Optimizing Transportation Problems with Multiple Objectives

Virtually all models developed for transportation problems have focused upon the optimization of a single objective criterion, namely the minimization of total transportation costs. They have generally neglected or often ignored the multiple conflicting objectives involved in the problem, the priority structure of these objectives, various environmental constraints, unique organizational values of the firm, and bureaucratic decision structures. However, in reality these are important factors which greatly influence the decision process of transportation problems. In this study the goal programming approach is utilized in order to allow for the optimization of multiple conflicting goals while permitting an explicit consideration of the existing decision environment.     

A Statistical Investigation and Optimization of an Industrial Radiography Inspection Process for Aero‐engine Components

Obtaining a high‐quality radiographic image is imperative in a radiography inspection process as it improves the identification of flaws in aero‐engine parts, which in turn enhances the reliability and safety of aircrafts. Existing methods to improve the radiographic inspection process are ad hoc and rely heavily on the experiences of radiographers. The radiographic images obtained from X‐rays have dual conflicting quality features, contrast sensitivity and spatial resolution, and have to satisfy a density reading constraint. This paper investigates an industrial radiography inspection process using statistical design of experiments (DOE) and analysis to determine optimal design settings for the process. The investigation adapts from the standard response surface methodology (RSM) and provides a promising alternative to the current methods. It has several key features such as the sequential DOE to first determine the feasible region imposed by the film density constraint, a sliding‐level system design to handle the irregular region, and an optimization formulation to optimize the dual image quality responses simultaneously. It provides a systematic approach to analyzing processes with secondary response constraints, and provides a quantitative basis for selecting optimum process settings. To evaluate the effectiveness of the statistical models obtained for industrial radiography, the probability of detection methodology is used to compare the optimum process settings recommended. Copyright © 2005 John Wiley & Sons, Ltd.     

Multiresponse systems optimization using a goal attainment approach

A goal attainment approach to optimize multiresponse systems is presented. This approach aims to identify the settings of control factors to minimize the overall weighted maximal distance measure with respect to individual response targets. Based on a nonlinear programming technique, a sequential quadratic programming algorithm, the method is proved to be robust and can achieve good performance for multiresponse optimization problems with multiple conflicting goals. Moreover, the optimization formulation may include some prior work as special cases by assigning proper response targets and weights. Fewer assumptions are needed when using the approach as compared to other techniques. Furthermore, the decision-maker's preference and the model's predictive ability can easily be incorporated into the weights' adjustment schemes with explicit physical interpretation. The proposed approach is investigated and compared with other techniques through various classical examples in the literature.     

Using constraints and their value for optimization of large ODE systems

We provide analytical tools to facilitate a rigorous assessment of the quality and value of the fit of a complex model to data. We use this to provide approaches to model fitting, parameter estimation, the design of optimization functions and experimental optimization. This is in the context where multiple constraints are used to select or optimize a large model defined by differential equations. We illustrate the approach using models of circadian clocks and the NF-κB signalling system.     

Cost effective simulation-based multiobjective optimization in the performance of an internal combustion engine

Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.     

A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach

A desirability function approach has been widely used in multi‐response optimization due to its simplicity. Most of the existing desirability function‐based methods assume that the variability of the response variables is stable; thus, they focus mainly on the optimization of the mean of multiple responses. However, this stable variability assumption often does not apply in practical situations; thus, the quality of the product or process can be severely degraded due to the high variability of multiple responses. In this regard, we propose a new desirability function method to simultaneously optimize both the mean and variability of multiple responses. In particular, the proposed method uses a posterior preference articulation approach, which has an advantage in investigating tradeoffs between the mean and variability of multiple responses. It is expected that process engineers can use this method to better understand the tradeoffs, thereby obtaining a satisfactory compromise solution.     

Artificial intelligence implementation in the APS process diagnostic

Thermal spray process is a technique of coating manufacturing implementing a wide variety of materials and processes. This technique is characterized by up to 150 processing parameters influencing the coating properties. The control of the coating quality is needed through the consideration of a robust methodology that takes into account the parameter interdependencies, the process variability and offers the ability to quantify the processing parameter-process response relationships. The aim of this work is to introduce a new approach based on artificial intelligence responding to these requirements. A detailed procedure is presented considering an artificial neural network (ANN) structure which encodes implicitly the physical phenomena governing the process. The implementation of such a structure was coupled to experimental results of an optic sensor controlling the powder particle fusion state before the coating formation. The optimization steps were discussed and the predicted results were compared to the experimental ones allowing the identification of the control factors.     

Multiresponse optimization with consideration of probabilistic covariates

In many complex experiments, nuisance factor may have large effects that must be accounted for. Covariates are one of the most important kinds of nuisance factors that can be measured but cannot be controlled within the experimental runs. In this paper a novel approach is proposed, based on goal programming, to find the best combination of factors so as to optimize multiresponse‐multicovariate surfaces with consideration of location and dispersion effects. Furthermore, it is supposed that several covariates considered in the experiment have probability distributions of known form. One objective is to find the most probable values of each covariate. For this purpose, a multiobjective mathematical optimization model is proposed and its efficacy is demonstrated by two numerical examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust Parameter Design: A Review

Parameter design is an engineering methodology intended as a cost‐effective approach for improving the quality of products and processes. The assumption is that there are both controllable factors (control variables) and uncontrollable/difficult to control factors (noise variables) that operate on the quality characteristic of a process. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic while minimizing the variation imposed on the process via the noise variables. Parameter design was popularized in the mid 1980s by Japanese quality consultant Genichi Taguchi. A panel discussion edited by Nair summarized important responses to Taguchi's ideas and methodology. In the last decade, there have been many applications and new developments in this important area. This review paper focuses largely on the work done since 1992, but a historical perspective of parameter design is also given. Copyright © 2003 John Wiley & Sons, Ltd.     

An integrated approach to tolerance synthesis,process selection and machining parameter optimization problems

Tolerance synthesis, process selection and machining parameter optimization have been recognized as key issues to ensure product quality and reduce production cost. Although the three issues are closely interrelated, they are rarely addressed simultaneously. This often leads to inconsistent and conflicting decisions. This paper reports an integrated approach for simultaneously addressing these issues subject to their common constraints and considers both tangible and intangible cost criteria. Most commonly used machining processes such as milling, turning, drilling, reaming, boring and grinding have been taken into account. Particular attention has also been paid to multiple quality characteristics. Two example problems, one requiring rotational machining, the other involving planar machining, are solved to demonstrate the application of the proposed approach.     

GENERALIZED CENTER METHOD FOR MULTIOBJECTIVE ENGINEERING OPTIMIZATION

This paper presents a new approach to multiobjective engineering optimization: the generalized center method (GCM). A multiobjective problem is solved by calculating the centers of a sequence of level sets. These sets comprise intersections of the original constraints and level constraints imposed on objective functions. In view of the different dimensions and conflicting nature of multiple objectives, some scaling and trade-off procedures are implemented. Several engineering optimization examples are given to demonstrate the effectiveness of the proposed method.     

Process design for the environment: A multi-objective framework under uncertainty

 Designing chemical processes for the environment requires consideration of several indexes of environmental impact including ozone depletion, global warming potentials, human and aquatic toxicity, photochemical oxidation, and acid rain potentials. Current methodologies, such as the generalized waste reduction algorithm (WAR), provide a first step towards evaluating these impacts. However, to address the issues of accuracy and the relative weights of these impact indexes, one must consider the problem of uncertainties. Environmental impacts must also be weighted and balanced against other concerns, such as their cost and long-term sustainability. These multiple, often conflicting, goals pose a challenging and complex optimization problem, requiring multi-objective optimization under uncertainty. This paper will address the problem of quantifying and analyzing the various objectives involved in process design for the environment. Towards this goal, we proposed a novel multi-objective optimization framework under uncertainty. This framework is based on new and efficient algorithms for multi-objective optimization and for uncertainty analysis. This approach finds a set of potentially optimal designs where trade-offs can be explicitly identified, unlike cost-benefit analysis, which deals with multiple objectives by identifying a single fundamental objective and then converting all the other objectives into this single currency. A benchmark process for hydrodealkylation (HDA) of toluene to produce benzene modeled in the ASPEN simulator is used to illustrate the usefulness of the approach in finding environmentally friendly and cost-effective designs under uncertainty. Received: 8 February 2000 / Accepted: 10 March 2000     

A Robust Framework for Multi‐Response Surface Optimization Methodology

A robust multi‐response optimization framework is proposed for simultaneously optimizing multiple conflicting quality characteristics. Unlike prior methods, the proposed approach is insensitive to subjective inputs like target specifications and improves optimization process for correlated responses. The effectiveness of the proposed approach is demonstrated and compared with existing methods considering two examples from the literature. The proposed method yields similar results consistently for different assigned target values demonstrating repeatability of the model, hence demonstrating insensitivity to assigned subjective target values. Furthermore, the study also considers multiple correlated design characteristics issue to achieve better trade‐off during design optimization. Copyright © 2013 John Wiley & Sons, Ltd.     

Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.     

Optimization of process plans using a constraint-based tabu search approach

A computer-aided process planning system should ideally generate and optimize process plans to ensure the application of good manufacturing practices and maintain the consistency of the desired functional specifications of a part during its production processes. Crucial processes, such as selecting machining resources, determining set-up plans and sequencing operations of a part should be considered simultaneously to achieve global optimal solutions. In this paper, these processes are integrated and modelled as a constraint-based optimization problem, and a tabu search-based approach is proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure from good manufacturing practices (penalty function) are the optimization evaluation criteria. Precedence constraints from the geometric and manufacturing interactions between features and their related operations in a part are defined and classified according to their effects on the plan feasibility and processing quality. A hybrid constraint-handling method is developed and embedded in the optimization algorithm to conduct the search efficiently in a large-size constraint-based space. Case studies, which are used for comparing this approach with the genetic algorithm and simulated annealing approaches, and the proposed constraint-handling method and other constraint methods, are discussed to highlight the performance of this approach in terms of the solution quality and computational efficiency of the algorithm.     

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

Nested coloured timed Petri nets for production configuration of product families

Production configuration is as an effective technique to deal with product variety while maintaining production stability and efficiency. It involves a diverse set of process elements (e.g., machines, operations), a high variety of component parts and assemblies and many constraints arising from product and process variety. Production configuration entails the selection and subsequent arrangement of process elements into complete production processes and the final evaluation of configured multiple alternatives. To better understand production configuration and its implementation, we study the underlying logic for configuring production processes using a dynamic modelling and visualisation approach. This is accomplished by developing a new formalism of nested coloured timed Petri nets (PNs). In view of the inherent modelling difficulties, in the formalism three types of nets–process nets, assembly nets and manufacturing nets–together with a nested net system are defined. Using an industrial example of vibration motors, we show how the proposed formalism can be applied to specify production processes at different levels of abstraction to achieve production configuration.     

An alternative desirability function for achieving ‘six sigma’ quality

To achieve high process yields or ‘six sigma’ quality, engineers often need to evaluate and optimize processes that are characterized by multiple quality characteristics. Existing desirability functions weigh together multiple objectives but they have a number of limitations. Most importantly, available desirability functions do not explicitly account for the combined effect of the mean and the dispersion of the quality characteristic. Therefore, it is easy to incur excessive expenditures or unknowingly to fail to achieve targeted yields. In this paper, a desirability function is proposed that addresses these limitations. This function conservatively estimates the ‘effective yield’ under assumptions described in the ‘six sigma’ literature. We use an arc‐welding application to illustrate how the proposed desirability function can yield a substantially higher level of quality as well as a more accurate assessment of the process capability than available alternatives. We suggest that the proposed desirability function should be used to facilitate multicriterion optimization when dispersion data are available. Copyright © 2003 John Wiley & Sons, Ltd.