The concurrent pursuit of renewable energy (RE) transition and circular economy (CE) objectives, introduces complex multi objective trade-offs among economic cost, environmental impact, social benefit, and energy security. Existing frameworks seldom integrate causal decision-making with multi-objective optimization under a unified structure. This paper proposes a novel integrated framework combining Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), a Chaotic Multi-Objective Honey Badger Algorithm (C-IMOHBA), and the VIKOR ranking method. The principal algorithmic contribution is the replacement of pseudo-random number generation in IMOHBA with a Tent-Logistic hybrid chaotic map. This deterministic integration effectively avoids premature convergence and eliminates degenerate zero security solutions that comprise 7% of the vanilla IMOHBA archive. Criteria weights derived from a five-expert DEMATEL-ANP elicitation prioritize environmental impact (32.13%) and economic cost (28.07%). These weights are applied in VIKOR to rank 300 Pareto-optimal solutions generated under IRENA-validated constraints. Numerical experiments confirm that C-IMOHBA achieves superior diversity and convergence metrics in 89.6 seconds, outperforming the 94.4 seconds vanilla baseline. The top-ranked compromise solution demonstrates a balanced operational profile: an LCOE of $19.08/MWh, emissions of 15.27 kg CO₂eq/MWh, employment of 1.827 jobs/MW, and a robust energy security index of 34.53. Ultimately, the framework provides a structured, reproducible decision-support methodology for policymakers addressing sustainable RE-CE system design.
Given the rapid expansion of solar energy projects, it is essential to have supply chains that are data-driven and efficient․ However, the solar energy supply chain is fragmented․ A systematic literature review based on the PRISMA model was conducted to investigate the current state of digital and artificial intelligence-based supply chain integration in the solar energy industry in the United Arab Emirates․ Studies that meet the criteria will be published between 2014 and 2026 from various databases and analyzed for relevance to the research questions proposed here․ The review will investigate various technologies, the successful factors for implementing them in supply chains, the challenges of using these technologies in supply chains, the functions of supply chains in the solar energy industry, and the performance of those supply chains utilizing these technologies․ The outcomes of this study will contribute to the discussion of how AI and digital technologies could be applied to enhance the integration of supply chains in the solar energy industry․
Presenting author: Rodayna Ibrahim Hassan Mohamed Ibrahim
Abstract
The transition from fossil fuels requires the deployment of highly efficient green hydrogen supply chains. This paper presents a five-echelon network design capturing the flow of green hydrogen from renewable feedstocks to large-scale power generation and heavy industrial end-users. A bi-objective mixed-integer linear programming model, solved by a goal programming approach, is proposed to balance economic costs and environmental emissions. The model determines the optimal production and storage locations, transport modes, and distribution flows. A case study conducted in Egypt illustrates the model's effectiveness and highlights significant structural trade-offs between cost and emissions objectives. Eventually, this research offers decision-makers an actionable framework for balancing economic feasibility and sustainability goals.
Towfique Rahman, Md. Abdul Moktadir, Yong Wu and Yousaf Ayub
Presenting author: Towfique Rahman
Abstract
The contemporary global energy landscape has become increasingly vulnerable due to large-scale supply chain (SC) disruptions caused by geopolitical instabilities, cyberattacks, and the transition to renewable energy sources. This research applies a reliability-enhanced fuzzy cognitive map (FCM) approach to systematically analyze the interdependencies and dynamic evolution of the identified 15 key mitigation strategies for the global energy SC. By integrating structured expert elicitation with a reliability-optimized aggregation procedure, the study identifies both upstream strategic drivers and steady-state operational priorities necessary for building a resilient energy SC. Dynamic simulations reveal that while geopolitical intelligence and diplomatic cooperation hold high initial importance, they primarily serve to activate downstream measures such as public-private emergency coordination platforms which emerge as the highest operational priority. The findings provide a framework for prioritizing and integrating strategies, such as artificial intelligence (AI) enabled analytics and strategic diversification, to enhance energy SC resilience.
This paper studies an identical parallel-machine scheduling problem with DeJong’s learning effect and interval-wise energy limits. Each job has a nominal processing time and a power rate. Processing times are position dependent and decrease according to DeJong’s learning model, while the total energy consumption within each interval must remain below a predefined limit. The objective is to minimize the makespan while maintaining energy feasibility. Three constructive heuristics are considered: Modified Longest Time First (MLTF), Modified Shortest Time First (MSTF), and Random Scheduling (RS). In addition, Simulated Annealing (SA) is applied as an improvement method initialized from the feasible MLTF schedule. The computational study is based on a benchmark of 5760 generated instances. The methods are evaluated using number of wins, average relative gap, and runtime. Computational results show that SA achieved the strongest overall performance, obtaining 3072 wins (53.34% win rate) and the lowest average relative gap of 0.035976. Among the constructive heuristics, MSTF achieved the strongest baseline performance.
