Traditional traffic assignment model suffers from its criteria which are travel time only, travel time and distance, travel time and reliability. All of these criteria influence route choice behavior. multi-criteria u...
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ISBN:
(数字)9781510649767
ISBN:
(纸本)9781510649767;9781510649750
Traditional traffic assignment model suffers from its criteria which are travel time only, travel time and distance, travel time and reliability. All of these criteria influence route choice behavior. multi-criteria user equilibrium model considering travel time, travel time reliability and travel distance was proposed. It was proved that the week multi-criteria user equilibrium model is a generalization of user equilibrium (UE) model and travel time budget (TTB) model. multi-criteria and multi-class user equilibrium (MMUE) model was established to overcome the shortcoming of multi solution of multi-criteria user equilibrium model. Quasi-method of successive average (QMSA) was designed to solve the MMUE model. An example was used to test the speed and accuracy of proposed algorithm. And the differences of MMUE, UE and TTB model were compared. Experiments had shown that the QMSA algorithm could be quickly convergent. It is more realistic to consider the different travel value for each traveler in MMUE model.
Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to cor...
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ISBN:
(纸本)9780738133669
Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to correct the sensor errors of the UAV. This problem can be abstracted into a path planning problem and solved by ant colony algorithm. Considering the uncertainty of correction failure in these calibration points. This paper presents an improved ant colony algorithm and set up the "Enhanced pheromone volatilization strategy" to ensure that the UAV could reach the destination with the greatest possibility in this uncertain situation. We verify our algorithm on public data sets**. On data set 1, our algorithm has a 100% probability of reaching the destination, while the traditional ant colony algorithm has only a 61% probability of reaching the destination. On data set 2, our algorithm has a 56% probability of reaching the destination, while the traditional ant colony algorithm cannot find a path can reach the destination. The algorithm code*** in this paper is simple to implement, strong robustness, and can be extended to other scenarios.
Traditional inverse DEA models could be called inverse radial DEA because they are based on radial efficiency measures. Due to the neglect of slacks in evaluating the efficiency score, inverse radial DEA may mislead d...
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Traditional inverse DEA models could be called inverse radial DEA because they are based on radial efficiency measures. Due to the neglect of slacks in evaluating the efficiency score, inverse radial DEA may mislead decision-making in some cases where slacks play important roles. In this paper, we proposed an integrated framework of inverse DEA called inverse non-radial DEA since it is based on non-radial DEA by multi-objective programming, which covers existing inverse DEA models. To further illustrate the inverse non-radial DEA, we construct the concrete mathematical formula of inverse SBM and some properties. In contrast to the radial approach, inverse non-radial DEA can overcome the error caused by ignoring slacks and provides more valuable information about inputs and outputs for decision-making by considering slacks. Although inverse non-radial DEA models are usually non-linear, we can convert it into a one-dimensional search problem about efficiency score, which can be solved by many existing efficient algorithms. A practical example is provided to demonstrate the advantages of inverse non-radial DEA models over inverse radial DEA models.
This paper addresses an integrated relief network design problem for pharmaceutical items. The proposed bi-objective model accounts for perishability of pharmaceutical items, mobility of relief facilities, and benefit...
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This paper addresses an integrated relief network design problem for pharmaceutical items. The proposed bi-objective model accounts for perishability of pharmaceutical items, mobility of relief facilities, and benefits from a cooperative coverage mechanism in designing the network. A min-max robust model is developed to tackle the demand uncertainty. Several numerical experiments are conducted to explore the performance of the robust model. Also, by conducting a real case study, useful managerial insights are derived through performing several sensitivity analyses. The numerical results reveal that using the min-max robust model enhances the pharmaceutical relief network's effectiveness and efficiency considerably.
Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be...
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Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this paper, environmental concerns are considered in formulating a mathematical model to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. We develop a multi-objective mixed-integer linear programming formulation to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization of facilities. To demonstrate the efficiency of the proposed optimization model, we design a green meat supply chain network for Southern Ontario, Canada. A solution approach based on augmented epsilon-constraint method is employed to solve the proposed model. As a result, a set of Pareto-optimal solutions is obtained. The set of Pareto-optimal solutions gives decision-makers the opportunity to make a trade-off between economic, environmental, and capacity utilization objectives. Our example shows that it is possible to keep emissions reasonably low without incurring high total costs. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature.
In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted...
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In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted voltage deviation index (LVDI) to quantify network voltage deviation. Secondly, this paper proposes a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously. Then, a multi-objective adaptive robust optimization method is proposed to obtain robust Pareto solutions under uncertainties. Accordingly, solution algorithms based on different multi-objective programming algorithms and a column-and-constraint generation algorithm are developed and systematically compared. The proposed method is verified through comprehensive tests on the IEEE 123-bus system and simulation results demonstrate high effectiveness of the LVDI, high efficiency of the solution algorithms and full operating robustness of the proposed VVC method against any uncertainty realization.
Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision ***,...
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Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision ***,the existing weapon system-of-systems(SoS)is tightly *** of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon *** the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important *** paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio ***,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping *** service collaboration network to calculate the flexibility and connectivity of each service portfolio is then *** multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.
We consider the constrained multi-objective optimization problem of finding Pareto critical points of difference of convex functions. The new approach proposed by Bento et al. (SIAM J Optim 28:1104-1120, 2018) to stud...
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We consider the constrained multi-objective optimization problem of finding Pareto critical points of difference of convex functions. The new approach proposed by Bento et al. (SIAM J Optim 28:1104-1120, 2018) to study the convergence of the proximal point method is applied. Our method minimizes at each iteration a convex approximation instead of the (non-convex) objective function constrained to a possibly non-convex set which assures the vector improving process. The motivation comes from the famous Group Dynamic problem in Behavioral Sciences where, at each step, a group of (possible badly informed) agents tries to increase his joint payoff, in order to be able to increase the payoff of each of them. In this way, at each step, this ascent process guarantees the stability of the group. Some encouraging preliminary numerical results are reported.
Based on the uncertain conditions such as uncertainty in blood demand and facility disruptions, and also, due to the uncertain nature of blood products such as perishable lifetime, distinct blood groups, and ABO-Rh(D)...
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Based on the uncertain conditions such as uncertainty in blood demand and facility disruptions, and also, due to the uncertain nature of blood products such as perishable lifetime, distinct blood groups, and ABO-Rh(D) compatibility and priority rules among these groups, this paper aims to contribute blood supply chains under uncertainty. In this respect, this paper develops a bi-objective two-stage stochastic programming model for managing a red blood cells supply chain that observes above-mentioned issues. This model determines the optimum location-allocation and inventory management decisions and aims to minimize the total cost of the supply chain includes fixed costs, operating costs, inventory holding costs, wastage costs, and transportation costs along with minimizing the substitution levels to provide safer blood transfusion services. To handle the uncertainty of the blood supply chain environment, a robust optimization approach is devised to tackle the uncertainty of parameters, and the TH method is utilized to make the bi-objective model solvable. Then, a real case study of Mashhad city, in Iran, is implemented to demonstrate the model practicality as well as its solution approaches, and finally, the computational results are presented and discussed. Further, the impacts of the different parameters on the results are analyzed which help the decision makers to select the value of the parameters more accurately.
Managing platelets supply chain network has proved challenging. Besides stochastic demand, the high perishability of platelets and the diversity of their demands make the management more intricate. As a motivation to ...
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Managing platelets supply chain network has proved challenging. Besides stochastic demand, the high perishability of platelets and the diversity of their demands make the management more intricate. As a motivation to conduct this paper, we investigate a real-world case study facing a variety of platelet demands to satisfy. Being the first-ever study, we contribute a practical method for the efficient design and planning of a multiple platelet-derived products supply chain network. As the most perishable blood product, platelets need to be maintained fresh. Thus, we suggest a bi-objective model make a tradeoff relationship between the network costs and platelets' freshness. Further, we account for two realistic features that the products are categorized into three main types with respect to their application and lifetime, and hospitals are prioritized based on their specialty and the population of patients they cover. To cope with the uncertainty and objectivemultiplicity, we develop a mixed approach. The network robustness under uncertainty is controlled by a robust method and the Pareto solutions of the conflicting objectives are obtained via an interactive approach. Further, we take into account real-world scenarios that the network facilities may face disruptions and utilize a robust scenario-based approach to deal with the disruption scenarios. The results demonstrate that although simultaneous demand fluctuation and disruption increase both logistics costs and delivery time, the proposed model is capable of achieving robust solutions that a little increase in the logistic costs obtains a considerable reduction in the level of relative regret. Further, the network will benefit from a favorable saving in the logistic costs only by a little increase in the storage time.
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