جستجوی مقالات مرتبط با کلیدواژه « fuzzy programming » در نشریات گروه « فنی و مهندسی »

In today's dynamic and unpredictable world, the planning and management of humanitarian supply chains hold paramount importance. Efficient logistics management is crucial for effectively delivering essential aid and resources to affected areas during disasters and emergencies, ensuring timely support and relief to vulnerable populations. In this research, we addressed a novel humanitarian supply chain network design problem that considers product differentiation and demand uncertainty. Specifically, we simultaneously incorporate non-perishable, perishable, and blood products as critical components of the network. The problem is formulated as a multi-objective mixed-integer linear programming model aiming to minimize the total cost and total traveled distance of products by making location, allocation, and production decisions. To enhance realism, we account for demand uncertainty in affected areas. To tackle this challenging problem, we proposed a two-phase solution methodology. Firstly, we employed a robust optimization approach to establish a deterministic counterpart for the stochastic model. Subsequently, an efficient fuzzy programming-based approach reformulates the model into a single-objective form, effectively accommodating decision-makers' preferences. Numerical instances are solved to investigate the performance of the model and solution methodologies. The results demonstrate the effectiveness of our fuzzy approach in finding non-dominated solutions, enabling decision-makers to explore trade-offs. Also, sensitivity analyses were conducted to provide more insights. Finally, some suggestions are presented to extend the current work by feature researchers.

The problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this issue has led to the modeling of a financial resource allocation problem for sustainable projects under uncertainty in this article. A fuzzy programming method was used to control model parameters and GSSA, GA, and SSA algorithms were used to solve the model. In the mathematical model, the goal was to optimize the objective function consisting of predicted return, investment risk, and project sustainability. Mathematical calculation results showed that meta-heuristic algorithms have high efficiency in achieving optimal solutions in a short time. so that the average time to solve them was less than 10 seconds. Also, the calculation results showed that increasing the uncertainty rate leads to increasing the value of the objective function and creating a distance from the optimal point. This is due to increasing costs and decreasing profits in sustainable projects. Finally, usage the TOPSIS method, the ranking of solving algorithms was done, and the GSSA algorithm was the most efficient algorithm among other algorithms with a desirability weight of 0.846.

This study investigates the joint production planning and warehouse layout under uncertainty. Today’s competitive business world needs to be investigated by models which are capable of considering uncertain nature of the problems, especially when the historical data is not available or the level of uncertainty is high. Joint production planning and warehouse layout problems is almost a novel and new area in both academics and practice. For warehousing problem, the eventually of rental warehouses and new allocations is enabled in each planning horizon period. A bi-objective MILP model is proposed and fuzzy distributed parameters and chance constraints are taken into considerations. One of the objective functions deals with the cost associated parameters and variables while the second one minimizes the fluctuations of the work labor in each planning period. A simple test problem along with a case study is investigated by the proposed model. The obtained results prove the applicability of the proposed model in real-world scale problems.

This paper deals with a multi- objective linear fractional programming problem involving probabilistic parameters in the right- hand side of the constraints. These probabilistic parameters are randomly distributed with known means and variances through the use of Uniform and Exponential Distributions. After converting the probabilistic problem into an equivalent deterministic problem, a fuzzy programming approach is applied by defining a membership function. A linear membership function is being used for obtaining an optimal compromise solution. The stability set of the first kind without differentiability corresponding to the obtained optimal compromise solution is determined. A solution procedure for obtaining an optimal compromise solution and the stability set of the first kind is presented. Finally, a numerical example is given to clarify the practically and the efficiency of the study.

This paper proposes a mathematical model for ride-sharing vehicles with a common destination. A number of cars should assign to individuals by a company to pick up other participants in their way to the common destination. Traveling time as an important parameter is considered an uncertain parameter to enhance the applicability of the model which is formulated using fuzzy programming and necessity concept. Moreover, to have a better solution with better productivity, maximizing the earliest departure time of the individuals is considered beside of minimizing total traveling time. This helps to make justice among individuals for departure time. Goal programming is employed to work with objective functions and solve the model. Furthermore, a numerical example is implemented on the model to evaluate the applicability of the model which indicates the efficiency of employing fuzzy programming and considering both of the objective functions using goal programming. Results of the numerical example indicate the importance of considering both of the objective functions together in which ignoring each of them leads to inefficient solutions.

Emergency blood distribution seeks to employ different means in order to optimize the amount of blood transported while timely provision. This paper addresses the concept of blood distribution management in disastrous conditions and develops a fuzzy scenario-based bi-objective model whereas blood compatibility concept is incorporated in the model, and the aim is to minimize the level of unsatisfied demand of affected areas (AAs) while minimizing the cost of the supply chain. The blood supply chain network under investigation consists of blood suppliers (hospitals or blood centers), blood distribution centers (BDCs), and AAs. Demand and capacity, as well as cost, are the sources of uncertainty and in accordance with the nature of the problem, the fuzzy-stochastic programming method is applied to deal with these uncertainties. After removing nonlinear terms, Ɛ-constraint solves the bi-objective model as a single objective one. Finally, we apply a case from Iran to show the applicability of the model, results prove the role of blood distribution management in decreasing the unsatisfied demand about 38%.

This paper presents an application of interactive fuzzy goal programming to the nonlinear multi-objective reliability optimization problem considering system reliability and cost of the system as objective functions. As the decision maker always have an intention to produce highly reliable system with minimum cost, therefore, we introduce the interactive method to design a high productivity system here. This method plays an important role to maximize the worst lower bound to obtain the preferred compromise solution which is close to the best upper bound of each objective functions. Until the preferred compromise solution is reached, new lower bounds corresponding to each objective functions will be determined based on the present solution to develop the updated membership functions as well as aspiration levels to resolve the proposed problem. Considering judgmental vagueness of decision maker, here we consider the resources as trapezoidal fuzzy numbers and apply total integral value of fuzzy number to transform into crisp one. To illustrate the methodology and performance of this approach, numerical examples are presented and evaluated by comparing with the other method at the end of this paper.

Nowadays, the design of a strategic supply chain network under the incidence of disruption is regarded as one of the important priorities of governments. Supplying sustainable petrochemical products is considered as a strategic goal by managers who require reliable infrastructure design. Crisis conditions such as natural disasters and sanctions have a destructive effect on the raw materials and product flows. On the other hand, the uncertainty of input parameters affects the business environment and intensifies the condition of disruption. In the present research, a new model of resilient supply chain network is introduced in a critical condition, which consists in a combination of reactive and preventive resilient strategies. In order to deal with the parametric uncertainties caused by changes in the business environment and inadequate knowledge, an effective hybrid possibilistic-flexible robust programming method was presented. The proposed model was capable of controlling the adverse effects of uncertainties and risk-aversion level of output decisions. The extended model was analyzed in the national project of polyethylene strategic supply chain network using real data, which included the flexibility of demand, capacity, and lead time components. The results indicated that optimality and feasibility robustness were guaranteed by presenting efficiency solutions.

Production is a key economic activity with potential long-term social benefits that can be thoroughly realised only if governments comply with their duties towards domestic production. Governments are responsible for the production of sustainable agricultural products via appropriate allocation of subsidies and regulation of price policies that would help take advantage of the potentials underlying agricultural production. In this paper, a model is developed to investigate the interaction between two decision makers in the stackelberg game, government as leader and agriculture as follower, with the ultimate aim of providing benefits to all sectors in the society in the sustainable agriculture paradigm. The proposed model is validated and its efficiency demonstrated via a case study of cotton production as a strategic agricultural production. The model is first solved using a combination of fuzzy mathematical and grey quadratic programming methods to account for the inherent uncertainty in a number of problem parameters. The model is then analyzed against various government-producer interaction scenarios and finally, the analysis results are compared.

This paper proposes an innovative robust-fuzzy method for multi-objective, multi-period supplier selection problem under multiple uncertainties. This approach integrates robust optimization and fuzzy programming. Uncertain parameters are modeled as random variables that take value within a symmetrical interval. However, due to the complexity or ambiguity of some real world problems and specially the nature of some of the available input data, the length of interval is also highly uncertain. This ambiguity motivated us to present a new approach, which can be applicable to multiple uncertainties conditions. Thus, in our approach the half-length of these intervals is also represented by fuzzy membership function. We develop a model and a solution approach to select suppliers by considering risk. The proposed method is applied to a real case of supplier selection in automobile industry under uncertainty and ambiguity conditions. To verify the proposed model, we evaluated the results by simulation technique and compared values of objective function under different scenarios.

Development of the infrastructure of alternative fuel stations is one of the best ways to extend the use of alternative fuel vehicles. Hence, constructing refueling stations with minimum cost is an important issue. On the other hand, considering the exact value of cost is not match with real cases. In this regard, the cost of building station is considered as a trapezoidal fuzzy value and a mathematical fuzzy programming model is presented in this paper. In order to solve the fuzzy model, first the model is converted to an interval programming model, then the equivalent bi-objective crisp model of the interval programming problem is written. Finally, two interactive fuzzy solution approaches are used to solve the respective bi-objective crisp model. The results show that the performance of the solution approaches is the same.

Management and scheduling of flights and assignment of gates to aircraft play a significant role to improve the performance of the airport, due to the growing number of flights and decreasing the flight times. This research addresses the assignement and scheduling problem of runways and gates simultaneously. Moreover, this research is the first study that considers the constraint of unavailability of runway’s and the uncertain parameters relating to both areas of runway and gate assignment. One of the distinguishing contributions of the proposed model is that the problem is formulated as a bi-level bi-objective one. The leader objective function minimizes the total waiting time for runways and gates for all aircrafts based on their importance coefficient. Meanwhile, the total distance traveled by all passengers in the airport terminal is minimized by a follower objective function. To solve the proposed model, Benders’ decomposition method is applied. Empirical data are used to show the validation and application of the proposed model. A comparison shows the effectiveness of the model and its significant impact on decreasing the costs.

Lean manufacturing is a strategic concern for companies which conduct mass production and it has become even more significant for those producing in a project-oriented way by modularization. In this paper, a bi-objective optimization model is proposed to design and plan a supply chain up to the final assembly centre. The delivery time and the quality in the procurement and low fluctuation of the production are the most important lean production principles that are considered. Because of the long-horizon planning and the subjective data gathered, it is necessary to handle uncertainty. Therefore, a robust credibility-based fuzzy programming (RCFP) approach is proposed to perform the robust optimization and to obtain the crisp equivalent of an MILP model using the chance constraint programming method in terms of simultaneous credibility measurement. A real industrial case study is provided to present the usefulness and applicability of the proposed model and programming approach.

Most of existing researches for multi response optimization are based on regression analysis. However, the artificial neural network can be applied for the problem. In this paper, two approaches are proposed by consideration of both methods. In the first approach, regression model of the controllable factors and S/N ratio of each response has been achieved, then a fuzzy programming has been applied to find the optimal factor's levels. In the second approach, a tuned Artificial Neural Network (ANN) is used to relate controllable factors and overall exponential desirability function then Genetic Algorithm(GA) is used to find factors optimum value. Mentioned approaches have been discussed in a real case study of oil refining industry. Experimental results for the suggested levels confirm efficiency of the both proposed methods; however, the Neural Network based approach shows more suitability in our case study.

Among the various existing models for the warehousing management, the simultaneous use of private and public warehouses is as the most well-known one. The purpose of this article is to develop a queuing theory-based model for determining the optimal capacity of private warehouse in order to minimize the total corresponding costs. In the proposed model, the available space and budget to create a private warehouse are limited. Due to the ambiguity, some parameters are naturally simulated by expert-based triangular fuzzy numbers and two well-known methods are applied to solve the queuing-based fuzzy programming model and optimize the private warehouse capacity. The numerical results for three cases confirm that unlike the previous approaches, the proposed one may easily and efficiently be matched with various lines of manufacturing environments and conditions.

This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized benders decomposition along with genetic algorithm to include minimum up/down time constraints are proposed, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, the MATLAB software, and CPLEX and KNITRO solvers are used. To verify the proposed technique and algorithm, two case studies that are IEEE 14 and 118 bus systems are implemented for optimal generation scheduling, and reliability issues. Finally, a comparison with other solution techniques has been given.

Aggregate production planning is a medium-term production planning to determine the production plan to satisfy fluctuating demand. In this paper, a robust approach is used to formulate the aggregate production planning that some parameters such as production costs and customer demand are fuzzy variables. The concept of entropy is used to reduce the sensitivity of noisy data and to obtain a more robust aggregate production plan based on the proposed model. Finally, a numerical example is presented to explain the model solution. In addition the robustness of proposed model solutions are compared with other classical fuzzy programming approach.

Recovery of used products is receiving much attention recently due to growing environmental concern. In this paper, we address the carbon footprint based problem arising in closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed or be remanufactured to be sold as new ones again. Given this environment, an optimization model for a closed-loop supply chain in which the carbon emission is expressed in terms of environmental constraints, namely carbon emission constraints, is developed. These constraints aim at limiting the carbon emission per unit of product supplied with different transportation mode. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, the fuzzy mathematical programming is introduced for uncertain modeling. Therefore, the statistical approach towards possibility to synthesize fuzzy information is utilized. So, using defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. The model is applied to an illustrative example of an uncertain green supply chain (GSC).