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This paper deals with uncapacitated single and multiple allocation p-hub maximal covering problems (USApHMCP and UMApHMCP) with binary and partial covering criteria. We present new mixed-integer programming formulations of the considered problems, which are valid for both binary and partial coverage cases. The efficiency of the proposed formulations is evaluated through computational experiments on smaller-size instances, and compared with the state-of-the art models from the literature. The obtained results indicate that the new UMApHMCP formulation outperforms the existing one for both coverage criteria in the sense of solutions' quality and running times. In order to solve instances of larger problem dimension, we develop two heuristic methods based on variable neighborhood search: general VNS (GVNS) for USApHMCP and basic VNS (BVNS) for UMApHMCP. The proposed GVNS and BVNS involve the same shaking procedure in order to hopefully escape local minima traps, while local search phases in GVNS and BVNS use different neighborhood structures in accordance with applied allocation schemes. Computational experiments conducted on smaller-size instances showed that both GVNS and BVNS almost instantly reach all known optimal solutions. In addition, the proposed GVNS and BVNS showed to be very efficient when solving large and large-scale hub instances with up to 1000 nodes, which were not previously considered as test instances for the considered problems. Both GVNS and BVNS provided best solutions on challenging USApHMCP and UMApHMCP instances for both coverage cases in short running times, which indicates their potential to be applied to similar problems. [ABSTRACT FROM AUTHOR]

In this paper we present the solution to a weekly log-truck scheduling problem (LTSP) integrating the routing and scheduling of trucks where all goods are transported in full truckloads. We must take into account pick-up and delivery requirements, multiple products, inventory levels, and lunch breaks. The objective is to minimize the overall transportation cost including wait times and the empty and loaded distance traveled. Our solution is based on a two-phase approach. The first phase involves an integer linear program that determines the destinations of full truckloads. The second phase uses an implicit integer linear program based on an arc formulation to ensure that the trucks are routed and scheduled at a minimum cost. Experiments have been conducted using Cplex 12.4.0, and almost all instances were solved within six hours with a reasonable gap. [ABSTRACT FROM AUTHOR]