. The use of e nd-of-life and end -of-use products to recover parts and raw materials can mitigate the severity of the increasing price of raw materials, the dis ruption of global supply chains for critical raw materials (e.g., chips and rare earth elements), and reduce the environmental impacts. Furthermore, circular economy strategies can improve scheduling by shorten ing the completion times of the components . This paper investigates the effects of implementing circular economy strategies (repair, reuse, and re -manufacturing) at the scheduling level in a manufacturing system involving disassembly, re -manufacturing, and assembly operations . A set of eight priority rules modify the job priority and the strategy implementation . The results show th at including circular economy strategies through disassembly can reduce the makespan , but scheduling is pivotal to manag ing the frequent change s in the quality of end -of-life produ cts and their volumes and the current production order mix . Introduction Industry 4.0 (I4.0) and Circular Economy (CE) paradigms have been leading the innovation in manufacturing companies and scholars’ interests for at least a decade. Furthermore, current research highlights the enabling role of I4.0 technologies in implementing CE practices while addressing the manufacturing challenges of mass customisation macrotrend [1]. I4.0 provides enabling technologies for CE from a twofold point of view: (i) advanc ed manufacturing systems with a high degree of flexibility and reconfigurability, (ii) digitalisation and data -driven approaches to allow the design and management of more complex systems [2]. Adopting I4.0 technologies and moving to the CE paradigm is important for the international competitive advantage of manufacturing companies [3]. The supply of raw materials has become critical [ 4], especially for importing countries like Italy [ 5], because of the disruptions of global supply chains that began with t he Covid19 pandemic and propagated due to the recent geopolitical conflicts [ 6]. At the same time , the advent of mass customisation and the transition towards sustainable development are increasing product varieties, fluctuations in product demand, and the need to increase product life -cycle through, for example, repairing and re -manufacturing [ 7]. Manufacturing companies, especially Small and medium -sized enterprises (SMEs ), struggle to address the increasing complexity of the demand and supply sides. Moreover, there are some barriers to the effective implementation of I4.0 , particularly its enabling role for the CE transition. The main three barriers are (i) the interoperab ility among different processes, (ii) the modelling of the processes and their integration to optimise the system, and (iii) the coordination and management of the entire manufacturing system and the digital counterparts that support it. [1]. CE actions ex ponentially increase the severity of these barriers because of the many cycles and flows added to the manufacturing system through the 9Rs (Refuse, Rethink, Reduce, Reuse, 2 Repair, Refurbish, Re -manufacture, Repurpose, Recycle, Recover) [ 8]. This complexity increases the risk of using new tools and machines in an obsolete way [ 9]; for example, optimising the stand - alone processes can result inefficient from the point of view of the entire system. A wider and more flexible implementation of CE strategies within manufacturing system s involves the introduction of disassembling operations [10]. Disassembly enhances the reuse and re - manufacturing of components from recovered end -of-life and end-of-use products , their repair, or their recycling [11]. However, disassembling operations may jeopardise manufacturing performance. For example, they lead to several flows of generally low -value items requiring space and generating holding costs, with different market demand s for each disassembled part [12]. In this context , production planning and control approaches are crucial , especially in the many available strat egies offered by the CE paradigm , combin ing technologies to optimise overall performance and overcoming challenges, also in system design [1 3]. Moreover, additional source s of uncertainty must be considered, such as quality issues, low value of recovered c omponents, uncertainties in quality and volumes of provided products , and the fact that disassembly operations are mainly performed by workers rather than robots [1 4]. In the literature, s cheduling problems involving disassembly, re-manufacturing, and reassembly operations focus on reduc ing tardiness and makespan [15] and on finding the minimal operation sequence to disassemble returned end -of-life or use products ( namely, returns ) [16]. However, scheduling problems with disassembl y are NP -hard; small problems can be solved by finding optimal solutions , while industrial -scale problems require heuristic and approxi mated approaches [17]. Heuristics have been applied to families of products [ 18], multi -objective stochastic scheduling problem s [19], and multi -product scheduling problems [2 0]. Priority rules are mainly used for scheduling problems because of ease of understanding and implementation and good performance [21], especially when calibrated on every single workstation [ 22]. From the production planning aspect , the literature is focused on the solution approach, while , from the disassembly point of view, the literature investigates the technical and economic performance. Instead , the literature neglects the intersection with CE strategies and approaches intertwined with sustainability that require a simultaneous multi -dimensional assessment [ 23]. This paper investigates th e impact of including CE strategies in a scheduling problem for a system characteri sed by disassembly, re -manufacturing, and assembly workstations , including quality control and returns repair . The schedules are identified by applying eight priority rules derived from t he liter ature and combined to deal with the CE strategies. Each priority rule is applied to a scenario with specific conditions of finished product demand and volumes of reparable and irreparable end -of-life and use products, which are exploited to recover parts and components. Problem description The inclusion of CE strategies and disassembly operations complicate s the scheduling problem . Apart from the standard scheduling decisions, the inclusion of CE strategies also includes decisions about the strategy each job (products, returns, or components) must follow . The impact of these further decisions on manufacturing performance is investigated in t his paper by consider ing a realistic manufacturing system based on a structure diffused in the literature that assumes three main production areas: disassembly, processing, and reassembly. The s cheduling problem includes the following further decisions : (i) allocating recovered products to repair or disassembly workstations , (ii) deciding which of the components recovered from the disassembly will be re-manufactured rather than reused as is. Furthermore, the system must deal with increased system variability because the availability of the return depend s on the quantity of end-of-life and use products disposed of by consumer s. Also , the quality level of the returns can make them irreparable or particularly long and expensive t o recover 3 their components. Therefore, to consider the impact on manufacturing performance of these sources of uncertainty, a scenario analysis investigates (i) different combinations of production orders and (ii) quality of returns, and (iii) different quantit ies of returns compared to the total product ion orders . Finally , priority rules are investigated to manage synchronisation between production orders and returns disassembly in the different scenarios . In fact, d isassembly operations may fast saturate buffers with components of low values required by production activit ies at different times and in different quantities . Fig. 1 shows the studied system implementing the following CE strategies: repair, re - manufacturing, and reuse. It consists of the following five single -server stations , identified by grey circles in the figure : disassembly (D), manufacturing (M), assembly (A), quality inspection (Q), and repair (R). Fig. 1. Manufacturing system with five single -server workstations for the following operations: disassembly (D), manufacturing (M), assembly (A), quality inspection (Q), and repair (R). The proposed system produces two types of finished products (FP): type N is a new top premium product, while type O is the basic version. Unlike FP-O, production orders for FP -N can be satisfied through repaired returns (REP). There are two types of return s (green arcs in Fig. 1) : the first can be repaired (R), while the second (B) can be disassembled . The recovered products of type B are stored in a buffer (I B) and provide components (C) and raw materials (R). Components C are supplied by other companies (virgin resources) or recovered from the disassembled products and reused without any processing activity. At the same time, raw materials R are provided by other companies (virgin resources) or retrieved from the disassembled returns, but they are re - manuf actured within the system to obtain manufactured components of the set M. The FP-N and FP-O are produced by assembling components from set C and manufactured parts from set M. Raw materials, components, and manufactured parts are clustered into three group s: (a) parts and components necessary for FP-N, i.e., R N, CN, and M N, respectively; (b) raw materials, parts, and components necessary for FP-O, i.e., R O, CO, and M O, respectively; c) raw materials, parts, and components necessary for both products, i.e., R B, CB, and M B, respectively. Assume that component C N can be reused for FP-N without processing , or it can be re-manufactured to become a part of M O. 4 FP-N, FP-O, and R are subjected to a quality inspection that: verif ies the quality leve l for items FP-N and FP-O and identif ies the necessary tasks for repairing items R. Design of Experiment Discrete Event Simulation is used to study the system in various scenarios. T he scenarios are characterised by various proportions of the two types of returns and eight priority rule s to find the sequence of jobs processed in each of the five workstations. The simulation model is developed and evaluated in Arena 16.2. The experiment investigates the makespan because of its importance in manufacturing since it represents the time required to satisfy all the production orders . System processing times are proportionally reduced together with job arrival time to have a sufficient number of observations in a limited amount of time (1 -2 work sh ifts of 8 hours) to improve comprehension of CE strategies on makespan. Assembly and quality control are the most time -consuming activities because they include setups , packaging, and small reparations. At the same time, the full return disassembly is considered a destructive activity to quickly recover key components such as chipboards, metallic frames and bodies, and small electric motors. In Table 1 , the fixed parameters are reported : the processing times of workstat ions and the total number of production orders that must be satisfied. Table 1. Fixed parameters of the simulation model. Param eter Value Description 𝑡𝐷 0.3 [min ] Processing time of disassembly workstation 𝑡𝑀 0.44 [min ] Processing time of manufacturing workstation. 𝑡𝐴 0.7 [min ] Processing time of assembly workstation. 𝑡𝑄𝐼 0.67 [min ] Processing time of the quality inspection workstation. 𝑡𝑅 3 [min ] Processing time of repairing workstation. N+O 600 [u] The total number of production orders of both types. The intertwined effects of variability and system characteristics can influence the makespan . Therefore the following three factors have been considered : • R/B. Ratio between the end -of-life and use of recovered products that can be repaired (R) out of those that can be disassembled (B): 1, 0.5, and 1.5. • (DN+DO)/(R+B). Ratio of the demand of finished products DN and DO out of the recovered reparable and irreparable end -of-life and use products: 1, 0.5, and 1.5. • DN/DO. Ratio between the demand for the premium level products of type FPN (DN) of the basic products of type FPO (DO): 1, 0.5, and 1.5. For each combination of factors, eight p riority rules are tested to model priority to one finished product or the other, the priority to reduce pressure on buffers, the priority in repairing strate gy, or balance priority to all finished products and strategies : Rule 1. Priority in all the workstations to the operations for FP-N and repairing activities to satisfy production orders. Rule 2. Priority in all the workstations to the operations for FP-O and repairing activities to satisfy production orders. Rule 3. Priority in manufacturing and assembly workstations to the operations that decrease buffer levels, while in quality inspection, FP-N has higher priority than FP-O, while repairing activities have the highest priority to satisfy production orders. Rule 4. Priority in manufacturing and assembly workstations to the operations that decrease buffer levels, while in quality inspection, FP-O has higher priority than FP-N, while repairing activities have the highest priority to satisfy production orders. 5 Rule 5. Same priority in all the workstations to both production orders by serv ing the type with the maximum number of remaining orders. The highest priority is given to repairing activities. Rule 6. Priority in manufacturing and assembly workstations to the operations that decrease buffer levels, balanced priority in quality inspec tion and maximum priority to repairing activities. Rule 7. Same priority in all the workstations. Rule 8. Priority in manufacturing and assembly workstations to the operations that decrease buffer levels, balanced priority in quality inspection. Results a nd discussion According to CE strategies, finished products can be repaired, reused as is or disassembled to recover components and raw materials, which, in turn, can be reused or re-manufactured. However, the impact of introducing these st rategies on the required time to complete a set of given production orders ( makespan ) is unclear, and it also depends on other system characteristics (variability in production order types and in the volumes and the quality level of available returns) . Fig. 2 shows the effects of the considered characteristics on the makespan . Specifically, from left to right: the proportion of returns devoted to the disassembly out of those repair ed, the proportion of production orders covered by returns, the number of production orders that can be satisfied by repaired products out of the other production order type, and the eight priority policies. The number of returns can increase the makespan from around 480 minutes (one production shift) to 640 minutes (two production shifts). Fig. 2. The main effect graphs show the effects of the single factors on the makespan . Repairable returns have a s hort flow time within the system because they only require quality control and repair operation s to satisfy production order s rather than disassembly, re - manufacturing, and reassem bly. In fact, the presence of more production orders satisfiable through repaire d returns reduces the makespan (DN/DO = 1.5). However, on average, the disassembly strategy, which provide s components and resources for both product types, led to a lower makespan than the increasing repair strategy (B/R = 0.5) . In general, priority rules that aims to a balanced satisfaction or production order types (5 and 7) lead to smaller m akespan ; hence, they are probably not the best strategies at all , but they can guarantee a good performance under many different conditions . 6 Fig. 3 shows the pairwise interactions on the makespan by highlighting that all the factors influence it since each level of the factor has a marker in a different level of the makespan . Also, there is an amplifying effect on the makespan between the propo rtion of return types and the number of returns (first frame in the top-left part of the figure ). Therefore, in the case of many returns (1 .5) and reparable returns larger than the others, the makespan increases (positive slope of the blue line). Fig. 3 . The pairwise effects of the four factors influencing the makespan . The pairwise interaction plot is not able to cap ture the real effe ctiveness of priority rules since it considers an average makespan of all the factors except for the two whose int eraction is investigated. However, from the interaction between priority rules and production order mix (DN/DO, the last frame in the bottom), it can be seen that priority rules 1 ( higher priority to FP - N) and 2 (high priority to FP -O) are not so bad how t hey appear in the other frames . Still, they are effective when applied during specific production order mixes. Conversely, priority rules 5 and 7 appear much more flexible since , on average , they led to a low makespan . In contrast, priority rules that focus on buffers (3, 4, 6, 8) mitigate the extreme effects of unbalanced rules towards one specific production order (3 and 4 lead to more robust and on average makespan than 1 and 2) , but, when applied to the entire system they are wi dely influenced by return availability, returns quality, and production mix (6 and 8 lead to different result for different colour lines) . Conclusion This paper investigates the impacts on the makespan of implementing some of the Circular Economy (CE) strategies ( reuse, re -manufacturing, and repair ) in a manufacturing system that exploits end -of-life and end -of-use products to recover components and satisfy the demand for new finished products . A scenario analysis evaluate d through the Discrete Event Simulation model has been created to assess eight priority rules applied to the same system with different characteristics in production order types, types and numbers of returned end -of-life and use products. Systems that include disassembly operations coupled with processing and reassembly are spreading because of the new laws and regulations regarding CE, the disruption of global supply 7 chains, and the increasing lead times in the supply of critical raw materials. Therefore, the discussion of the results of the paper could provide technical and operational insights regarding the characteristics of manufacturing systems that foster or dampen the transition towards the manufacturing paradigm that includes CE strategies. The results show that synchronising CE strategies (the numbers and the types of recovered products) with the production orders (numbers and types) reduces the makespan . However, CE strategies make short -term production planning more complex , and scheduling is important to improve technical performance . Furthermore, potential disequilibria between the types of returns and the types of production orders can be mitigated through priority rules. Finally, priority rules deeply affect the system performance , and they must follow the frequent changes in the system condition since they are not robust to the high uncertainty levels considered in this paper . Future research will deepen the many critical issues related to the disassembly processes by intertwining them with the adoption of CE strategies . It will address other indicators , such as WIP and the total consumption of virgin materials . Also, other sustainable alternatives should be investigated, like industrial symbiosis. Acknowledgement This study was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next -Gener ationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11 -10-2022, PE00000004). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission c an be considered responsible for them.