Abstract: In this paper, we consider the vehicle routing problem
with mixed fleet of conventional and heterogenous electric vehicles
and time dependent charging costs, denoted VRP-HFCC, in which
a set of geographically scattered customers have to be served by a
mixed fleet of vehicles composed of a heterogenous fleet of Electric
Vehicles (EVs), having different battery capacities and operating
costs, and Conventional Vehicles (CVs). We include the possibility
of charging EVs in the available charging stations during the routes
in order to serve all customers. Each charging station offers charging
service with a known technology of chargers and time dependent
charging costs. Charging stations are also subject to operating time
windows constraints. EVs are not necessarily compatible with all
available charging technologies and a partial charging is allowed.
Intermittent charging at the depot is also allowed provided that
constraints related to the electricity grid are satisfied.
The objective is to minimize the number of employed vehicles and
then minimize the total travel and charging costs.
In this study, we present a Mixed Integer Programming Model and
develop a Charging Routing Heuristic and a Local Search Heuristic
based on the Inject-Eject routine with different insertion methods. All
heuristics are tested on real data instances.
Abstract: The purpose of Grid computing is to utilize
computational power of idle resources which are distributed in
different areas. Given the grid dynamism and its decentralize
resources, there is a need for an efficient scheduler for scheduling
applications. Since task scheduling includes in the NP-hard problems
various researches have focused on invented algorithms especially
the genetic ones. But since genetic is an inherent algorithm which
searches the problem space globally and does not have the efficiency
required for local searching, therefore, its combination with local
searching algorithms can compensate for this shortcomings. The aim
of this paper is to combine the genetic algorithm and GELS (GAGELS)
as a method to solve scheduling problem by which
simultaneously pay attention to two factors of time and number of
missed tasks. Results show that the proposed algorithm can decrease
makespan while minimizing the number of missed tasks compared
with the traditional methods.
Abstract: In this paper newly reported Cosh window function is
used in the design of prototype filter for M-channel Near Perfect
Reconstruction (NPR) Cosine Modulated Filter Bank (CMFB). Local
search optimization algorithm is used for minimization of distortion
parameters by optimizing the filter coefficients of prototype filter.
Design examples are presented and comparison has been made with
Kaiser window based filterbank design of recently reported work.
The result shows that the proposed design approach provides lower
distortion parameters and improved far-end suppression than the
Kaiser window based design of recent reported work.