Three-layer supply chain management using an improved genetic algorithm

Supply-chain network design is a fundamental problem for most organizations. The optimization of the network allows for efficient management of operations in the entire supply chain. The supply chain involves all activities concerning the flow of goods from raw material to delivery to the consumer, as well as the flow of information throughout the process. This study investigates a three-level supply chain with a single-period model comprising a number of manufacturers and a set of distributors and consumers. A novel methodology was presented to limit the random behavior of genetic operators in solving the problem. The problem was modeled aiming to minimize the total cost of the system. Moreover, the production capacity was considered limited and insufficiency was assumed unacceptable. Given their inherent complexity, this category of problems is recognized as NP-hard type. Therefore, this study employed an improved genetic algorithm to solve the problem that—according to the results—was found to be highly effective.

Keywords:

Supply Chain , Production–Distribution Planning , Crossover , Genetic Algorithm , Optimization