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In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics. [ABSTRACT FROM AUTHOR]

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm. [ABSTRACT FROM AUTHOR]

Multiple-route job shop scheduling problem (MRJSP) is a generalization of job shop scheduling problem in which each job may have more than one route for its production and the numbers of operations associated to the alternative routes of the job are not necessarily equal. MRJSP is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem, necessitating the use of heuristic approaches or dispatching rules for its solution. In this paper, mathematical formulation of MRJSP is first presented. Then, a two-phase algorithm is proposed to deal with the two major sub-problems in MRJSP, namely, the route selection and the sequencing sub-problems. In order to evaluate the effectiveness of the proposed algorithm, several problems (in small, medium, and large sizes) are designed and solved using the proposed algorithm. The problems are also solved using some other dispatching rules, and comparisons are provided. The computational results show that the proposed algorithm generates high-quality schedules in a timely fashion. [ABSTRACT FROM AUTHOR]