In large cities all around the world, individual and motorized traffic is still prevalent. this circumstance compromises the quality of living, and moreover, space inside cities for parking individual vehicles for mov...
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ISBN:
(数字)9783030386290
ISBN:
(纸本)9783030386290;9783030386283
In large cities all around the world, individual and motorized traffic is still prevalent. this circumstance compromises the quality of living, and moreover, space inside cities for parking individual vehicles for movement is scarce and is becoming even scarcer. thus, the need for a greener means of transportation and less individual vehicles inside the cities is demanded and rising. An already accepted and established solution possibility to these problems are public bike sharing systems (PBS). Such systems are often freely available to people for commuting within the city and utilize the available space in the city more efficiently than individual vehicles. When building or extending a PBS, a certain optimization goal is to place stations inside a city or a part of it, such that the number of bike trips per time unit is maximized under certain budget constraints. In this context, it is also important to consider rebalancing and maintenance costs as they introduce substantial supplementary costs in addition to the fixed and variable costs when building or extending a PBS. In contrast to the literature, this work introduces a novel approach which is particularly designed to scale well to large real-world instances. Based on our previous work, we propose a multilevel refinement heuristic operating on hierarchically clustered input data. this way, the problem is coarsened until a manageable input size is reached, a solution is derived, and then step by step extended and refined until a valid solution for the whole original problem instance is obtained. As an enhancement to our previous work, we introduce the following extensions. Instead of considering an arbitrary integral number of slots for stations, we now use sets of predefined station configurations. Moreover, a local search is implemented as refinement step in the multilevel refinement heuristic and we now consider real-world input data for the city of Vienna.
We propose a new approach to solve combinatorialoptimization problems. Our approach is simple to implement but powerful in terms of performance and speed. We combine the strengths of a meta-heuristic approach withth...
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We propose a new approach to solve combinatorialoptimization problems. Our approach is simple to implement but powerful in terms of performance and speed. We combine the strengths of a meta-heuristic approach withthe integerprogramming method by partitioning the problem into two interrelated subproblems, where the higher level problem is solved by the metahueristic and the lower level problem is solved by integerprogramming. We discuss the selection of key variables to facilitate an effective partitioning, and test our approach on two real world crossdocking problems, which is very popular in this part of the world. Our experimental results indicate that our new approach is very promising.
One main concern of voting theory is to determine a procedure for choosing a winner from among a set of candidates, based on the preferences of the voters or, more ambitiously, for ranking all the candidates or a part...
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Airline crew pairing optimization problem (CPOP) aims to find a set of flight sequences (crew pairings) that cover all flights in an airline’s highly constrained flight schedule at minimum cost. Since crew cost is se...
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A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic...
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this paper addresses the problem of partitioning a set of vectors into two subsets such that the sums per every coordinate should be exactly or approximately equal. this problem, introduced by Kojic [8], is called the...
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A cardinality constrained knapsack problem is a continuous knapsack problem in which no more than a specified number of nonnegative variables are allowed to be positive. this structure occurs, for example, in areas su...
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We consider the problem of designing a transportation network for hazardous materials (HTNDP). For HTNDP, it was shown that deciding whether there exists an optimal path of risk 0 is NP-hard. A natural way to handle N...
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With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic...
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ISBN:
(纸本)9780769556703
With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. the proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. the optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. the proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.
the basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discuss...
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the basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discussed in the present paper. the effectiveness of the considered method is demonstrated by the possibility of the use of different form of objective functions, various and numerous nonlinear constraints and different types of design variables (continuous, discrete, real, integer). the sensitivity of the algorithm to the choice of the evolution strategy parameters is also discussed herein. the generality of the evolution strategy is illustrated by the analysis of three examples dealing with: the design of helical springs, the buckling of cylindrical composite panels and the buckling of pressure vessels with domed heads.
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