# Reconfigurable Supply Chain Selection: Literature Review, Research Roadmap and New Trends
## 2 Literature Review
### Configuration Choice Approaches in a Reconfigurable Context
#### 2.3.2 Approaches Using Reconfiguration Indicators  
In the context of reconfigurable manufacturing systems and supply chains, several approaches were proposed for the selection of the optimal configuration according to several criteria. The authors of [30] applied the Non-Dominated Genetic Sorting Algorithm (NSGA-II) for the selection of the optimal configuration based on the total cost which consists of the production cost, maintenance cost and configuration change (reconfiguration) cost, as well as the production time and reconfiguration time. Within the same context, [31] proposed an approach to select a set of machines able to perform all the operations required to realize the production using a multi-objective approach based on two criteria, namely the total completion time and the perturbation caused by the unpredicted unavailability of selected machines. The authors of [32] developed a machine selection approach for RMS design to ensure the best process plan that provides customized production. Using NSGA, the authors proposed a multi-objective approach based on maximizing the system flexibility index and minimizing the total completion time.  
Based on the reduction of the reconfiguration effort, [PERSON] et al. [18] proposed a configuration selection approach with several aspects (arrangement of machines, equipment selection and assignment of operations) that consists in minimizing the effort of reconfiguration over a planning horizon based on cost and availability criteria. The authors of [33] proposed a configuration selection approach by considering three criteria, namely cost optimization, machine capacity, and reconfiguration effort which refers to the effort of changing from one configuration to another by applying NSGA-II to determine the set of near-optimal solutions and the Technique for Order Preference by Similarity to Ideal Solution method to rank the pareto frontiers. In their proposed approach, [PERSON] and [PERSON] [19] also included cost, capacity and reconfiguration effort as part of the production line configuration selection. The authors of [34] introduced a new composite metric for product flow configuration selection for RMS that included nine relevant factors, namely investment cost, reconfiguration time, product lead time, throughput, reliability, system availability, operational capability and reconfiguration effort/smoothness.  
Despite their importance in selecting the optimal configuration, few works have focused on reconfigurability characteristics for configuration selection in the context of reconfigurable manufacturing systems and supply chains. The authors of [35] addressed the configuration selection problem to determine the optimal configuration based on four fundamental criteria, namely productivity, convertibility, scalability and cost which can be applied to other types of manufacturing systems. The authors of [36] proposed an approach to configuration selection in RMS based on their degree of reconfigurability at both the system and machine level measured according to three indicators: modularity, convertibility, diagnosability. In the same context, [PERSON] et al. [37] presented a comprehensive decision making approach for selecting an optimal configuration for the single part reconfigurableflow line based on cost, machine utilization, operational capacity, machine reconfigurability and configuration convertibility, which represent the selection criteria. Based on the six reconfigurability characteristics, [PERSON] et al. and [PERSON] et al. [20,26] proposed an approach to evaluate reconfigurability by formulating these characteristics mathematically. This approach allows to choose the most reconfigurable configuration objectively and from a quantitative evaluation, while considering the uncertainty in the decision-making process.  
Table 2 shows a classification of the proposed approaches in the context of configuration selection according to the application levels, criteria and methods used.