The environmental consciousness of society and globally competitive market have considerably increased thanks to the scientific studies, media, governmental and non-governmental organizations. In this regard, environm...
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The environmental consciousness of society and globally competitive market have considerably increased thanks to the scientific studies, media, governmental and non-governmental organizations. In this regard, environmental factors have been considered within the supplier selection process which is a major decision point in supply chains. Hence, in addition to the optimization of the traditional criteria, green criteria have also started to take its place in the supplier selection problem. In this study, an integrated methodology including the Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) and a modified two-phase fuzzy goal programming model are proposed to better address this selection problem in a multi-item/multi-supplier/multi-period environment. The detailed steps are explicitly provided within the proposed methodology. In this respect, the criteria importance weights are determined via IF-TOPSIS which enables the opportunity to handle the vagueness within the evaluation process of decision-makers. Afterward, the obtained importance weights are used in the modified two-phase fuzzy goal programming model for selecting the best suppliers. An application in the air filter industry is performed to demonstrate the validation of the proposed methodology. Consequently, the proposed methodology successfully provides the best selection of suppliers by satisfying both classic and green criteria. (C) 2020 Elsevier B.V. All rights reserved.
Demand and supply pattern for most products varies during their life cycle in the markets. In this paper, the author presents a transportation problem with non-linear constraints in which supply and demand are symmetr...
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Demand and supply pattern for most products varies during their life cycle in the markets. In this paper, the author presents a transportation problem with non-linear constraints in which supply and demand are symmetric trapezoidal fuzzy value. In order to reflect a more realistic pattern, the unit of transportation cost is assumed to be stochastic. Then, the non-linear constraints are linearized by adding auxiliary constraints. Finally, the optimal solution of the problem is found by solving the linear programming problem with fuzzy and crisp constraints and by applying fuzzy programming technique. A new method proposed to solve this problem, and is illustrated through numerical examples. multi-objective goal programming methodology is applied to solve this problem. The results of this research were developed and used as one of the Decision Support System models in the Logistics Department of Kayson Co.
With the improvement of people's living standards and the desire to pursue quality of travel, the number of people who choose self-guided itineraries is increasing sharply. However, the problem is that planning ro...
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ISBN:
(数字)9780784482933
ISBN:
(纸本)9780784482933
With the improvement of people's living standards and the desire to pursue quality of travel, the number of people who choose self-guided itineraries is increasing sharply. However, the problem is that planning routes during the trip are exhausting, and the efficiency of transportation. In response to this and considering the psychology of people making the most of their holidays along with current boom in chain travel, the paper proposes a frame and an algorithm that aims to optimize the travel route. This paper studies route planning for chain travel of city groups within a specific range based on cluster analysis. The algorithm refers to the solution of TSP, knowledge in Graph Theory, and is suitable for optimizing travel itineraries for multiple cities for several days, characterized by providing users with time and money-saving options. It reduces waste on the road, waiting times for travelers, and provides convenience for self-guided itineraries.
This paper studies an integrated optimization model of production planning with partner selection in a networked manufacturing system. An integrated multi-objective programming model is proposed and a numerical exampl...
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ISBN:
(纸本)9783642384271;9783642384264
This paper studies an integrated optimization model of production planning with partner selection in a networked manufacturing system. An integrated multi-objective programming model is proposed and a numerical example is illustrated. The result shows the effectiveness and feasibility of the model. The model is suitable for the decision of production planning in networked manufacturing environment.
In this paper, we consider a fuzzy multi-choice linear programming problem where some of the parameters and decision variables are trapezoidal type fuzzy numbers. In order to defuzzify a general fuzzy quantity the con...
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In this paper, we consider a fuzzy multi-choice linear programming problem where some of the parameters and decision variables are trapezoidal type fuzzy numbers. In order to defuzzify a general fuzzy quantity the concept of nearest trapezoidal fuzzy number is introduced. By assuming all the decision variables as trapezoidal fuzzy number, the objective function and the left hand side of constraints are approximated to their nearest trapezoidal fuzzy number. Interpolating polynomials are formulated for all multi-choice type parameters. multi-choice type parameters are replaced by polynomials with integer variables. Then an equivalent multi-objective non linear programming problem is established. First and second objective functions represent left and right modal values of the trapezoidal type fuzzy number, where as the third and fourth objective functions represent the left and right spreads of the trapezoidal type fuzzy number. By applying lexicographic method optimal solution is obtained. In addition, a case study on a garment manufacture company is presented to demonstrate the solution procedure.
For Intelligent Transportation Systems(ITS), dynamic traffic route choice is one of the most important technologies. Practical applications have shown that static route choice models can not meet passengers' trave...
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ISBN:
(纸本)9781479932795
For Intelligent Transportation Systems(ITS), dynamic traffic route choice is one of the most important technologies. Practical applications have shown that static route choice models can not meet passengers' travel demands, because route choice is a typical multi-objective programming problem affected by many dynamic factors, such as real-time traffic state, link travel time, traffic incident, intersection delay and so on. In this paper, I proposed a multi-objective dynamic route choice function with the minimum of link travel time and distance as the target, which travelers paid most attention to when they chose the route. Also I got travel time reliability as the model constraint to against the complexity and indetermination of traffic flow. And I obtained the link travel time by short-term forecasting based on measured traffic data of the network. Finally, in order to test its reliability, I established a simulation road network for real-time traffic data, and solved the model I put forward by improved genetic algorithm. Fortunately, I obtained an exciting result.
The aim of this paper is to highlight the role of the Decision Support System within the field of multi-criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create multi-Criteria Decisio...
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The aim of this paper is to highlight the role of the Decision Support System within the field of multi-criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create multi-Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20-year period in which MCDSS was used as a decision-making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision-making methods such as analytic hierarchy process, Utility Additive, and Goal programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright (C) 2014 John Wiley & Sons, Ltd.
A multi-objective operation model is proposed in this paper to deal with a multi-product, multi-period supply chain consisting of a supplier and a producer. In this supply chain, the amount of raw materials supply and...
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ISBN:
(纸本)9787811240559
A multi-objective operation model is proposed in this paper to deal with a multi-product, multi-period supply chain consisting of a supplier and a producer. In this supply chain, the amount of raw materials supply and their prices, ultimate products demand and their prices are uncertain. The uncertainties of prices are described by the uncertain intervals;the supplies and demands are described as a scenario set with certain probability. The model is constructed as a multi-objective programming problem to satisfy several conflict objectives, such as the operating coordination of supply chain, making the maximum profit of all participants as much as possible, and robustness of decision to uncertain markets. The result of a numerical example shows that the model we proposed is robust.
Data envelopment analysis (DEA) is a well-known approach to measuring operations performance of decision-making units (DMUs). Moreover, the balanced scorecard (BSC) is a methodology for strategic planning of organizat...
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Data envelopment analysis (DEA) is a well-known approach to measuring operations performance of decision-making units (DMUs). Moreover, the balanced scorecard (BSC) is a methodology for strategic planning of organizations and measuring their internal performance. DEA generally uses quantity measures, whereas BSC applies quality measures. In the real world, DMUs usually have complex structures such as multi-level or hierarchical structures. In these structures, there are multiple levels and each level utilizes inputs to produce outputs separately, where the outputs of the previous level are the inputs to the next level. In the present paper, we apply BSC to the design of a multi-level structure. BSC encompasses the four perspectives of finance, customers, internal processes, and learning & growth. Furthermore, we apply DEA to multi-stage or hierarchical structures based on BSC. For this purpose, two different approaches, the cooperative model versus the multi-objective model, are proposed. Cooperative models such as bargaining games are game theoretic approaches to evaluate DMUs. In this paper, the DEA-game model uses the bargaining concept to link perspectives in BSC, while each perspective is considered separately as an objective in the multi-objective DEA model. In addition, two approaches are compared based on the related results. We also employ data on Iranian cement companies to show the different capabilities of each approach. Our findings demonstrate that the DEA-game model is capable of differentiating the cement companies from each other more effectively.
This paper presents a new modified technique for order preference by similarity to ideal solution (M-TOPSIS) approach for unraveling stochastic fuzzy multi-level multi-objective fractional decision making problem (ML-...
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This paper presents a new modified technique for order preference by similarity to ideal solution (M-TOPSIS) approach for unraveling stochastic fuzzy multi-level multi-objective fractional decision making problem (ML-MOFDM) problem. In the proposed model the coefficients and the scalars of the fractional objectives have a fuzzy nature. The right-hand sides are stochastic parameters also, both of the left-hand side coefficients and the tolerance measures are fuzzy kind. In this manner, the deterministic-crisp ML-MOFDM model of stochastic fuzzy ML-MOFDM can be gotten utilizing chance constrained strategy with predominance plausibility criteria and the alpha-cut methodology. In literature, almost all works on multi-level fractional programming are the crisp version, in which they convert the fractional functions into a linear one using a first order Taylor series which causes rounding off error. The proposed M-TOPSIS approach presents a new method for solving such problem without approximating or changing the nature of the problem. An algorithm to clear up the M-TOPSIS approach, just as illustrative numerical model is displayed.
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