چكيده به لاتين
Nowadays, manufacturing industries increasingly require integrated planning to make production and distribution decisions. Since perishable products have a specific maximum useful lifetime, useless after their lifetime, integrating decisions on the production and distribution of these products is of far greater importance. Accordingly, lack of attention to integrating decisions for perishable products results in the failure to deliver on time and reduces their quality. On the other hand, due to the advancement of technology and the customers’ changing expectations, considering the competition plays an essential role in establishing or developing any production-distribution company. In the present thesis, a production, inventory, and distribution problem is taken into account so that, firstly, the competition through the supply chain and the competition with the other supply chains are considered. Secondly, the solution has enough robustness regarding the uncertainty of the parameters. In fact, the production and distribution problem in a supply chain of perishable product suppliers with a specific lifetime is surveyed. In the supposed supply chain, on the one hand, factories and distribution centers work together in an integrated manner on the location, production, inventory, and distribution decisions. Meanwhile, vertical competition occurs between the production-distribution unit and the retailers. Then, the competition between the concerned supply chain and other similar product supply chains is investigated, as well as a comprehensive model is developed. In this model, in addition to price, distance from the facilities, and service level, the freshness factor of the perishable product is also considered an important competitive factor. To encourage retailers, three strategies, including perished product return, discount, and credit period policies, are employed to develop a new demand function. Besides, a robust fuzzy stochastic programming is utilized to cope with the operational uncertainty of the demand and some cost parameters. Due to the NP-hard nature of the problem, two solution approaches, including the adaptive large neighborhood search algorithm and a heuristic one based on Bander’s decomposition and genetic algorithms, are devised. Eventually, a case study is conducted to demonstrate the applicability and efficiency of the developed models. Also, several problems in different sizes are simulated to investigate the convergence of the proposed solution approaches. The computational results confirm the efficient performance of the proposed models and solution methods.
