Skip to main content

2014 | Buch

Optimization and Control Methods in Industrial Engineering and Construction

insite
SUCHEN

Über dieses Buch

This book presents recent advances in optimization and control methods with applications to industrial engineering and construction management. It consists of 15 chapters authored by recognized experts in a variety of fields including control and operation research, industrial engineering and project management. Topics include numerical methods in unconstrained optimization, robust optimal control problems, set splitting problems, optimum confidence interval analysis, a monitoring networks optimization survey, distributed fault detection, nonferrous industrial optimization approaches, neural networks in traffic flows, economic scheduling of CCHP systems, a project scheduling optimization survey, lean and agile construction project management, practical construction projects in Hong Kong, dynamic project management, production control in PC4P and target contracts optimization.

The book offers a valuable reference work for scientists, engineers, researchers and practitioners in industrial engineering and construction management.

Inhaltsverzeichnis

Frontmatter
Robustness of Convergence Proofs in Numerical Methods in Unconstrained Optimization
Abstract
Numerical methods to solve unconstrained optimization problems may be viewed as control systems. An important principle in dynamic control system theory is that control policies should be prescribed in a feedback manner rather than in an open loop manner. This is to ensure that the outcomes are not sensitive to small errors in the state variables. A standard proof in numerical methods in unconstrained optimization like the Zoutendijk method is, from the control theory point of view, an open loop type of analysis as it studies what happens along a total trajectory for various initial state variables. In this chapter, an example is constructed to show that the eventual outcome and convergence to a global minimum point or otherwise can be very sensitive to initial values of the state variable. Convergence of a numerical method in unconstrained optimization can also be established by using the Lyapunov function theorem. The Lyapunov function convergence theorem provides feedback type analysis and thus the outcomes are robust to small numerical errors in the initial states. It requires that the level sets of the objective function are properly nested everywhere in order to have global convergence. This means the level sets of the objective function must be topologically equivalent to concentric spherical surfaces.
B. S. Goh, W. J. Leong, K. L. Teo
Robust Optimal Control of Continuous Linear Quadratic System Subject to Disturbances
Abstract
In this chapter, the robust optimal control of linear quadratic system is considered. This problem is first formulated as a minimax optimal control problem. We prove that it admits a solution. Based on this result, we show that this infinite-dimensional minimax optimal control problem can be approximated by a sequence of finite-dimensional minimax optimal parameter selection problems. Furthermore, these finite-dimensional minimax optimal parameter selection problems can be transformed into semi-definite programming problems or standard minimization problems. A numerical example is presented to illustrate the developed method.
Changzhi Wu, Xiangyu Wang, Kok Lay Teo, Lin Jiang
A Linearly-Growing Conversion from the Set Splitting Problem to the Directed Hamiltonian Cycle Problem
Abstract
We consider a direct conversion of the, classical, set splitting problem to the directed Hamiltonian cycle problem. A constructive procedure for such a conversion is given, and it is shown that the input size of the converted instance is a linear function of the input size of the original instance. A proof that the two instances are equivalent is given, and a procedure for identifying a solution to the original instance from a solution of the converted instance is also provided. We conclude with two examples of set splitting problem instances, one with solutions and one without, and display the corresponding instances of the directed Hamiltonian cycle problem, along with a solution in the first example.
Michael Haythorpe, Jerzy A. Filar
Optimum Confidence Interval Analysis in Two-Factor Mixed Model with a Concomitant Variable for Gauge Study
Abstract
Measurements often include a variety sources of variability due to measurement systems. The measurement systems study is performed to determine if the measurement procedure is appropriate for monitoring a manufacturing process. The measurement systems study here focuses on determining the amount of variability in a two-factor mixed model with a concomitant variable and no interaction in situations where two variables are correlated. The Analysis of variance is performed for the model and variabilities in the model are represented in a linear combination of variance components. Optimum confidence intervals are constructed using Modified Large Sample approach and Generalized Inference approach in order to determine the variability such as repeatability, reproducibility, parts, gauge, and the ratio of variability of parts to variability of gauge. A numerical example is provided to compute the optimum confidence intervals to investigate the variability in the model.
Dong Joon Park, Min Yoon
Optimization of Engineering Survey Monitoring Networks
Abstract
This chapter considers the various ways in which engineering survey monitoring networks, such that those used for tracking volcanic and large-scale ground movements, may be optimized to improve the precision. These include the traditional method of fixing control points, the Lagrange method, free net adjustment, the g-inverse method, and the Singular Value Decomposition (SVD) approach using the pseudo-inverse. A major characteristic of such inverse problem networks is that the system is rank deficient. This deficiency is solved using either exterior (i.e. a priori) or inner constraints. The former requires additional resources to provide the control points. In contrast, inner constraints methods do not require the imposition of external control and offer higher precision because the network geometry is preserved.
Willie Tan
Distributed Fault Detection Using Consensus of Markov Chains
Abstract
We propose a fault detection procedure appropriate for use in a variety of industrial engineering contexts, which employs consensus among a group of agents about the state of a system. Markov chains are used to model subsystem behaviour, and consensus is reached by way of an iterative method based on estimates of a mixture of the transition matrices of these chains. To deal with the case where system states cannot be observed directly, we extended the procedure to accommodate Hidden Markov Models.
Dejan P. Jovanović, Philip K. Pollett
Engineering Optimization Approaches of Nonferrous Metallurgical Processes
Abstract
The engineering optimization approaches arising in nonferrous metallurgical processes are developed to deal with the challenges in current nonferrous metallurgical industry including resource shortage, energy crisis and environmental pollution. The great difficulties in engineering optimization for nonferrous metallurgical process operation lie in variety of mineral resources, complexity of reactions, strong coupling and measurement disadvantages. Some engineering optimization approaches are discussed, including operational-pattern optimization, satisfactory optimization with soft constraints adjustment and multi-objective intelligent satisfactory optimization. As an engineering optimization case, an intelligent sequential operating method for a practical Imperial Smelting Process is illustrated. Considering the complex operating optimization for the Imperial Smelting Process, with the operating stability concerned, an intelligent sequential operating strategy is proposed on the basis of genetic programming (GP) adaptively designed, implemented as a multi-step state transferring procedure. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution with compact individuals. The optimal solution gained by evolution is a sequential operating program of process control, which not only ensures the tendency to optimization but also avoids violent variation by operating the parameters in ordered sequences. Industrial application data are given as verifications.
Xiaofang Chen, Honglei Xu
Development of Neural Network Based Traffic Flow Predictors Using Pre-processed Data
Abstract
Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on tr aining in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches.
Kit Yan Chan, Cedric K. F. Yiu
Economic Scheduling of CCHP Systems Considering the Tradable Green Certificates
Abstract
Due to the fossil fuel crisis contributed by the explosive growth in energy demand, combined cooling heating and power (CCHP) systems which can jointly supply electricity and hot/cold have become the mainstream of energy generation technology. In this chapter, tradable green certificate mechanism is firstly introduced to operation of CCHP system, and the impacts of tradable green certificate on the scheduling of CCHP system are studied. And then, based on the probability distribution of wind speed and solar radiation intensity as well as the copula join function, the joint probability distribution of maximum available output of multiple solar and wind farms is built. The economic dispatch model for multi-energy complementary system considering the TGC was proposed to maximize renewable energy utilization. This model aims at minimizing total system cost whist fulfilling the constraints of power system stable operation and hot/cold water pipes safe operation. After that, in order to address the non-convex scheduling optimization problem, global descent method is applied, which can continuously update the local optimal solutions by global descent function, and find global optimal solution. Finally, one modified IEEE 14-bus system is used to verify the performance of the proposed model and optimization solver.
Hongming Yang, Dangqiang Zhang, Ke Meng, Mingyong Lai, Zhao Yang Dong
Optimizations in Project Scheduling: A State-of-Art Survey
Abstract
Project scheduling is concerned with an optimal allocation of resources to activities realized over time. To survive in today’s competitive environment, efficient scheduling for project development becomes more and more important. The classical project scheduling is based on the critical path method (CPM) in which resources required are assumed unlimited. This is however impractical. To overcome CPM’s drawback, several techniques and optimizations have been proposed in project scheduling literature. In this chapter, we will present a state-of-art survey on project scheduling from the optimization point of view. In particularly, we will focus on the advancements of optimization formulations and solutions on project scheduling in the recent years.
Changzhi Wu, Xiangyu Wang, Jiang Lin
Lean and Agile Construction Project Management: As a Way of Reducing Environmental Footprint of the Construction Industry
Abstract
Construction industry effects the environment through its outputs and its process (i.e. causing CO\(_{2}\) emissions, exploitation of raw materials, energy consumption). There is need to reduce its environmental footprint of the construction industry with the help of efficient and effective construction project management, where possible benchmarking with management principles and applications in manufacturing industry. Such a key concept originated and adapted from manufacturing industry is lean and agile construction which can contribute to the reduction of environmental footprint of the construction industry, enabling especially reduction in waste, increasing value added activities. For this reason, this chapter focuses on the construction project management with respect to the agility and leanness perspective. It provides an indepth analysis of the whole project life cycle phases based on lean and agile principles.
Begum Sertyesilisik
Managing Construction Projects in Hong Kong: Analysis of Dynamic Implications of Industrial Improvement Strategies
Abstract
Boosting performance levels is one of the critical concerns of increasingly demanding construction industry clients. Focusing on building services installations in this chapter, poor practices in the site installation stage increase non-value-adding defective and demolition works, as well as consequential rework, affecting the overall project performance. Based on a series of face-to-face interviews with experienced practitioners and a focus group exercise, this chapter presents the mapping of various interacting and fluctuating behaviours patterns during the site installation stage of building services in construction projects, with the aid of a generic system dynamics model. Through a real case project for initializing the model, several scenarios were examined to test the behaviour patterns and characteristics of various influential improvement strategies. Drawing on long established industrial engineering principles in the manufacturing industry, some particularly useful concepts have been selected and modified in this chapter for addressing the causes of the identified production shortcomings. This chapter concludes that attention should be paid to prerequisite conditions and readiness of downstream processes prior to on-site installation, improvement of workmanship during installation and integration of self, successive and cross check mechanisms so as to avert the downward spiral of typical vicious cycles that have contributed to poor project performance.
Sammy K. M. Wan, Mohan M. Kumaraswamy
Dynamic Project Management: An Application of System Dynamics in Construction Engineering and Management
Abstract
Computer simulation is one of the most widely utilized tools for operational research in construction engineering and management. Although discrete event simulation (DES) has been extensively utilized in construction, system dynamics (SD) has received relatively little attention despite its great potential to address dynamic complexity in construction projects, which are inherently complex, dynamic and involve multiple feedback processes and non-linear relationships. This chapter introduces dynamic project management (DPM), an SD-based new construction project modeling approach, which has been successfully applied to deal with dynamic complexities in diverse infrastructure and building projects. Particularly, this chapter introduces three major theoretical foundations of DPM: a holistic approach, a system structure-oriented approach, and the incorporation of control time delays. This chapter is expected to serve as a useful guideline for the application of SD in construction and to contribute to expanding the current body of knowledge in construction simulation.
Sangwon Han, SangHyun Lee, Moonseo Park
A Lean Framework for Production Control in Complex and Constrained Construction Projects ( $$\mathrm{PC}^{4}\mathrm{P}$$ PC 4 P )
Abstract
Production conditions in construction are different than in the manufacturing industry.
Søren Lindhard, Søren Wandahl
Optimization in the Development of Target Contracts
Abstract
Target contracts are commonly used in construction and related project industries. However, research to date has largely been qualitative, and there is no universal agreement on how any sharing of project outcomes should be allocated between contracting parties. This chapter demonstrates that by formulating the sharing problem in optimization terms, specific quantitative results can be obtained for all the various combinations of the main variables that exist in the contractual arrangements and project delivery. Such variables include the risk attitudes of the parties (risk-neutral, risk-averse), single or multiple outcomes (cost, duration, quality), single or multiple agents (contractors, consultants), and cooperative or non-cooperative behaviour. The chapter gives original, newly derived results for optimal outcome sharing arrangements. The chapter will be of interest to academics and practitioners interested in the design of target contracts and project delivery. It provides an understanding of optimal sharing arrangements within projects, broader than currently available.
S. Mahdi Hosseinian, David G. Carmichael
Metadaten
Titel
Optimization and Control Methods in Industrial Engineering and Construction
herausgegeben von
Honglei Xu
Xiangyu Wang
Copyright-Jahr
2014
Verlag
Springer Netherlands
Electronic ISBN
978-94-017-8044-5
Print ISBN
978-94-017-8043-8
DOI
https://doi.org/10.1007/978-94-017-8044-5