博弈联盟模糊收益的分配

提出了联盟模糊收益合理分配的一种新方法.首先,在模糊收益α截集上定义了α合理分配集,分析了该分配集与模糊收益Shapley值的关系.接着,给出了模糊收益的α合理Shapley分配函数,对其性质进行了讨论.然后,构造了模糊合理Shapley分配,证明其连续性,得到了联盟模糊收益与模糊合理Shapley分配具有包含关系的结论.     

Variable neighborhood search for minimum cost berth allocation

The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well as earliness or tardiness of completion, for all ships. We assume ships can arrive at any given time, i.e., before or after the berths become available. The resulting problem, which subsumes several previous ones, is expressed as a linear mixed 0–1 program. As it turns out to be too time-consuming for exact solution of instances of realistic size, a Variable Neighborhood Search (VNS) heuristic is proposed, and compared with Multi-Start (MS), a Genetic Search algorithm (GA) and a Memetic Search algorithm (MA). VNS provides optimal solutions for all instances solved to optimality in a previous paper of the first two authors and outperforms MS, MA and GA on large instances.     

Work allocation to stations with varying learning slopes and without buffers

In this paper, we address the problem of allocating the work elements, belonging to the products of a lot, to the stations of an assembly line so as to minimize the makespan. The lots that are processed on the assembly line are characterized by a low overall demand for each product. There is no buffer permitted in between the stations, and the line operates under learning. In particular, the stations’ learning slopes are assumed to be different. We present a procedure to determine the optimal assignments of the workload to the stations under learning variability and show that it considerably affects these assignments.     

Partial integration of frequency allocation within antenna positioning in GSM mobile networks

In this article we propose to partially integrate the antenna positioning (APP) and frequency allocation problems (FAP). The traditional wireless network design process examines these two major issues sequentially in order to avoid the very high complexity associated with the simultaneous resolution of the two problems. The proposed integration involves the introduction of interference protection guarantees within the APP. It is customary to define such guarantees in an intermediate step and to use them as input to FAP, in order to protect against interference in critical areas. The proposed approach consists of selecting these protections while solving the APP, allowing the optimization procedure to exploit the degrees of freedom that this would offer. Results on two real-life problem instances indicate a significant improvement in interference levels and resource utilization.     

敏捷供应链管理中最优激励方案的实现问题研究

合理的利益分配策略可以稳定敏捷供应链的运营过程,加速敏捷供应链目标的实现.基于委托-代理理论,研究了敏捷供应链利益分配中最优激励方案的设计问题,得出在信息对称条件下由于合作伙伴企业的努力水平可以为主导企业所观测,因而容易制定最优激励方案并实现利益分配.而在更多的信息不对称条件下,需要对合作伙伴企业的产出结果进行监督和评估,通过不同努力水平下的分布概率间接地获得努力水平信息.该结论对于敏捷供应链利益分配系统的设计与实现具有重要指导意义.对最优激励方案在敏捷供应链利益分配中的实现过程进行了说明,通过适当的评价指标体系和评价方法,实现了利益分配中考虑努力水平等因素的激励策略.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

Interference and priority aware resource allocation in coexisting WBANs using game models

Wireless body area networks (WBANs) have become an integral part of our health monitoring system. However, the specific challenges offered by a dense co-existence of heterogeneous WBANs have not been properly addressed. The interference between the coexisting WBANs which cause throughput degradation and energy wastage can be mitigated by suitable channel and slot allocations. In this work, a Stackelberg game model with pricing is used for resource scheduling among multi-class WBANs. The access points (APs) act as leaders who decide the prices to be paid by the normal WBAN users for reusing the resources allocated to the critical WBAN users. The normal WBAN users act as followers which use the pricing information and a distributed learning algorithm that guarantees Pareto-optimal solution. As a benchmark for comparison purpose, we propose a centralized resource scheduling which is formulated as an optimization problem where the objective is to maximize system throughput while guaranteeing the quality of service (QoS) requirements of multi-class WBANs. To efficiently solve this problem, a column generation method is proposed. Numerical results are provided to show the efficiency and effectiveness of the proposed game model.     

A single-channel single-winner auction model for homogeneous channel allocation in CRNs

Cognitive radio (CR) is a novel intelligent technology which enables opportunistic access to temporarily unused licensed frequency bands. A key functionality of CR is to distribute free channels efficiently amongst Secondary Users (SUs) boosting spectrum usage to assist the escalating wireless applications world wide. In this context, this paper introduces a channel allocation mechanism which enables SUs (CR enabled unlicensed users) to dynamically access unused spectrum bands to fulfill their spectrum needs. We model the channel allocation problem as a sealed-bid single-sided auction which primarily aims at maximizing the overall spectrum utilization. Market based spectrum auctions in CR networks motivate licensed users to participate and lease their under utilized radio resources to gain monetary benefits. Sequential bidding is applied to this model for auctioning homogeneous channels, which reduces communication overhead. Bid submission takes into account two major CR constraints, namely, dynamics in spectrum opportunities and differences in channel availability time, which on incorporation provide disruption free data transmission to the SUs. We reduce resource wastage in this model by performing multiple auction rounds. Application of second price auction determines winning bidders and their respective payments to auctioneer. The design of our auction mechanism is supported with the proofs of truthfulness and individually rational properties. Furthermore, experimental results indicate that our model outperforms an existing auction method. Spectrum utilization values show 22 to 75% improvement in our model with changing number of SUs, and 23 to 93% improvement in our model with changing number of channels.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).