This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.

Aviation manufacture cell scheduling is generally multi-constraint and multi-objective flexible job-shop scheduling, and is more complicated than classical job-shop because of flexible processing route, multi-restriction, multi-objective, dynamic disturb and complex model. The model of aviation manufacture cell scheduling is set up, and accordingly adaptive ant colony algorithm is brought forward. The adaptive mechanism and the genetic principle are introduced into the algorithm to accelerate convergence and avoid stagnation. Finally, the aviation manufacture cell scheduling of Xi’an Aero-Engine (Group) Ltd. is well solved by the proposed methods.

This paper studies a just-in-time job-shop scheduling problem (JITJSSP) in which each operation has an earliness cost or a tardiness cost if it is completed before or after its due date and the objective function is to minimize the total earliness and tardiness costs of all operations. In order to solve this problem, an improved genetic algorithm (IGA) is introduced in this paper. IGA utilizes an operation-based scheme to represent schedules as chromosomes. Then, each chromosome is processed through a three-stage mechanism. Firstly, the semi-active decoding process is employed to expand the search space of solutions and guarantee comprehensive solutions. Secondly, the greedy insertion mechanism for tardy operations is executed to move the tardy operations left to the appropriate idle time to reduce the tardiness costs. Finally, the greedy insertion mechanism for early operations is proposed to shift the early operations right to the suitable idle time to decrease the earliness costs. After the maximum number of generations is reached, IGA continues with selection, crossover and mutation. The experimental results finally show that most of solutions on the benchmarks are improved by our algorithm.

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