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This article presents a framework and develops a formulation to solve a path planning problem for multiple heterogeneous Unmanned Vehicles (UVs) with uncertain service times for each vehicle-target pair. The vehicles incur a penalty proportional to the duration of their total service time in excess of a preset constant. The vehicles differ in their motion constraints and are located at distinct depots...
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...
Some classic and complex problems in Operations Research consist of simplified versions of real logistic and supply chain management applications. One common and successful, but approximated approach for coping with these problems considers the system of interest isolated from its environment. In such a case, the links to the real world may be reduced to a set of parameters associated with probabilistic...
The real network traffic characteristics makes it difficult to estimate origin-destination flows only from statistics for routers' interfaces. The presented approach, based on passively measured link traffic statistical properties, estimates the flows, allowing for flow interdependence and seasonality. Rescaled profile of the total daily traffic in the network is used to compensate deterministic component...
Decentralized security software deployment is important to achieve reliable and secure network operations. In this paper, we consider the joint traffic routing and security software deployment problem. In this problem, the network can make decisions about routing, security protection and recovery software to minimize total energy consumption of all nodes in the network. Specifically, the network has...
Opportunistic networks (ON) allow for routing in networks where contemporaneous end-to-end path are unstable. Due to uneven and fluctuant node density, both links and nodes in our uncertain environment may be inherently unreliable and disconnections may be long-lived. A critical challenge for ONs is determining routes through the network without ever having an end-to-end connection. Owing to these...
In this paper, we propose a distributed algorithm for optimal routing in wireless multi-hop networks. We build our approach on a recently proposed model for stochastic routing, whereby each node selects a neighbor to forward a packet according to a given probability distribution. Our solution relies on dual decomposition techniques with regularization, that can significantly improve on the slow convergence...
In order to propose a novel discrete differential evolution algorithm for stochastic vehicle routing problems (SVRP), the two bit wise operators of the computer language are introduced. In the algorithm, the individuals are represented as natural numbers, and new mutation and revised operators are developed for this representation. Computational simulations and comparisons based on Benchmark Problem...
Decision rights affect the performance of of supply chain as well as the optimal decisions. In this paper, we develop retailer-led model of decentralized optimization, supplier-led model of decentralized optimization and the model of inventory-transportation integrated optimization(ITIO) based on delivery capacity and discount sale price. Study shows that supplier and retailers' optimal strategies...
A lot of attention has been devoted to flow routing in networks by the past. Usually, each commodity to be routed is characterized by a single deterministic traffic value. However, telecommunication traffic is well known to be highly variable in time. The current paper revisits usual network routing models, in considering traffic stochasticity, with the aim of better modeling traffic multiplexing...
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...
Motivated by an industry project with a small package shipping company in France, we study a vehicle routing problem with stochastic travel and service times that considers the influence of driver familiarity with routes and customers on routing efficiency. Our approach forgoes any fixing of delivery areas thus maintaining routing flexibility. Driver specific travel and service times give drivers...
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...
An improved vehicle coordination strategy for vehicle routing problem (VRP) based on SWEEP was proposed in order to solving single-depot VRP with stochastic demands. In this strategy, the vehicle routing that customers were not served by basic vehicle (BV) is re-optimized using SWEEP rules, then these customers are severed by SWEEP vehicle (SV) in order that the total serve time will be less and the...
We describe a kind of supply chain optimization problem as a commodity stream routing problem upon a stochastic flow network. We divide the optimization problem to two parts: Firstly, establish model to calculate the optimal commodity stream allocation policy on all minimal paths; secondly, convert these allocation policy on all minimal paths to the optimal routing policy on all arcs. A multi-objective...
There is great interest in building an accurate theoretical model of IT support organizations, for several purposes such as optimal workforce allocation and what-if scenario analysis. However, the complexity of real-life IT support organizations makes it extremely hard to model their organizational, structural and behavioral processes. While the adoption of stationary stochastic processes to model...
Adaptive cross-layer designs exploit channel state information (CSI) to optimize wireless networks operating over fading channels. Capitalizing on convex optimization, duality theory and stochastic approximation tools, this paper develops channel-adaptive algorithms to allocate resources at the transport, network, link, and physical layers. Optimality here refers to maximizing a sum-utility of the...
Assigning and scheduling vehicle routes in stochastic traffic network is a crucial management problem. Vehicle routing problem (VRP) is a combinational optimization problem, it belongs to the NP-hard problem theoretically. VRP with time windows and capacity constraint in stochastic traffic network was studied considering the state of traffic network changing randomly under the action of external factors...
In this paper, a simulation optimization method for campus bus routing, which allows the vehicle divert from its current destination, is given. Vehicle routing that can divert a vehicle away from its fixed route in response to a new customer request is beneficial to campus bus routing for its efficiency in quick response and saving cost especially when the density of customer requests is low. A simulation...
As part of solutions to the technical problem of logistics distribution, vehicle routing problem (VRP) is getting more and more attention in academics and enterprises, it belongs to the NP-hard problem theoretically. VRP with time windows and capacity constraint in stochastic traffic network was studied based on travel time reliability . Firstly, travel time was expressed as a random variable according...
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