Inter-operator resource and service sharing, which can utilize the network resources more efficiently and improve users' quality of services, is regarded as a promising method for the operators to construct future...
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ISBN:
(纸本)9781538674628
Inter-operator resource and service sharing, which can utilize the network resources more efficiently and improve users' quality of services, is regarded as a promising method for the operators to construct future multi-operator networks. Particularly, an appropriate user association scheme is quite essential to enhance the network capacity. In this paper, our objective is to maximize the capacity of a multi-operator network by optimizing the user association scheme. Specifically, we investigate three inter-operator sharing scenarios, i.e., service-sharing scenario, spectrum-sharing scenario, and full-sharing scenario. Then, we propose a fractional programming (FP) based algorithm for the user association optimization problem. As inter-operator service sharing enables users to be served by other operators, a sharing coefficient is introduced for each operator to indicate its service level agreement (SLA) for regulating the users' association behaviors. As shown in the simulation results, the user association scheme obtained by the proposed algorithm can achieve a higher capacity than other alternative schemes. In comparison with the scenario without inter-operator sharing, both inter-operator spectrum sharing and service sharing can improve network capacity significantly, and the largest capacity is realized under the full-sharing scenario.
The issue of transportation is a particular type of mathematical programming that facilitates searching for and determining an optimal distribution network, considering the set of suppliers and recipients. This paper ...
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The issue of transportation is a particular type of mathematical programming that facilitates searching for and determining an optimal distribution network, considering the set of suppliers and recipients. This paper uses a numerical example to present a solution to a transport problem utilizing classical computation methods, i.e., the northwest corner, the least cost in a matrix, and the VAM approximation method. The objective of the paper was to develop tools in the form of algorithms that would then be implemented in three various computing environments (R, GNU Octave, and Matlab) that allow us to optimize transport costs within an assumed supply network. The model involved determining decision variables and indicating limiting conditions. Furthermore, the authors interpreted and visualized the obtained results. The implementation of the proposed solution enables users to determine an optimal transport plan for individually defined criteria.
With the emergence of environmental problems, the implementation of electric vehicles in the transport sector presents a solution that meets environmental and economic objectives. For this reason, electric vehicles (E...
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With the emergence of environmental problems, the implementation of electric vehicles in the transport sector presents a solution that meets environmental and economic objectives. For this reason, electric vehicles (EVs) have become increasingly popular as a mainstream transportation solution, opportunities to recharge the vehicle away from home have become a critical issue, and it needs a long waiting time, with a risk of electrocution. One of the solutions to avoid these disadvantages is the wireless charging EVs. For that reason, the main contribution of this work is to propose the strategic location of inductive power transmitters especially when there are several routes between an origin and a destination. Our goal is to find a compromise between the cost of installing the power transmitters and the cost of the battery while maintaining the quality of the vehicle routing. To show the efficiency of our mathematical model and resolution method, we compare our results with the results found in the literature.
Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptation...
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Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks. In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development.
This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker's pr...
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This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker's preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.
This study proposes an architecture (hardware and software) for real-time monitoring and control system for a cold supply chain. The architecture includes different physical components and how they are interconnected ...
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This study proposes an architecture (hardware and software) for real-time monitoring and control system for a cold supply chain. The architecture includes different physical components and how they are interconnected and their structure, behavior, functions, and decision-making processes. The system is based on RFID-WSN-GPS, where collected data are transferred in real time to a controller serving as a decision-making unit. The controller analyzes, stores, predicts, and intervenes whenever faults occur. mathematical models corresponding to different supply chain configurations were developed in this study. Different cost components (such as products, transportation, lost sales, and inventory-related costs) and risk mitigation decisions (such as stopping transportation and rerouting shipments) are considered in the models. A case study of grape transportation was conducted. In this case study, four different supply chain configurations were compared to demonstrate the utility of the proposed system in terms of cost savings. The results show cost reductions of up to 3.39% depending on the different system configurations and the set of decisions considered. Sensitivity analysis was conducted to evaluate the effect of failure probability and cost components on cost savings.
Portfolio optimization is about building an investment decision on a set of candidate assets with finite capital. Generally, investors should devise rational compromise to return and risk for their investments. Theref...
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Portfolio optimization is about building an investment decision on a set of candidate assets with finite capital. Generally, investors should devise rational compromise to return and risk for their investments. Therefore, it can be cast as a biobjective problem. In this work, both the expected return and conditional value-at-risk (CVaR) are considered as the optimization objectives. Although the objective of CVaR can be optimized with existing techniques such as linear programming optimizers, the involvement of practical constraints induces challenges to exact mathematical methods. Hence, we propose a new algorithm named F-MOEA/D, which is based on a Pareto front evolution strategy and the decomposition based multiobjective evolutionary algorithm. This strategy involves two major components, i.e., constructing local Pareto fronts through exact methods and picking the best one via decomposition approaches. The empirical study shows F-MOEA/D can obtain better approximations of the test instances against several alternative multiobjective evolutionary algorithms with a same time budget. Meanwhile, on two large instances with 7964 and 9090 assets, F-MOEA/D still performs well given that a multiobjective mathematical method does not finish in 7 days.
Data on inter-district food flows are typically not collected and are thus unavailable for most sub-Saharan African (SSA) countries and for many parts of world. Given the volatile and frequent regionally specific defi...
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Data on inter-district food flows are typically not collected and are thus unavailable for most sub-Saharan African (SSA) countries and for many parts of world. Given the volatile and frequent regionally specific deficits in food production in Malawi, evidence on food flows under different scenarios is needed for food policy decisions. This paper develops a spatially explicit mathematical programming model for the Malawian food sector to calibrate inter-district food flows and to assess how transport cost variations affect these flows. The food sector modeling approach we develop and implement allows for a natural estimation of inter-district trade flows in data sparse environments. In addition, we restrict crop mixes to those within the range of observed historical crop land use unlike modeling approaches that are prone to overspecialization. The calibration results for our baseline model indicate that about 7% of Malawian maize production flows between districts as compared to 66% for rice, 74% for beans, and 46% for groundnuts. A simulation experiment of varying unit transport costs shows that reductions in per unit transport costs increase the share of production that is traded inter-regionally, although the traded shares vary among the crops included in our model. The effectiveness of spatially targeted food production and marketing policies in Malawi therefore depends on these baseline food flows and the associated inter-district trade costs. Future research agenda on generating agricultural statistics in Malawi should focus on introducing intra-national commodity flow surveys.
Autonomous equipment for crop production is on the brink of commercialization in the United States but federal, state, and local policies could affect commercial viability and hinder adoption. This article examines th...
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Autonomous equipment for crop production is on the brink of commercialization in the United States but federal, state, and local policies could affect commercial viability and hinder adoption. This article examines the farm-level implications of both a speed restriction and on-site supervisory regulations. The rules reduce the profitability of autonomous machinery, and for some scenarios autonomous machines are no longer an economically viable alternative to conventional machinery. Regulations also increase the optimal number autonomous machines required and influence production practices. Smaller farms have more flexibility in supporting the rules because they have more to gain from the use of autonomous equipment.
The dual hesitant fuzzy set (DHFS) is an effective mathematical approach to deal with the data which are imprecise, uncertain or incomplete information. DHFS is an extension of hesitant fuzzy sets (HFS) which encompas...
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The dual hesitant fuzzy set (DHFS) is an effective mathematical approach to deal with the data which are imprecise, uncertain or incomplete information. DHFS is an extension of hesitant fuzzy sets (HFS) which encompass fuzzy sets (FS), intuitionistic fuzzy sets (IFS), HFS, and fuzzy multisets as a special case. DHFS consist of two parts, that is, the membership and non-membership degrees which are represented by two sets of possible values. Therefore, in accordance with the practical demand these sets are more flexible and provide much more information about the situation. The aim of this paper is to develop an effective methodology for solving matrix games with payoffs of triangular dual hesitant fuzzy numbers (TDHFNs). The flaws of the existing approach to solve matrix games with TDHFNs payoffs are pointed out. Moreover, to resolve these flaws, novel, general and corrected approach called Mehar approach is proposed to obtain the optimal strategies for TDHFNs matrix games. In this methodology, the concepts and ranking order relations of TDHFNs are defined. A pair of bi-objective linear programming models for matrix games with payoffs of TDHFNs is derived from two auxiliary dual hesitant fuzzy programming models based on the ranking order relations of TDHFNs defined in this paper. An effective methodology based on the weighted average method is developed to determine optimal strategies for two players. In this approach, it is verified that any matrix game with TDHFNs payoffs always has a TDHFNs equilibrium value. Finally, a numerical experiment is incorporated to illustrate the applicability and feasibility of the proposed Mehar approach in TDHFNs matrix game. The obtained results are compared with the results obtained by the previous approaches for solving TDHFNs matrix game.
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