2008
1
4
4
103
Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the MahalanobisTaguchi System
2
2
The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the Adjoint Matrix Approach to MTS for vehicle braking to identify a reduced set of useful variables in multidimensional systems.
1

281
292


Elizabeth A.
Cudney
University of Missouri – Rolla, Rolla, Missouri 65409 USA
University of Missouri – Rolla, Rolla, Missouri
Iran


Kioumars
Paryani
Lawrence Technological University, Southfield, Massachusetts 02139 USA
Lawrence Technological University, Southfield,
Iran


Kenneth M.
Ragsdell
University of Missouri – Rolla, Rolla, Missouri 65409 USA
University of Missouri – Rolla, Rolla, Missouri
Iran
MahalanobisTaguchi system (MTS)
Mahalanobis distance (MD)
Adjoint matrix
Pattern Recognition
Orthogonal array (OA)
Signaltonoise ratio (SN)
Mahalanobis space (reference group)
[[1] AlOtum, H.M.(2003), Morphological operators for color image processing based on Mahalanobis##distance measure, Optical Engineering, 42 (9); 25952606.##[2] Anandan, P., Irani M.(2002), Factorization with uncertainty, International Journal of Computer##Vision, 49 (23); 101116.##[3] Asada, M. (2001), Wafer yield prediction by the MahalanobisTaguchi system, IEEE International##Workshop on Statistical Methodology, 6; 2528.##[4] GarciaLagos, F., Joya, G., Marin, F.J., Sandoval F. (2003), Modular power system topology##assessment using Gaussian potential functions, IEEE ProceedingsGeneration Transmission and##Distribution, 150 (5); 635640.##[5] Hayashi, S., Y. Tanaka, Kodama E. (2001), A new manufacturing control system using Mahalanobis##distance for maximizing productivity, IEEE Transactions, 15 (4); 5962.##[6] Manly, B.F.J. (1994), Multivariate Statistical Methods: A Primer; Chapman & Hall, London.##[7] Shen, H., Carter, J.F., Brereton, R.G., Eckers C. (2003), Discrimination between tablet production##methods using pyrolysisgas chromatographymass spectrometry and pattern recognition, Analyst,##128(3); 287292.##[8] Taguchi, G., Jugulum R. (2002), The MahalanobisTaguchi strategy; John Wiley & Sons, Inc., New##[9] Taguchi, S.(2000), MahalanobisTaguchi system, ASI Taguchi Symposium, Detroit, MI.##[10] Wu, Y. (2004), Pattern recognition using Mahalanobis distance, TPD Symposium, Journal of Quality##Engineering Forum, 12 (5); 787795.##]
A Goal Programming Method for Finding Common Weights in DEA with an Improved Discriminating Power for Efficiency
2
2
A characteristic of data envelopment analysis (DEA) is to allow individual decision making units (DMUs) to select the most advantageous weights in calculating their efficiency scores. This flexibility, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a multiple objective linear programming (MOLP) approach for generating a common set of weights in the DEA framework.
1

293
303


A.
Makui
Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
Department of Industrial Engineering, Iran
Iran


A.
Alinezhad
Islamic Azad UniversityScience & Research Branch, Tehran, Iran.
Islamic Azad UniversityScience & Research
Iran


R.
Kiani Mavi
Islamic Azad UniversityScience & Research Branch, Tehran, Iran.
Islamic Azad UniversityScience & Research
Iran


M.
Zohrehbandian
Department of Mathematics, Islamic Azad UniversityKaraj P.O.Box 31485313, Karaj, Iran.
Department of Mathematics, Islamic Azad University
Iran
MOLP
Goal Programming
DEA
Efficiency
Ranking
Weight restrictions
[[1] Bouyssou D. (1999), Using DEA as a tool for MCDM: some remarks; Journal of the Operational##Research Society 50(9); 974978.##[2] Charnes A., Cooper W.W. (1961), Management Models and Industrial Applications of Linear##Programming; John Wiley, New York.##[3] Charnes A., Cooper W.W., Rhodes E. (1978), Measuring the efficiency of decision making units;##European Journal of Operational Research 2; 429444.##[4] Doyle J.R., Green R.H. (1994), Efficiency and crossefficiency in DEA: derivatives, meanings and##uses; Journal of the Operational Research Society 45; 567578.##[5] Estellita Lins M.P., Angulo Meza L., Moreira da Silva A.C. (2004), A multiobjective approach to##determine alternative targets in data envelopment analysis; Journal of the Operational Research##Society 55; 10901101.##[6] Giokas D. (1997), The use of goal programming and data envelopment analysis for estimating efficient##marginal costs of outputs; Journal of the Operational Research Society 48(3); 319323.##[7] Golany B. (1988), An interactive MOLP procedure for the extension of DEA to effectiveness analysis;##Journal of the Operational Research Society 39(8); 725734.##[8] Golany B., Yu G. (1995), A goal programmingdiscriminant function approach to the estimation of an##empirical production function based on DEA results; Journal of Productivity Analysis 6; 171186.##[9] Jahanshahloo G.R., Memariani A., Lotfi F.H., Rezai H.Z. (2005), A note on some of DEA models and##finding efficiency and complete ranking using common set of weights; Applied Mathematics and##Computation 166; 265281.##[10] Joro T., Korhonen P., Wallenius J. (1998), Structural comparison of data envelopment analysis and##multiple objective linear programming; Management Science 44; 962970.##[11] Kao C., Hung H.T. (2005), Data envelopment analysis with common weights: the compromise##solution approach; Journal of the Operational Research Society 56; 11961203.##[12] Karsak E.E., Ahiska S.S. (2005), Practical common weight multicriteria decisionmaking approach##with an improved discriminating power for technology selection; International Journal of Production##Research 43(8); 15371554.##[13] Kornbluth J. (1991), Analysing policy effectiveness using cone restricted data envelopment analysis;##Journal of the Operational Research Society 42; 10971104.##[14] Roll Y., Cook W.D., Golany B. (1991), Controlling factor weights in data envelopment analysis; IIE##Transactions 23(1); 29.##[15] Roll Y., Golany B. (1993), Alternate methods of treating factor weights in DEA; Omega 21(1); 99##[16] Stewart T.J. (1996), Relationships between data envelopment analysis and multicriteria decisionanalysis;##Journal of the Operational Research Society 47(5); 654665.##[17] Xiao Bai L., Reeves G.R. (1999), A multiple criteria approach to data envelopment analysis; European##Journal of Operational Research 115; 507517##]
A New Solution for the Cyclic MultiplePart Type ThreeMachine Robotic Cell Problem based on the Particle Swarm Metaheuristic
2
2
In this paper, we develop a new mathematical model for a cyclic multiplepart type threemachine robotic cell problem. In this robotic cell a robot is used for material handling. The objective is finding a part sequence to minimize the cycle time (i.e.; maximize the throughput) with assumption of known robot movement. The developed model is based on Petri nets and provides a new method to calculate cycle times by considering waiting times. It is proved that scheduling problem of a robotic cell is unary NPcomplete. Achieving an optimal solution for this type of complex, largesized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. In this paper we implement an algorithm based on the particle swarm optimisation (PSO) method for solving the problem. To validate the developed model and solution algorithm, various test problems are examined some of which are of smallsize and some other of largesize. The computational results show that the proposed algorithm achieves optimum solutions for small sized problems, while for largesized problems this algorithm can find suitable solutions in acceptable time.
1

304
317


N.
Kamalabadi
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University
Iran


S.
Gholami
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat
Iran


A.H.
Mirzaei
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Department of Industrial Engineering, Tarbiat
Iran
Cyclic blocking flowshop
Particle swarm optimisation
Robotic cell
scheduling
[[1] Asfahl C.R. (1992), Robots and manufacturing automation; 2nd Edition, Wiley, New York.##[2] Agnetis A. (2000), Scheduling nowait robotic cells with two and three machines; European Journal of##Operational Research 123; 303314.##[3] Agnetis A., Pacciarelli D. (2000), Part sequencing in threemachine nowait robotic cells; Operations##Research Letters 27; 185192.##[4] Brauner N., Finke G. (1999), On a conjecture about robotic cells: New simplified proof for the threemachine##case; INFOR 37(1); 2036.##[5] Bagchi T.P., Gupta J.N.D., Sriskandarajah C. (2006), A Review of TSP based approaches for flow##shop scheduling; European Journal of Operational Research 169; 816 854.##[6] Crama Y., Klundert J.J. van de. (1999), Cyclic scheduling in 3machine robotic flow shops; Journal of##Scheduling 2; 3554.##[7] Crama Y., Kats V., Klundert, J.J. van de, Levner E. (2000), Cyclic scheduling in robotic flow shops;##Annals of Operations Research: Mathematics of Industrial Systems 96; 97124.##[8] Drobouchevitch I.G., Sethi S.P., Sriskandarajah C. (2006), Scheduling dual gripper robotic cell oneunit##cycles; European Journal of Operational Research 171; 598631.##[9] Dawande M., Geismar H.N., Sethi S.P., Sriskandarajah C. (2005), Sequencing and scheduling in##robotic cells:Recent developments; Journal of Scheduling 8; 387426.##[10] Gultekin H., Akturk M.S., Karasan O.E. (2006), Cyclic scheduling of a 2machine robotic cell with##tooling constraints; European Journal of Operational Research 174; 777–796.##[11] Gultekin H., Akturk M.S., Karasan O.E. (2007), Scheduling in a threemachine robotic flexible##manufacturing cell; Computers & Operations Research 34; 2463–2477.##[12] Hall N. G., Kamoun H., Sriskandarajah C. (1997), Scheduling in robotic cells: Classification, two and##three machine cells; Operations Research 45; 421439.##[13] Hall N. G., Kamoun H., Sriskandarajah C. (1998), Scheduling in robotic cells: Complexity and steady##state anhlysis; European Journal of Operational Research 109; 4365.##[14] Hu X., Shi Y., Eberhart R. (2004), Recent advances in particle swarm, in Proc. of CEC2004, Congress##on Evolutionary Computation, 1; 90–97.##[15] Kennedy J., Eberhart R. (1995), Particle swarm optimization, in Proc. of the IEEE international##conference on neural networks (Perth, Australia), 1942–1948.##[16] Maggot J. (1984), Performance Evaluation of Concurrent Systems Using Petri Nets; inform.##processing lett., 18(1); 713.##[17] Sriskandarajah C., Hall N.G., Kamoun H., Wan H. (1998), Scheduling large robotic cells without##buffers; Annals of Operations Research 76; 287–321.##[18] Sethi S.P., Sriskandarajah C., Sorger G., Blazewicz J., Kubiak W. (1992), Sequencing of parts and##robot moves in a robotic cell; International Journal of Flexible Manufacturing Systems 4; 331358.##[19] Shi Y., Eberhart R. (1998), "A modified particle swarm optimizer", in Proc. of the IEEE international##conference on evolutionary computation, 69–73,.##[20] Tasgetiren MF., Sevkli M., Liang YC., Gencyilmaz G. (2004), "Particle swarm optimization algorithm##for single machine total weighted tardiness problem". In: Proc. of the IEEE congress on evolutionary##computation, Oregon: Portland, 14121419.##]
Machine Cell Formation Based on a New Similarity Coefficient
2
2
One of the designs of cellular manufacturing systems (CMS) requires that a machine population be partitioned into machine cells. Numerous methods are available for clustering machines into machine cells. One method involves using a similarity coefficient. Similarity coefficients between machines are not absolute, and they still need more attention from researchers. Although there are a number of similarity coefficients in the literature, they do not always incorporate the important properties of a similarity coefficient satisfactorily. These important properties include alternative routings, processing time, machine capacity (reliability), machine capability (flexibility), production volume, product demand, and the number of operations done on a machine. The objectives of this paper are to present a review of the literature on similarity coefficients between machines in CMS, to propose a new similarity coefficient between machines incorporating all these important properties of similarity, and to propose a machine cell heuristic approach to group machines into machine cells. An example problem is included and demonstrated in this paper.
1

318
344


Ibrahim H.
Garbie
Department of Mechanical Engineering, Helwan University, Helwan, Cairo, 11792, EGYPT
Department of Mechanical Engineering, Helwan
Iran


Hamid R.
Parsaei
Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA
Department of Industrial Engineering, University
Iran


Herman R.
Leep
Department of Industrial Engineering, University of Louisville, Louisville, KY, 40292, USA.
Department of Industrial Engineering, University
Iran
Cellular Manufacturing
Similarity coefficients
Machine cells
[[1] Aljaber N., Baek W., Chen C.L. (1997), A Tabu search approach to the cell formation##problem; Computers & Industrial Engineering 32; 169185.##[2] Chang P. T., Lee E.S. (2000), A multisolution method for cell formationexploring practical##alternatives in group technology manufacturing; Computers and Mathematics with##Applications 40; 12851296.##[3] Gunasingh K.R., Lashkari R.S. (1989), Machine grouping problem in cellular manufacturing##systems: An integer programming approach; International Journal of Production Research##27; 14651473.##[4] Gupta T. (1991), Clustering algorithms for the design of a cellular manufacturing systemAn##analysis of their performance; Computers & Industrial Engineering 20; 461468.##[5] Gupta T. (1993), Design of manufacturing cells for flexible environmental considering##alternative routing; International Journal of Production Research 31; 12591273.##[6] Gupta T., Seifoddini H. (1990), Production data based similarity coefficient for machinecomponent##grouping decisions in the design of a cellular manufacturing system; International##Journal of Production Research 28; 12471269.##[7] Islam K.M.S., Sarker B.R. (2000), A similarity coefficient measure and machineparts##grouping in cellular manufacturing systems; International Journal of Production Research##38; 699720.##[8] Lee M.K., Luong H.S., Abhary K. (1997), A genetic algorithm based cell design considering##alternative routing; Computer Integrated Manufacturing Systems 10; 93107.##[9] Leem C.W., Chen J.J. G. (1996), Fuzzysetbased machinecell formation in cellular##manufacturing; Journal of Intelligent Manufacturing 7; 355364.##[10] Lozano S., Canca D., Guerrero F., Garcia J.M. (2001), Machine grouping using sequence##based similarity coefficients and neural network; Robotics and Computer Integrated##Manufacturing 17; 399404.##[11] Luong L.H.S. (1993), A cellular similarity coefficient algorithm for the design of##manufacturing cells; International Journal of Production Research 31; 17571766.##[12] Luong L.H.S., Kazerooni M., Abhary K. (2001), Genetic algorithms in manufacturing system##design. In Computational Intelligence in Manufacturing Handbook, edited by Jun Wang et##al., Boca Raton, FL (CRC Press LLC).##[13] McAuley J. (1972), Machine grouping for efficient production; The Production Engineer 52;##[14] Mosier C. (1989), An experiment investigating the application of clustering procedures and##similarity coefficients to the GT machine cell formation problem; International Journal of##Production Research 27; 18111835.##[15] Nair G.J., Narendran T.T. (1998), CASE: A clustering algorithm for cell formation with##sequence data; International Journal of Production Research 36; 157179.##[16] Nazarlo D., Ramirez B. (2000), Application of mixed integer programming to cellular##manufacturing; Engineering Valuation and Cost Analysis 2; 373386.##[17] Ponnambalam S.G., Aravindan P. (1994), Design of cellular manufacturing systems using##objective functional clustering algorithms; International Journal of Advanced Manufacturing##Technology 9; 390397.##[18] Probhakaran G., Janakiraman T.N., Sachithanandam M. (2002), Manufacturing databased##combined dissimilarity coefficient for machine cell formation; International Journal of##Advanced Manufacturing Technology 19; 889897.##[19] Ramabhatta V., Nagi R. (1998), An integrated formulation of manufacturing cell formation##with capacity planning and routing; Annals of Operations Research 77; 7995.##[20] Seifoddini H. (1988), Incorporation of the production volume in machine cells formation in##group technology applications. Recent Developments in Production Research, edited by A.##Mital, (Elsevier Science Publishers B.V., Amsterdam), 562570.##[21] Seifoddini H. (1989), A probabilistic approach to machine cell formation in group##technology. International Conference of Institute of Industrial Engineers, Toronto, Ontario,##Canada, May 1417, 625629.##[22] Seifoddini H., Djassemi M. (1991), The production databased similarity coefficient versus##Jaccard”s similarity coefficient; Computers & Industrial Engineering 21; 263266.##[23] Seifoddini H., Djassemi M. (1996), Merits of the production volume based similarity##coefficient in machine cell formation; Journal of Manufacturing Systems 14; 3544.##[24] Seifoddini H., Wolfe P.M. (1986), Application of the similarity coefficient method in group##technology; IIE Transactions; 271277.##[25] Seifoddini H., Tjahana B. (1999), Partfamily formation for cellular manufacturing: A case##study at Harnischfeger; International Journal of Production Research 37; 32633273.##[26] Shaferm S.M., Rogers D.F. (1993), Similarity and distance for cellular manufacturing. Part##II: An extension and comparison; International Journal of Production Research 31; 1315##[27] Viswanthan S. (1996), A new approach for solving the Pmedian problem in group##technology; International Journal of Production Research 34; 26912700.##[28] Waghodekar P.H., Sabu S. (1984), Machinecomponent cell formation in group technology:##MACE; International Journal of Production Research 22; 937948.##[29] Wilhelm W.E., Chiou C.C., Chang D.B. (1998), Integrating design and planning##considerations in cellular manufacturing; Annals of Operations Research 77; 97107.##[30] Won Y. (2000), New Pmedian approach to cell formation with alternative process plans;##International Journal of Production Research 38; 229240.##[31] Won Y., Kim S. (1997), Multiple criteria clustering algorithm for solving the group##technology problem with multiple process routings; Computers & Industrial Engineering 32;##[32] Yasuda K., Yin Y. (2001), A dissimilarity measure for solving the cell formation problem in##cellular manufacturing; Computers & Industrial Engineering 39; 117.##[33] Yin Y., Yasuda K. (2002), Manufacturing cells design in consideration of various production##factors; International Journal of Production Research 40; 885906.##]
Generalized Cyclic Open Shop Scheduling and a Hybrid Algorithm
2
2
In this paper, we first introduce a generalized version of open shop scheduling (OSS), called generalized cyclic open shop scheduling (GCOSS) and then develop a hybrid method of metaheuristic to solve this problem. Open shop scheduling is concerned with processing n jobs on m machines, where each job has exactly m operations and operation i of each job has to be processed on machine i . However, in our proposed model of GCOSS, processing each operation needs more than one machine (or other resources) simultaneously. Furthermore, the schedule is repeated more than once. It is known that OSS is NPhard. Therefore, for obtaining a good solution for GCOSS, which is obviously NPhard, a hybrid algorithm is also developed. This method is constructed by hybridizing ant colony optimization (ACO), beam search and linear programming (LP). To verify the accuracy of the method, we also compare the results of this algorithm with the optimal solution for some special problems.
1

345
359


Mohammad
Modarres
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran
Industrial Engineering Department, Sharif
Iran


Mahsa
Ghandehari
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran
Industrial Engineering Department, Sharif
Iran
Open shop scheduling
Cyclic open shop scheduling
Metaheuristic
ACO, Beam search
[[1] Blum C. (2005), BeamACOHybridizing ant colony optimization with beam search: an application to##open shop scheduling; Computers and Operations Research 32; 15651591.##[2] Deuber W., Zhu X. (1997), Circular Coloring of Weighted Graphs; Journal of Graph Theory 23; 365##[3] Dorigo M., Stutzle T. (2004), Ant colony optimization; Boston, MA: MIT Press.##[4] Garey M.R., Johnson D.S. (1979), Computers and intractability; A guide to the theory of NPCompleteness,##Freeman, San Francisco.##[5] Kubale M., Nadolski A. (2005), Chromatic scheduling in a cyclic open shop; European Journal of##Operation Research; 164(99); 585591.##[6] Liaw C.F. (2003), An efficient tabu search approach for the twomachine preemptive open shop##scheduling; computers & Operations Research 30, 20812095.##[7] Ow P.S., Morton T.E. (1988), Filtered beam search in scheduling; International Journal of Production##Research 26; 297307.##[8] Stutzle T, Hoos H.H. (2000), MAXMIN ant system; Future Generation Computer Systems 16(8);##[9] Zhu X. (1992), Starchromatic numbers and products of graphs; Journal of Graph Theory 16; 557##]
Study of Scheduling Problems with Machine Availability Constraint
2
2
In real world scheduling applications, machines might not be available during certain time periods due to deterministic or stochastic causes. In this article, the machine scheduling with availability constraints for both deterministic and stochastic cases with different environments, constraints and performance measures will be discussed. The existing body of research work in the literature will be completely reviewed and the NPcomplete models will be identified.
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360
383


Hamid Reza
Dehnar Saidy
Young Researchers Club, Tehran’s Science and Research Branch, Islamic Azad University, Tehran, Iran
Young Researchers Club, Tehran’s Science
Iran


Mohammad
Taghi TaghaviFard
Faculty of Accounting and Management, Allameh Tabataba’i University, Tehran, Iran
Faculty of Accounting and Management, Allameh
Iran
sequencing
scheduling
Unavailability period
Resumable
Breakdown
NPhard
[[1] Adiri I., Bruno J., Frostig E., Rinnooy Kan A.H.G. (1989), Single machine flowtime scheduling##with a single breakdown; Acta Informatica 26; 679696.##[2] Aggoune R. (2002), Ordonnancement d’Ateliers sous Contraintes de Disponibilité des Machines;##Ph.D. Thesis, Universite de Metz; France.##[3] Aggoune R. (2004a), TwoJob Shop Scheduling Problems with Availability Constraints. In:##Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling##(ICAPS 2004), Whistler (Canada).##[4] Aggoune R. (2004b), Minimizing the makespan for the flow shop scheduling problem with##availability constraints; European Journal of Operational Research 153; 534543.##[5] Aggoune R., Portmann M.C. (2006), Flow shop scheduling problem with limited machine##availability: A heuristic approach; International Journal of Production Economics 99; 415.##[6] Albers S., Schmidt G. (1999), Scheduling with Unexpected Machine Breakdowns. In: Computer##Science; Vol. 1671, Proceedings of the Third International Workshop on Approximation Algorithms##for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial##Algorithms and Techniques, SpringerVerlag: London, 269280.##[7] Albers S., Schmidt G. (2001), Scheduling with unexpected machine breakdowns; Discrete Applied##Mathematics 110(23); 8599.##[8] Albers S., Schmidt G. (2004), Scheduling with Unexpected Machine Breakdowns; In:##Randomization, Approximation, and Combinatorial Optimization (Algorithms and Techniques),##Springer: Berlin/ Heidelberg, Volume 1671; 269280.##[9] Allahverdi A. (1995), Twostage Production Scheduling with Separated Setup Times and Stochastic##Breakdowns; Journal of the Operational Research Society 46(7); 896904.##[10] Allahverdi A. (1996), Twomachine proportionate flowshop scheduling with breakdowns to##minimize maximum lateness; Computers & Operations Research 23; 909916.##[11] Allahverdi A. (1997), Scheduling in stochastic flowshops with independent setup, processing and##removal times; Computers and Operations Research 24(10); 955960.##[12] Allahverdi A., Mittenthal J. (1998), Dual criteria scheduling on a twomachine flowshop subject to##random breakdowns; International Transactions in Operational Research 5; 317324.##[13] Allaoui H., Artiba A. (2006), Scheduling twostage hybrid flow shop with availability constraints;##Computers and Operations Research 33(5); 13991419.##[14] Apon A., Wilbur L. (2003), AmpNet  a highly available cluster interconnection network;##Proceedings IEEE International Symposium on Parallel and Distributed Processing.##[15] Balas E., Lancia G., Serafini P., Vazacopoulos A. (1998), Job shop scheduling with deadlines;##Journal of Combinatorial Optimization 1(4); 329353.##[16] Birge J., Frenk J.B.G., Mittenthal J., Rinnooy Kan A.H.G. (1990), Single machine scheduling subject##to stochastic breakdowns; Naval Research Logistics 37; 661677.##[17] Birge J., Glazebrook K.D. (1988), Assessing the effects of machine breakdowns in stochastic##scheduling; Operations Research Letters 7(6); 267 271.##[18] Blazewicz J., Breit J., Formanowicz P., Kubiak W., Schmidt G. (2001), Heuristic algorithms for the##twomachine flowshop problem with limited machine availability; Omega Journal 29; 599608.##[19] Blazewicz J., Dell'Olmo P., Drozdowski M., Maczka P. (2003), Scheduling multiprocessor tasks on##parallel processors with limited availability; European Journal of Operational Research 149; 377##[20] Blażewicz J., Drozdowski M., Formanowicz P., Kubiak W., Schmidt G. (2000), Scheduling##preemtable tasks on parallel processors with limited availability; Parallel Computing 26(9); 1195##[21] Blażewicz J., Ecker K., Pesch E., Schmidt G., Węglarz J. (1996), Scheduling Computer and##Manufacturing Processes; Springer; Berlin.##[22] Braun O., Lai T.C., Schmidt G., Sotskov Y.N. (2002), Stability of Johnson’s schedule with respect##to limited machine availability; International Journal of Production Research 40(17); 43814400.##[23] Breit J. (2004), An improved approximation algorithm for twomachine flow shop scheduling with##an availability constraint; Information Processing Letters 90 (6); 273278.##[24] Breit J., Schmidt G., Strusevich V.A. (2001a), Twomachine open shop scheduling with an##availability constraint; Operations Research Letters 29(2); 6577.##[25] Brucker P., Garey M.R., Johnson D.S. (1977), Scheduling equallength tasks under treelike##precedence constraints to minimize maximum lateness; Mathematics of Operations Research 2; 275##[26] Cai X., Wu X., Zhou X. (2005), Dynamically optimal policies for stochastic scheduling subject to##preemptiverepeat machine breakdowns; IEEE Transactions on Automation Science and Engineering##2(2); 158172.##[27] Canon C., Billaut J.C., Bouquard J.L. (2003), The onemachine sequencing problem with##availability constraints; Technical Report 271, Laboratoire d’Informatique de Universitè de Tours;##Tours (France).##[28] Carlier J. (1982), The onemachine sequencing problem; European Journal of Operational Research##11; 4247.##[29] Chan F.T.S., Wong T.C., Chan L.Y. (2006), Flexible jobshop scheduling problem under resource##constraints; International Journal of Production Research 44(11); 20712089.##[30] Chen W.J. (2007), Scheduling of jobs and maintenance in a textile company; International Journal##of Advanced Manufacturing Technology 31; 737742.##[31] Cheng T.C.E., Liu Z. 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