Search

The online version of the original article can be found at https://doi.org/10.1007/s10479-022-05005-7Correction to: Annals of Operations Researchhttps://doi.org/10.1007/s10479-022-05005-7This correction is published as several notations were overlooked by typesetter during proofing.Original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Fariba Goodarzian; Vikas Kumar and Peiman GhasemiReported by Author; Author; Author [Extracted from the article]

In this study, we present a novel fuzzy control strategy that considers the influence of lead times on nonlinear supply chain management (SCM) systems. The authors have constructed a fuzzy control model for an SCM model based on nonlinear phenomena with lead times, drawing inspiration from Takagi-Sugeno's (T-S) fuzzy control system. Additionally, the authors have designed a fuzzy H ∞ control approach to mitigate the effects of lead times, switch between sub-models, and address customers' stochastic requests, all while considering the concept of maximal overlapped rules groups. The control of nonlinear supply chain management is implemented in a manner that ensures asymptotic stability and facilitates smooth transitions from one subsystem to another. The introduction of the membership function into the fuzzy system results in reduced fluctuations in the system's variables. The key contributions of the research include the design of a fuzzy H ∞ control approach, leveraging maximal overlapping-rules group (MRRG) to ensure stability through localized definite positive matrices identification. This strategy achieves two primary objectives: asymptotic stability of the supply chain system and smooth switching between nonlinear SCM elements. The authors demonstrate the effectiveness of their proposed method through comprehensive comparisons with the fuzzy H ∞ control technique. The study provides a valuable insight into managing lead times in SCM through dynamic and stable control methodologies. Finally, a comprehensive comparison is provided between the suggested H ∞ management approach and the fuzzy H ∞ control strategy. This comparison is demonstrated through two-stage nonlinear numerical simulations, keeping SCM lead times in mind. Movement results are presented to illustrate the effectiveness of our suggested algorithm. • A fuzzy control for mitigating the effects of lead times. • Considering stochastic demands from customers. • MRRG in T-S fuzzy system linked to control ensures stability. • Proposed control ensures smooth switching in nonlinear supply chain elements. • Linked to fuzzy H_∞ control, our method is paralleled for effective demonstration. [ABSTRACT FROM AUTHOR]

Recently, sustainable supply chains have emerged to emphasize the importance of social and environmental concerns along with economic factors in supply chain management. In this context, there is a necessity for mathematical models that indicate environmental aspects and the social effects of the supply chain network. In the present study, a new multi-product production, distribution, and transportation, in which the economic, environmental, and social effects under CO2 emission to fill this gap are presented. Additionally, service technology under various criteria in the social aspects and time window for the earliest and latest arrival time of products to main distribution centers are provided. Also, other novelties of this paper, the environmental effects to open main and local distribution centers, warehouses, and pharmacies are considered as environmental effects. So, the main contributions of this paper are to develop a multi objective, dynamic production and distribution planning using time windows in service technology of sustainable pharmaceutical supply Chain Network. In this regard, the mathematical model is formulated as a mixed-integer linear programming model. Moreover, a Benders decomposition approach is appropriately extended to solve the presented model. In the proposed approach, the problem is decomposed into two models of sub-problem and a master. The master problem is developed by means of preprocessing and valid inequalities. The general and relative efficiency of the model and approach is experimentally assessed. The pharmaceutical production and distribution system of France is considered as a real case study in this paper. Eventually, the results indicate that the proposed approach considerably outperforms, and the efficiency of the developed model is verified through a set of sensitivity analyses. [ABSTRACT FROM AUTHOR]