scholarly journals Modeling of complex structured processes using discrete iterative networks and petri nets

2021 ◽  
Vol 2131 (3) ◽  
pp. 032004
Author(s):  
A Korneev ◽  
T Lavrukhina ◽  
T Smetannikova ◽  
Yu Glazkova
Keyword(s):  
Petri Nets ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Processing Mode ◽  
Functional Relationships ◽  
Multi Stage ◽  
Optimal Value ◽  
Spatially Distributed ◽  
Alternative Solutions ◽  
Production Conditions

Abstract The paper considers the use of discrete iterative networks and Petri nets in the modeling of complex structured processes. In general, the production process is represented as a composition of machines. Probabilistic finite machine are modeled to select optimal solutions from a certain set of alternative solutions. Alphabets of finite and probabilistic machines are formed on the basis of discrete optimization methods when modeling multi-stage productions. The processing mode adaptation block is used to change the alphabets when the production conditions are changed. Using the alphabet generation block allows you to choose the optimal value of the alphabets of the random variables under study. During modeling complex systems and developing algorithms for managing them, the presence of ambiguous functional relationships between factors and quality indicators is taken into account. Methods of modeling complex spatially distributed objects based on a hierarchy of machines belonging to a predetermined finite number of machine types using iterative networks and Petri nets are described.

scholarly journals On quantitative stability in infinite-dimensional optimization under uncertainty

2021 ◽  
Author(s):  
M. Hoffhues ◽  
W. Römisch ◽  
T. M. Surowiec
Keyword(s):  
Stochastic Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Under Uncertainty ◽  
Optimal Solutions ◽  
Probability Metrics ◽  
Pde Constrained Optimization ◽  
Quantitative Stability ◽  
Infinite Dimensional ◽  
Optimal Value

AbstractThe vast majority of stochastic optimization problems require the approximation of the underlying probability measure, e.g., by sampling or using observations. It is therefore crucial to understand the dependence of the optimal value and optimal solutions on these approximations as the sample size increases or more data becomes available. Due to the weak convergence properties of sequences of probability measures, there is no guarantee that these quantities will exhibit favorable asymptotic properties. We consider a class of infinite-dimensional stochastic optimization problems inspired by recent work on PDE-constrained optimization as well as functional data analysis. For this class of problems, we provide both qualitative and quantitative stability results on the optimal value and optimal solutions. In both cases, we make use of the method of probability metrics. The optimal values are shown to be Lipschitz continuous with respect to a minimal information metric and consequently, under further regularity assumptions, with respect to certain Fortet-Mourier and Wasserstein metrics. We prove that even in the most favorable setting, the solutions are at best Hölder continuous with respect to changes in the underlying measure. The theoretical results are tested in the context of Monte Carlo approximation for a numerical example involving PDE-constrained optimization under uncertainty.

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.

Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems

2018 ◽  
pp. 205-230
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Search Space ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Path Representation ◽  
Representation Technique ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets

2019 ◽  
Vol 14 (2) ◽  
pp. 360-384 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Petri Nets ◽  
Information Sharing ◽  
Bullwhip Effect ◽  
Colored Petri Nets ◽  
Content Type ◽  
Multi Stage ◽  
The Impact ◽  
Serial Supply Chains

PurposeInformation distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.Design/methodology/approachA simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.FindingsThe bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.Research limitations/implicationsInformation sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.Originality/valueColored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.

scholarly journals Analysis of Economic Indicators of Complex Production Processes

2018 ◽  
Vol 7 (3.5) ◽  
pp. 7
Author(s):  
Korneev A.M ◽  
Abdullakh L.S
Keyword(s):  
Forecast Model ◽  
Complete Information ◽  
Economic Indicators ◽  
Management Effectiveness ◽  
Technological Factors ◽  
Production Processes ◽  
Multi Stage ◽  
Spatially Distributed ◽  
The Cost ◽  
Production Resources

The article describes the methodology for describing the economic indicators of management effectiveness and decision-making under conditions of complex multi-stage productions. The algorithm and the forecast model of the need for production resources are presented, that allow providing more complete information on costs and help in pricing for various products, significantly reducing the response time to economic and technological situation changes. Characteristics of technology parameters are linked to a multi-stage production process. As the semi-finished product passes through the processing stages, the values of the technological factors are fixed. Methods for estimating the influence of parameters of complex spatially-distributed systems on costs are presented. Important elements of costs that affect the product value are determined. Detailing the cost elements for the technological operations under study is carried out, the boundaries, where the largest amount of resources is spent, are determined. 

scholarly journals Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5537
Author(s):  
Martin Nell ◽  
Alexander Kubin ◽  
Kay Hameyer
Keyword(s):  
Induction Machine ◽  
Optimization Methods ◽  
Induction Machines ◽  
Parameter Selection ◽  
Pattern Search ◽  
Machine Model ◽  
Multi Stage ◽  
Selection Approach ◽  
Optimization Parameters ◽  
Selection Of

Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.

Statistical Inference of Second-Order Cone Programming

2018 ◽  
Vol 35 (06) ◽  
pp. 1850044
Author(s):  
Jiani Wang ◽  
Liwei Zhang
Keyword(s):  
Objective Function ◽  
Second Order ◽  
Optimal Solutions ◽  
Random Vectors ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Second Order Cone ◽  
Analysis Theory ◽  
Optimal Value ◽  
Sum Of Norms

The randomness of the second-order cone programming problems is mainly reflected in the objective function and the constraints both having random vectors. In this paper, we discuss the statistical properties of estimates of the respective optimal value and optimal solutions when the random vectors are estimated by their sample both in the objective function and the constraints, which are based on perturbation analysis theory of second-order cone programming. As an example we consider the problem of minimizing a sum of norms with weights.

The Method of Optimal Phasor Measurement Unit Placement

2013 ◽  
Vol 798-799 ◽  
pp. 298-301
Author(s):  
Wen Jun Zhang ◽  
Jian Jun Xu ◽  
Long Xing
Keyword(s):  
Optimization Methods ◽  
Phasor Measurement Unit ◽  
Measurement Unit ◽  
Stable Operation ◽  
System Failure ◽  
Optimal Solutions ◽  
Constraint Condition ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Full Network

Taking the full network observability of power system and the least number of PMU as objective, to appearing fault situation in the grid, this paper proposes Differential Evolution and Particle Swarm Optimization (DEPSO) algorithm in view of the system failure rate. The improved DEPSO algorithm is global optimization, the algorithm takes the constraint condition of fault rate into account during the course of seeking optimal solutions. At the end, through the examples show that the algorithm compares with the existing optimization methods, which can reduce the number of PMU configuration and achieve completely observability of the system, at the same time, and stable operation of the system, through the simulation results verify feasibility and valibity of the algorithm.

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.

Approaches To Optimization For Movable Shading Systems:a Review Of Optimization Methods And Tools

2021 ◽  
pp. 123-134
Author(s):  
Yesim Keskinel ◽  
Mustafa Emre Ilal
Keyword(s):  
Energy Consumption ◽  
Optimization Methods ◽  
Building Performance ◽  
Optimal Solutions ◽  
Future Projections ◽  
Computational Optimization ◽  
Research Findings

Studies show that movable shading systems have lots of benefits for building performance. Minimizing energy consumption and maximizing daylight usage are natural expectations when using these systems. To find optimal solutions for these systems, different methods have been used. Today, optimization methods are used to solve this problem. In the literature, there are few studies about optimization of movable shading systems. This paper aims to identify different movable shading systems, optimization types, and computational optimization tools that are used. Research findings and future projections based on the reviewed papers are summarized.

Sign in / Sign up

Export Citation Format

Share Document