Fuzzy Optimization
Malihe Niksirat; Majid Abdolrazzagh nezhad
Abstract
Purpose: In this paper, a Binary Fuzzy Linear Programming Problem (BFLPP) with fuzzy objective function and fuzzy constraints is considered. The purpose of this paper is to propose a new approach that solves the problem based on kerre’s adapted method that maintains the assumption of being fuzzy ...
Read More
Purpose: In this paper, a Binary Fuzzy Linear Programming Problem (BFLPP) with fuzzy objective function and fuzzy constraints is considered. The purpose of this paper is to propose a new approach that solves the problem based on kerre’s adapted method that maintains the assumption of being fuzzy in the solving process. Therefore, the solution is more consistent with the conditions of uncertainty governing the problem.
Methodology: In this paper, a new fuzzy branch-and-bound approach based on Kerre's adapted method is proposed to solve the fuzzy binary integer programming problem. In each node of the branch-and-bound tree, the linear relaxation of the fuzzy problem is solved with a new fuzzy simplex method based on Kerre’s adapted method.
Findings: Numerical examples are presented to illustrate the proposed method step by step and the results are compared with other approaches that solve fuzzy binary integer programming problems.
Originality/Value: Unlike the available defuzzification procedures and fuzzy ranking functions in the literature of the research problem, the proposed approach considers the assumption of being fuzzy in the solution process and thus offers a more realistic solution.
Fuzzy Optimization
Malihe Niksirat
Abstract
Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers ...
Read More
Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers at certain stations to avoid overcrowding.Methodology: The purpose of this paper is to develop decision support tools to prevent congestion of vehicles. Also, in order to consider the real conditions, two types of fuzzy and scenario-based uncertainty are considered. A dynamic nonlinear integer programming model is introduced to obtain the optimal service pattern for vehicles that are ready to be dispatched. To overcome the combined uncertainty of the problem, possibility theory has been proposed as a new fuzzy stochastic programming approach that has significant advantages.Findings: The model is clearly strikes a balance between observing social distancing by reducing the capacity of vehicles and reducing the waiting time of passengers who lose services. Numerical examples are provided to illustrate the proposed concepts and model and to compare the results.Originality/Value: The proposed decision support model can suggest service patterns for different lines service and can assess public transport operators to evaluate the advantages and disadvantages of implementing epidemic-based service patterns due to operational advances and demand level of travelers.
Management and operational budgeting
Malihe Niksirat; Seyed Hadi Nasseri
Abstract
Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan ...
Read More
Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan by managers. Accordingly, in this paper, a neuro-fuzzy network model is proposed to forecast the number of cases and deaths in countries that are most affected by this disease. The performance of the proposed neuro-fuzzy network has been compared with time series forecasting neural network as well as radial basic functions neural networks. The proposed model is able to predict the number of cases and deaths from the disease for a period of the next 15 days at a lower error rate.