Improving the Efficiency of Stochastic Vehicle Routing: A Partial Lagrange Multiplier Method

Cao, Zhiguang; Guo, Hongliang; Zhang, Jie; Niyato, Dusit; Fastenrath, Ulrich

doi:10.1109/tvt.2015.2480964

Cited by 56 publications

(26 citation statements)

References 30 publications

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“…However, if we can convert our problem to a linear program (LP) without loss of precision, we can solve it in polynomial time. In this section we introduce the partial Lagrange multiplier method [36], which solves a sequence of LPs with at worst a polynomial number of steps.…”

Section: Partial Lagrange Multiplier Methodsmentioning

confidence: 99%

“…This proof is given in Appendix B due to space constraints. Since we have proved that g( λ) is concave, we can assume that ∇g( λ) ≤ G, where G is a value that we define in (35) and (36). Further, each individual Lagrange multiplier value, denoted by λ i , is bounded above, as well as by the defined bound of λ i ≥ 0.…”

Section: Proof Of Convergencementioning

confidence: 99%

“…With this established, we can write the convergence proof from [36], which we provide in Appendix C for reference. This proof is a known result from convex optimization, and thus we know that g…”

Section: Proof Of Convergencementioning

confidence: 99%

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A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Chase

Niyato

Wang

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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Abstract-As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-asa-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.

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Section: Partial Lagrange Multiplier Methodsmentioning

confidence: 99%

Section: Proof Of Convergencementioning

confidence: 99%

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A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Chase

Niyato

Wang

et al. 2019

IEEE Trans. Dependable and Secure Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is recognized that, if + = 1, then () = ( ′ ). By using the Lagrangian multiplier method 34 for relaxing the constraint in (22), the problem  ′ is relaxed as follows: where i j is the Lagrangian multiplier for the constraint x i j = y i j . The Lagrangian relaxation ( ) can be separated into two problems.…”

Section: Joint Energy and Latency Optimizationmentioning

confidence: 99%

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Dao

Jang

et al. 2018

Trans Emerging Tel Tech

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Recently, the emergence of fog computing and big internet of things (IoT) data have been considered as the main representatives identifying fifth‐generation mobile networks. In fifth generation, cloudization is extended from the core to the access tiers, referred to as fog radio access networks. Fog radio access networks provide ultralow latency offloading services to a massive number of IoT devices in their proximity. In this paper, we propose a joint energy and latency optimization (JELO) scheme for upstream IoT offloading services in fog radio access networks. The JELO scheme controls the offloaded task assignment among fog‐enabled eNodeBs (fog nodes) with strict consideration of the systematic resources and specific characteristics of individual tasks. The joint objective function aims at red optimizing the energy red consumption and offload latency for entire networks. Simulation results demonstrate that the JELO scheme outperforms existing approaches in terms of the energy consumption and load balancing while maintaining IoT service satisfaction.

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“…Zhao et al propose an offensive strategy based on a virtual tangent circle for robotic fish competition [7]; Gao et al propose a method for path planning of robotic fish balls based on fuzzy logic and geometry [8]; Xie et al propose fuzzy control based steering control algorithm [9]. Guo et al propose several path finding and path planning methods for smart agents, which consider the criterion of 'faster' or 'arriving on tome' in the route optimization [10][11][12][13][14]. In terms of robustness research, ke haokang also proposed the corresponding stability path planning strategy [15].…”

Section: Introductionmentioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

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To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, "adjusting position" or "pushing ball" defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real-time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

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Improving the Efficiency of Stochastic Vehicle Routing: A Partial Lagrange Multiplier Method

Cited by 56 publications

References 30 publications

A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

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