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Aiming at the joint optimisation problem of uncertain distributed manufacturing and preventive maintenance (PM) for semiconductor wafers considering multi-energy complementary (UDMPM-MEC), a bi-level integrated scheduling model is constructed with interval makespan, total interval carbon emissions and total interval cost as the optimisation objectives. The upper model is used to obtain the joint optimisation scheduling scheme of distributed manufacturing and PM, and the lower model determines the energy allocation scheme on this basis. Given the problem characteristics, we propose an improved multi-objective whale swarm algorithm (IMOWSA). By designing jobs-to-factories allocation strategy, synchronous scheduling maintenance strategy, multi-energy complementary energy allocation decoding strategy, individual distance calculation strategy, multi-neighbourhood search strategy and deduplication strategy, the local and global search are effectively balanced, and the convergence precision is improved. A large number of simulation experiments and comparative analyses show that the IMOWSA algorithm is feasible and effective to solve this problem. [ABSTRACT FROM AUTHOR]

This study mainly considers the scheduling problems with learning effects, where the learning rate is a random variable and obeys a uniform distribution. In the first part, we introduce a single machine model with location-based learning effects. We have given the theoretical proof of the optimal solution for the five objective functions. In the second part, we study the problem with group technology. Both intra-group and inter-group have location-based learning effects, and the learning rate of intra-group jobs follows a uniform distribution. We also give the optimal ranking method and proof for the two problems proposed. [ABSTRACT FROM AUTHOR]

The Telecommunications Services of Trinidad and Tobago Limited (TSTT) has demonstrated its approach to modernization via integration of technological systems with international telecommunications providers. With the recognition of TSTT’s need to develop an international telecommunication system capable of 95% nation coverage with rich broadband content, Chinese company Huawei leapfrogged into Trinidad and Tobago’s developing market. Huawei achieved what Western providers did not and gained strategic partnership with TSTT in a short period of time, given the government-to-government support for trade, TSTT’s telecommunication demand, and Huawei’s growth plans. Despite the functional advantages derived from Huawei, the partnership was not without risk given the speed and liability of newness of greenfield investments. If the TSTT and Huawei partnership is to be sustainable, we must model the relationship and then identify weaknesses in a bid to remove them. Huawei’s approach to investment in the Latin American and Caribbean region is grounded in the model presented in this article, which follows: the creation of a leapfrog environment, an internationalization strategy, and risk management. The needs of a technology-deprived partner changes as they meet industry standards and investing firms must seek to understand and anticipate future needs or risk competition from an innovative supplier.

From the perspective of the surgical scheduling problem in emergency, this paper considers the fatigue effect and the truncated learning effect caused by medical staffs' long-time work, regards the surgical scheduling problem as the hybrid flow-shop scheduling problem and proposes a three-stage surgical scheduling model which includes preoperative, intraoperative and postoperative. The improved grey wolf optimization is used to solve the emergency surgical scheduling model, and the effectiveness of the model and the algorithm are tested through simulation experiments. The results show that the improved grey wolf optimization algorithm can solve the emergency surgical scheduling model more effectively and get better scheduling results. [ABSTRACT FROM AUTHOR]