Mohamed Nafea

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.

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.