Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem

References

• Allahverdi, A, 2004. A new heuristic for m-machine flowshop-scheduling problem with bicriteria of makespan and maximum tardiness, Comput. Oper. Res. 31 (2004), pp. 157–180.
   Web of Science ®Google Scholar
• Allahverdi, A, 2003. The two- and m-machine flowshop-scheduling problems with bicriteria of makespan and mean flowtime, Eur. J. Oper. Res. 147 (2003), pp. 373–396.
   Web of Science ®Google Scholar
• Baker, KR, 1984. Sequence rules and due-date assignments in a job shop, Manag. Sci. 30 (1984), pp. 1093–1104.
   Web of Science ®Google Scholar
• Barr, RS, Golden, BL, Kelly, JP, Resende, MGC, and Stewart, W.R., 1995. Designing and reporting on computational experiments with heuristic methods, J. Heurist. 1 (1995), pp. 9–32.
   Google Scholar
• Bauer, A, Bullnheimer, B, Hartl, RF, and Strauss, C, 1999. "An ant colony optimization approach for the single machine total tardiness problem". In: 1999 Congress on Evolutionary Computation. 1999. pp. 1445–1450.
   Google Scholar
• Bilge, U, Kirac, F, Kurtulan, M, and Pekgun, P, 2004. A tabu search algorithm for parallel machine total tardiness problem, Comput. Oper. Res. 31 (2004), pp. 397–414.
   Web of Science ®Google Scholar
• Birman, M, and Mosheiov, G, 2004. A note on a due-date assignment on a two-machine flow-shop, Comput. Oper. Res. 31 (2004), pp. 473–480.
   Web of Science ®Google Scholar
• Botee, HM, and Bonabeau, E, 1998. Evolving ant colony optimization, Adv. Complex Sys. 1 (1998), pp. 149–159.
   Google Scholar
• Brah, SA, and Hunsucker, JL, 1991. Branch-and-bound algorithm for the flowshop with multiple processors, Eur. J. Oper. Res. 51 (1991), pp. 88–99.
   Web of Science ®Google Scholar
• Bullnheimer, B, Hartl, RF, and Strauss, C, 1997. Applying the ant system to the vehicle routing problem, in Proceedings of the 2nd International Conference on Metaheuristics, (1997).
   Google Scholar
• Chang, P-C, Hsieh, J-C, and Lin, S-G, 2002. The development of gradual-priority weighting approach for the multi-objective flowshop-scheduling problem, Int. J. Prod. Econ. 79 (2002), pp. 171–183.
   Web of Science ®Google Scholar
• Chang, P-T, and Lo, Y-T, 2001. Modelling of job-shop scheduling with multiple quantitative and qualitative objectives and a GA/TS mixture approach, Int. J. Comput.-Integr. Manuf. 14 (2001), pp. 367–384.
   Web of Science ®Google Scholar
• Cheng, R, Gen, M, and Tsujimura, Y, 1996. A tutorial survey of job-shop-scheduling problems using genetic algorithms — I. Representation, Comput. Ind. Eng. 30 (1996), pp. 983–997.
   Web of Science ®Google Scholar
• Cheng, TCE, and Diamond, JE, 1995. Scheduling two job classes on parallel machines, IIE Trans. 27 (1995), pp. 689–693.
   Web of Science ®Google Scholar
• Chou, FD, and Lee, CE, 1999. Two-machine flowshop scheduling with bicriteria problem, Comput. Ind. Eng. 36 (1999), pp. 549–564.
   Google Scholar
• Colorni, A, Dorigo, M, and Maniezzo, V, 1991. "Distributed optimization by ant colonies". In: Varela, F, and Bourgine, P, eds. First European Conference Artif. Life. Paris, France: Elsevier; 1991. pp. 134–142.
   Google Scholar
• Colorni, A, Dorigo, M, Maniezzo, V, and Trubian, M, 1994. Ant system for job-shop scheduling, Belg. J. Oper. Res. Stat. Comput. Sci. 34 (1994), pp. 39–53.
   Google Scholar
• Daniels, RL, 1994. Incorporating performance information into multi-objective scheduling, Eur J. Oper. Res. 77 (1994), pp. 272–286.
   Google Scholar
• Djellab, H, and Djellab, K, 2002. Preemptive hybrid flowshop-scheduling problem of interval orders, Eur. J. Oper. Res. 137 (2002), pp. 37–49.
   Web of Science ®Google Scholar
• Dorigo, M, and Gambardella, LM, 1997a. Ant colonies for the travelling salesman problem, BioSystems 43 (1997a), pp. 78–81.
   Google Scholar
• Dorigo, M, and Gambardella, LM, 1997b. Ant Colony System: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Computat. 1 (1997b), pp. 53–66.
   Google Scholar
• Dorigo, M, and Maria, L, 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Computat. 1 (1997), pp. 53–66.
   Google Scholar
• Dorigo, M, and Stützle, V, 2004. Ant Colony Optimization. London: MIT Press; 2004.
   Google Scholar
• Dorigo, M, Caro, GD, and Gambarsella, LM, 1999. Ant algorithms for discrete optimization, Artif. Life 5 (1999), pp. 137–172.
   PubMed Web of Science ®Google Scholar
• Dorigo, M, Maniezzo, V, and Colorni, A, 1996. Ant system: optimization by a colony of cooperating agents, IEEE Trans. Sys. Man Cybernet. B 26 (1996), pp. 29–41.
   PubMed Web of Science ®Google Scholar
• Dorigo, M, Maniezzo, V, and Colorni, A, 1991. Positive Feedback as a Research Strategy. 1991, Technology Report No. 91–016 (Politecnico di Milano, Milan).
   Google Scholar
• Esquivel, S, Ferrero, S, Gallard, R, Salto, C, Alfonso, H, and Schutz, M, 2002. Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop-scheduling problem, Knowledge-Based Sys. 15 (2002), pp. 13–25.
   Google Scholar
• Framinan, JM, Leisten, R, and Ruiz-Usano, R, 2002. Efficient heuristics for flowshop sequencing with the objectives of makespan and flowtime minimization, Eur. J. Oper. Res. 141 (2002), pp. 559–569.
   Web of Science ®Google Scholar
• Frey, DD, Engelhardt, F, and Greitzer, EM, 2003. A role for ‘one-factor-at-a-time’ experimentation in parameter design, Res. Eng. Des. 14 (2003), pp. 65–74.
   Web of Science ®Google Scholar
• Gambardella, LM, and Dorigo, M, 1995. .
   Google Scholar
• Ghedjati, F, 1999. Genetic algorithms for the job-shop-scheduling problem with unrelated parallel constraints: heuristic mixing method machines and precedence, Comput. Ind. Eng. 37 (1999), pp. 39–42.
   Web of Science ®Google Scholar
• Goldberg, DE, 1989. Genetic Algorithm in Search, Optimization and Machine Learning. Reading, MA: Addision-Wesley; 1989.
   Google Scholar
• Gravel, M, Price, WL, and Gagné, C, 2002. Scheduling continuous casting of aluminium using a multiple objective colony optimization metaheuristic, Eur. J. Oper. Res. 143 (2002), pp. 218–229.
   Web of Science ®Google Scholar
• Gupta, JND, Kruger, K, Lauff, V, Werner, F, and Sotskov, YN, 2002. Heuristics for hybrid flow shops with controllable processing times and assignable due dates, Comput. Oper. Res. 29 (2002), pp. 1417–1439.
   Web of Science ®Google Scholar
• Gupta, JND, 1988. Two-stage, hybrid flowshop-scheduling problem, J. Oper. Res. Soc. 39 (1988), pp. 359–364.
   Web of Science ®Google Scholar
• Hundal, TS, and Rajgopal, J, 1988. An extension of Palmer's heuristic for the flow-shop-scheduling problem, Int. J. Prod. Res 26 (1988), pp. 1119–1124.
   Web of Science ®Google Scholar
• Ishibuchi, H, and Murata, T, 1998. A multi-objective genetic local search algorithm and its application to flowshop scheduling, IEEE Trans. Sys. Man Cybernet. C Appl. Rev. 28 (1998), pp. 392–403.
   Web of Science ®Google Scholar
• Ishibuchi, H, Yoshida, T, and Murata, T, 2003. Balance between genetic search and local search in mimetic algorithms for multiobjective permutation flowshop scheduling, IEEE Trans. Evol. Computat. 7 (2003), pp. 204–223.
   Web of Science ®Google Scholar
• Itoh, K, Huang, D, and Enkawa, T, 1993. Twofold look-ahead search for multi-criterion job shop scheduling, Int. J. Prod. Res. 31 (1993), pp. 2215–2234.
   Web of Science ®Google Scholar
• Kacem, I, Hammadi, S, and Borne, P, 2002. Approach by localization and multiobjective evolutionary optimization for flexible job-shop-scheduling problems, IEEE Trans. Sys. Man Cybernet. 32 (2002), pp. 1–13.
   Web of Science ®Google Scholar
• Kim, C-O, Min, H-S, and Yih, Y, 1998. Integration of inductive learning and neural networks for multi-objective FMS scheduling, Int. J. Prod. Res. 36 (1998), pp. 2497–2509.
   Web of Science ®Google Scholar
• Kyparisis, GJ, and Koulamas, C, 2001. A note on weighted completion time minimization in a flexible flow shop, Oper. Res. Lett. 29 (2001), pp. 5–11.
   Web of Science ®Google Scholar
• Lee, I, 2001. Artificial intelligence search methods for multi-machine two-stage scheduling with due date penalty, inventory, and machining costs, Comput. Oper. Res. 28 (2001), pp. 835–852.
   Google Scholar
• Maniezzo, V, and Carbonaro, A, 2000. An ANTS heuristic for the frequency assignment problem, Future Generat. Comput. Sys. 16 (2000), pp. 927–935.
   Google Scholar
• McMullen, PR, 2001. An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives, Art. Intell. Eng. 15 (2001), pp. 309–317.
   Google Scholar
• McMullen, PR, 2000. Proceedings of the YOR 11 Conference.
   Google Scholar
• Mellor, P, 1966. A review of job shop scheduling, Oper. Res. Q. 17 (1966), pp. 161–170.
   Google Scholar
• Merkle, D, Middendorf, M, and Schmeck, H, 2002. Ant colony optimization for resource-constrained project scheduling, IEEE Trans. Evol. Computat. 6 (2002), pp. 333–346.
   Web of Science ®Google Scholar
• Min, HS, Yih, Y, and Kim, C-O, 1998. A competitive neural network approach to multi-objective FMS scheduling, Int. J. Prod. Res. 36 (1998), pp. 1749–1765.
   Web of Science ®Google Scholar
• Mosheiov, G, and Oron, D, 2004. A note on the SPT heuristic for solving scheduling problems with generalized due dates, Comput. Oper. Res. 31 (2004), pp. 645–655.
   Web of Science ®Google Scholar
• Murata, T, Ishibuchi, H, and Tanaka, H, 1996. Multi-objective genetic algorithm and its applications to flowshop scheduling, Comput. Ind. Eng. 30 (1996), pp. 957–968.
   Web of Science ®Google Scholar
• Nagar, A, Haddock, J, and Heragu, S, 1995. Multiple and bicriteria scheduling: a literature survey, Eur. J. Oper. Res. 81 (1995), pp. 88–104.
   Web of Science ®Google Scholar
• Nawaz, M, 1983. A heuristic algorithm for the ‘M’-machine, ‘N’ job flow-shop sequencing problem, Manag. Sci. 11 (1983), pp. 91–95.
   Web of Science ®Google Scholar
• Neppalli, VR, Chen, CL, and Gupta, J, 1996. Genetic algorithms for two-stage bicriteria flowshop problem, Eur. J. Oper. Res. 95 (1996), pp. 356–373.
   Web of Science ®Google Scholar
• Nowicki, E, and Czeslaw, S, 1998. The flow shop with parallel machines: a tabu search approach, Eur. J. Oper. Res. 106 (1998), pp. 226–253.
   Web of Science ®Google Scholar
• Rajendran, C, and Ziegler, H, 1999. Heuristics for scheduling in flowshops and flowline-based manufacturing cells to minimize the sum of weighted flowtime and weighted tardiness of jobs, Comput. Ind. Eng. 37 (1999), pp. 671–690.
   Google Scholar
• Rajendran, C, and Ziegler, H, 2003. Scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence-dependent setup times, Eur. J. Oper. Res. 149 (2003), pp. 513–522.
   Web of Science ®Google Scholar
• Saaty, TL, 1994. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. PA: RWS: Pittsburgh; 1994.
   Google Scholar
• Santos, DL, Hunsucker, JL, and Deal, DE, 1995. Flowmult: permutation sequences for flow shops with multiple processors, J. Informat. Optimiz. Sci. 16 (1995), pp. 351–366.
   Google Scholar
• Sawik, T, 2000. Mixed-integer programming for scheduling flexible flow lines with limited intermediate buffers, Math. Comput. Model. 31 (2000), pp. 39–52.
   Web of Science ®Google Scholar
• Sawik, T, 2002. An exact approach for batch scheduling in flexible flowlines with limited intermediate buffers, Math. Comput. Model. 36 (2002), pp. 461–471.
   Web of Science ®Google Scholar
• Serafini, P, 1996. Scheduling jobs on several machines with the job splitting property, Oper. Res. 44 (1996), pp. 617–628.
   Web of Science ®Google Scholar
• Sivrikaya-Serifoðlu, F, and Ulusoy, G, 1999. Parallel machine scheduling with earliness and tardiness penalties, Comput. Oper. Res. 26 (1999), pp. 773–787.
   Google Scholar
• Sridhar, J, and Rajendran, C, 1993. Scheduling in a cellular manufacturing system: a simulated annealing approach, Int. J. Prod. Res. 31 (1993), pp. 2927–2945.
   Web of Science ®Google Scholar
• Stützle, T, and Hoos, HH, 2000. MAX–MIN ant system, Future Generat. Comput. Sys. 16 (2000), pp. 889–914.
   Web of Science ®Google Scholar
• Stützle, T, and Hoos, HH, 1997. "The MAX–MIN ant system and local search for the traveling salesman problem". In: Bäck, T, Michalewicz, Z, and Yao, X, eds. IEEE International Conference on Evolutionary Computation. Piscataway, NJ: IEEE Press; 1997. pp. 309–314.
   Google Scholar
• T’kindt, V, Monmarché, N, Tercient, F, and Laügt, D, 2002. An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop-scheduling problem, Eur. J. Oper. Res. 142 (2002), pp. 250–257.
   Web of Science ®Google Scholar
• Wardono, B, and Fathi, Y, 2004. A tabu search algorithm for the multi-stage parallel machine problem with limited buffer capacities, Eur. J. Oper. Res. 155 (2004), pp. 380–401.
   Web of Science ®Google Scholar
• Zwaan, SVD, and Marques, C, 1999. Ant colony optimisation for job shop scheduling, Proceedings of the Third Workshop on Genetic Algorithms and Artificial Life (1999).
   Google Scholar