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Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When...
Network utility maximization is often applied for the cross-layer design of wireless networks considering known wireless channels. However, realistic wireless channel capacities are stochastic bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. Based on NUM theory we develop a framework for scheduling, routing, congestion control and power...
In real life optimization problems, it is very important to have high quality solutions (optimal). But when uncertainty becomes part of the optimization problem, solutions should be optimal and robust to the uncertain environmental changes. This paper focuses on finding robust optimal solution for the vehicle routing problem with stochastic demands VRPSD. In this case when the uncertainty of the customers...
In this paper, we propose a mathematical model for a dynamic vehicle routing problem (VRP) to minimize unmet demand with stochastic demands and real-time vehicle control in large-scale emergencies. In this context, we consider that one depot may not supply all demand nodes with sufficient medicine that they need in limited time. The problem involves multiple vehicles with various capacities and instantaneous...
The Vehicle Routing Problem (VRP) is of considerable economic significance in logistic systems as it manages the distribution of goods to make an efficient transportation system. Considering a practical application, this paper solves a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modeled as a stochastic variable as opposed to conventional VRP. To deal...
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