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727
Planning UMTS Base Station Location: Optimization Models with Power Control and Algorithms
 IEEE Transactions on Wireless Communications
, 2003
"... Classical coverage models, adopted for secondgeneration cellular systems, are not suited for planning universal mobile telecommunication system (UMTS) base station (BS) location because they are only based on signal predictions and do not consider the traffic distribution, the signal quality requir ..."
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Cited by 64 (11 self)
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Classical coverage models, adopted for secondgeneration cellular systems, are not suited for planning universal mobile telecommunication system (UMTS) base station (BS) location because they are only based on signal predictions and do not consider the traffic distribution, the signal quality requirements, and the power control (PC) mechanism. In this paper, we propose discrete optimization models and algorithms aimed at supporting the decisions in the process of planning where to locate new BSs. These models consider the signaltointerference ratio as quality measure and capture at different levels of detail the signal quality requirements and the specific PC mechanism of the wideband CDMA air interface. Given that these UMTS BS location models are nonpolynomial (NP)hard, we propose two randomized greedy procedures and a tabu search algorithm for the uplink (mobile to BS) direction which is the most stringent one from the traffic point of view in the presence of balanced connections such as voice calls. The different models, which take into account installation costs, signal quality and traffic coverage, and the corresponding algorithms, are compared on families of small to largesize instances generated by using classical propagation models.
The ThreeDimensional Bin Packing Problem
 Operations Research
, 2000
"... The problem addressed in this paper is that of orthogonally packing a given set of rectangularshaped boxes into the minimum number of rectangular bins. The problem is strongly NPhard and extremely difficult to solve in practice. Lower bounds are discussed, and it is proved that the asymptotical wo ..."
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Cited by 57 (6 self)
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The problem addressed in this paper is that of orthogonally packing a given set of rectangularshaped boxes into the minimum number of rectangular bins. The problem is strongly NPhard and extremely difficult to solve in practice. Lower bounds are discussed, and it is proved that the asymptotical worstcase performance of the continuous lower bound is 1/8. An exact algorithm for filling a single bin is developed, leading to the definition of a exact branchandbound algorithm for the threedimensional bin packing problem, which also incorporates original approximation algorithms. Extensive computational results, involving instances with up to 60 boxes, are presented: it is shown that many instances can be solved to optimality within a reasonable time limit.
Stateoftheart exact and heuristic solution procedures for simple assembly line balancing
, 2003
"... ..."
The Dynamic and Stochastic Knapsack Problem with Deadlines
 Operations Research
, 1996
"... In this paper a dynamic and stochastic model of the wellknown knapsack problem is developed and analyzed. The problem is motivated by a wide variety of realworld applications. Objects of random weight and reward arrive according to a stochastic process in time. The weights and rewards associated w ..."
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Cited by 54 (0 self)
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In this paper a dynamic and stochastic model of the wellknown knapsack problem is developed and analyzed. The problem is motivated by a wide variety of realworld applications. Objects of random weight and reward arrive according to a stochastic process in time. The weights and rewards associated with the objects are distributed according to a known probability distribution. Each object can either be accepted to be loaded into the knapsack, of known weight capacity, or be rejected. The objective is to determine the optimal policy for loading the knapsack within a fixed time horizon so as to maximize the expected accumulated reward. The optimal decision rules are derived and are shown to exhibit surprising behavior in some cases. It is also shown that if the distribution of the weights is concave, then the decision rules behave according to intuition. Keywords: dynamic programming, sequential stochastic resource allocation This research was supported by the National Science Foundati...
Maximizing The System Value While Satisfying Time And Energy Constraints
, 2002
"... this paper may be copied or distributed royalty free without further permission by computerbased and other informationservice systems. Permission to republish any other portion of this paper must be obtained from the Editor ..."
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Cited by 49 (4 self)
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this paper may be copied or distributed royalty free without further permission by computerbased and other informationservice systems. Permission to republish any other portion of this paper must be obtained from the Editor
The Dynamic and Stochastic Knapsack Problem
 Operations Research
, 1998
"... The Dynamic and Stochastic Knapsack Problem #DSKP# is de#ned as follows: Items arrive according toaPoisson process in time. Each item has a demand #size# for a limited resource #the knapsack# and an associated reward. The resource requirements and rewards are jointly distributed according to a kn ..."
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Cited by 49 (1 self)
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The Dynamic and Stochastic Knapsack Problem #DSKP# is de#ned as follows: Items arrive according toaPoisson process in time. Each item has a demand #size# for a limited resource #the knapsack# and an associated reward. The resource requirements and rewards are jointly distributed according to a known probability distribution and become known at the time of the item's arrival. Items can be either accepted or rejected. If an item is accepted, the item's reward is received, and if an item is rejected, a penalty is paid. The problem can be stopped at any time, at which time a terminal value is received, whichmay depend on the amount of resource remaining. Given the waiting cost and the time horizon of the problem, the objective is to determine the optimal policy that maximizes the expected value #rewards minus costs# accumulated. Assuming that all items have equal sizes but random rewards, optimal solutions are derived for a variety of cost structures and time horizons, and recursive algorithms for computing them are developed. Optimal closedform solutions are obtained for special cases. The DSKP has applications in freight transportation, in scheduling of batch processors, in selling of assets, and in selection of investment projects.
A minimal algorithm for the 01 Knapsack Problem.
 Operations Research
, 1994
"... Although several large sized 01 Knapsack Problems (KP) may be easily solved, it is often the case that most of the computational eort is used for preprocessing, i.e. sorting and reduction. In order to avoid this problem it has been proposed to solve the socalled core of the problem: A Knapsack ..."
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Cited by 49 (10 self)
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Although several large sized 01 Knapsack Problems (KP) may be easily solved, it is often the case that most of the computational eort is used for preprocessing, i.e. sorting and reduction. In order to avoid this problem it has been proposed to solve the socalled core of the problem: A Knapsack Problem de ned on a small subset of the variables. But the exact core cannot be identi ed without solving KP, so till now approximated core sizes had to be used.
Serverstorage virtualization: Integration and load balancing in data centers
 In Proceedings of IEEE/ACM Supercomputing
, 2008
"... Abstract—We describe the design of an agile data center with integrated server and storage virtualization technologies. Such data centers form a key building block for new cloud computing architectures. We also show how to leverage this integrated agility for nondisruptive load balancing in data ce ..."
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Cited by 49 (8 self)
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Abstract—We describe the design of an agile data center with integrated server and storage virtualization technologies. Such data centers form a key building block for new cloud computing architectures. We also show how to leverage this integrated agility for nondisruptive load balancing in data centers across multiple resource layers servers, switches, and storage. We propose a novel load balancing algorithm called VectorDot for handling the hierarchical and multidimensional resource constraints in such systems. The algorithm, inspired by the successful Toyoda method for multidimensional knapsacks, is the first of its kind. We evaluate our system on a range of synthetic and real data center testbeds comprising of VMware ESX servers, IBM SAN Volume Controller, Cisco and Brocade switches. Experiments under varied conditions demonstrate the endtoend validity of our system and the ability of VectorDot to efficiently remove overloads on server, switch and storage nodes. I.
Approximating the SingleSink Link Installation Problem in Network Design
, 1998
"... We initiate the algorithmic study of an important but NPhard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a specified length for each edge, and a specified demand, dem v , for each source node v. (2) ..."
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Cited by 49 (2 self)
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We initiate the algorithmic study of an important but NPhard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a specified length for each edge, and a specified demand, dem v , for each source node v. (2) A small set of cable types, where each cable type is specified by its capacity and its cost per unit length. The cost per unit capacity per unit length of a highcapacity cable may be significantly less than that of a lowcapacity cable, reflecting an economy of scale, i.e., the payoff for buying at bulk may be very high. The goal is to design a minimumcost network that can (simultaneously) route all the demands at the sources to the sink, by installing zero or more copies of each cable type on each edge of the graph. An additional restriction is that the demand of each source must follow a single path. The problem is to find a route from each source node to the sink and to assign ca...
Random knapsack in expected polynomial time
 IN PROC. 35TH ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING (STOC2003
, 2003
"... We present the first averagecase analysis proving a polynomial upper bound on the expected running time of an exact algorithm for the 0/1 knapsack problem. In particular, we prove for various input distributions, that the number of Paretooptimal knapsack fillings is polynomially bounded in the num ..."
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Cited by 48 (10 self)
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We present the first averagecase analysis proving a polynomial upper bound on the expected running time of an exact algorithm for the 0/1 knapsack problem. In particular, we prove for various input distributions, that the number of Paretooptimal knapsack fillings is polynomially bounded in the number of available items. An algorithm by Nemhauser and Ullmann can enumerate these solutions very efficiently so that a polynomial upper bound on the number of Paretooptimal solutions implies an algorithm with expected polynomial running time. The random input model underlying our analysis is quite general and not restricted to a particular input distribution. We assume adversarial weights and randomly drawn profits (or vice versa). Our analysis covers general probability distributions with finite mean and, in its most general form, can even handle different probability distributions for the profits of different items. This feature enables us to study the effects of correlations between profits and weights. Our analysis confirms and explains practical studies showing that socalled strongly correlated instances are harder to solve than weakly correlated ones.