Nowadays, rough set theory has become an invaluable tool to represent the uncertainty in different optimization problems because of its aspect of considering agreement and knowledge of all the experts and hence addres...
详细信息
Nowadays, rough set theory has become an invaluable tool to represent the uncertainty in different optimization problems because of its aspect of considering agreement and knowledge of all the experts and hence addressing more realistic decisions. Motivated by the nature of rough sets, in this study we investigate an unbalanced multi-objective fixed-charge transportation problem in which all the decision variables as well as coefficients of the objective functions and constraints are represented by rough intervals. A new method has been proposed to solve an unbalanced fully rough multi-objective fixed-charge transportation problem in which, firstly, an unbalanced fully rough multi-objective fixed-charge transportation problem transformed into a balanced fully rough multi-objective fixed-charge transportation problem. Then three approaches, namely, fuzzy programmingtechnique, goal programming technique and weighted-sum method are applied for obtaining the Pareto-optimal solution of the transformed balanced fully rough multi-objective fixed-charge transportation problem. In weighted-sum method, analytic hierarchy process has been used to determine the weights corresponding to objective functions. A comparison is drawn between the Pareto-optimal solutions which are derived from different approaches. Since the obtained solution is in a rough environment, it provides a wide range to help the decision maker to extract the best compromise solution. Finally, a case study is solved to show the contribution of the article in the field of decision-making and transportation.
Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the...
详细信息
Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goalprogramming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models' results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic-environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the v
暂无评论