BUILDING MATHEMATICS MODELING FOR SOLVED TRANSPORTATION PROBLEMS AND OPTIMIZING WITH MORE FOR FEWER ALGORITHMS IN THE BUSINESS COMMUNITY

Abstract

Mathematical modeling supports the development of the business world and the industrial world, especially in transportation (Widana, 2020). Many emerging algorithms are combined with the adoption of changes in the form of real problems modeled into mathematical problems. The modeling aims to optimize to produce the most optimal objective function value based on the formed constraints. Optimization of the mathematical model can be assisted by several optimization algorithms, such as the More for Less Algorithm. The algorithm, as a form of looping, is based on the constraint function with the specified parameters until it reaches the final objective function, namely optimization related to time and cost(Muftikhali et al., 2018). In general, it is hoped that supply and demand will be balanced in transportation. However, in reality, often in field conditions, mixed transportation problems result in non-optimal costs. The study results show that the More for Less Algorithm can provide the most optimal solution according to the allocation of factory requests to shops with the minimum cost. The dual variable matrix index is positive, so the solution to the transportation problem with mixed constraints is called optimal. The solutions obtained in the transportation case study with mixed constraint functions in the field of the Amarta Bakery industry community business are as follows:with a total cost of 580 (in units of money). Combinations - factory-to-store combinations for companies are interpreted as first factory at the first store with an allocation of 90 product units, first factory at the second shop with an allocation of 110 product units, second factory at the second shop with an allocation of 0 product units, factory the second factory at the third store with an allocation of 70 units of product, and the third factory at the third store with an allocation of 0 units of product. The number zero means that to carry out efficiency and optimization according to mixed constraints, it is necessary to make conditions where factories do not distribute to shops.