## 1 Introduction  
Nowadays, in order to survive in the competitive market, manufacturing enterprises have changed the mode of production from mass production to customized and on-demand production with small batches and short cycles. Flexible manufacturing systems (FMS) can produce a mid-volume and a mid-variety of products and allow rapid changes between them [1]. However, due to external and internal fluctuations, the machining environment changes dynamically and the optimal process plan and schedule becomes less efficient or even infeasible. In order to solve the aforementioned problems, many research efforts have been made by using the advanced technologies, such as Internet of Things (IoT) [2], cloud computing (CC) [3], cyber-physical systems (CPS) [4], and service-oriented technologies (SOT) [5]. Based on these types of research and technology, real-time and multi-source manufacturing data and logistics data are easier to access than ever before [6]. The advances in IoT and CC have provided promising opportunities to further address the aforementioned problems. Firstly, real-time and accurate information of manufacturing things can be collected by using embedded devices and sensors, such as radio frequency identification (RFID) sensors, etc. Real-time information capturing and integration architecture of the internet of manufacturing things has been presented to support optimal control and decision during manufacturing execution [7]. Secondly, researchers and practitioners have proposed a variety of cloud services, ranging from product design, manufacturing, performance analysis, and exception diagnosis. For example, an architecture of scientific workflow management system based on a cloud manufacturing service platform has been proposed to formalize and structure distributed scientific processes [8]. An FMS is a computerized and highly automated production system that integrates a set of computer numerically-controlled machines and a material handling system [9]. In an FMS, the automated guided vehicle (AGV) is widely used for transportation among production cells and warehouses. Petri nets (PNs) are widely used to model, analyze, and control FMSs [10; 11] and other discrete event systems (DES) [12; 13] since they can well depict the relation between equipment status and operations. The simultaneous scheduling of different components of FMS has drawn research attentions in recent years. For instance, [PERSON] and [PERSON] investigated the simultaneous scheduling of machines and AGVs using a Petri net approach [14]. [PERSON] et al. studied the simultaneous scheduling of machines and tools in an FMS [15]. However, in real production, the manufacturing environment changes dynamically because of uncertainties and disturbances, which makes the schedule less efficient or infeasible [16]. Moreover, the scheduling tasks are complicated and time-consuming. With respect to the topic of production and logistics in manufacturing systems, a lot of research work has been done, such as simultaneous scheduling of production and logistics [17]. Only a few researchers have focused on works dealing with IoT-based production-logistics collaboration [18; 19]. Despite significant achievements, several challenges still exist in the production-logistics collaboration. These challenges are summarized as follows:  
1. How can the advantages of IoT and CC technology be leveraged to model the behaviors of key production and logistics equipment and make the equipment'smart? Generally, key equipment such as machines and AGVs simply execute the commands of the preset programs or workers. The equipment is not aware and cannot make decisions based on dynamic changes in the manufacturing environment. Thus, they cannot adjust the schedule according to real-time scenarios. 2. How can a production-logistics collaboration strategy be designed to undertake active response and self-organizing configuration? In traditional manufacturing systems, the behaviors of both machines and AGVs are event-driven.