An Approach to Solving Transport Problems through the M-Method

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Miglena Ivanova ◽  
Keyword(s):  
Discrete Distribution ◽  
Probability Analysis ◽  
Transport Problem ◽  
Optimal Planning ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Transport Problems ◽  
Stated Problem ◽  
Freight Wagons

This paper presents an approach to solving transport problems by using the M-Method with optimal planning of freight wagon logistics. This method is applied in an example transport problem for minimizing empty mileage of open and covered freight wagons, considering their interchangeability and through overview of all combinations of interchangeable open and covered wagons in stations with a freight wagon shortage. The paper also presents an approach for probability analysis of two-dimensional discrete distribution (Crosstabs), generated by the SPSS program, including the optimal solutions of the stated problem which have been reached through the M-Method.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

A Simulated Annealing-Based Approach to Three Dimensional Component Packing

1994 ◽  
Author(s):  
Simon Szykman ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Computational Approach ◽  
Vlsi Circuits ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Layout Problem ◽  
Component Layout ◽  
General Component

Abstract This paper introduces a computational approach to three dimensional component layout that employs simulated annealing to generate optimal solutions. Simulated annealing has been used extensively for two dimensional layout of VLSI circuits; this research extends techniques developed for two dimensional layout optimization to three dimensional problems which are more representative of mechanical engineering applications. In many of these applications, miniaturization trends increase the need to achieve higher packing density and fit components into smaller containers. This research addresses the three dimensional packing problem, which is a subset of the general component layout problem, as a framework in which to solve general layout problems.

Optimization of Bicycle Frames Using Genetic Algorithms

2007 ◽  
Author(s):  
Yuan Mao Huang ◽  
Kuo Juei Wang
Keyword(s):  
Genetic Algorithms ◽  
Factor Of Safety ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Dimensional Model ◽  
Equilibrium Equations ◽  
Fitness Value ◽  
Two Dimensional Model ◽  
Bicycle Frame ◽  
The Mean

A bicycle frame is optimized for the lightest weight by using genetic algorithms in this study. Stresses of five rods in the bicycle frame less than the material yielding strength with consideration of the factor of safety are the constraints. A two-dimensional model of the frame is created. Equilibrium equations are derived and loads acting on rods are determined. A known function is used to verify feasibility of the program generated. Effects of the mutation rate, the crossover rate and the number of generation on the mean and the standard deviation of the fitness value are studied. The optimal solutions with the outer diameters and the inner diameters of the front frame rods to be 0.040 m and 0.038 m, respectively, the outer diameters and the inner diameters of the rear frame rods to be 0.024 m and 0.021m, respectively, and the weight of the bicycle frame to be 0.896 kg are recommended for the bicycle frame.

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