Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston",
  title =        "Enhancing Branch-and-Bound Algorithms for Order
                 Acceptance and Scheduling with Genetic Programming",
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "124--136",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "doi:10.1007/978-3-662-44303-3_11",
  abstract =     "Order acceptance and scheduling (OAS) is an important
                 planning activity in make-to-order manufacturing
                 systems. Making good acceptance and scheduling
                 decisions allows the systems to use their manufacturing
                 resources better and achieve higher total profit.
                 Therefore, finding optimal solutions for OAS is
                 desirable. Unfortunately, the exact optimisation
                 approaches previously proposed for OAS are still very
                 time consuming and usually fail to solve the problem
                 even for small instances in a reasonable computational
                 time. In this paper, we develop a new branch-and-bound
                 (B&B) approach to finding optimal solutions for OAS. In
                 order to design effective branching strategies for B&B,
                 a new GP method has been proposed to discover good
                 ordering rules. The results show that the B&B
                 algorithms enhanced by GP can solve the OAS problem
                 more effectively than the basic B&B algorithm and the
                 CPLEX solver on the Mixed Integer Linear Programming
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston