scholarly journals Algorithm for Solving Problem of Designing Regional Logistics Infrastructure

2021 ◽  
Vol 20 (4) ◽  
pp. 352-356
Author(s):  
R. B. Ivut ◽  
P. V. Popov ◽  
P. I. Lapkovskaya ◽  
N. E. Sheveleva
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Methodological Approach ◽  
Optimal Location ◽  
Transport Infrastructure ◽  
Regional Logistics ◽  
Initial Stage ◽  
Nested Sets ◽  
Three Stages ◽  
The Cost

The paper considers an algorithm for solving the problem of finding the optimal location of key objects of transport and warehouse infrastructures within the framework of a methodological approach to designing logistics infrastructure in the territory of the region of the countries. The methodological approach includes three stages. At the first stage, areas  are determined where it is advisable to locate key objects of the regional logistics infrastructure. Further, using the models developed by the authors, the linking of warehouse infrastructure objects on the ground has been carried out and, taking into account the designed warehouse network, the optimal dislocation of transport infrastructure objects has been determined.  To find the optimal locations for the objects for regional logistics infrastructure facilities, the authors propose an algorithm that is applicable both for building warehouse and transport infrastructures due to the similarity of the models. The algorithm is based on the method of constructing a sequence of plans. At the initial stage, the final expansion is constructed for the set of plans under consideration. For a given set, a minorant has been determined for the cost function associated with the placement and maintenance of infrastructure facilities, the movement of goods, and the haul of an empty vehicle. After that, an iterative algorithm has been formed that determines the sequence of optima of the minorant on a sequence of nested sets. At the first step, an element of the set of plans has been found that minimizes the minorant, at the next step, the found element is excluded from the set under consideration, and a new optimum is sought on the remaining set for which the minorant takes the minimum value. To eliminate multiple plans, it is advisable to use dynamic programming procedures. The limits of applicability  of the method for constructing a sequence of plans are determined by the ability to construct an extension of the set of plans for placing objects, select a minorant on it, and build an algorithm for ordering optima.

Energy Cost Optimization of HVAC Loads Under Time-Varying Electricity Price Signals

2016 ◽  
Author(s):  
Saeid Bashash
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Energy Cost ◽  
Optimal Operation ◽  
Electricity Price ◽  
Programming Approach ◽  
Time Varying ◽  
Temperature Deviation ◽  
Price Signals ◽  
The Cost

This paper presents a dynamic programming approach to optimize energy cost of multiple interacting household appliances such as air conditioning systems and refrigerators with temperature flexibility, under time varying electricity price signals. We adopt a first order differential equation model with a binary (ON-OFF) switching control function for each load. An energy cost minimization problem is then formulated with a pair of constraints on the temperature lower and upper bounds, as well as an equality condition on the initial and final temperature states. We use dynamic programming to compute cost-optimal control inputs and temperature trajectories for a given electricity price profile and ambient temperature condition. To account for temperature deviation from its desired setpoint, a quadratic temperature deviation penalty is added to the cost function. Moreover, to minimize the control input chattering for equipment protection, the cost function is expanded to also minimize the number of on-off switching events. Results for the different weighting combinations of the optimization objectives provide useful insights on the optimal operation of individual and multiple interacting HVAC loads. In particular, we observe that the loads are desynchronized under the cost-optimal operation, in the presence of local (renewable) power generation. The presented optimization algorithm and observed results can lead to the development of novel model predictive and rule-based feedback control policies for optimal energy management in households.

scholarly journals Designing Supporting Structures of Passenger Ropeways of Minimum Cost Based on Modular Intermediate Towers of Discretely Variable Height

2020 ◽  
Vol 6 (4) ◽  
pp. 265-277
Author(s):  
Alexander V. Lagerev ◽  
Igor A. Lagerev
Keyword(s):  
Urban Development ◽  
Minimum Cost ◽  
Transport Infrastructure ◽  
Optimal Placement ◽  
Promising Alternative ◽  
Large Cities ◽  
Initial Stage ◽  
Calculation Results ◽  
The Cost ◽  
The City

AbstractPassenger ropeways are a promising alternative for the development of public transport infrastructure in large cities. However, the construction of ropeways has a rather high cost and requires taking into account a significant number of restrictions associated with the features of the existing urban development and the placement of urban infrastructure. The main objective of this research is to develop optimization models that minimize the total cost of modular intermediate towers of a discretely variable height and a rope system due to the optimal placement and selection of the height of these towers, taking into account the features of the surface topography and urban development. The proposed modular principle for the construction of intermediate towers also enables the cost of construction to be further reduced. As a specific example, the design of a ropeway in the city of Bryansk, which has a complex terrain, is considered. The developed models are conveniently used at the initial stage of the design of the ropeway to compare the cost of various options for the location of the ropeway route in order to reduce the risk of error when choosing the least expensive option. The calculation results can serve as a guide for a preliminary assessment of the number and height of intermediate towers, their installation locations on the ground and the characteristics of the cable system.

On the Assignment of Customers to Parallel Queues

1992 ◽  
Vol 6 (4) ◽  
pp. 495-511 ◽  
Author(s):  
Arie Hordijk ◽  
Ger Koole
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Optimal Policy ◽  
The State ◽  
Arrival Process ◽  
Single Server ◽  
Finite Buffers ◽  
Parallel Queues ◽  
Routing Policy ◽  
The Cost

This paper considers routing to parallel queues in which each queue has its own single server and service times are exponential with nonidentical parameters. We give conditions on the cost function such that the optimal policy assigns customers to a faster queue when that server has a shorter queue. The queues may have finite buffers, and the arrival process can be controlled and can depend on the state and routing policy. Hence our results on the structure of the optimal policy are also true when the assigning control is in the “last” node of a network of service centers. Using dynamic programming we show that our optimality results are true in distribution.

Adaptive Dynamic Programming Applied to a 6DoF Quadrotor

2011 ◽  
pp. 102-130 ◽  
Author(s):  
Petru Emanuel Stingu ◽  
Frank L. Lewis
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Control Policy ◽  
Adaptive Dynamic Programming ◽  
Optimal Controller ◽  
Policy Action ◽  
Adaptive Dynamic ◽  
Quadrotor Helicopter ◽  
The Cost ◽  
Number Of Iterations

This chapter discusses how the principles of Adaptive Dynamic Programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning. The controller (actor) changes its control policy (action) based on stimuli received in response to its actions by the critic (cost function, reward). There is a cause and effect relationship between action and reward. Reward acts as a reinforcement signal that leads to learning of what actions are likely to generate it. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Are Roundabouts Safe and Economically Viable Replacing Conventional Diamond Interchange Ramp Terminals?

2021 ◽  
pp. 036119812110088
Author(s):  
Boris Claros ◽  
Beau Burdett ◽  
Madhav Chitturi ◽  
Andrea Bill ◽  
David A. Noyce
Keyword(s):  
Empirical Bayes ◽  
Methodological Approach ◽  
Cost Savings ◽  
Weighted Distribution ◽  
Crash Rates ◽  
Design Considerations ◽  
Rate Analysis ◽  
The Cost ◽  
Benefit Cost ◽  
Service Life Cycle

Roundabout implementations at traditional intersections have been shown to be effective at reducing severe crashes. Roundabouts have also been implemented at interchange ramp terminals; however, limited research is available. In this study, 25 roundabout ramp terminal implementations were evaluated. The methodological approach consisted of Empirical Bayes for safety effectiveness and crash cost changes, crash type weighted distribution, crash rate analysis of bypass configuration, and cost of implementation. Roundabouts were effective at reducing fatal and injury crashes when replacing existing interchange diamond ramp terminals: 65% reduction for roundabouts replacing stop-controlled ramp terminals and 41% reduction for roundabouts replacing signal-controlled ramp terminals. Observed crash type weighted distributions are provided to visualize the frequency and location of crashes within roundabout ramp terminals for design considerations. Exit ramp and outside crossroad approaches with right-turn bypass showed significantly lower crash rates than designs without bypass. The crash cost analysis showed that roundabouts replacing diamond ramp terminals yielded crash cost savings of between $95,000 and $253,000 per site per year (69% to 54% decrease in crash costs). Considering crash costs savings only, the cost of implementation should be less than $1.9 million for a roundabout replacing a stop-controlled ramp terminal and less than $5.1 million for a roundabout replacing a signal-controlled ramp terminal to accomplish benefit-cost ratios greater than one for a service life cycle of 20 years. Costs are in 2019 dollars.

scholarly journals A Constrained Markovian Diffusion Model for Controlling the Pollution Accumulation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1466
Author(s):  
Beatris Adriana Escobedo-Trujillo ◽  
José Daniel López-Barrientos ◽  
Javier Garrido-Meléndez
Keyword(s):  
Dynamic Programming ◽  
Dirichlet Problem ◽  
Stochastic Control ◽  
Finite Time ◽  
Time Horizon ◽  
Closed Loop ◽  
Programming Techniques ◽  
Pollution Accumulation ◽  
Finite Time Horizon ◽  
The Cost

This work presents a study of a finite-time horizon stochastic control problem with restrictions on both the reward and the cost functions. To this end, it uses standard dynamic programming techniques, and an extension of the classic Lagrange multipliers approach. The coefficients considered here are supposed to be unbounded, and the obtained strategies are of non-stationary closed-loop type. The driving thread of the paper is a sequence of examples on a pollution accumulation model, which is used for the purpose of showing three algorithms for the purpose of replicating the results. There, the reader can find a result on the interchangeability of limits in a Dirichlet problem.

Sorting permutations by fragmentation-weighted operations

2020 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Alexsandro Oliveira Alexandrino ◽  
Carla Negri Lintzmayer ◽  
Zanoni Dias
Keyword(s):  
Approximation Algorithms ◽  
Computational Biology ◽  
Cost Function ◽  
Traditional Approach ◽  
Upper Bounds ◽  
Evolutionary Distance ◽  
Lower And Upper Bounds ◽  
Approximation Factor ◽  
New Type ◽  
The Cost

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations. The traditional approach is to consider that any rearrangement has the same probability to happen, and so, the goal is to find a minimum sequence of operations which sorts the permutation. However, studies have shown that some rearrangements are more likely to happen than others, and so a weighted approach is more realistic. In a weighted approach, the goal is to find a sequence which sorts the permutations, such that the cost of that sequence is minimum. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about the lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for five versions of this problem involving reversals and transpositions. We also give bounds for the diameters concerning these problems and provide an improved approximation factor for simple permutations considering transpositions.

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

Contour Extraction of Cerebrovascular Based on Maximum Optimization Cost

2011 ◽  
Vol 204-210 ◽  
pp. 1415-1418
Author(s):  
De Jiang Zhang ◽  
Na Na Dong ◽  
Xiao Mei Lin
Keyword(s):  
Dynamic Programming ◽  
Blood Vessel ◽  
High Efficiency ◽  
High Accuracy ◽  
Differential Method ◽  
Cost Curve ◽  
Contour Extraction ◽  
Target Image ◽  
The Cost ◽  
Conventional Algorithm

By studying the conventional algorithm of contour extraction, a new method of contour extraction in blood vessel of brain is proposed based on the MOC maximum optimization cost. First of all, the theory computes the gray differential of the image by conventional differential method to build the cost space. Then, by using dynamic programming theory, the maximum optimization cost curve in the space is extracted to serve as the specific cerebrovascular profile. The experiments show that this method ensures high efficiency in extracting cerebrovascular contour and a high accuracy in positioning cerebrovascular contour, and it diminishes the target image ambiguity caused by noise to improve the anti-interference ability of Contour extraction.

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