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Abstract：The production scheduling method of aerospace structural parts with dual resource constraints of critical equipment and operator is investigated, where structural parts' processing time varies with the operator's ability, and the key components and processes have specific equipment and operator requirements constraints. A mathematical model of the flexible job shop scheduling with dual resource constraints (FJSSDR) is constructed to describe the equipment、operator and process information of the parts. A nested ant colony-genetic hybrid algorithm (NACGHA) is designed to solve the FJSSDR problem. Aiming at the dual resource selection problem, the mapping relationship between resource selection problem and ant colony parallel search is structured. Genetic algorithm is used to solve the optimal scheduling problem by taking the selected double resource constraints as the processing constraint information of the job. The equipment processing sequence is optimized with avoiding conflict of key operator resources, where the objectives of fullest utilization of critical equipment and makespan minimization are considered. The ant colony algorithm pheromones are updated by integrating the objective value obtained by ant colony and genetic algorithm to improve the performance. At last, a scheduling case of aerospace structural parts production shop is analyzed. The case study demonstrates that the proposed NACGHA method has good performance on FJSSDR in terms of reducing resource total load, avoiding resource conflict and improving the utilization of key equipment.

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As the most important core process in the dyeing and finishing workshop of knitting companies, the dyeing process has the characteristics of multi-variety, small-batch, parallel machine processing of multiple types, and high cost in equipment cleaning, which render the dyeing scheduling problem a bottleneck in the production management of a dyeing and finishing workshop. In this paper, the dyeing process scheduling problem in dyeing and finishing workshops is described and abstracted, and an optimized mathematical model of dyeing scheduling is constructed with the goal of minimizing the delay cost and switching cost. Constraints such as multiple types of equipment, equipment capacity, weights of orders and equipment cleaning time are considered. For the sub-problem of equipment scheduling in the dyeing scheduling problem, a heuristic rule that considers equipment utilization and order delay is proposed. For the sub-problem of order sorting of the equipment in the dyeing scheduling problem, a hybrid genetic algorithm with a variable neighbourhood search strategy has been designed to optimize sorting. The algorithm proposed in this paper has been demonstrated via case simulation to be effective in solving the scheduling problem in dyeing and finishing workshops.

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Job-shop scheduling is a difficult type of production planning problem, of which the primary characteristic is that the processing route of each job is different. Job shop scheduling belongs to the special class of NP-hard problems.Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In most studies on job-shop scheduling problems, the objective is usually to determine the sequence of jobs to minimize the makes pan. The due date request of the key jobs, the availability of key machine, the average wait-time of the jobs and the similarities between jobs and so on are also the objectives to be considered synthetically in real manufacturing process. In this paper, the job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. First, the description of this problem is given with its model. Then, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illustrative example is given to testify the validity of this algorithm.

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