نوع مقاله : پژوهشی
1 گروه مهندسی صنایع، دانشکده فنی مهندسی، دانشگاه سمنان، سمنان، ایران
2 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه یزد، یزد، ایران
عنوان مقاله [English]
In this paper, the problem of simultaneous production planning, inventory control, transportation, and pricing of perishable goods (with limited lifetime) in a two-stage supply chain is investigated. Extensive research has examined on each of the important supply chain sub-problems, including production and inventory planning, distribution and transportation planning, and pricing, separately. On the other hand, global optimum solution can be achieved when these sub-problems are solved simultaneously and in the form of an integrated model. However, less research has focused on integrating these decisions. There are also many research papers that assume that inventory items can be stored indefinitely to meet future demands. While there are certain types of products that over time, either decay or become obsolete and, as a result, become unused. Perishable goods include food, vegetables, human blood, photographic films, etc. that have a maximum shelf life to use. If the product is perishable, then there will be more need for integrated decision-making. Another important issue to consider is the uncertainty of the available data. In other words, the parameters influencing these decisions are not deterministic, and this uncertainty must be controlled to minimize the possibility of losses associated with the decisions. A non-deterministic multi-period optimization model, in which demand uncertainty depends on the product price and the remaining periods, is proposed to solve the problem. In the proposed model, robust possibility planning is used to deal with uncertainty. To validate the proposed model and solution approach, data from a case study (taken from Patron Company, which produces green mortar and is used in the steel industry) were used. The results of computational experiments show that by applying the proposed approach, while making integrated decision-making, supply chain costs can be reduced by an average of 16%. Also, comparing the proposed robust possibility approach with the nominal approach in uncertainty control, it is observed that the maximum and average deviation from optimality are reduced by 46% and 11%, respectively.