Development of Exact and Heuristic Optimization Methods for Safety Improvement Projects at Level Crossings under Conflicting Objectives

2021 ◽  
pp. 108296
Author(s):  
Prashant Singh ◽  
Junayed Pasha ◽  
Ren Moses ◽  
John Sobanjo ◽  
Eren E. Ozguven ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Heuristic Optimization ◽  
Level Crossings ◽  
Safety Improvement ◽  
Conflicting Objectives

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Flexible Job Shop Models for Solving Scheduling and Layout Problems Using Coloured Petri Nets

2014 ◽  
Vol 598 ◽  
pp. 638-642
Author(s):  
José Eloundou ◽  
David Baudry ◽  
M’hammed Sahnoun ◽  
Abdelaziz Bensrhair ◽  
Anne Louis ◽  
...  
Keyword(s):  
Petri Nets ◽  
Programming Language ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Optimization Methods ◽  
Heuristic Optimization ◽  
Coloured Petri Nets ◽  
Flexible Job Shop ◽  
Cpn Tools ◽  
Manufacturing Problems

In this paper we propose models for solving both the layout manufacturing problems and the scheduling manufacturing systems. These models are based on Coloured petri Nets. The particularity of our models is the possibility to include complex programmable functions inside the petri nets models. In our case the programming language is SML/NJ. The advantage of programming language is the possibility to use Heuristics or Meta-heuristic optimization methods inside the Coloured Petri Nets (CPN) model without the necessity to relaunch the simulation of the model at each step of optimization. For describing, analysing and simulating our models we will use CPN Tools.

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