Peri-urban agriculture (PA) offers significant social, economic, and environmental benefits but also presents challenges. By leveraging opportunities and addressing challenges, PA can become more sustainable. In tropical regions, integrating high-value crops such as coffee and rubber into forest garden models could support PA. Expanding High-Value Intercropping Forest Garden (HIFG) synergies into a multi-farmer coalition of pesticide-free coffee, pesticide-free rubber, and free-range chicken farmers can enhance resource sharing and waste management, improving sustainability and efficiency. This study examines the evolutionary game dynamics of a four-party cooperation model, involving the local government and three smallholder farmers (CFA: Coffee family A, RFB: Rubber family B, and CFC: Chicken family C). The aim is to optimize resource sharing and waste management to increase efficiency and reduce waste. We develop a multifaceted evolutionary game model to analyze stakeholder behaviors under government incentives. Stability analysis identifies Evolutionarily Stable Strategies (ESSs) within this framework. MATLAB simulations, based on HIFG data from Vietnam’s central highlands, validate the results. Our findings highlight the importance of government support, subsidies, and the costs incurred in resource sharing and waste management. These factors are key to achieving a stable equilibrium and promoting sustainable farming. The research emphasizes the role of government policies in fostering cooperation, resource optimization, and waste reduction, ultimately contributing to a more efficient and resilient agricultural supply chain. The paper concludes with recommendations to strengthen resource sharing and circular economy practices in urban agriculture, advancing the Sustainable Development Goals (SDGs).

This paper addresses the critical challenge of identifying cost-effective coalitions of social media influencers to promote environmental sustainability (ES) messages under budget constraints. Traditional influencer marketing often relies on heuristics that ignore relational dependencies and heterogeneous agent costs, leading to redundant coverage and suboptimal resource allocation. To overcome these limitations, we introduce a novel Relational Cooperative Game (RG) framework that formalizes pre-determined dependencies among influencers and followers using closure operators, enabling a portfolio of polynomial-time identification algorithms for Minimal Winning Coalitions (MWCs). We further extend this model to the Weighted Cost-Relational Game (WCRG) to optimize campaigns with heterogeneous influencer costs. We prove that finding a Minimum-Cost Winning Coalition (MCWC) is NP-hard via reduction from weighted set cover and propose two complementary algorithms: (1) a Greedy Cost–Benefit (GCB) algorithm that operates in polynomial time and empirically achieves optimal solutions across all tested instances; a logarithmic approximation guarantee is established for the restricted single-antecedent model; and (2) an Integer Linear Programming (ILP) formulation enhanced with Strongly Connected Component (SCC) preprocessing to handle cyclic dependencies and yield exact optimal solutions for moderate instances. Extensive empirical validation, ranging from a representative six-agent cyclic scenario to large-scale synthetic networks (up to 300 agents), confirms the framework’s robustness and scalability. Results demonstrate that GCB consistently achieves optimal solutions (approximation ratio = 1.000×) with subsecond runtime (<0.2 s) and minimal memory overhead (<50 MB), while ILP-SCC leverages graph condensation for rapid exact solving. Compared to size-based baselines, WCRG achieves up to 95.2% cost savings by systematically leveraging cost-efficient micro-influencers, empirically validating that minimizing coalition size does not guarantee cost efficiency. These findings establish WCRG as a scalable, budget-aware optimization toolkit for maximizing the impact of sustainability campaigns through relational coalition design.

Scheduling is one of the activities which have a great impact on the communication performance of optical burst switching networks. There have been many scheduling algorithms proposed but most of them are online. Recently, several algorithms of group scheduling have been published, but they have the complexity of NP-complete. Moreover, the timeslot of group scheduling is predetermined and fixed without considering the dependence on the rate of arriving data. This article proposes a new algorithm of group scheduling which has the linear complexity and has the timeslot of group scheduling that could be changeable and adapt to the rate of arriving data. The analyses and evaluations of the effectiveness based on the simulation results will confirm the advantages of our algorithm.

Group scheduling is an operation whereby control packets arriving in a time slot schedule their bursts simultaneously. Normally, those bursts that are of the same wavelength are scheduled on the same channel. In cases where the support of full wavelength converters is available, such scheduling can be performed on multiple channels for those bursts that are of an arbitrary wavelength. This paper presents a new algorithm for group scheduling on multiple channels. In our approach, to reach a near-optimal schedule, a maximum-weight clique needs to be determined; thus, we propose an additional algorithm for this purpose. Analysis and simulation results indicate that an optimal schedule is almost attainable, while the complexity of computation and that of implementation are reduced.