## 1 Introduction
To develop a self-organized and self-adaptive CPS, let us first review the architecture of a CPS. The design of a CPS can be divided into five levels [24], including the connection level, conversion level, cyber level, cognition level and configuration level. The collection of information from sensors, controllers or related systems in CPSs is conducted at the connection level. The transformation of the raw data into useful information is performed at the conversion level. The cyber level plays a central role in the architecture since it enables a CPS to interact with other CPSs and the environment. The cognition level constructs a virtual model based on the information gathered from all the components of the CPS. Knowledge of the CPS is generated at the cognition level to support decision-making. The configuration level deals with the self-configuration and self-optimization issues in the CPS. Planning and scheduling are two important issues in the Industry 4.0 literature [25; 26] and are at the configuration level in CPS design [27]. The scheduling issue has been studied in [3; 10; 11] under the premise of pre-specified workflows of processes and capabilities of resources in CPSs. However, the workflows of processes and capabilities/efficiency of resources may change dynamically. In addition, the energy consumption factor is not considered in [3; 10; 11]. Developing a method to determine the configuration of workflows and resources is urgent. Process planning aims to determine the configuration of the production process and the associated resources involved. It is a preliminary step for developing a self-organized and self-adaptive CPS and solving the subsequent scheduling problem [28]. The planning of CPSs with logistic models has been studied in [29]. The process planning issue in CPS design has been studied in [30]. However, the sustainability factor of CPSs is not considered in [30]. This paper focuses on the problem of determining the configuration of workflows/resources to compose processes with desirable properties in planning sustainable CPSs. Time and energy consumption are two important factors in developing production processes for sustainable CPSs. The former focuses on the issue of meeting the order demand in a timely manner, whereas the latter is directly related to the sustainability issue in the manufacturing sector. Therefore, time and energy consumption factors must be taken into consideration in the production planning processes in sustainable CPSs. In addition to time and energy consumption factors, planning production processes in CPSs must be based on the operational requirements/constraints of the processes and the real-time information available from the IoT infrastructure. The planning of processes in sustainable CPSs is an important issue to timely meet demand and sustainability development goals. The ever-shrinking life cycle of technologies leads to frequent changes in processes. This makes the planning of processes a challenging task. To plan processes in the presence of process changes, an effective planning method must be developed to automate the planning task. A CPS consists of entities in cyber space and physical space. Cyber World models are used to capture the capability of entities in the CPS. To address the planning problem, proper modeling tools must be used to construct the Cyber World models for the CPS. Petri nets are applied to model and manufacturing systems [31] due to their capability to capture discrete events, as well as synchronous/asynchronous and concurrent operations in production systems. In particular, Discrete Timed Petri Nets (DTPNs) have been applied to represent the Cyber World models for the CPS, solve the scheduling problems [3,10] and assess the influence of failures on the CPS [11]. In this study, DTPNs are adopted as the Cyber World models for the CPS. However, the problem addressed in this study is to determine the DTPNs necessary to meet the manufacturing requirements of sustainable CPSs instead of analyzing given DTPNs.