Model for Planning and Sizing Curbside Parking Lanes in Urban Networks

2018 ◽  
Vol 2672 (20) ◽  
pp. 1-11 ◽  
Author(s):  
Chrysanthi Gkini ◽  
Christina Iliopoulou ◽  
Konstantinos Kepaptsoglou ◽  
Eleni I. Vlahogianni
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Programming Model ◽  
Urban Traffic ◽  
Traffic Networks ◽  
Mathematical Programming Model ◽  
Urban Network ◽  
Urban Networks ◽  
Adverse Impacts ◽  
Parking Demand

Curbside parking is associated with various adverse impacts on urban traffic networks and is rarely recommended. However, there are cases where parking demand dictates the establishment of on-street parking lanes. Proper planning of the number and type of curbside parking lanes to be located is essential for maximizing roadway capacity and minimizing the resulting impacts of parking operations on the network’s performance. This paper develops a bi-level mathematical programming model for planning and sizing curbside parking lanes in an urban network. The model is solved using a genetic algorithm and demonstrated for a medium-sized urban network.

OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?

2018 ◽  
Vol 21 (06n07) ◽  
pp. 1850022 ◽  
Author(s):  
MEHRDAD AGHA MOHAMMAD ALI KERMANI ◽  
REZA GHESMATI ◽  
MASOUD JALAYER
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Mathematical Programming ◽  
Real World ◽  
Programming Model ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Message Content ◽  
Mathematical Programming Model ◽  
The Real

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

scholarly journals A Genetic Algorithm for the Double Row Layout Problem

2020 ◽  
Vol 22 (2) ◽  
pp. 85-92
Author(s):  
Achmad Pratama Rifai ◽  
Setyo Tri Windras Mara ◽  
Putri Adriani Kusumastuti ◽  
Rakyan Galuh Wiraningrum
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Material Handling ◽  
Programming Model ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Layout Problem ◽  
Double Row ◽  
Handling Cost ◽  
Material Handling Cost

The double row layout problem (DRLP) is an NP-hard and has many applications in the industry. The problem concerns on arranging the position of  machines on the two rows so that the material handling cost is minimized. Although several mathematical programming models and local heuristics have been previously proposed, there is still a requirement to develop an approach that can solve the problem efficiently. Here, a genetic algorithm is proposed, which is aimed to solve the DRLP in a reasonable and applicable time. The performances of the proposed method, both its obtained objective values and computational time, are evaluated by comparing it with the existing mathematical programming model. The results demonstrate that the proposed GA can find relatively high-quality solutions in much shorter time than the mathematical programming model, especially in the problem with large number of machines.

scholarly journals A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Hai Shen ◽  
Lingyu Hu ◽  
Kin Keung Lai
Keyword(s):  
Mathematical Programming ◽  
Input Data ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Topsis Method ◽  
Data Set ◽  
Interval Extension ◽  
Interval Valued

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

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