A Tripartite Evolutionary Game Analysis on The Co-construction of Fresh Food Supply Chain System with Blockchain Technology

Eliminating Information asymmetry and food safety risks in the fresh food supply chain with the help of blockchain technology has become one of the hot topics in academic and agricultural fields. However, as the non-rational stakeholders in the fresh food supply chain, ensuring that the three parties can actively participate in the construction of the blockchain system is a complex problem. This paper constructs a tripartite evolutionary game model among the cooperative(farmers), fresh food retailers, and third-party logistics providers, and then obtains the replicator dynamics function of stakeholders and analyzes the evolution trend in the co-construction of the fresh food supply chain system with blockchain technology. The results show that three Evolutionary Stable Strategies (ESS) are accepted by three players under specific parameters, and the equilibrium point remains stable under external interference. Moreover, the existence of an ideal cooperative state where all three players choose to participate actively has been confirmed. When blockchain systems generate additional benefits, they can incentivize active participation from stakeholders, and consumer attitudes toward traceable agricultural products actively correlate with these benefits. The blockchain protocol restricts participants, but excessively harsh penalties may inhibit sales and the involvement of third-party logistics (3PL)

Integrating The Entropy and Picture Fuzzy Set Methods to Process Supplier Selection Issues

Many factors must be considered when selecting suitable and stable suppliers. It directly affects the success and sustainable development of supply chains, is a complex multi-criteria decision-making (MCDM) problem, and is a core issue for supply chains. Due to the COVID-19 outbreak, the supply and demand of supply chains worldwide have been severely imbalanced. The overall economic environment remains uncertain after the epidemic, so it is challenging for experts to effectively apply traditional MCDM research methods to evaluate and select suppliers. Therefore, this study integrates the entropy and picture fuzzy set methods and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach to process supplier selection issues. In the numerical verification, this study uses aquatic product e-commerce for supplier selection as an illustrative example to further verify the effectiveness and applicability of the proposed methods. The calculation results were compared with the typical fuzzy set, the intuitionistic fuzzy set, and the original picture fuzzy set. The numerical verification results comparing the different listed calculation methods show that the proposed method can overcome the unclear, missing, or incomplete evaluation information and effectively handle the MCDM problem in the fuzzy environment

Determination of The Location Layout of Electricity Agriculture Tractors In Open Landscape Conditions with The P-Median Chance Constrained Mixed Integer Mathematics Model

Agriculture has continuously evolved in terms of technology and economics throughout human history. From ancient times to the present, it has consistently embraced technological innovations to produce more efficient and higher-quality products. Over the past two decades, the rise of electric vehicles has emphasized the importance of electric tractors in agriculture. The widespread adoption of Agricultural Electric Tractors can lead to more sustainable and ecologically friendly farming practices. Although agricultural electric tractors are more environmentally friendly compared to traditional internal combustion tractors, the limited availability of solar-powered charging stations poses a significant barrier to their widespread use. This study aims to develop a mixed-integer mathematical model to determine the optimal locations for solar-powered charging stations in open-field agricultural areas for Agricultural Electric Tractors. The Chance-Constrained optimization model is compared with a deterministic model to evaluate the performance of the proposed mathematical model. Given that solving the deterministic model is an NP-hard problem, a Binary Genetic Algorithm was employed as a solution approach. This comparison focuses on assessing the effectiveness of the Chance-Constrained model in handling uncertainty, highlighting differences in solution quality, computational efficiency, and robustness. Additionally, the study identifies the locations for off-grid (solar-powered) charging stations for electric vehicles in agricultural settings. The results obtained from the mathematical model, where only one solar-powered charging station provides service, have been tested using the Arena simulation program and subsequently interpreted

Reverse Logistics Path Optimization Based on Hybrid Dung Beetle Optimization Algorithm

Reverse logistics optimization in urban environments faces significant challenges due to dispersed collection points and complex obstacle distributions, resulting in inefficient routing and high operational costs. To address these limitations, this study develops a novel hybrid optimization algorithm that strategically integrates Dung Beetle Optimization (DBO) with Simulated Annealing (SA) for multi-collector path planning. The hybrid approach enhances DBO's search capabilities by incorporating SA's probabilistic acceptance mechanism, effectively preventing premature convergence to suboptimal solutions. Using a two-dimensional grid model to represent urban collection environments, experimental validation demonstrates substantial performance improvements: the hybrid algorithm achieves 14.39% shorter paths than standalone DBO and 5.23% improvement over SA alone, while exhibiting faster convergence across diverse network configurations. These results confirm the method's effectiveness for sustainable reverse logistics operations in complex urban scenarios

Optimizing Faculty Hiring in Higher Education Using Model Predictive Control

As in any other sector, the objective of human resource manpower planning in academia is to avoid or to minimize a shortage or surplus of specific types of labor. In academia, the service offered is education, and the labor force is the lecturers and the professors. Hiring faculty members can have a positive socio-economic impact by improving education, driving innovation, supporting the local economy, and enhancing community development. Planning the manpower in academia is crucial for the future of the university. Our main tool is Model Predictive Control, which has received great interest during the last decades in the process industries, especially in chemical processes. Goodwin et al. (2001) report more than 2000 applications of Model Predictive Control. In this paper, we are using Model Predictive Control to obtain the optimal hiring rates for a university given its current and target faculty headcounts. A numerical example shows the effectiveness and the efficiency of the proposed method

Intelligent Network Orchestration System for Efficient Deployment and Management of Distributed Deep Neural Networks in Dynamic Network Environments

The evolution of distributed deep neural networks (DNNs) in device-edge-cloud architectures necessitates adaptive network control mechanisms to meet stringent latency requirements. Existing software-defined networking (SDN) solutions exhibit limitations in dynamic resource orchestration and cross-layer optimization for AI workloads. We present INOS (Intelligent Network Orchestration System), an SDN-based framework integrating network function virtualization to enable QoS-aware network slicing for prioritized DNN traffic and deep reinforcement learning-driven task offloading across heterogeneous compute nodes. Through NS-3 simulations replicating industrial IoT scenarios, INOS demonstrates quantifiable improvements in latency reduction and resource efficiency compared to static resource allocation baselines. The system architecture extends SDN control plane capabilities with AI-native decision modules, addressing key challenges in service function chaining for distributed intelligence

Metal Supply Chain Coordination with Revenue-sharing Contract: A Case Study in Khuzestan Steel Company

This paper aims to model a Metal Supply Chain to optimize the price and order quantity under decentralized and coordinated decision-making strategies in a two-level supplier-manufacturer chain. Modeling of this supply chain was performed concerning the leadership of one member and the following of another member using the Stackelberg game model. In this study, a revenue-sharing contract is used to create a coordination strategy in the steel supply chain, and sensitivity analysis on key variables is performed. The results of this research can help improve the profitability of the members in the MSC under a coordinated approach using a revenue-sharing contract compared to a decentralized one. The results of the sensitivity analysis demonstrated that with the increase in product quality, the total profit of the supply chain increases in both coordinated and decentralized modes, while the coordinated strategy yields a greater increase in total profit than the decentralized one

Low-Carbon Operation Decision of Green Packaging Supply Chain Considering Production and Marketing Cooperation

Given the cooperative nature of the production and marketing segments of the supply chain, this paper investigates the manufacturer’s low-carbon traceable production and the distributor’s low-carbon marketing strategies in a manufacturer-led secondary supply chain under a carbon cap-and-trade policy. The paper proposes the pass-contract model to enhance the cooperative nature of supply chain actors and constructs four Stackelberg game models. The study shows that both low-carbon marketing strategies by distributors and the introduction of traceability technologies by manufacturers can increase the profits of supply chain actors; the sensitivity coefficient of traceability and the sensitivity coefficient of marketing efforts at the brand end positively correlate with the profits of manufacturers and distributors. Under the cost-sharing model, when the share ratio is within a reasonable range, it not only leads to the highest level of carbon emission reductions (CER) in the supply chain but also maximizes supply chain profits. Adopting green packaging strategies, such as cost-sharing contracts, production and sales balancing, traceability technology, and low-carbon marketing, is essential for supply chain members to achieve sustainable development and environmental benefits

Enhancing Resilience in Electricity Supply Chains: A SCOR Model Approach for Vertically Integrated Utilities

This study introduces an innovative application of the Supply Chain Operations Reference (SCOR) model in a vertically integrated electricity supply chain. In this context, utilities manage all activities from primary energy procurement, generation, and transmission to distribution, which poses unique challenges due to process interdependence. The framework's comprehensive approach—covering plan, source, make, deliver, return, and enable—serves as a diagnostic and benchmarking tool that enhances supply chain resilience, reliability, responsiveness, flexibility, cost efficiency, and asset management. By aligning standardized supply chain methodologies with the specific requirements of the electricity industry, this study effectively identifies potential bottlenecks and risks across the integrated supply chain. This application differentiates this study from the existing literature and provides new actionable insights for sustainable supply chain management practices in the electricity sector

Conflict-Free Path Planning for Autonomous Transport Vehicles

Autonomous Transport Vehicles (ATVs) are flexible robotic systems for transportation tasks in intra-logistics. It is very important to determine the most cost-efficient routes for ATVs within factory environments. Conflicts occur if more than one ATV moves at the same point in the same time interval. Conflicts cause unwanted delays, and it is crucial to determine the conflict-free routes for ATVs on the shop floor. Efficiently planning conflict-free paths for multiple AGVs and minimizing overall task completion time are essential for assessing system performance. In the study, a NonConflict-A* (NC-A*) algorithm that is based on the A* algorithm has been proposed to solve conflict problems in multiple ATVs. Unlike studies in the literature, the proposed algorithm performs pathfinding, conflict detection, and conflict resolution simultaneously. The NC-A* algorithm detects the conflicts while obtaining the paths, chooses the conflict resolution strategy that is the least costly according to the conflict types and produces the most cost-efficient route. The proposed algorithm aims to minimize the total route duration time while ensuring system safety by detecting and resolving all potential conflicts. The algorithm is tested for different conflict types of multiple ATVs traveling at various speeds. The solutions of the algorithm show that the algorithm determines all of the conflicts, selects the appropriate solution strategy and generates conflict-free routes