Solving multi-objective supply chain management using non-dominated sorting genetic algorithm

Abstract

Focusing on production processes is the decisive factor in managing an efficient supply chain that leads to the company's success. The objective constraints in the model include all the goals the company seeks to achieve and the level to achieve for each. In addition to clarifying the contribution of each decision variable in achieving the specified levels of the different goals, the conclusions reached are the results that prove the possibility of solving a problem. Applying the mathematical model according to the demand for parts (derived from the demand for the final product) contributed significantly to saving the stock of raw materials, as (100) refers to the quantity that is kept as a regular stock for the first week and varies from one week to another according to the change in demand. As a result of reducing the stock of materials, the costs associated with it will decrease, and the difference can be seen in the total costs of storing raw materials and semimanufactured parts, which is estimated at (47929.1) Iraqi dinars) for the storage of materials and parts for all weeks, according to the planning periods established by the company. By applying the genetic algorithm, the total storage costs were calculated, and it was (13024.8) Iraqi dinars, which is the most critical indicator of success in improving the supply chain performance.