It is vital to pre-position a certain amount of relief supplies at pre-disaster. But governments are often confronted with a dilemma of coordinating inventory cost and stock-out cost in pre-positioning relief supplies. Pre-positioning strategies for relief supplies depend on the natural characteristic of relief supplies and the most urgent needs at post-disaster. Therefore, we classify relief supplies by their natural characteristic and priority, and introduce an option contract into relief supplies pre-positioning system which consists of a single government and an agreement enterprise. Specially, a consumable and relatively urgent relief supplies pre-positioning (C-RUP) model and a non-consumable and urgent relief supplies pre-positioning (NC-UP) model are established. The optimal decisions of the government and the enterprise are derived, respectively. The relief supply chain coordination is achieved with the option contract. Under channel coordination, the two models can not only improve the government’s emergency ability and support capacity, but also reduce the government’s inventory risk when compared to the government single pre-positioning model. Moreover, we give out the conditions that the two sides can reach a win-win situation. Lastly, we propose important managerial insights for pre-positioning strategies related to different types of relief supplies.

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

Mining scrapers as an important part of scraper conveyors are highly prone to wear and fatigue failure. A new scraper capable of turning sliding friction into rolling friction was designed to limit wear and reduce failure rate. To determine the safety and reliability of the new scraper, numerical pulling force measurement was conducted on its physical model and finite element analysis was performed on its 3D model based on SolidWorks Simulation. The results were then compared with data of the traditional scraper. Numerical pulling force measurement results indicated impressively lower friction for the new scraper. Stress, strain, and displacement distributions obtained by static stress analysis based on SolidWorks Simulation proved conformance with the strength and deflection standards. Damage percentage and total life nephograms yielded from fatigue analysis indicated no significant life reduction. Numerical pulling force measurement combined with analysis based on SolidWorks Simulation can help reduce the production cost and development cycle. It plays a great role in determining the safety, reliability, and stability of the new scraper.