(10) 1197-1208.
9
7. Ceri, S., & Pelagatti, G. (1984).Distributed Databases: principles and systems, NewYork: McGraw-Hill.
10
8. Ehrgott, M. (2005).Multicriteria optimization, 2nd Edition. Berlin Heidelberg New York, Springer.
11
9. Elmasri, R. & Navathe, B. (1999).Fundamentals of database systems, 3rd Edition, NewYork: Addison-Wesley.
12
10. Hariri, A.M.A., & Potts, C.N.(1997).A branch and bound algorithm for the two-stage assembly scheduling problem,
13
European Journal of Operation Research , 103(3) 547–556.
14
11. Haouari, M., & Daouas, T. (1999). Optimal scheduling of the 3-machine assembly-type flow shop, RAIRO Operation Research,
15
33(4) 439–445.
16
12. Hong-Sen, Y., Xiao-Qinn, W., & Fu-Li, X. (2014). A hybrid electromagnetism-like algorithm for two-stage assembly flow shop scheduling problem.International Journal of Production Research, In Press.
17
13. Keyvanfar, V., Mahdavi, Iraj., & Komaki, G.H.M. (2013). Single machine scheduling with controllable processing times tominimize total tardiness and earliness.Computers and industrial Engineering, 65(1) 166-175.
18
14. Khorshidian, H., Javadian, N., Zandieh, M., Rezaeian, J., & Rahmani, K.(2011). A genetic algorithm for JIT single machine scheduling with preemption and machine idle time.Expert Systems with Applications, 38(7) 7911-7918.
19
ORIGINAL_ARTICLE
Solving Single Machine Sequencing to Minimize Maximum Lateness Problem Using Mixed Integer Programming
Despite existing various integer programming for sequencing problems, there is not enoughinformation about practical values of the models. This paper considers the problem of minimizing maximumlateness with release dates and presents four different mixed integer programming (MIP) models to solve thisproblem. These models have been formulated for the classical single machine problem, namely sequenceposition(SP), disjunctive (DJ), linear ordering (LO) and hybrid (HY). The main focus of this research is onstudying the structural properties of minimizing maximum lateness in a single machine using MIPformulations. This comparison helps us know the characteristics and priority of different models inminimizing maximum lateness. Regarding to these characteristics and priorities, while solving the latenessproblem in the procedure of solving a real-world problem, we apply the lateness model which yields insolution in shortest period of time and try not to use formulations which never lead to solution for large-scaleproblems. Beside single machine, these characteristics are applicable to more complicated machineenvironment. We generate a set of test problems in an attempt to solve the formulations, using CPLEXsoftware. According to the computational results, a detailed comparison between proposed MIP formulationsis reported and discussed in order to determine the best formulation which is computationally efficient andstructurally parsimonious to solve the considering problem. Among the four presented formulations,sequence-position (SP) has the most efficient computational time to find the optimal solution.
http://jqepo.shahed.ac.ir/article_187_3f676765e00168e9e04dbfd9252521c4.pdf
2015-02-26T11:23:20
2018-08-20T11:23:20
33
42
10.22070/jqepo.2015.187
Single machine scheduling
Mixed integer programming
Maximum lateness
Release date
Amir Hossein
Parsamanesh
a.parsamanesh@shahed.ac.ir
true
1
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Rashed
Sahraeian
sahraeian@shahed.ac.ir
true
2
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, University College of Engineering, Shahed University, Tehran, Iran
AUTHOR
1. Pinedo, M., & Hadavi, K. Scheduling: Theory, Algorithms and systems development, in operations research proceedings 1991, W. Gaul, et al., Editors. 1992, Springer Berlin Heidelberg. p. 35-42.
1
2. Wagner, H.M.( 1959). An integer linear-programming model for machine scheduling. Naval Research Logistics Quarterly, 6(2):p. 131-140.
2
3. Manne, A.S.( 1960). On the job-shop scheduling problem. Operations Research, 8(2) pp. 219--223.
3
4. Blazewicz, J., M. Dror, & Weglarz, J.( 1991). Mathematical programming formulations for machine scheduling: A survey.European Journal of Operational Research, 51(3) pp. 283-300.
4
5. Queyranne, M., & Schulz, A.S. Polyhedral approaches to machine scheduling.Technical report 408/1994. Department of
5
Mathematics, Technical University of Berlin, Berlin, Germany, 1994.
6
6. Unlu, Y. and Mason, S.J. ( 2010). Evaluation of mixed integer programming formulations for non-preemptive parallel machine scheduling problems.Computers & Industrial Engineering, 58(4) pp. 785-800.
7
7. Baker, K.R. & B. Keller. ( 2010). Solving the single-machine sequencing problem using integer programming. Computers & Industrial Engineering, 59(4) pp. 730-735.
8
8. Herr, O., and Goel, A. ( 2014). Comparison of two integer programming formulations for a single machine family scheduling problem to minimize total tardiness. Procedia CIRP, 19(0) pp. 174-179.
9
9. Bahalke, U., et al. ( 2010). Genetic and tabusearchalgorithmsforthesingle machinescheduling problemwith sequence-dependent set-uptimes and deteriorating jobs. International Journal of Engineering, 23 pp. 227-234
10
10. Sels, V. & Vanhoucke, M. ( 2012). A hybrid genetic algorithm for the single machine maximum lateness problem with release times and family setups. Computers & Operations Research, 39(10) pp. 2346-2358.
11
11. Lu, C.-C., Lin, S-W., and Ying ,K.-C. ( 2012). Robust scheduling on a single machine to minimize total flow time. Computers & Operations Research, 39(7) pp. 1682-1691.
12
12. Moghaddam, A., F. Yalaoui, L.& Amodeo. ( 2015). Efficient meta-heuristics based on various dominance criteria for a singlemachine bi-criteria scheduling problem with rejection. Journal of Manufacturing Systems, 34(0) pp. 12-22.
13
13. Senthilvel, A.N., Maheswari, S.U., and Hemamalini, T. (2014). Heuristic robust algorithm to optimize sequencing of jobs on a single machine. Procedia Materials Science, 5(0) p. 1473-1481.
14
14. Graham, R.L., et al.(1979). Optimization and approximation in deterministic sequencing and scheduling: a survey, in Annals of Discrete Mathematics, E.L.J. P.L. Hammer and B.H. Korte, Editors, Elsevier. p. 287-326.
15
15. Lenstra, J.K., Rinnooy Kan, A.H.G., and Brucker, P. (1977) . Complexity of machine scheduling problems, in Annals of Discrete Mathematics, E.L.J.B.H.K. P.L. Hammer and G.L. Nemhauser, Editors, Elsevier. p. 343-362.
16
16. Balas, E., On the facial structure of scheduling polyhedra, in Mathematical Programming Essays in Honor of George B. Dantzig Part I, R.W. Cottle, Editor. 1985, Springer Berlin Heidelberg, p. 179-218.
17
17. Queyranne, M., and Wang,Y. (1991). Single-machine scheduling polyhedra with precedence constraints. Math Operation Research, 16(1) p. 1-20.
18
18. Dyer, M.E. and Wolsey, L.A. ( 1990). Formulating the single machine sequencing problem with release dates as a mixed integer program. Discrete Applied Mathematics, 26(2–3) p. 255-270.
19
ORIGINAL_ARTICLE
Simultaneous Monitoring of Multivariate-Attribute Process Mean and Variability Using Artificial Neural Networks
In some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. In this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribute processeswhose quality characteristics are correlated. The proposed neural network-based methodology not onlydetects separate mean and variance shifts, but also can efficiently detect simultaneous changes in meanvector and covariance matrix of multivariate-attribute processes. The performance of the proposed neuralnetwork-based methodology in detecting separate as well as simultaneous changes in the process is evaluatedthorough a numerical example based on simulation in terms of average run length criterion and the resultsare compared with a statistical method based on the combination of two control charts that are developed formonitoring the mean vector and covariance matrix of multivariate-attribute processes, respectively. Theresults of model implementation on numerical example show the superior detection performance of theproposed NN-based methodology rather than the developed combined statistical control charts.
http://jqepo.shahed.ac.ir/article_188_db89132e09a36c98cd375afffd946530.pdf
2015-02-26T11:23:20
2018-08-20T11:23:20
43
54
10.22070/jqepo.2015.188
Average run length
Covariance matrix
Mean vector
Multi-layer perceptron neural network
Multivariate-attribute process
Mohammad Reza Maleki
Maleki
true
1
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed University, Tehran, Iran
AUTHOR
Amirhossein
Amiri
amiri@shahed.ac.ir
true
2
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed University, Tehran, Iran
LEAD_AUTHOR
1. Ahmadzadeh, F. (2011). Change point detection with multivariate control charts by artificial neural network.The International Journal of Advanced Manufacturing Technology, 1-12, published online. DOI: 10.1007/s00170-009-2193-6
1
2. Amiri, A., Maleki, M. R., & Doroudyan, M. H. (2015). Monitoring Variability of multivariate-attribute processes using artificial neural network. Production and Operations Management, 5 (2) 21-36.
2
3. Aparisi, F., Avendaño, G., & Sanz, J. (2006). Techniques to interpret T 2 control chart signals. IIE Transactions, 38(8) 647-657.
3
4. Aparisi, F., García‐Bustos, S., & Epprecht, E. K. (2014). Optimum multiple and multivariate Poisson statistical control charts. Quality and Reliability Engineering International, 30(2) 221-234.
4
5. Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview.
5
Quality and Reliability Engineering International, 23(5) 517-543.
6
6. Brian Hwarng, H., & Wang, Y. (2010). Shift detection and source identification in multivariate autocorrelated processes.
7
International Journal of Production Research, 48(3) 835-859.
8
7. Cheng, C. S. (1995). A multi-layer neural network model for detecting changes in the process mean. Computers & Industrial
9
Engineering, 28(1) 51-61.
10
8. Cheng, C. S., & Cheng, H. P. (2011). Using neural networks to detect the bivariate process variance shifts pattern.
11
Computers & Industrial Engineering, 60(2) 269-278.
12
9. Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons.
13
10. Doroudian, M. H., Amiri, A., Root transformation method for monitoring correlated variable and attribute quality characteristics.Proceedings of 11th Islamic Countries Conference on Statistical Sciences (ICCS-11), Lahore, Pakistan, December 19-21, 2011.
14
11. Doroudyan, M. H., & Amiri, A. (2013). Monitoring multivariate–attribute processes based on transformation techniques.The International Journal of Advanced Manufacturing Technology, 69(9-12) 2161-2172.
15
12. Golnabi, S., & Houshmand, A. A. (1999). Multivariate shewhart x-bar chart. Inter Stat, 4.
16
13. Hotelling, H. (1947). Multivariate quality control. Techniques of statistical analysis. New York: McGraw-Hill, 111-184.
17
14. Hwarng, H. B. (2008). Toward identifying the source of mean shifts in multivariate SPC: a neural network approach.
18
International Journal of Production Research, 46(20) 5531-5559.
19
15. Kang, L., & Brenneman, W. A. (2011). Product defect rate confidence bound with attribute and variable data.Quality and
20
Reliability Engineering International, 27(3) 353-368.
21
16. Li, J., Tsung, F., & Zou, C. (2014). Multivariate binomial/multinomial control chart. IIE Transactions, 46(5) 526-542.
22
17. Maleki, M. R., Amiri, A., & Doroudyan, M. H. (2012). Monitoring multivariate-attribute processes using artificial neural
23
network. 42th conference on Computers and Industrial Engineering, Cape Town, South Africa. (CIE42).
24
18. Maleki, M. R., Amiri, A., & Rasouli, M. (2013). Monitoring Variability of Multivariate-attribute Processes Using EWMA
25
Control Charts Based on NORTA Inverse Technique. 3rd International Conference on Production and Industrial Engineering, Jalanhar, India, (CPIE-2013), pp. 1390-1393.
26
19. Memar, A.O. & Niaki, S.T.A. (2011). Multivariate variability monitoring using EWMA control charts based on squared
27
deviation of observations from target.Quality and Reliability Engineering International, 27(8) 1069-1086.
28
20. Niaki, S. T. A., & Abbasi, B. (2005). Fault diagnosis in multivariate control charts using artificial neural networks.
29
Quality and Reliability Engineering International, 21(8) 825-840.
30
21. Niaki, S. A., & Abbasi, B. (2008). Detection and classification mean-shifts in multi-attribute processes by artificial neural networks. International Journal of Production Research, 46(11) 2945-2963.
31
22. Salehi, M., Kazemzadeh, R. B., & Salmasnia, A. (2012). On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes. Applied Soft Computing, 12(9) 2973-2984.
32
23. Shang, Y., Tsung, F., & Zou, C. (2013). Statistical process control for multistage processes with binary outputs.
33
IIE Transactions, 45(9) 1008-1023.
34
24. Topalidou, E., & Psarakis, S. (2009). Review of multinomial and multiattribute quality control charts. Quality and Reliability
35
Engineering International , 25(7) 773-804.
36
25. Yeh, A. B., Huwang, L., & Wu, C. W. (2005). A multivariate EWMA control chart for monitoring process variability with
37
individual observations. IIE Transactions, 37(11) 1023-1035.
38
26. Yeh, A. B., Li, B., & Wang, K. (2012). Monitoring multivariate process variability with individual observations via penalisedlikelihood estimation. International Journal of Production Research, 50(22) 6624-6638.
39
27. Yu, J. B., & Xi, L. F. (2009). A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Systems With Applications, 36(1) 909-921.
40
28. Yu, J., Xi, L., & Zhou, X. (2009). Identifying source (s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble. Engineering Applications of Artificial Intelligence, 22(1) 141-152.
41
ORIGINAL_ARTICLE
A Modified Benders Decomposition Algorithm for Supply Chain Network Design under Risk Consideration
In today’s competitive business environment, the design and management of supply chainnetwork is one of the most important challenges that managers encounter. The supply chain network shouldbe designed for satisfying of customer demands as well as minizing the total system costs. This paper presentsa multi-period multi-stage supply chain network design problem under demand uncertainty. The problem isformulated as a two-stage stochastic program. In the first-stage, strategic location decisions are made, whilethe second-stage contains the tactical decisions. In our developed model, conditional value-at-risk (CVaR) asan effective risk measure is used to produce first-stage decisions in which the loss cost in the second-stage isminimized. In addition, a modified Benders decomposition algorithm is developed to solve the model exactly.The computational results on a set of randomly generated problem instances demonstrate the effectiveness ofthe proposed algorithm in terms of the solution quality.
http://jqepo.shahed.ac.ir/article_189_9f12969da53b62bc3f6298ef62d6ae46.pdf
2015-02-26T11:23:20
2018-08-20T11:23:20
55
66
10.22070/jqepo.2015.189
Benders decomposition
Conditional Value-at-Risk
Supply chain network design
Two-stage stochastic programming
Uncertain demand
Nima
Hamta
nima.hamta@aut.ac.ir
true
1
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad
Fattahi
true
2
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Mohsen
Akbarpour Shirazi
true
3
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Behrooz
Karimi
behrooz.karimi@gmail.com
true
4
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
1. Simchi-Levi, D., Kaminsky, P. & Simchi-Levi, E., Designing and Managing the Supply Chain: The Definitive Guide for the Business Professional, McGraw-Hill, New York, 2004.
1
2. Amiri, A. (2006). “Designing a distribution network in a supply chain system: formulation and efficient solution procedure”,European Journal of Operational Research, 171(2), pp. 567-576.
2
3. Z. Drezner, H.W. Hamacher (Eds.), Facility Location: Applications and Theory, Springer, New York, 2004.
3
4. S. Nickel, J. Puerto, Location Theory: A Unified Approach, Springer, New York, 2005.
4
5. C.S. ReVelle, H.A. Eiselt.(2005) Location analysis: A synthesis and survey, European Journal of Operational Research, 165(1) 1-19.
5
6. M.T. Melo, S. Nickel, F. Saldanha-da-Gama. (2009) . Facility location and supply chain management – A review, European Journal of Operational Research, 196(2) 401-412.
6
7. Klose, A., Drexl, A. (2005). Facility location models for distribution system design, European Journal of Operational
7
Research 162(1) 4-29.
8
8. Georgiadis, M.C., Tsiakis, P., Longinidis, P. & Sofioglou, M.K. (2011). Optimal design of supply chain networks under
9
uncertain transient demand variations. Omega, 39(3) 254-272.
10
9. Pishvaee, M.S., Jolai, F. and Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics
11
network design.Journal of Manufacturing Systems, 28(4) 107-114.
12
10. Birge, J.R. & Louveaux, F.,Introduction to Stochastic Programming, Springer-Verlag, New York, 1997.
13
11. Rockafellar, R.T. and Uryasev, S. ( 2002). “Conditional value-at-risk for general loss distributions”,Journal of Banking &
14
Finance, 26(7) pp. 1443-1471.
15
12. Benders, J.F.( 1962). “Partitioning procedures for solving mixed-variables programming problems”,Numerische Mathematik,
16
Vol. 4, pp. 238-252.
17
13. MirHassani, S.A., Lucas, C., Mitra, G., Messina, E. & Poojari, C.A. ( 2000). “Computational solution of capacity planning models under uncertainty”,Parallel Computing, 26(5) pp. 511-538.
18
14. Santoso, T., Ahmed, S., Goetschalckx, M. & Shapiro, A., “A stochastic programming approach for supply chain network design under uncertainty”,European Journal of Operational Research,167(1) pp. 96-115, 2005.
19