In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

This paper studies the problem of assigning tasks to a workforce with different skills. The problem is modeled as an unrelated parallel scheduling problem, incorporating sequence-dependent setup times (UPMSPSDST). Exact methods generally are not able to solve real large problems of UPMSPSDST. Hence, this research introduces an efficient, straightforward metaheuristic solution leveraging the Ant Colony Optimization (ACO) algorithm. The objective is to minimize the total completion time while assigning jobs to unrelated parallel machines with sequence-dependent preparation times. The algorithm establishes a threshold for improving the Ants (solutions) to select only promising ants for the improvement phase, thereby reducing the computational effort performed by local search operators. The proposed ACO algorithm maintains a basic structure and could be extended to solve other scheduling problems. A set of test instances available in the literature has been used to validate the efficiency of the proposed methodology. In addition, the results have been compared with the best previously published works. The ACO algorithm improves 30% of the best-known solutions (BKS) and reaches 30% of the BKS. The results show that the average performance of the ACO algorithm exceeds the average performance of the methods used by the best previously published works for the UPMSPSDST.

The Flexible flow shop (FFS) is defined as a multi-stage flow shops with multiple parallel machines. FFS scheduling problem is a complex combinatorial problem which has been intensively studied in many real world industries. This review paper gives a comprehensive exploration review on the FFS scheduling problem and guides the reader by considering and understanding different environmental assumptions, system constraints and objective functions for future research works. The published papers are classified into two categories. First is the FFS system characteristics and constraints including the problem differences and limitation defined by different studies. Second, the scheduling performances evaluation are elaborated and categorized into time, job and multi related objectives. In addition, the resolution approaches that have been used to solve FFS scheduling problems are discussed. This paper gives a comprehensive guide for the reader with respect to future research work on the FFS scheduling problem.