A hybrid particle-swarm tabu search algorithm for solving job shop scheduling problems

Hao GAO, Sam KWONG, Baojie FAN, Ran WANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

62 Citations (Scopus)


This paper proposes a method for the job shop scheduling problem (JSSP) based on the hybrid metaheuristic method. This method makes use of the merits of an improved particle swarm optimization (PSO) and a tabu search (TS) algorithm. In this work, based on scanning a valuable region thoroughly, a balance strategy is introduced into the PSO for enhancing its exploration ability. Then, the improved PSO could provide diverse and elite initial solutions to the TS for making a better search in the global space. We also present a new local search strategy for obtaining better results in JSSP. A real-integer encode and decode scheme for associating a solution in continuous space to a discrete schedule solution is designed for the improved PSO and the tabu algorithm to directly apply their solutions for intensifying the search of better solutions. Experimental comparisons with several traditional metaheuristic methods demonstrate the effectiveness of the proposed PSO-TS algorithm.
Original languageEnglish
Pages (from-to)2044-2054
JournalIEEE Transactions on Industrial Informatics
Issue number4
Early online date23 Jul 2014
Publication statusPublished - Nov 2014
Externally publishedYes


  • Global search
  • job shop scheduling
  • particle swarm optimization (PSO)
  • tabu search (TS)


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