9,417 research outputs found
Deriving consensus rankings via multicriteria decision making methodology
Purpose - This paper seeks to take a cautionary stance to the impact of the
marketing mix on customer satisfaction, via a case study deriving consensus
rankings for benchmarking on selected retail stores in Malaysia.
Design/methodology/approach - The ELECTRE I model is used in deriving
consensus rankings via multicriteria decision making method for benchmarking
base on the marketing mix model 4P's. Descriptive analysis is used to analyze
best practice among the four marketing tactics.
Findings - Outranking methods in consequence constitute a strong base on
which to found the entire structure of the behavioral theory of benchmarking
applied to development of marketing strategy.
Research limitations/implications - This study looks only at a limited part
of the puzzle of how consumer satisfaction translates into behavioral outcomes.
Practical implications - The study provides managers with guidance on how to
generate a rough outline of potential marketing activities that can be used to
take advantage of capabilities and convert weaknesses and threats.
Originality/value - The paper interestingly portrays the effective usage of
multicriteria decision-making and ranking method to help marketing managers
predict their marketing trends
A new approach for transport network design and optimization
The solution of the transportation network optimization problem actually requires, in most cases, very intricate and powerful computer resources, so that it is not feasible to use classical algorithms. One promising way is to use stochastic search techniques. In this context, Genetic Algorithms (GAs) seem to be - among all the available methodologies- one of the most efficient methods able to approach transport network design and optimization. Particularly, this paper will focus the attention on the possibility of modelling and optimizing Public Bus Networks by means of GAs. In the proposed algorithm, the specific class of Cumulative GAs(CGAs) will be used for solving the first level of the network optimization problem, while a classical assignment model ,or alternatively a neural network approach ,will be adopted for the Fitness Function(FF) evaluation. CGAs will then be utilized in order to generate new populations of networks, which will be evaluated by means of a suitable software package. For each new solution some indicators will be calculated .A unique FF will be finally evaluated by means of a multicriteria method. Altough the research is still in a preliminary stage, the emerging first results concerning numerical cases show very good perspectives for this new approach. A test in real cases will also follow.
Comparison of PBO solvers in a dependency solving domain
Linux package managers have to deal with dependencies and conflicts of
packages required to be installed by the user. As an NP-complete problem, this
is a hard task to solve. In this context, several approaches have been pursued.
Apt-pbo is a package manager based on the apt project that encodes the
dependency solving problem as a pseudo-Boolean optimization (PBO) problem. This
paper compares different PBO solvers and their effectiveness on solving the
dependency solving problem.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083
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