In this paper, the performance of non-orthogonal multiple access (NOMA) based cooperative spectrum sharing in hybrid satellite-terrestrial networks (HSTNs) is investigated, where the primary satellite network recruits the secondary terrestrial network as a cooperative relay. To improve the fairness and spectrum utilization under cooperative spectrum sharing (i.e. overlay paradigm of cognitive radio), the NOMA power allocation profile is determined by instantaneous channel conditioning at the second temporal phase. The closed-form outage probability and approximated ergodic capacity expressions for the primary user (PU) and the secondary user (SU) are derived by decode-and-forward (DF) relay protocols, where the generalized Shadowed-Rician fading and Nakagami-m fading are considered for satellite links and terrestrial links, respectively. Simulation results are conducted for validation of the theoretical derivation and analysis of the impact of key parameters, and prove the superiority of NOMA comparing to conventional orthogonal multiple access (OMA) schemes on cooperative spectrum sharing in HSTNs. Besides, the fairness analysis between the PU and the SU is introduced by Jain's fairness index (JFI). INDEX TERMS Non-orthogonal multiple access (NOMA), hybrid satellite-terrestrial networks (HSTNs), cooperative spectrum sharing, outage probability (OP), ergodic capacity.

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Customer self-pickup, offered as an option at most distribution centers, can provide flexible service times and save operational costs. Customers can either choose to self-pickup their demand or to have it delivered by a traditional way. At each customer point, the delivery demand is split, with the amount depending on the service and personal characteristics. In this situation, how to efficiently locate distribution centers and route deliveries becomes a vital problem for express companies that has not been studied in the literature. In this paper, for the first time, we propose a mathematical programming model for optimizing the location-routing problem with split demand (LRP-SD), together with a delivery ratio analysis model to predict self-pickup and delivery demand. To adapt the model to real-world cases, two heuristics as used in large-scale simulation-based optimization are devised and implemented. One is biogeography-based optimization (BBO) for solution speed, and the other is an adaptive large neighborhood search (ALNS) for solution quality. The two algorithms are compared using real data from a Shanghai-based express delivery company.

Abstract:This article studies how to allocate a scare emergency medical resource at the beginning of large-scale disaster. One salient problem in emergency rescue is how to coordinate the resource among injuries of different degrees in different periods. The injuries are classified into three levels and given different priorities. Waiting cost with resources shortage, deteriorated cost with delay and transferring cost to other hospitals are introduced into the decision-making process. Basing on incorporating the costs into revenue function, a Markov decision process (MDP) model is proposed to establish the optimal action policies in different periods. A dynamic algorithm is proposed to derive the optimal solution. With a numerical experiment, the improvement of MDP is displayed and some managerial suggestions are also provided.Keywords: emergency management; medical resource allocation; Markov decision process; multi-priority injuries; operational research.