In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.

Supply chain design (SCD) is highly addressed in the operational research. Moreover, sustainability plays a significant role in the supply chain performance. This paper gives a literature review on the subject of sustainable supply chain design (SSCD) for perishable products during 2014-2020. A summary table is developed to compare between the different research papers to provide a deeper understanding for the literature. The papers are compared according to different classifications such as: perishability, sustainability, objective functions, modelling approaches, software tools used and also the field of application. Finally, a list of potential issues for future research opportunities is recommended.

Integrating high-performance computer vision (CV) into Industry 4.0 environments remains a challenge due to the computational disparity between state-of-the-art (SOTA) models and resource-constrained edge hardware. This study proposes a hardware-aware optimization framework designed to bridge this gap, focusing on real-time object detection for high-speed, omnidirectional conveyor systems. Unlike conventional benchmarking, the proposed framework employs a multi-stage optimization pipeline—integrating backbone refinement, hyperparameter tuning, and quantization—to transition diverse architectures from baseline configurations ($M_{base}$) to hardware-optimized variants ($M_{opt}$).The framework’s efficacy is validated using a custom-built standalone experimental platform detecting package features, brands, and disruptions on an omnidirectional-wheeled conveyor. A comprehensive comparative analysis is conducted across a heterogeneous edge ecosystem, including the NVIDIA Jetson Nano (GPU), Raspberry Pi 4 (CPU), and Google Coral (TPU). Our findings demonstrate that through systematic tuning, the YOLOv10n variant emerged as the superior architecture, achieving a precision of 98.1% and an mAP_50:95 of 81.22%. Post-deployment characterization reveals that the optimized YOLOv10n model on the NVIDIA Jetson Nano achieved a peak inference speed of 25 frames per second (FPS), successfully striking the “Pareto-optimal” balance between predictive accuracy and real-time processing. The primary contributions of this work include a reproducible optimization methodology, a comparative performance map across three distinct hardware backends, and the release of a specialized industrial conveyor dataset.

Over recent years, location-routing problems have become popular, since they tackle multiple major decisions in supply chains. With the focus now on sustainable supply chains, the problem has become sought-after, with the emphasis on complying with goals set by world leaders such as complying with environmental rules and social equity. As a result, this has opened multiple research directions within the location-routing problem. In the literature, the focus has merely been on two of the sustainability pillars, the environmental and economic pillars, with no integration of the social pillar. In this article, the aim is to integrate the social pillar alongside the environmental and economic ones by modeling the system as a capacitated two-echelon location-routing problem tackling multiple scenarios. Under the umbrella of optimization technology, the algorithm used to solve the problem is a genetic algorithm. This article also demonstrates the process of designing the experimentation phase and selecting variables aiming to fine-tune the models. Multiple parent selection methods and crossover methods were tested, among other variables. The algorithm has proven its success in finding a near-optimum value when compared to the benchmark solution, with an error less than 0.05%. Tournament has performed better as a parent selection method in contrast to stochastic universal sampling, and has proved to be more stable in the face of the stochastic noise induced in the models. This study shows that the social pillar, like the other two pillars, can be integrated in the location-routing problem at an extensive level, beyond what is normally implemented.

The world is not the same since late December 2019 when Coronavirus pandemic started to emerge in Wuhan, China and soon it began to spread all over the world affecting countries in Asia, Europe, North America and even Africa. Accordingly it was declared by the WHO as a pandemic in the 11th of March 2020 which raised the emergency of public health on an international basis. Although the supply chain has faced lots of epidemics and pandemics in the last decades, but COVID-19 is said to be unique in its effect. Since the effect of the COVID-19 pandemic on the supply chain was not studied from change management perspective. Accordingly the current paper tried to understand the degree of changes that the pandemic resulted in within supply chains in the light of the organizational change management literature and punctuated equilibrium theory. Data was collected by conducting semi-structured interviews with supply chain managers in different organizations. The results showed that the degree of change has two dimensions, the first is planned or emergent change, and the second one is that change can be developmental, transformational, or transitional.