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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, the Uncertain CARP (UCARP) is investigated. In UCARP, the demands of tasks and the deadheading costs of edges are stochastic and one has to design a robust solution for all possible environments. A problem model and a robustness measure for solutions are defined according to the requirements in reality. Three benchmark sets with uncertain parameters are generated by extending existing...
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...
We consider the energy-efficient network resource allocation that minimizes a cost function of average user powers for multi-hop wireless networks. A class of fair cost functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal routing, scheduling and power control schemes are developed. Relying on stochastic optimization...
Vehicle Routing Problem has been approved a NP problem and it belongs to classical Combination Optimization hard problem. An effective algorithm based on Important Sampling is designed to solve the model which named Vehicle Routing Problem with Weight Coefficients and Stochastic Demands (WVRPSD). The optimal importance sampling distribution function was obtained by making use of the expection constructed...
Vehicle routing problem belongs to classical Combination Optimization hard problem and has been extensively studied by many researchers. The main purpose of this paper is to establish the model which named Vehicle Routing Problem with Weight Coefficients and Stochastic Demands (WVRPSD) and propose an effective algorithm based on Important Sampling to solve this model. The optimal importance sampling...
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...
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...
We introduce a problem in which demands arrive stochastically on a line segment, and upon arrival, move with a fixed velocity perpendicular to the segment. We design a receding horizon service policy for a vehicle with speed greater than that of the demands, based on the translational minimum Hamiltonian path (TMHP). We consider Poisson demand arrivals, uniformly distributed along the segment. For...
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...
The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. Vehicle Routing Problem has been approved a NP problem and it belongs to classical Combination Optimization hard problem. An effective algorithm based on cross-entropy is designed to solve the model which named Vehicle Routing Problem with Weight Coefficients and Stochastic...
In this paper, we consider joint optimization of end-to-end data transmission and resource allocation for Wireless-Infrastructured Distributed Cellular Networks (WIDCNs), where each base station (BS) in a cell is connected with its neighboring BSs via wireless links, and a mobile station (MS) can access one or multiple adjacent BSs simultaneously through time-varying OFDMA channels. The communications...
VMI (vendor managed inventory) policy coordinates effectively the antinomy between the inventory and the transportation within logistics distribution system. Under VMI mode, integrating inventory and transportation of the supplier and the demander as a whole is vital to achieve the optimization of total distribution cost. In view of typical many-one distribution network in the modern distribution...
Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. In such complex designs, a primary design goal is to limit the power consumption of the chip. Power consumption depends on capacitance, which depends on the length of wires on the chip and the number of vias which connect wires on different layers of the chip. We use ant colony...
This paper provides a tutorial treatment of recent stochastic network optimization techniques, including Lyapunov network optimization, backpressure, and max-weight decision making. A new technique of place holder bits that improves delay for networking problems with general costs is also presented. An example application is given for the problem of energy-aware scheduling and routing in a wireless...
Given a universe U of n elements and a weighted collection l of m subsets of U, the universal set cover problem is to a-priori map each element u epsi U to a set S(u) epsi l containing u, so that X sube U is covered by S(X)=UuepsiXS(u). The aim is finding a mapping such that the cost of S(X) is as close as possible to the optimal set-cover cost for X. (Such problems are also called oblivious or a-priori...
In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems. In this paper, through an analysis of the constructive procedure of the solution in the ant colony system (ACS), a vehicle routing problem (VRP) is examined and a hybrid ant colony system coupled with a stochastic local...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
Simulated evolution (SimE) is an evolutionary metaheuristic that has produced results comparable to well established stochastic heuristics such as SA, TS and GA, with shorter runtimes. However, for problems with a very large set of elements to optimize, such as in VLSI placement and routing, runtimes can still be very large and parallelization is an attractive option. Compared to other metaheuristics,...
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