جستجوی مقالات مرتبط با کلیدواژه « برنامه ریزی امکانی استوار » در نشریات گروه « صنایع »

Customers now care more than ever about the sustainability and reliability of products packaging. This research considers a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions. Triangular fuzzy numbers have been used for non-deterministic parameters and a robust probabilistic programming approach with Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this study is of mixed integer linear programming type, which is implemented Augmented Epsilon Constraint (AEC) method in GAMS software to solve it and find Pareto optimal solutions. The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the Carton-Making Industry. The obtained results indicate the existence of a conflict between the three objective functions. With this account, decision-makers should demand lower profits for increased environmental protection and improved reliability compared to the situation where only the economic aspect is considered. The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers.

In this paper, the problem of simultaneous production planning, inventory control, transportation, and pricing of perishable goods (with limited lifetime) in a twostage supply chain is investigated. Extensive research has examined each of the important supply chain subproblems, including production and inventory planning, distribution and transportation planning, and pricing, separately. On the other hand, the 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 assuming inventory items can be stored inde nitely to meet future demands. While there are certain types of products that either decay or become obsolete over time and, as a result, become unused. Perishable goods include food, vegetables, human blood, photographic lms, etc. which 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 in uencing 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, by comparing the proposed robust possibility approach with the nominal approach in uncertainty control, it is observed that the maximum and average deviations from optimality are reduced by 46% and 11%, respectively.

The provision of an efficient master plan which is able to integrate the procurement, production and distribution plans is a critical need in the way of achieving the competitive advantage in today’s marketplace. In this paper, a supply chain master planning problem of a drug supply chain is taken into account. The considered drug supply chain includes multiple suppliers, one manufacturer and multiple distribution centers. In this paper, a multi-objective possibilistic mixed integer linear programming model (MOPMILP) which minimizes the total logistics cost and maximizes the total value of supplier selection aggregate function is developed. It should be noted that both economic and environmental criteria are considered in the supplier selection objective function to support the green and sustainable purchasing approach. Then to cope with the input parameters tainted with high degree of uncertainty, a new effectual robust possibilistic programming (RPP) model is elaborated. The proposed robust possibilistic programming model is able to appropriately adjust the degree of feasibility and optimality robustness of output decisions against business-as-usual uncertainty. Also the proposed robust optimization model can be appropriately applied in the cases in which reliable and sufficient historical data is not available for imprecise parameters (i.e., most of the real-life problems). To show the usefulness and effectiveness of the proposed robust possibilistic programming model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and felicity of the developed approach.