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With the continuous development of optical fiber access and other network infrastructure, cloud conference, online education, online office, cloud virtual-reality (VR) and other innovative services requiring high bandwidth are widely developed, which puts forward higher requirements on network bandwidth, delay, jitter and stability. Upgrading the legacy optical fiber network, building a gigabit optical broadband network, and promoting the further extension of optical fiber network to the enterprises help to improve the operation efficiency of information infrastructure and solve the problem of network coverage. High-quality business and enterprise broadband services can be realized, which stimulate potential consumer demand, and lay a solid foundation for the sustainable development of internet services. The business-enterprise full-optical network technology and the applications are studied, the related industrial chain and standardization progress are introduced, and the application mode and development trend are analyzed in this paper.

The unrelated parallel machine scheduling problem with sequence dependent setup times (UPMSP-SDST) addressed in this study refers to allocating jobs among a given number of machines and determining their processing sequence on each machine, to minimize the makespan (i.e., the maximum completion time). To deal with large-scale UPMSP-SDST with higher efficiency, this study presents an enhanced adaptive large neighborhood search (EALNS) algorithm with various destroy and repair operators and an efficiency-enhancement mechanism. The efficiency-enhancement mechanism is mainly composed of a simplified calculation method and a hierarchical comparison mechanism, which are applied to improve the implementation process of the greedy-based operators. The simplified calculation method obtains a new makespan by an incremental or decremental transformation, to avoid reluctant calculations. The hierarchical mechanism refines the comparison of different removal or insertion strategies of the operators, thereby arresting high-quality solutions with more metrics. The proposed algorithm is tested on 1640 instances, and numerical results demonstrate the superior performance of the EALNS over the existing methods, especially for large-scale problems. Notably, 834 instances’ best-known solutions are updated from this study. In addition, deep analysis of the impact of the distribution of setup time on the performance of the algorithm is provided, which further verifies the potential wide applicability of the proposed EALNS.

The effective distribution of relief to an emergency logistics system plays a crucial role during the disaster response phase. Considering stochastic characteristics of relief demand, this study investigates the robust optimization of a multi-objective multi-period location-routing problem for epidemic logistics, a special emergency logistics, with uncertain scenarios. A corresponding robust multi-objective multi-period optimization model is proposed, which aims to determine the optimal location of temporary relief distribution centres and route planning simultaneously. The optimization objectives include the total travel time, the total cost, and the disutility of relief service. To solve the above optimization model, a preference-inspired co-evolutionary algorithm with Tchebycheff decomposition (PICEA-g-td) is given. The performance of the proposed PICEA-g-td is evaluated by comparing it with NSGA-II, MOEA/D and PICEA-g. The experimental results show that the proposed algorithm performs better than the other three algorithms in terms of the solution quality. Finally, some useful management insights are obtained.

With the development of modern cities, metro are playing an increasingly important role in urban traffic. A biochemical terrorist attack in a metro station can cause many casualties. Therefore, more attention should be paid to metro emergency management. This paper presents a guidance scheme based on an improved empirical formula to optimize emergency evacuation. Additionally, the guidance evacuation model combined the social force model with pedestrian characteristics to study the influence of pedestrian behaviors on guidance evacuation. The experiment results show the following: (1) A guidance scheme based on an improved empirical formula can significantly reduce the casualties and improve the efficiency of evacuation; (2) The guidance evacuation model can be used to find the reasonable guidance locations and the combination of guides. (3) Pedestrian characteristics must be taken into account in the research of the guidance evacuation. Analyzing the behavioral characteristics of pedestrian can provide improvement measures for guidance evacuation. This paper focuses on the simulation experiments of emergency evacuation in metro station, which can provide practical references for developing an efficient and safe emergency evacuation scheme.