Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry, the coordinated scheduling problem of parallel machine production and transportation is investigated. The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance. A non-cooperative game model is established, considering the competition and self-interest behavior of jobs from different customers for machine resources. The job from different customers is mapped to the players in the game model, the corresponding optional processing machine and location are mapped to the strategy set, and the makespan of the job is mapped to the payoff. Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model. A Nash equilibrium solution algorithm based on the genetic algorithm (NE-GA) is designed, and the effective solution of approximate Nash equilibrium for the game model is realized. The fitness function, single-point crossover operator, and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium. Rules are also designed to avoid the generation of invalid offspring chromosomes. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. Compared with other algorithms such as heuristic algorithms (FCFS, SPT, and LPT), the simulated annealing algorithm (SA), and the particle swarm optimization algorithm (PSO), experimental results show that the proposed NE-GA algorithm has obvious performance advantages.     

沿海产业合作污染治理的微分对策

构建了基于连续时间的海洋产业和陆域产业关于沿海环境建设项目中投资合作的微分对策动态模型,运用汉密尔顿-亚柯比-贝尔曼方程求出海洋产业和陆域产业在Nash非合作博弈、Stackelberg主从博弈及协同合作博弈情形下的最优策略,并对反馈均衡结果进行了比较。研究结果显示,与另外两种博弈情形相比,协同合作博弈情形下的系统收益最大,沿海污染存量最少;Stackelberg主从博弈情形下的收益水平与污染存量都优于Nash非合作博弈情形下的收益水平和污染存量,且在Stackelberg主从博弈情形下,海洋产业的激励因子在沿海环境投资建设中具有协调和激励作用,可以提高海陆产业双方以及沿海产业整个系统的收益,得出该情形下海洋产业和陆域产业在沿海环境投资建设中的系统最优收益分配比例,使得沿海产业总系统利润最大化。     

两级供应链中合作广告的博弈分析

研究了由单一制造商和单一零售商组成的二级供应链的合作广告策略。采用博弈论方法构建了模型并结合数值仿真,在考虑制造商与零售商各自的广告成本的情形下,分别考察了非合作静态博弈、Stackelberg主从博弈以及Nash合作博弈下的最优广告策略。通过比较,得到了制造商和零售商选择合作情形下的广告水平、订货量和整条供应链收益均优于不合作情形的结论,制造商的全国性广告水平与自身的广告成本呈负相关,而与零售商的广告成本呈正相关,为供应链成员选择广告策略提供了理论依据。     

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.     

动态联盟收益分配问题的博弈研究

借鉴委托-代理理论建立了动态联盟的收益分配模型,分析了动态联盟收益分配过程应考虑的因素,并以此为基础利用博弈论的相关模型分析动态联盟的博弈行为,其中包括两种非合作策略下的博弈和一种合作策略下的博弈,并通过实例论证了动态联盟进行合作机制设计和应用的必要性.     

Fair power control for wireless ad hoc networks using game theory with pricing scheme

Resource allocation in wireless ad hoc networks is usually modelled in a non-cooperative game theoretic framework with the objective of maximising individual utility. However, the selfishness of autonomous users under such framework may lead to throughput unfairness which only benefits certain users. To alleviate this unfairness problem, the authors propose a payment-based power control scheme using game theory where each user announces a set of price coefficients that reflects different compensations paid by other users for the interference they produce. Users who generate higher interference are required to pay more by transmitting at a lower power to give other users a fairer chance of sharing the throughput.Without any incentive to play fairly, users could misbehave by broadcasting high price coefficients to force other users to transmit at a lower power. The authors treat this problem casting it into a price game which resembles a Prisoner's Dilemma game. Users who play this game iteratively will behave cooperatively and broadcast the price coefficients truthfully. Together with analytical proof, the proposed approach is shown to converge to Nash equilibrium where at this point it is able to provide a fairer throughput share among users at the expense of a slight loss in total throughput.     

Game theoretic approach for channel assignment and power control with no-internal-regret learning in wireless ad hoc networks

In wireless ad hoc networks, co-channel interference can be suppressed effectively through proper integration of channel assignment (CA) and power control (PC) techniques. Unlike centralised cellular networks where CA and PC can be coordinated by base stations, the integration of CA and PC into infrastructureless wireless ad hoc networks where no global information is available is more technically challenging. The authors model the CA and PC problems as a non-cooperative game, in which all wireless users jointly pick an optimal channel and power level to minimise a joint cost function. To prove the existence and uniqueness of Nash equilibrium (NE) in the proposed non-cooperative CA and PC game (NCPG), the authors break the NCPG into a CA subgame and a PC subgame. It is shown that if NE exists in these two subgames, the existence of NE in the NCPG is ensured. Nonetheless, due to unpredictable network topology and diverse system conditions in wireless ad hoc networks, the NCPG may encounter the dasiaping-pongdasia effect that renders NE unattainable. By incorporating a call-dropping strategy and no-internal-regret learning into the NCPG, an iterative and distributed algorithm that ensures convergence to NE is proposed. It is shown through simulation results that the proposed approach leads to convergence and results in significant improvements in power preservation and system capacity as compared with the popular distributed dynamic CA technique incorporated with PC.     

On describing the solution sets of generalized Nash games with shared constraints

In this paper we use parametric variational inequality problems for the purpose of describing entire solution sets of generalized Nash games with shared constraints. We prove two theoretical results and we introduce a computational method that practitioners can implement in applied problems modeled as generalized Nash games, under assumptions present in the current literature. Further, we give illustrative examples of how our computational technique is used to derive solution sets of known generalized Nash games previously not solved by existing techniques. We close with the presentation of an applied problem formulated as a generalized Nash game, namely a model of a joint implementation environmental accord between countries. We discuss the possible advantages of modeling it within a generalized Nash game framework.     

Modeling the design quality competition for durable products

In this paper we model the design quality competition in durable product markets. We use a duopolistic non-cooperative game theoretic framework. Each firm controls the design quality level and the price of its product. The resulting control theoretic model is solved under certain functional assumptions to describe the Nash equilibrium design quality and pricing strategies of the two competitors. We compare open and closed loop design quality and pricing strategies. We use an extensive set of numerical experiments to investigate the effects of various parameters of the model on the optimal policies. We conclude with a brief summary of our results and some useful managerial insights derived from the model.     

Multi-period hedging and coordination in a prefabricated construction supply chain

In this paper, a multi-period buffer space hedging coordination between a building contractor (BC) and a logistics provider (LP) is investigated. In order to facilitate the application of the construction lead-time hedging (CLTH) strategy, adopted by the BC, extra buffer space needs to be reserved at LP’s intermediate warehouse for contingency usage. This strategy is defined as ‘buffer space hedging (BSH)’ and it increases the pressure of LP on involving extra storage and maintenance cost. Two coordination mechanisms are adopted for solving this BSH problem. One scheme is by introducing a cost-sharing term. A Nash game model is studied to find the individual optimal decisions. Another scheme is a cooperative game model with proper side-payments. We show that adjusting the BSH amount for each review period benefits the supply chain. Both coordination mechanisms enable a win–win outcome. Especially, if system resources are sufficient, the cooperative game outperforms the Nash game. Numerical experiments further demonstrate that the benefit of the proposed model is more significant under the case with lower unit enlarging/rearrangement cost, higher tardiness penalty and unpredictable and uncontrollable construction process-determined assembled and installed prefabs.     

Efficient unmanned aerial vehicle formation rendezvous trajectory planning using Dubins path and sequential convex programming

Trajectory planning of formation rendezvous of multiple unmanned aerial vehicles (UAVs) is formulated as a mixed-integer optimal control problem, and an efficient hierarchical planning approach based on the Dubins path and sequential convex programming is proposed. The proposed method includes the assignment of rendezvous points (high level) and generation of cooperative trajectories (low level). At the high level, the assignment of rendezvous points to UAVs is optimized to minimize the total length of Dubins-path-based approximate trajectories. The assignment results determine the geometric relations between the UAVs’ goals, which are used as equality constraints for generating trajectories. At the low level, trajectory generation is treated as a non-convex optimal control problem, which is transformed to a non-convex parameter optimization and then solved via sequentially performing convex optimization. Numerical experiments demonstrate that the proposed method can generate feasible trajectories and can outperform a typical nonlinear programming method in terms of efficiency.     

供应不确定下制造商的采购策略:回收或双渠道采购

在考虑供应商供应不确定的情形下,构建制造商在不同市场结构下的采购策略选择模型,研究发现:①垄断市场中,当供应不确定性较大时,制造商会选择回收策略;当供应商的不确定性较小时,制造商选择双渠道采购。②竞争市场中对比制造商的均衡利润发现,当供应商供应不确定性大时,两制造商均选择回收策略达到纳什均衡;当供应不确定性处于中间水平时,两制造商选择差异化的采购策略更有利可图;当供应商供应不确定性小时,上游供应商之间的竞争激烈,双渠道采购为市场的均衡策略。此外,还探讨了供应不确定性的变化对制造商的利润影响。     

考虑消费者辨识能力的冷链博弈分析

消费者辨识能力是指消费者对所购产品或服务品质的辨别能力。针对忽略消费者辨识能力将得出不准确结论，给决策者的抉择造成误导的问题，采用完全信息静态博弈方法，分别构建了政府、冷链产品企业和消费者之间的不考虑消费者辨识能力及考虑消费者辨识能力的混合策略博弈模型。通过分析Nash均衡解及期望收益，对比发现，忽略消费者辨识能力将低估政府部门的期望收益，同时对企业和消费者的期望收益也预估不准确。因此，在实际问题中应关注消费者对产品的辨识能力。     

The impact of strategic inventory and procurement strategies on green product design in a two-period supply chain

This study analyses the impact of power structures and strategic inventory on the development-intensive and marginal-cost-intensive green product types under three procurement strategies. The results suggest that (i) in the manufacturer-Stackelberg game, the retailer retains strategic inventory to earn higher profits. The retailer's decision improves profit for the manufacturer and greening level of the product; (ii) for the marginal-cost-intensive green product, the power structures and procurement strategies cannot make any impact on the greening level and the retailer cannot build up strategic inventory under retailer-Stackelberg game; (iii) under the Nash game, the procurement decision creates conflict between the supply chain members for marginal-cost-intensive green products; (iv) if the retailer does not maintain strategic inventory or procures product in a single lot, then the manufacturer prefers to produce marginal-cost-intensive products and retailer prefers to sale development-intensive products to receive maximum profits under manufacturer-Stackelberg game. The optimal preferences are concurrent under retailer-Stackelberg game, but not under the Nash game; (v) single-period equilibrium solutions may exhibit sub-optimal characteristics, but two-period planning can lead to exemplary outcomes in the perspective of the greening level and profits of the supply chain members.     

Research on Sensor Network Coverage Enhancement Based on Non-Cooperative Games

Coverage is an important issue for resources rational allocation, cognitive tasks completion in sensor networks. The mobility, communicability and learning ability of smart sensors have received much attention in the past decade. Based on the deep study of game theory, a mobile sensor non-cooperative game model is established for the sensor network deployment and a local information-based topology control (LITC) algorithm for coverage enhancement is proposed. We both consider revenue of the monitoring events and neighboring sensors to avoid nodes aggregation when formulating the utility function. We then prove that the non-cooperative game is an exact potential game in which Nash Equilibrium exists. The proposed algorithm focuses on the local information of the neighboring sensors and decides sensors’ next action based on the actions of the other sensors, which maximizes its own utility function. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate that the proposed algorithm can enlarge the coverage of the entire monitoring area while achieving effective coverage of the events.     

基于基站间合作的下行干扰协调方法及其性能评估

从基站间合作的角度研究了蜂窝系统下行多小区干扰协调问题.引入了合作虚拟小区的概念,并提出了三扇区合作虚拟小区规划方案和双扇区合作虚拟小区规划方案.所提方案在实现小区间干扰协调的同时,还可使系统实现同频组网和无缝切换.仿真结果显示,与传统的频率复用方式相比,合作虚拟小区方案能很好地平衡小区内不同位置频谱效率的分布,改善边缘用户的性能.其中,双扇区合作方案可使系统中断概率比频率复用方式(1,3,3)下降5%以上,而且其扇区吞吐量与频率复用方式(1,3,1)相当.     

A new strategy for direct tool path generation from measured points

Conventional algorithms for tool path generation from measured points do not use multi-resolution and region-by-region strategies, the advantages of which have been demonstrated, to generate region-by-region tool paths. This paper presents a new strategy for generating tool paths from measured points directly by using multi-resolution and region-by-region strategies. Given a set of measured points, for rough-cuts we select a fewer number of measured points than control points to construct a coarse B-spline surface and then generate rough-cut tool paths. For finish-cuts, we select a large number of measured points as control points to construct a fine B-spline surface and then generate region-by-region finish-cut tool paths.     

The effect of model uncertainty on cooperation in sensorimotor interactions

Decision-makers have been shown to rely on probabilistic models for perception and action. However, these models can be incorrect or partially wrong in which case the decision-maker has to cope with model uncertainty. Model uncertainty has recently also been shown to be an important determinant of sensorimotor behaviour in humans that can lead to risk-sensitive deviations from Bayes optimal behaviour towards worst-case or best-case outcomes. Here, we investigate the effect of model uncertainty on cooperation in sensorimotor interactions similar to the stag-hunt game, where players develop models about the other player and decide between a pay-off-dominant cooperative solution and a risk-dominant, non-cooperative solution. In simulations, we show that players who allow for optimistic deviations from their opponent model are much more likely to converge to cooperative outcomes. We also implemented this agent model in a virtual reality environment, and let human subjects play against a virtual player. In this game, subjects'' pay-offs were experienced as forces opposing their movements. During the experiment, we manipulated the risk sensitivity of the computer player and observed human responses. We found not only that humans adaptively changed their level of cooperation depending on the risk sensitivity of the computer player but also that their initial play exhibited characteristic risk-sensitive biases. Our results suggest that model uncertainty is an important determinant of cooperation in two-player sensorimotor interactions.     

Dynamic corporate social responsibility (CSR) strategies in oligopoly

This paper investigates dynamic and strategic (oligopolistic) interactions with respect to their corporate social responsibility (CSR) activities. In order to separate dynamic from strategic effects, the paper proposes a differential game and derives the open loop and Markov perfect Nash equlibria with the cooperative solution as benchmark. The Markov strategies are in stark contrast to the open loop equilibrium that is close to cooperation. This suggests that strategic aspects are indeed crucial. This rat race, once triggered, can explain fast and wide growth of CSR and not necessarily underlying overall efficiency considerations.     

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00366-x