Linear Programming Algorithm
1.INTRODUCTION The traffic control requires optimized route control in case of train schedule disruptions, by automatic route setting and real-time schedule alteration. The basic functions are : Route tracking Traffic control Priority decision functions The basic purpose of train scheduling in a railway network is to adjust train service provisions according to the traffic demands, limitations imposed by physical network and the maintenance and renewal necessities. It also involves locomotive stock and train crew planning. In engaged networks, there are lots of stations with a few hundred trains per day while the track often contains cumbersome layouts, multiple intertwined in-line, out-line, one-way/two-way through-stand. On the other …show more content…
Hybrid optimization algorithm for solving non-convex NLP/MINLP problems with constraints 1.1 BASIC CONCEPTS (a) LINEAR PROGRAMMING: Linear programming (LP; also called linear optimization) is a method to obtain the best result (such as maximum profit or lowest cost) whose requirements and constraints are depicted by linear relationships, in a mathematical model. Linear programming is a special sub-case of mathematical programming (mathematical optimization). On a more clear note, linear programming is a method for the optimization of a linear objective function, subject to linear equality and linear inequality constraints[47]. Its best domain of region is a convex polytope, which is a set of intersection of finitely many half spaces, each of which is constituted of a linear inequality. Its chief function is a real-valued affine (linear) function defined on this polyhedron. If such a point exists where this function has the smallest or the largest value, then a linear programming algorithm is used to find the value. Linear programs are problems that can be expressed in canonical form as Maximize c^Ty Subject to Ky ≤ b and y ≥ 0 where y represents the vector of variables (to be determined), c and b are vectors of (known) …show more content…
A simple mathematical formula is followed to move the particles around in the search space. When an optimized space is found, the last viewed space is un-recorded and the new position is stored. This leads to utilization of memory. This improvisation of positions leads to a better direction of movement. This process is iterated with the hope that a better solution will be found, though there is no Cen percent guarantee to it. Formally, let f: ℝn → ℝ be the cost function which must be minimized. The function takes a candidate solution as argument in the form of a vector of real numbers and produces a real number as output which indicates the objective function value of the given candidate solution. The gradient of f is not known. The goal is to find a solution a for which f(a) ≤ f(b) for all b in the search-space, which would mean a is the global minimum. Maximization can be performed by considering the function h = -f instead. Let S be the number of particles in the swarm, each having a position xi ∈ ℝn in the search-space and a velocity vi ∈ ℝn. Let pi be the best known position of particle i and let g be the best known position of the entire swarm. A basic PSO algorithm is
Nt1330 Unit 5 Term Paper
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Nt1310 Unit 4 Assignment
977 Words | 4 PagesFirstly I used LIBRARY IEEE, USE IEEE.STD_LOGIC_1164.ALL, and USE IEEE.STD_LOGIC_UNSIGNED.ALL as my libraries for this part implementation. I have taken all the three input signals R, G, B (all are unsigned) of an integer type which can have values from 0 to 255 (since they are all 8 bit inputs) as my input ports and Saturation (STD_LOGIC_VECTOR) as my output ports. The algorithm for computation of saturation is given by equation 5. The first thing to do is compare the magnitude of the three input signals R, B and G, to get the minimum signal comparisons has to be done among the input signals. Since minimum value is required no need to include the maximum value condition in the code, so Condition if (r<b) gives us the minimum value as GREEN.
Mat 540 Week 8 Assignment
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Nt1310 Unit 4
984 Words | 4 PagesNote that in the above equations, the $R_{sp}(b_j)$, $\forall b_j \in B$, $RB^M(u_i)$, $\forall u_i \in U$, and $RB^S(u_i)$, $\forall u_i \in U$, are unknown variables. The objective function of the above formulation is to maximize the estimated total amount of data, i.e., to maximize the network throughput. The constraint C1 restricts the split data rate $R_{sp}(b_j)$, $\forall b_j \in B^C$, should be less than $b_j$'s input data rate $R_{in}(b_j)$. The C2 demands that the $D^M_p(u_i)$ cannot be larger than the summation of (i) UE $u_i$'s input data volume at MeNB in the upcoming $I_t$, i.e., $R_{agg}^M(u_i) \times I_t$ and (ii) the remaining data located at MeNB $D_r^M(u_i)$. The C3 restricts the $D^S_p(u_i)$ on SeNBs, and the idea is similar
Nt1310 Unit 5 Lab Essay
90 Words | 1 Pages{Objective Function} f$(X)$, X= (x1, x2, x3, x4 $...$xd) \Generate the initial population of n fireflies, Xi, i = 1, 2, $...$, n \Light intensity Ii at Xi is determined by f (Xi) \Define the light absorption coefficient $gamma$ \While (t $<$Ij) \ hspace{2cm} Move firefly i towards j; \ hspace{1cm}End if \ hspace{1cm}Vary attractiveness with distance i via exp[- $ gamma r^{2}$] \ hspace{1cm}End for j \ hspace{1cm}End for i \ hspace{1cm}Rank the fireflies and find the current global best solution g* \End while \Post-process the results \
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Relative Placement Analysis
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Pinewood Derby: Data Analysis
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Mousetrap Car Lab Report
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Nt1330 Unit 3 Csr
787 Words | 4 PagesHowever, other constraints can be set as well, e.g., the part-of-speech tag of a specific token in the expression itself or before or after the temporal expression. For the normalization, it use normalization resources containing mappings between an expression and its value in standard format. Furthermore, linguistic clues are applied to normalize ambiguous expressions. For example, the tense of a sentence may indicate the temporal relation between an expression and its reference time.
NFPA-1001 Training Paper
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Persuasive Speech: Mutations And Space Exploration
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Master Budget Analysis
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