جستجوی مقالات مرتبط با کلیدواژه "برنامه ریزی چندهدفه" در نشریات گروه "مدیریت"

The availability of energy is a vital aspect of a nation’s economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focused on the former, which relies on real-time pricing. Our objective was to develop a multi-objective mathematical model that considers load response programs for smart energy grids.

The study employed a scenario-based approach and classified the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices were marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario was created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model was developed that aimed to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model was run using the GAMS programming language. In addition, the study evaluated the impact of load response programs on enhancing objective functions.

The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively.

Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost-effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions.

Iran's current electricity generation systems rely on large-scale power plants situated far from consumers, utilizing fossil fuels as their primary source. However, as demand for electricity surges, greenhouse gas emissions, electricity losses, and reliability issues have become prevalent. Smart cities have emerged as a promising solution to these challenges, with smart grids being a critical component of their energy systems. Efficient energy management necessitates the optimization of resource usage, minimization of costs and environmental risks, and maximization of reliability. This study proposes a multi-objective optimization model for managing a smart grid's energy. The model aims to minimize costs, pollution, and peak consumption while simultaneously maximizing reliability. To account for uncertainties such as renewable energy output, demand, and price, a stochastic approach is utilized. The problem is formulated as a mixed integer linear programming model, and the Pareto solutions are obtained using the epsilon constraint method. In order to achieve the most optimal Pareto solutions, we have employed the use of the fuzzy satisfiability method. Results indicate that the smart energy grid can effectively reduce energy consumption, costs, and pollution while significantly increasing system reliability.

In this paper, inspired by a real case study, a hierarchical approach is presented for supplier development and supporting its critical decisions in each stage. Supplies needing development and their eligible suppliers for development are first identified using the best-worst multi-criteria decision-making method. The related decision criteria are selected based on the literature review and subjective opinions of procurement experts. Finally, a bi-objective mathematical model is proposed for selecting and allocating candidate development plans to each candidate supplier. The objectives of the proposed model include maximizing the degree of achievement of the development goals of the buyer and maximizing the net present value of the development plans’ portfolio (i.e. the selected development plans for eligible suppliers). The computational results and derived managerial insights demonstrate the effectiveness and applicability of the proposed approach and its ability to support supplier development decisions for the buyer.

Finding a subset of features is an issue that has been widely used in a variety of fields such as machine learning and statistical pattern recognition. Since increasing the number of features increases the computational cost of a system, it seems necessary to develop and implement systems with minimum features and acceptable efficiency.

Considering objective, it's developmental research and in terms of two Meta-heuristic algorithms, namely genetic algorithm (GA) and multi-objective non-dominated sorting genetic algorithm (NSGA II). The multi-objective method compared to the single-objective method has reduced the number of features to 50% in all instances; it doesn't make much difference in classification accuracy. The proposed method is applied on six datasets of credit data, and the results were analyzed using two common classifiers namely, support vector machine (SVM) and K-nearest neighbors (KNN). Comparing two classifiers applied on datasets, K- nearest neighbors (KNN) compared to the support vector machine (SVM) has shown relatively better performance in increasing the classification accuracy and reducing the number of attributes.

Genetic algorithm and multi objective non-dominated sorting genetic algorithm have a good performance in increasing the accuracy of classification and reducing the number of attributes in feature selection problem of multi-class data. The results also indicate an increase in classification accuracy, simultaneously with a significant decrease in the number of features in both KNN and SVM methods.

According to the results, the proposed approach has a high efficiency in features selection problem.

Supplier selection is one of the most critical problems in the supply chain management that helps firms to evaluate and select appropriate suppliers. Different conflicting objectives could be considered in these kinds of problems. Most important objectives are: minimizing the cost, maximizing the quality, and maximizing the reliability. In this paper the set of suppliers are considered as a system and they are selected so that maximizing the reliability of the selected set and maximizing the quality of the received products, and also minimizing the purchasing cost. On the other hand, in practice exact data are not accessible, therefore, fuzzy theory is utilized to evaluate the suppliers. In addition, suppliers usually use discount mechanism to attract more buyers and to achieve more profits. In this paper supplier selection problem is modeled considering all unit discount. Additionally, satisfaction of the demand depends on the risk attitude of the buyer. The satisfaction of the demand is considered as a fuzzy-probabilistic constraint. Finally, a numerical example is solved based on different risk attitudes of the buyer and related results are reported.

One of the most significant issues nowadays is natural disasters and their severe and devastating vital and financial effect. As in crisis situations, the time required to service and deliver relief goods is extremely limited, therefore, with regard to existing restrictions, the need to define special conditions for relief with a view to alleviating casualties is clearly evident. This research in the scope of a service supply chain defines a vital mobile medical service system based on the definition of mobile hospitals that serve patients with medical priorities in the affected areas. The two objectives of minimizing relief cost and service time, which are based on economic and social criteria, respectively, are intended to define the distribution locations, the optimal route of hospital delivery, and also the capacity of hospitals to serve in case of casualty. The problem model is solved using the constraint method by considering the objective function of the service time as the main objective, and the Pareto frontier of the model solution is presented.

Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition.

Knowledge Management (KM) can improve Supply Chain (SC) performance through some frameworks such as EFQM Excellence Model in order to achieve competitive advantage. In this paper first, the KM enablers in SC are classified based on EFQM enabler criteria. Then, the importance of KM enabler are evaluated by fuzzy Dematel-ANP. In addition, fuzzy AHP is applied to evaluate the importance of each KM enabler in knowledge sharing. In the research, the Multi-objective Mix-integer non-linear programming (MINLP) is used to optimize the performance of knowledge management and select KM strategy in each part of SC. Likewise, it is approved to select suitable members of SC for Research and Development unit of SC. The results represent that each part of SC should be developed by concentration on some KM enablers, and selection of a suitable strategy. In addition, they emphasis on the effect of each KM enabler on selection of suitable members.

Vehicle routing is one of the significant issues in the field of Logistics management and despite many endeavors for integrating this issue with other fields of logistics, most of its aspects require research and development. The selection of interdependent factors in vehicle routing is one of these approaches and it can have a considerable impact on price increase, income decrease, lost sale, dissatisfaction of customers and increase in returned goods. This issue is the rewritten classic model of vehicle routing by considering the approach of selecting a customer as one of the significant approaches in the distribution management. The main idea of this article is to consider the fact that the number of requests for each region is a fixed amount and if goods were distributed among several sellers in that region, all of the requests would be divided among these sellers, which is not desirable for the sellers and results in considerable distribution expenses. Above all, the satisfaction of ultimate consumers depends on broad distribution and if distributed among more ones, there would be greater satisfaction. Based on this idea, in the present research, a new formulation from this issue in the form of a two-purpose mathematical model has been presented by putting together the classic vehicle routing and the approach of selective distribution. In this research, firstly, a review of the literature of this subject has been carried out, and then the issue has been raised. Following that, based on the limiting assumptions describing the conditions of the issue, modeling of vehicle routing with the approach of selecting interdependent factors has been considered and mathematical modeling has been presented. This model with a set of sample data has been dealt with and its reliability has been assessed.

In the present study, the supplier selection problem from a new perspective is formulated and solved. For this purpose, minimizing cost and minimizing delivery time and maximizing reliability are considered as objectives of the problem. The purchasing cost in discounted status, the order cost, the transportation cost, the shortage cost and the carrying cost, the components forming the cost objective function. In this research, the design of reliability in parallel systems is benchmarked for supplier selection, so that the supplier's set is considered as a system and each supplier as a component. After mathematical modeling of the problem, the objective functions are described as fuzzy goals and a fuzzy decision approach has been used to rewrite the proposed three-objective model as a single-objective model. The production of a final solution rather than a set of Pareto's solutions is one of the advantages of the proposed method, which prevents decision-making confusion. In order to describe the function and potential application of the proposed method, the supplier selection problem with the actual data was solved.

The withdrawal Kanban system, by capability of data transferring in supply chain reduces different types of the waists such as inventories level and unnecessary movements. To achieve the aims of lean production, the parameters of the Kanban system such as the number of Kanban should be determined properly. The number of Kanban problem is a multi-objective problem which should met the aims of producers and suppliers simultaneously. In this paper, the objectives and constraints of withdrawal Kanban problem has been determined based on a case study in automobile supply chain. A mathematical integer multi-objective model with non-linear objects has been developed. Two sets of solutions are generated by the optimization model. A simulation model is developed to check the possibility and validity of solutions. The simulation studies show that one of the solutions can reduce up to 46 percent the inventory costs while increase 11 percent transportation costs compared to the current state.

The maximum capture problem seeks to find a suitable location for facilities in the network space and in a competitive condition. In this problem, the new company intends to enter the market with the aim of capturing more demand. In this study, the cost factor is considered to be a separate objective function and a bi-objective model is proposed. The number of facilities parameter is considered to be an interval and for its upper and lower bounds calculations two models are proposed. To obtain upper bound a model with the maximum capture objective and the maximum budget constraint and to obtain lower bound a model with the minimum cost objective and the minimum market share constraint. To solve the proposed model, a goal programming method is used. The steps of the research methodology and modelling are shown in a case study from Yazd city. The results show that if the weight of the objective functions is assumed to be equal and the investor neglect 7 % of the market share, 55% of initial investment could be saved.

In the single-mode resource-constrained project scheduling problem, it is assumed that each activity has a specified known execution time and resource consumption can be done only in one way. In practice, there are many cases in which the make span can be reduced by providing additional resources activities. In this case, each activity can be done in one of the procedures, which is called the multi-mode resource-constrained project scheduling. In this paper, the problem includes determination of the basic schedule for the project activities which may be done in several models and the precedence relations are met, While the project make-span, cost and resource fluctuations are minimal. In this research project scheduling problem network will model using ED simulation software and the results of the simulation and a meta-heuristic algorithm has been compared. Finally, management strategies to optimize the scheduling, i.e such minimize total time, cost and resource leveling, will be offered.

In this paper, the considered set covering issue is a stance of location-allocation issue in which the residents of each the eighteen district of region 6 of Tehran, have definite requirements to receive services (medical first aid) from some of emergency centers. We want to arrange so that at least one center, in each district, provides services to people who have been exposed to incidents. In this regard and to emplacement of these relay centers, a developed multi-objective model is used that has objectives such as minimizing the establishment cost of relay stations, maximizing the total covered population and minimizing the total distances between demand locations and allocated service providers. Since, each of the centers has only an ambulance and these ambulances being out of reach during providing services, possible limitations have been used in this model. So, in this research a combination of multi-objective decision making (MODM) methods and set covering problem (SCP) is used to specify the location of relay centers in 18 districts of region 6 in Tehran. The chosen solution is simulated annealing method that is performed using Matlab software. At the end, the model is implemented through the Ultra Innovative algorithm of particle mass optimization to compare to the answers of the Simulated Annealing algorithm. Results show that both algorithms give the same answers for the considered locations.

Today's world is more competitive than ever, and industry increasingly looks to supply chain management as a competitive weapon. the objective of this research is to suggest an integrated approach for Closed Loop Supply Chain network including two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Beside a fuzzy method is utilized to evaluate suppliers based on quantitative and qualitative criteria. The output of this stage is the weight of each supplier according to each part.in the second phase, we propose a Multi Objctive mixd-integer linear programming model to determine weight of suppliers and amount of item for purching from them(strategic decisions), as well as weight of remanufacturing subcontractor and dedicate product to them (tactical decisions), and find out the optimal number of part and products in CLSC network(operational decisions). The objective functions maximize profit of selling, minimize factory cost as well as cost of unsatisfied demand and rest of them maximize weight of suppliers and allocate return product between recoverable centers in factory and remanufacturing subcontractors. the mathematical programming model is validated through numerical analysis.

In this paper a new method is proposed to find the fuzzy optimal solution of fuzzy multi-objective linear programming problems (FMOLPp) with fuzzy right hand side and fuzzy decision variables. Due to the imprecise nature of available resources، determination of a definitive solution to the model seems imposible. Therefore، the proposed model is designed in order to make fuzzy decisions. The model resolves the deficiencies of previous models presented in this field and its main advantage is simplicity. To illustrate the efficiency of the proposed method، it is applied to the problem of allocating orders to suppliers. Due to the nature of the fuzzy solutions obtained from solving the model، the decision maker will be faced with more flexibility in decision making.