18,672 research outputs found
An efficient two-phase iterative heuristic for Collection-Disassembly problem
Closing the loop in the supply chains is one of the mandatory conditions for more sustainable development. The Collection-Disassembly Problem appears in the reverse part of the closed-loop supply chains. Its aim is to coordinate the activities of collection of end-of-life products from collection centres and their subsequent disassembly. The disassembly step is required for efficient remanufacturing and recycling of returned products. The Collection-Disassembly problem integrates such optimization problems as dynamic lot-sizing and vehicle routing in general cases. In this paper, we develop a Two-Phase Iterative Heuristic to efficiently address large size instances. The numerical tests show that the heuristic provides good solutions under acceptable computational time
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption
The past decade has seen a substantial increase in the use of small unmanned
aerial vehicles (UAVs) in both civil and military applications. This article
addresses an important aspect of refueling in the context of routing multiple
small UAVs to complete a surveillance or data collection mission. Specifically,
this article formulates a multiple-UAV routing problem with the refueling
constraint of minimizing the overall fuel consumption for all of the vehicles
as a two-stage stochastic optimization problem with uncertainty associated with
the fuel consumption of each vehicle. The two-stage model allows for the
application of sample average approximation (SAA). Although the SAA solution
asymptotically converges to the optimal solution for the two-stage model, the
SAA run time can be prohibitive for medium- and large-scale test instances.
Hence, we develop a tabu-search-based heuristic that exploits the model
structure while considering the uncertainty in fuel consumption. Extensive
computational experiments corroborate the benefits of the two-stage model
compared to a deterministic model and the effectiveness of the heuristic for
obtaining high-quality solutions.Comment: 18 page
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