Logistics services are among the essential activities on which institutions and companies rely to organize the movement of materials and products from production or storage locations to points of consumption. With the increasing volume of orders and the growing number of vehicles, routes, and delivery schedules, preparing logistics schedules manually has become increasingly complex. Such processes may result in higher transportation costs, delivery delays, or inefficient utilization of available resources. Therefore, Linear Programming (LP) provides an effective mathematical approach for making optimal decisions and developing more efficient logistics schedules by achieving better use of available resources while minimizing costs or time and improving service quality. Linear Programming is a mathematical optimization method used to identify the best possible solution to a problem from a set of available alternatives while satisfying a predefined set of constraints. In logistics services, it can be applied to determine the appropriate vehicle for each shipment, delivery times, and distribution routes while considering vehicle capacities, fleet size, drivers’ working hours, distances between storage and distribution locations, and required delivery deadlines. This approach transforms a real-world logistics problem into a mathematical model that can be processed computationally to identify an optimal solution. The design of a Linear Programming application for logistics scheduling is based on three main components: decision variables, the objective function, and constraints. Decision variables represent the information that the system needs to determine. For example, the variable (X_{ijt}) can be defined to take the value (1) if vehicle (i) is assigned to transport order (j) during time period (t), and (0) otherwise. This representation converts operational decisions into mathematical variables that can be processed through optimization algorithms. The objective function represents the outcome that the application seeks to achieve in the most efficient manner. The objective may be to minimize total transportation costs, reduce travel distances, decrease waiting and delay times, or achieve several of these objectives simultaneously. For example, an objective function can be formulated to minimize the total transportation, operating, and delay costs. The system then searches for a schedule that achieves the lowest possible cost while ensuring that all orders are fulfilled according to the specified requirements. The third component involves defining the constraints, which are the conditions that the resulting solution must satisfy. Major logistics scheduling constraints include vehicle capacity, the number of available vehicles, drivers’ working hours, order quantities, delivery deadlines, the number of warehouses and distribution points, and distances between origins and destinations. For example, the quantity of goods assigned to a vehicle must not exceed its capacity. In addition, the same vehicle should not be assigned to two shipments during the same period, while all orders must be delivered within the required time windows. The application can be developed through several sequential stages. The first stage involves collecting and entering the required data, including the number of vehicles, the capacity of each vehicle, warehouse and customer locations, order quantities, distances, working hours, and transportation and operating costs. In the second stage, these data are transformed into a Linear Programming mathematical model, after which an appropriate optimization algorithm or specialized mathematical optimization tool is used to determine the optimal solution. The application can be developed using programming languages such as Python and connected to specialized optimization libraries. In the final stage, the results are presented through a clear and user-friendly interface, such as a schedule showing the vehicle assigned to each order, departure and arrival times, proposed routes, utilized resources, and the total cost. Designing an application based on Linear Programming contributes to transforming logistics scheduling from a process largely dependent on experience and manual estimation into a systematic process based on data and mathematical modeling. It can reduce transportation and operating costs, improve the utilization of vehicles and human resources, minimize waiting and delivery delays, enhance distribution efficiency, and improve customer service. In the future, such applications can be further developed by integrating artificial intelligence, real-time data analysis, and traffic information, allowing schedules to be automatically adjusted when sudden changes occur in demand, road conditions, or vehicle availability. This integration can significantly enhance the flexibility and responsiveness of logistics systems and enable them to manage operational changes with greater efficiency.