Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Key Words: Joint replenishment problem, multiobjective, modified RAND, genetic algorithm

Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Key Words: Joint replenishment problem, multiobjective, modified RAND, genetic algorithm

___

  • M. Khouja, M. Michalewicz, S. Satoskar, “A comparison between genetic algorithms and the RAND method for solving the joint replenishment problem”, Production Planning and Control, Vol. 11, pp. 556–564, 2000.
  • K. Moon, B.C. Cha, “The joint replenishment problem with resource restriction”, European Journal of Operational Research, Vol. 173, pp. 190–198, 2006.
  • E. Arkin, D. Joneja, R. Roundy, “Computational complexity of un capacitated multi echelon production planning problems”, Operations Research Letters, Vol. 8, pp. 61–66, 1989.
  • M. Khouja, S. Goyal, “A review of the joint replenishment problem literature: 1989-2005”, European Journal of
  • Operational Research, Vol. 186, pp. 1–16, 2008.
  • C.S. Tsou, “Evolutionary Pareto optimizers for continuous review stochastic inventory system”, European Journal of Operational Research, Vol. 195, pp. 364–371, 2009.
  • H.M. Wee, C.C. Lo, P.H. Hsu, “A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment”, European Journal of Operational Research, Vol. 197, pp. 620–631, 2009.
  • M.K Starr, D.W. Miller, Inventory Control: Theory and Practice, Englewood Cliffs, NJ, USA, Prentice Hall, 1962.
  • E.S. Gardner Jr., D.G. Dannenbring, “Using optimal policy surfaces to analyze aggregate inventory tradeoffs”, Management Science, Vol. 25, pp. 709–720, 1979.
  • G. Padmanabhan, P. Vart, “Analysis of multi-item inventory systems under resource constrains: a non-linear goal programming approach”, Engineering Cost and Production Economics, Vol. 20, pp. 121–127, 1990.
  • J. Xu, L. Zhao, “A multi-objective decision-making model under fuzzy rough coefficient and its application to inventory problem”, Information Science, Vol. 180, pp. 679–696, 2010.
  • J. Xu, Y. Liu, ”Multi-objective decision making model under fuzzy random environment and its application to inventory problems”, Information Science, Vol. 178, pp. 2899–2914, 2008.
  • E. Silver, “A simple method of determining order quantities in joint replenishments under deterministic demand”, Management Science, Vol. 22, pp. 1351–1361, 1976.
  • S.K. Goyal, A.S. Belton, “On a simple method of determining order quantities in joint replenishments under deterministic demand”, Management Science, Vol. 25, pp. 604–610, 1979.
  • M. Kaspi, M.J. Rosenblatt, “An improvement of Silver’s algorithm for the joint replenishment problem”, IIE Transactions, Vol. 15, pp. 264–269, 1983.
  • F. Djeffal, T Bendib, “Multi-objective genetic algorithms based approach to optimize the electrical performances of the gate stack double gate (GSDG) MOSFET”, Microelectronics Journal, Vol. 42, pp. 661–666, 2011.
  • D.P. Singh, A. Khare, “Different aspects of evolutionary algorithms, multi-objective optimization algorithms and application domain”, International Journal of Advanced Networking and Applications, Vol. 2, pp. 770–775, 2011.
  • A. Konak, D.W. Coit, A.E. Smith, “Multi-objective optimization using genetic algorithms: a tutorial”, Reliability Engineering & System Safety, Vol. 91, pp. 992–1007, 2006.
  • E. Zitzler, M. Laumanns, L. Thiele, “SPEA2: improving the strength Pareto evolutionary algorithm”, in Evolution- ary Methods for Design Optimization and Control with Applications to Industrial Problems (K.C. Giannakoglou, D.T. Tsahalis, J. Periaux, T. Fogarty, editors), Zurich, Switzerland, Swiss Federal Institute of Technology, pp. 95–100, 2001.
  • U. ¨Ozkaya, F. G¨une¸s, “A modified particle swarm optimization algorithm and its application to the multi objective FET modeling problem”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 20, pp. 263–271, 20