The flexible job shop scheduling problem for processing machines and transportation vehicles (FJSP_PT) has garnered significant attention from academia and industry. Due to the inclusion of transportation vehicle scheduling in the scheduling problem of flexible manufacturing systems, solving FJSP_PT becomes more challenging and significantly more practically relevant compared to the flexible job shop scheduling problem. We summarize the assumptions, constraints, objective functions, and benchmarks of FJSP_PT. Then, statistical analysis is conducted on the literature up to 2023, including journals, number of articles published each year, and solution algorithms. We analyze recent literature on FJSP_PT, categorizing it based on algorithms into exact algorithms, heuristic algorithms, meta-heuristic algorithms, and swarm intelligence based algorithms. Finally, the research trends and challenges faced by FJSP_PT are summarized.

The flexible job-shop co-scheduling problem (FJCSP) for processing machines and automated guided vehicles (AGVs) in a flexible manufacturing system (FMS) has attracted more attention with the aim of improving production efficiency. In FMS, AGVs in charge of transporting jobs realize the flexible linkage of operations between different processing machines. The added interdependence between transporting and processing tasks brings more difficulties than the traditional flexible job-shop scheduling problem (FJSP). In this paper, the mathematical model of FJCSP is formulated to minimize the makespan. Considering the feature similarity of FJCSP with FJSP and AGV-routing problem in different cases, a multi-view modeling-based hybrid algorithm consisting of an estimation of distribution algorithm (EDA) and an ant colony optimization (ACO) is proposed. In EDA, a probability model abstracts the information in superior solutions about the operation sequencing and the rule selection for scheduling machines and AGVs. In ACO, a job-path pheromone model and an AGV-path pheromone model are designed to jointly select the job-machine-AGV combination with shorter processing time and transportation time. In the proposed hybrid algorithm, EDA and ACO generate solutions independently and achieve cooperation by sharing elites. An adaptive parameter is designed to regulate the use of the two methods to adapt to the varying demands of multi-view modeling in different cases and search stages. Furthermore, a local search with a three-layer operator based on the critical path method is proposed to balance exploration and exploitation in solution space. Finally, computational experiments involving a case study verified the advantage of the multi-view modeling-based hybrid algorithm in comparison with the state-of-the-art approaches.Note to Practitioners—This paper was motivated by the optimization problem of scheduling machines and automated guided vehicles (AGVs) in flexible manufacturing system (FMS). In FMS with AGVs, the transportation stages for jobs by AGVs significantly impact the overall production efficiency of the FMS and cannot be overlooked. This paper suggested a hybrid evolutionary algorithm using an estimation of distribution algorithm (EDA), an ant colony optimization (ACO) and a local search algorithm based on the critical path method. In the proposed hybrid algorithm, an adaptive parameter is introduced to regulate the utilization of EDA and ACO in generating a new population. This paper presents a mathematical characterization of the scheduling problem and subsequently outlines the step-by-step design of the hybrid algorithm. Computational experiments, including a case study, demonstrate that the hybrid algorithm exhibits adaptability to various instances and outperforms state-of-the-art approaches.