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This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment. [ABSTRACT FROM AUTHOR]

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers. [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]

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]

This paper addresses the unrelated parallel machine scheduling problem with job sequence- and machine-dependent setup times. The preemption of jobs is not permitted, and the optimization criteria are to simultaneously minimize total weighted flow time and total weighted tardiness. The problem has applications in industries such as TFT-LCD, automobile, and textile manufactures. In this study, a Pareto evolutionary approach is proposed to solve the bi-objective scheduling problem. The performance of this approach using different encoding and decoding schemes is evaluated and is compared with that of two multi-objective simulated annealing algorithms via a set of instances generated by a method in the literature. The experimental results indicate that the Pareto evolutionary approach using random key representation and weighted bipartite matching optimization method outperforms the other algorithms in terms of closeness metric, based on similar computation times. Additionally, although the proposed method does not provide the best distribution in terms of diversity metric, it found most of the reference solutions. [ABSTRACT FROM AUTHOR]

Considering alternative machines for operations, forbidden intervals during which machines cannot be available and a job’s batch size greater than one in the real manufacturing environment, this paper studies the batch splitting scheduling problem on alternative machines with forbidden intervals, based on the objective to minimize the makespan. A scheduling model is established, taking before-arrival set-up, processing, and transfer time into account. And a new hybrid parallel algorithm, based on differential evolution and genetic algorithm, is brought forward to solve both the batch splitting problem and the batch scheduling problem by assuming a common number of sub-batches in advance. A solution consists of the actual optimum number of sub-batches for each job, the optimum batch size for each sub-batch, and the optimum sequence of operations for these sub-batches. Experiments on the performance of the proposed algorithm under different common numbers of sub-batches are carried out. The results of simulations indicate that the algorithm is feasible and efficient. [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]