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As globalisation and competition grow rapidly, adopting a flexible manufacturing philosophy becomes more critical for manufacturers. Cellular Manufacturing System (CMS) is among one of the most favourable ones which has been deeply studied in academia since this philosophy was coined in 1925. To design the manufacturing system efficiently and effectively, the management team needs to have a comprehensive representation of the definitions, classifications, solution approach to cellular manufacturing design problems. In this study, exact methods, heuristic approaches, metaheuristic techniques, and artificial intelligence strategies for cellular manufacturing design are reviewed. A comparison of solution approaches is discussed and based on this analysis, some suggestions for future research are proposed. Moreover, we tried to highlight the importance of investigating sustainability decisions related to CMS problems, and integration of supply chain decisions with CMS decisions which have not been sufficiently studied to identify a research direction for researchers to model and analyse those issues. Finally, we briefly discuss the placement of CMS as one of the major production systems in the industry 4.0 evolution paradigm. Reviewing the adoption of industry 4.0 techniques by CMS sheds light on the contribution of methodological approaches in transition to intelligent CMS. [ABSTRACT FROM AUTHOR]

Flowshop scheduling methods that appear in the literature assume that products are processed in either batch mode or cyclic mode. In batch mode, a product starts after another product is completely processed. On the other hand, in cyclic mode, products are run in mixed mode with unit transfer. In this paper, a new scheduling method, the batch-cyclic scheduling method, is proposed to schedule the products. In the proposed method, multiple sets of products (Cyclic Sets) are selected first and then products in each cyclic set are run in mixed mode. Overall, the general solution approach consists of three phases: (1) family formation based on processing similarity; (2) subfamily formulation based on a processing time dissimilarity (proposed mathematical model); (3) product scheduling using the batch-cyclic method (proposed mathematical model). As in the cyclic method, batch-cyclic scheduling is feasible when setup times are negligible. Due to the lack of work in this newly proposed batch-cyclic method, it is compared with the batch method. Additionally, the performance of the three-phase approach is compared with the two-phase approach using 27 scenarios. The results show that the proposed three-phase approach minimises the number of machines and lowers makespan when the bottleneck machine shifts. [ABSTRACT FROM AUTHOR]

This paper uses a Genetic Algorithm (GA) to reduce total tardiness in an identical parallel machine scheduling problem. The proposed GA is a crossover-free (vegetative reproduction) GA but used for four types of mutations (Two Genes Exchange mutation, Number of Jobs mutation, Flip Ends mutation, and Flip Middle mutation) to make the required balance between the exploration and exploitation functions of the crossover and mutation operators. The results showed that use of these strategies positively affects the accuracy and robustness of the proposed GA in minimizing the total tardiness. The results of the proposed GA are compared to the mathematical model in terms of the time required to tackle the proposed problem. The findings illustrate the ability of the propounded GA to acquire the results in a short time compared to the mathematical model. On the other hand, increasing the number of machines degraded the performance of the proposed GA. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies a 3-dimensional pallet loading problem considering interlock stacking pattern, box dimensions, humidity, and storage time, where overlapping and overhanging are not allowed. Despite the importance of this problem in the literature, our work provides the first method that considers the environmental conditions such as 1) storage time and 2) humidity, and their tremendous impacts on the strength of the boxes, as has been observed widely in the DHL supply chain. Design/methodology/approach: This paper proposes a two-phase heuristic algorithm to solve a 3-dimensional pallet loading problem under real conditions (relative humidity, and storage time) considering interlock stacking patterns, where overlapping and overhanging are not allowed. In phase 1, the horizontal layer configuration is determined by block techniques. Three types of horizontal layers are created based on box dimensions perpendicular to the base. In phase 2, a novel mathematical model is propounded to improve the pallet volume utilization, and stability considering the pallet's maximum allowable height and weight, and the dynamic compression strength of boxes. The dynamic compression strength of boxes is calculated by the modified McKee formula. Two performance measures, pallet volume utilization, and stability (load height) are utilized to evaluate the performance of the proposed heuristic algorithm in real-world instances (DHL Supply Chain). Findings: The results illustrated that the dynamic compression strength of boxes decreases as the relative humidity and storage time increase. The load height changes dynamically along with box dimensions, box alignment, direction, relative humidity, and storage time. Increasing relative humidity and storage time and applying an interlock stacking pattern reduce the pallet utilization, however, enhance the pallet stability. Finally, the proposed heuristic algorithm's efficacy increases as the identical box dimensions' heterogeneity increases. Originality/value: It is believed in the supply chain where these characteristics are observed, the implementation of the heuristic algorithm will help them improve the pallet volume utilization and stability. [ABSTRACT FROM AUTHOR]

Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]