The selection of manufacturing processes for a given application is a complex problem of multicriteria decision‐making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision‐making approach from the list of available methods for a given application is a difficult task. This work suggests a methodology to assess different selection approaches, which are the technique for order of preference by similarity to ideal solution (TOPSIS), analytic hierarchy process (AHP), and VIKOR: stepwise procedure. This valuation was done depending on the following factors: number of alternative processes and criteria, agility through the process of decision‐making, computational complexity, adequacy in supporting a group decision, and addition or removal of a criterion. A case study in this study was presented to analyse the evaluation methodology. The criteria used to evaluate and identify the best manufacturing process were categorized into productivity, accuracy, complexity, flexibility, material utilization, quality, and operation cost. Five manufacturing processes were considered, including gravity die casting, investment casting, pressure die casting, sand casting, and additive manufacturing. The results showed that each approach was suitable for the problems of manufacturing process selection, in particular toward the support of group decision‐making and uncertainty modelling. Manufacturing processes were ranked based on their respective weights for AHP, TOPSIS, and VIKOR, and sand casting is the best. In terms of computational complexity, the VIKOR method performed better than TOPSIS and AHP. Moreover, the VIKOR and TOPSIS methods were better convenient to the selection of manufacturing processes for agility during the process of decision‐making, the number of alternative processes and criteria, adequacy in supporting a group decision, and addition or removal of a criterion.

Deadlock control approaches based on Petri nets are usually implemented by adding control places and related arcs to the Petri net model of a system. The main disadvantage of the existing policies is that many control places and associated arcs are added to the initially constructed Petri net model, which significantly increases the complexity of the supervisor of the Petri net model. The objective of this study is to develop a two-step robust deadlock control approach. In the first step, we use a method of deadlock prevention based on strict minimal siphons (SMSs) to create a controlled Petri net model. In the second step, all control places obtained in the first step are merged into a single control place based on the colored Petri net to mark all SMSs. Finally, we compare the proposed method with the existing methods from the literature.

Supplier selection problem has a major regard in terms of the performance of supply chain of an organization. Several various approaches were proposed, including the analytic hierarchy process, fuzzy analytic hierarchy process, and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS). However, no comparative researches of these three approaches related to the supplier selection problem have been carried out. Therefore, this article proposes a methodology to evaluate the selected approaches. The evaluation was conducted based on the following factors: agility during the decision process, computational complexity, number of criteria and alternative suppliers, and adequacy in supporting a group decision. The methodology is implemented in X company. The results show that each approach is convenient to the supplier evaluation and selection problem, particularly toward the support of group decision-making and uncertainty modeling. In terms of computational complexity, analytic hierarchy process performs better than fuzzy TOPSIS and fuzzy analytic hierarchy process. Moreover, the fuzzy TOPSIS approach is better suited to the supplier evaluation and selection in terms of agility during the decision process, the number of criteria and alternative suppliers, and the adequacy in supporting a group decision.

The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature.

Previously, different deadlock control strategies for automated manufacturing systems (AMSs) based on Petri Nets with reliable resources have been proposed. However, in real-world applications, resources may be unreliable. Therefore, deadlock control strategies presented in previous research studies are not suitable for such applications. To address this issue, this paper proposes a novel three-step deadlock control strategy for fault detection and treatment of unreliable resource systems. In the first step, a controlled system (deadlock-free) is obtained using the “Maximum Number of Forbidding First met Bad Markings Problem 1” (MFFBMP1), which does not consider resource failures. Subsequently, all obtained monitors are merged into a single monitor based on a colored Petri net. The second step addresses deadlocks caused by resource failures in the Petri net model using a common recovery subnet based on colored Petri nets. The recovery subnet is applied to the system obtained in the first step to ensure that the system is reliable. The third step proposes a hybrid approach that combines neural networks with colored Petri nets obtained from the second step, for the detection and treatment of faults. The proposed approach possesses the advantages of modular integration of Petri nets and can also learn neurons and reduce knowledge, similar to neural networks. Therefore, this approach solves the deadlock problem in AMSs and also detects and treats failures. The proposed approach was tested using an example from literature.

Various deadlock control policies for automated manufacturing systems with reliable and shared resources have been developed, based on Petri nets. In practical applications, a resource may be unreliable. Thus, the deadlock control policies proposed in previous studies are not applicable to such applications. This paper proposes a two-step robust deadlock control strategy for systems with unreliable and shared resources. In the first step, a live (deadlock-free) controlled system that does not consider the failure of resources is derived by using strict minimal siphon control. The second step deals with deadlock control issues caused by the failures of the resources. Considering all resource failures, a common recovery subnet based on colored Petri nets is proposed for all resource failures in the Petri net model. The recovery subnet is added to the derived system at the first step to make the system reliable. The proposed method has been tested using an automated manufacturing system deployed at King Saud University.