جستجوی مقالات مرتبط با کلیدواژه « linear programming » در نشریات گروه « مدیریت »

In this research, an optimal supply chain network for the collection and recycling of urban waste has been presented by using the double-objective mixed integer linear programming method. In the supply chain design of this research, in addition to the concept of recycling, the concept of a dedicated hub was also considered for each type of separated waste. The objective functions include an economic function to minimize investment costs and a social objective function to maximize the amount of recycling. To solve the large-scale problem accurately, the Lagrange release method has been used. To validate and confirm the efficiency, the model was implemented on a case study in the city of Karaj. According to the obtained results, in order to increase the amount of recycling in the waste supply chain network, more infrastructural and operational investments are needed in the area of forming hub centers. By increasing the amount of recycling, the harmful environmental and destructive effects caused by burying and burning mixed waste will be reduced. In this research, it was observed that the Lagrange release method is able to solve large-scale problems with appropriate accuracy and in less time compared to the commercial CPLEX solver. Therefore, the Lagrange release method can be used as an accurate solution to reduce the time of solving large-scale problems.

In Iran, Although the PET recycling industry is new, due to the appropriate economic return, many individuals and companies have turned to this industry. Creating a proper structure in the distribution and collection network of these products can provide a good base to replace its consumption with more environmentally friendly products. This paper aims to present a mixed integer linear programming (MILP) model for designing a supply chain network in which customer demand is dependent on the price offered by distribution centers. The proposed model addresses two main global trends in this industry: 1- Economizing the collection and recycling of bottles, 2- Price sensitivity analysis on demand for PET bottles. In addition, we applied a recently developed optimization algorithm (TLBO) to this problem with significant modifications. Since the standard version of TLBO is introduced for continuous representation methods and the representation method used in this paper (priority-based encoding) is and discrete method, modifications have been made to convert this method to a continuous one. Finally, the performance of this algorithm is assessed in terms of the quality of answers and the convergence speed and compared with two other meta-heuristic algorithms.

Nowadays, industries, especially the banking industry, are facing a much more complex environment than the past decade. Therefore, they have to adapt to the environment to obtain and maintain their competitive edge. The traditional banking system is no longer efficient enough, while a universal banking system can provide more varied services to a more diverse clientele. Considering the radical humanist paradigm in the analysis of social theories and a focus on the effectiveness of capable human resources for the correct implementation of universal banking in Iran, the present paper aims to propose a universal banking system focusing on the empowerment and skill development of bank employees. This study used the Delphi fuzzy method to select experts and classify and analyze their responses. Further, the Linear Programming and , FGTMA-FDEMATEL and screenwriting method was used to identify and investigate the criteria correlationsand map the network relationships. Arobust statisticalmethod was used to solve optimization problems based on data uncertainty.and lingo software was used to solve the problem. The results drawn from the model indicated that factors such as employee decision-making ability, responsibility towards executing a decision, access to decision tools, and employee self-efficacy sources influence the empowerment of employees and correct implementation of private, corporate,business, and investment banking. Further, they showed that sense of competence, motivation, participation, sense of effectiveness, and information technology influence universal banking. In addition, training and reward influence all types of universal banking

Nowadays, one of the basic problems of organizations, in establishing the system of performance evaluation, is to identify the key indicators. In recent years, among the valuation models and performance, it has been more attention to the balanced scorecard; but many of the balanced assessment projects face up to fail in action. The most important reasons behind the failure in the establishment of a weak balanced assessment is in the selection of the appropriate key indicators. Hence, this study tried to applying an appropriate method for providing a model for the selection of the performance appropriate key indicators by the help of the modified balanced scorecard model and the use of a linear programming and the Operating consequences of this approach will present as a case at Commercial Company in Kerman. This study is an applied research, its method is descriptive – analytic one. The population included 71 members of the technical staff of projects and storage installations of state Commercial Company in Kerman that 10 members were the managers of the company.

One of investments to increase the wealth of investors is investment in the stock exchange. The purpose of this paper is ranking of cement and plaster, petroleum products (production of petrochemicals refined products), tile and ceramic, automotive (manufacture of motor vehicles) industries of stock exchange to help the investors for investment decisions. This research aims to develop a methodology for solving MADM problems with both ratings of alternatives on attributes and weights being expressed with interval-valued intuitionistic fuzzy sets. In this methodology, a weighted absolute distance between intuitionistic fuzzy sets is defined using weights of intuitionistic fuzzy sets, two simpler auxiliary linear programming models being used to calculate the relative closeness coefficient of ideal solution, based on the concept of likelihood of interval numbers, we rank alternatives. The required data is extracted during 1393 to 1394 mainly through audited financial statements data from rahavard novin software. the final results of this study indicate that petroleum products, cement and plaster, tile and ceramic, automotive industries respectively, have the highest rank.

In this study, the strategy of effective asset allocation under uncertainty with the capability of risk control, transaction cost reduction and favorable return realization is investigated. In order to implement this strategy and to overcome the shortfalls of classic portfolio optimization models in dealing with uncertainty, the formation of an index fund using a robust approach and considering cardinality constraint became the agenda. Accordingly, in order to solve the index tracking problem, a linear programming model as minimizing the absolute deviation between the expected return of the index fund and that of the benchmark is presented. Considering the dimension of the solution space, a Meta heuristic genetic algorithm was implemented to solve the robust counterpart of the problem. The results of the analysis imply on the selection of 20 stocks as the index fund composition and indicate good performance of the index tracking funds based on criteria such as correlation, root mean square error and the excess return using out of sample data.

This research presents a mathematical model for oil and gas optimal allocation to different sectors in Iran using operations research techniques. Sectors are residential and commercial, transportation, industries, agriculture, exports, injection to oil reservoirs and power plans as a secondary energy producer. Optimal allocation of energy resources to end-uses from 2011 to 2021 has been done using a linear programming model regarding lifting of subsidies. The results provide scientific basic for the optimal allocation of energy resources in Iran.
This research presents a mathematical model for oil and gas optimal allocation to different sectors in Iran using operations research techniques. Sectors are residential and commercial, transportation, industries, agriculture, exports, injection to oil reservoirs and power plans as a secondary energy producer. Optimal allocation of energy resources to end-uses from 2011 to 2021 has been done using a linear programming model regarding lifting of subsidies. The results provide scientific basic for the optimal allocation of energy resources in Iran.

Concept of locating business units includes the most important factors in their success and one the most competitive advantage of industrial units. Entrepreneurs have been considered in this context، and different mathematical models have been developed. This paper aims to develop a decision support system based on a mathematical model in linear programming. In a comprehensive approach to this article، three general headings can be recognized. First، as background concepts related to decision support systems، expert systems and mathematics of location reviewed. On the second part، the foundations of mathematics and logic model used in this study is explained. Finally، the samples software designed to implement the model are introduced and as a case study finding a best place for a toy manufacturing is studied and based on the outputs of software the province of Qom have introduced.

Much research hasn’t been done in the area of project selection in the public sector. In this paper، we tried to choose projects in the public sector; a linear programming model is presented. In an effort to build the model، the decision criteria were identified using the Delphi method and then the criteria were reviewed at conceptual combining. Model was constructed in a linear، goal and mixed integer programming approach. After building the model، was tested with data on budget bill، the results were analyzed and the model was revised several times. The results of the model execution confirmed its validity. This type of model as a decision support system can be used for project selection in the public sector.

In this study a linear programming model has been presented which its objective is to optimize revenue of hotel. In this model، a special planning horizon has been considered and there are several reservation periods. Each period of reservation may have different prices and the model has an ability to consider different kinds of rooms. Two situations of demand have been considered as certain and uncertain. We use scenario-based method for uncertain demand which is simple but near to reality method in stochastic programming. The unique characteristic of this study is considering cancellation and no-show which is a key factor in amount of acceptation of hotel guests. The model is validated by numeric examples and it is solved by linear programming software، finally.

Hub location problem is further used in transportation and telecommunication networks (airlines، post delivery services، etc.) so origin-destination pairs، receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper، the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases، the cost will be reduced as well.

This paper evaluates the decision making process for import complete manufactured pieces versus import of partial pieces to assemble in Iran، taking into account the risk factors for a manufacturing company. Since this sort of decision making confront with several risks، it is necessary to establish a process for finding the risks associated with this kind of problems in order to decrease the effects of these risks in the process. Since the problem is classified as a Multiple Criteria Decision Making (MCDM) problem، Uncertain Analytical Hierarchy Process (UAHP) was used to find the most attractive alternative. Because the alternatives were identified from the first point، a bottom-up procedure was used to organize the hierarchy. In initial stage، the attributes which distinct from the alternatives were obtained by literature review and experts'' interviews. Then the attributes were grouped to upper level to establish the criteria. Three criteria were found from this stage. The criteria were product، partners، and environment which they encompassed 12 attributes. Forming the hierarchy and doing the uncertain pairwised comparisons، which considers a range of numbers instead of one single number for declaring the preference between two factors، a Linear Programming (LP) model with two types of objective functions were formed for each individual alternative. Each single LP model can express the maximum and minimum value of each individual alternative. The research''s results indicate the most appropriate alternative is to import the final product from India. The last preferred one was to import the parts of the final product from India. This study can be a suitable framework in supply chain management and purchasing decisions and risk evaluations because the major parts of manufacturing activities is always to decide about the selection of most preferred strategies for companies.

The development of Multiple Criteria Decision Making Techniques is to provide the possibility of using multiple criteria in decision making problems. Multi-criteria decision making problems primarily relate to choosing the best option among several available options and according to some indicators. One of the major issues in this regard is to determine the importance weights of decision criteria. Various methods such as Entropy, LINMAP and Saaty’s method have been presented for determining the weights of the indicators. In this paper, a non-linear model based on the logic of TOPSIS method is proposed to estimate the ideal weights. Simplicity, Reduction of received data from the decision makers in the evaluation process, and the possibility of considering his comments about the preference of indicators to each other can be cited as the advantages of this method.

Although linear programming is used widely in the world, its inefficiency in dealing with difficult problems is concerned. With the advancement in science and dealing with various problems, it tends to have problems in mass production in a short time. Heuristic and meta-heuristic techniques are the latest achievements of nonlinear programming for solving similar problem. One area that requires programming applications in mass production is NP-scheduling problems. This paper aims at modeling and comparing the two methods of Linear Programming and Ant Colony System Algorithm in flexible flow shop scheduling problem according to the number of jobs and machines. This study is based on comparing the index of time processing, the number of constraints, optimality, and the memory size of the random numbers. Using Quasi-experimental research method, software testing tools are C-sharp and Lingo for the ant colony algorithm and linear programming respectively. The results show that linear programming model has higher performance when machines and jobs are in low numbers; however, with the rise of the machines and jobs, Ant Colony System algorithm has proven high efficiency.

Performance budgeting has been an important theme of public expenditure management for decades. In the 1990s, however, a new wave of enthusiasm for performance budgeting began to sweep through governments. In recent years, however, the level of interest in organizational use of performance-based budgeting re-emerged with more details and support from researchers and practitioners. Performance-based budgeting refers to procedures or mechanisms intended to strengthen links between the funds provided to organization plans and their outcomes or outputs through the use of formal performance information in resource allocation decision making. Reviewing the history of other methods of budgeting showed that mathematical models of budgeting have been used wildly by researches. Regarding to the large number of parameters that are involved in process of performance-based budgeting, qualitative and subjective models could not lead to an optimum decision for budget. The purpose of this paper is to find a mathematical model for performance-based budgeting that could maintain the optimality and feasibility of allocation in uncertain condition of budgeting. The important factors that cause uncertainty are estimated values for cost derivers and future costs. We used robust optimization to develop performance-based budgeting mathematical models. Finally, presented models have been used in an Iranian bank for budgeting. The result showed that using robust models of performance based budgeting could improve the indicator of budget deviation in this organization.

In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and practical constraints. In order to increase the flexibility provided by the multiplicity of routings, the model distributes production volume of each part among alternative routes. Also, a constraint enforcing work load balancing among machines is included in the model. Due to the complexity and combinatorial nature of this model, an enhanced algorithm comprised of a genetic algorithm and a linear programming is proposed for solving the model. The proposed algorithm is tested by a range of test problems and compared with two algorithms from the literature. The computational results show that the proposed algorithm is effective and the proposed approach offers better solution.