A consideration of the integral variables of customer location, traffic flow, and road conditions to determine the best feasible delivery routes is a big challenge in Logistical operations. A poor routing strategy that delivers products places an ineffective gloss and eventually converts into high operating expenses, over-consumption of fuel, and shipment delays. The paper’s goal is to build a model for the logistics management of the company which aims for effective management of the truck allocation and vehicle routing using K-means clustering and TSP. K-means clustering is often used to classify the sites of delivery based on their closeness in space, hence simplifying the problem by reducing its dimensionality. The proposed algorithm considered customer location prioritization in deliveries, delivery task allocation, and truck allocation to enable timely delivery. Therefore, this paper presented a solution to enhance the logistics operations of beverage brand “KINZA” by optimizing its truck loading and delivery route. The model would ensure that each truck is able to travel optimally, with vehicle-routing algorithms applied in a way to avoid all unnecessary waste of time and distance. Finally, the main scope of this paper is to develop and design a dynamic logistics platform for the KINZA Company distribution network.

Additive manufacturing by direct metal fabrication represents one of the fastest-growing areas in material science and manufacturing. Modern manufacturing demands that parts be engineered to have high strength, be lightweight with complex geometrical details, and be suitable for operation upon completion. A very good example of such engineering-manufacturing involves the design and manufacturing of turbine blades for energy efficiency. On the other hand, topology-optimized lattice structures have huge potential and flexibility available to designers operating in the area of designing lightweight structures and high-strength ones at the same time, in contrast to solid form structures. The key issues involved in the research include designing graded density structures made from different lattice architectures for dense materials by characterization of the thermo-mechanical properties for a number of lattice settings in Gyroid, Diamond, Schwarz, Lidinoid, Split P, and Neovius lattices for varied parameters. This paper questions how appropriately the design structure functions in high-speed-rotating elements, such as turbine blades. The current research work will be aimed at the design, finite element analysis for simulation, and manufacturing through additive manufacturing of the turbine blades, considering several designs and lattice structures that satisfy the requirements of lightweight construction and high strength. A detailed preliminary design study has already been performed with the aim of justifying the idea presented in this paper and to create an initially validated basis. It therefore presents findings from the design of different lattice structures, supported by simulations that explain the potential, extent, and limitations of the proposed paper with regard to its general scope.

The primary challenge in the supply chain is minimizing travel distance and time between hubs and customers. Inappropriate assignment of vehicle routing results in travel distances longer than required, causing delays in achieving timely deliveries, and ultimately negatively affect the customer expectations routes. In this study, the selecting optimal vehicle routing has been addressed. This involves calculating the shortest possible that meets the demand effectively while adhering to various logistical constraints like warehouse fixed positions, demand variety, demand quantity, and destination locations. Dynamic programming has been developed where numerous time period-based fluctuations can be accommodated such as fluctuations in traffic, alternative routes availability, and changes in travel distances. The weighted demand cost matrix has been introduced to prioritize and cluster the group of customer nodes for the assignment of certain vehicles. Moreover, API google distance matrix (latitudes and longitudes) has been integrated into the model to extract live locations of source-and-demand nodes which are a function of different time periods of a day. The dynamic has optimized the vehicle routes and results in 30.9% reduction comparing the existing case. The validation was done through four more cases where different possibilities such as business expansion, network growth, demand fluctuations, and vehicle capacities.