An Evolutionary Algorithm and discrete event simulation for optimizing inspection strategies for multi-stage processes

The problem of determining the optimal inspection strategy for a given multi-stage production process, i.e. the inspection strategy that results in the lowest total inspection cost, while still assuring a required output quality, is modelled as a joint optimization of inspection location, type and inspection limits. A fusion between a discrete event simulation to model the multi-stage process subject to inspection and to calculate the resulting inspection costs, and an Evolutionary Algorithm (EA) to optimize the inspection strategies, is suggested.     

A general repeat inspection plan for dependent multicharacteristic critical components

A component is critical if it causes disaster or a very high cost upon failure. Multicharacteristic critical components exist in many systems. Such components could be a part of an aircraft, space shuttle, a special weapon system or a gas ignition system. In many situations, characteristics’ failures of these components are statistically dependent. In this paper, a new inspection plan for such components is proposed. A mathematical model that depicts the plan is developed and an example demonstrating the results of the model is given. The advantage of this model over the other model where independence of the characteristics’ failure is assumed for the case of dependency is illustrated. The model resulted in an average of 32.4% reduction in cost compared to the situation where the dependency case is solved assuming statistical independence.     

A non-parametric method for fitting ROC curves for evaluation of industrial inspection performance

The use of signal detection theory (SDT) in evaluation of the performance of an inspector on certain tasks requires the identification of the receiver operating characteristic (ROC) curve of the inspector. In this paper a functional form, Y = f(X), for the ROC equation is proposed, and two alternative approaches are developed for estimation of its parameters. This method does not require prior assumptions on the statistical distribution of the noise and signal-plus-noise. The application of the method is illustrated by the use of an example data set.     

Optimal inspection policy for three-state systems monitored by control charts

This paper presents the formulations of the expected long-run cost per time unit for a system monitored by a static control chart and by an adaptive control chart respectively. The static chart has a fixed sampling interval and a fixed sample size. The adaptive chart has a fixed sample size but variable sampling intervals. The system is supposed to have three states, normal working state, failure delay time state, and failed state. Two levels of repair are used to maintain the system. A minor repair is used to restore the system if a detectable defect is confirmed by an inspection. A major repair will be performed if the system fails. The expected cost per time unit for maintaining such a system is obtained. The objective of such analysis is to find an optimal sampling policy for the inspection process. An artificially generated data example and a real data example are used to compare the expected cost per time unit for both the static and adaptive control charts.     

Quality investment and inspection policy in a supplier–manufacturer supply chain

This paper examines a serial supply chain that consists of one supplier and one manufacturer, each having imperfect production and inspection processes. Both the supplier and the manufacturer invest in quality improvement actions in their production processes to reduce defective items being produced. In addition to quality investment, the supplier engages in outbound inspection before sending the components to the manufacturer, and the manufacturer engages in inbound inspection, when receiving the components from the supplier, and outbound inspection, before sending final products to customers. We investigate the supplier’s and the manufacturer’s quality investment and inspection strategies in four noncooperative games with different degrees of information revealed. We study the effects of inspection-related information on both parties’ equilibrium strategies and profits, and further assess, at equilibrium, the rationality of the penalty on defective components.     

Utilizing an approximative solution to obtain the real optimal solution for a production and inspection model

Previous studies have proposed an integrated production and inspection model for a deteriorating production system whose process is characterized by an exponential distribution. Since the simultaneous determination of the optimal scheduled inspection times and the optimal production run length is difficult, an approximative production and inspection solution is obtained under the condition that the optimal inspection policy is equally-spaced. That is, obtaining an approximative production run length and number of inspections. This study further investigates this approximative solution and demonstrates that how to utilize it to obtain the real optimal solution more efficiently.     

Mathematical programming formulations for approximate simulation of multistage production systems

Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This new way of representing discrete event systems paves the way to the creation of simpler mathematical programming models that reduce the complexity of the system analysis. The paper proposes an approximate representation for a class of production systems characterized by several stages, limited buffer capacities and stochastic production times. The approximation exploits the concept of a time buffer, modeled as a constraint that put into a temporal relationship the completion times of two customers in a sample path. The main advantage of the proposed formulation is that it preserves its linearity even when used for optimization and, for such a reason, it can be adopted in simulation–optimization problems to reduce the initial solution space. The approximate formulation is applied to relevant problems such as buffer capacity allocation in manufacturing systems and control parameters setting in pull systems.     

A method for determining the equilibrium states of dynamic systems

A heuristic method is presented for determining the equilibrium states of motion of dynamic systems, in particular, spacecraft. The method can also be applied to the solution of sets of linear or nonlinear algebraic equations. A positive-semidefinite functional is formed to convert the problem to that of finding those minimum points where the functional vanishes. The process is initiated within a selecteddomain of interest by random search; convergence to a minimum is obtained by a modified Davidon's deflected gradient technique. To render this approach feasible in the presence of constraints, the functional is modified to include penalty terms which cause the functional to approach infinity at the constraint boundaries. Close approximations to solutions near the constraint boundaries are found by applying Carroll's approach in successively reducing the weighting factors of the penalty terms. After finding a minimum, the local domain around this point is eliminated by adding to the functional an interior constraint term, representing the surface under a hypersphere centered at the minimum point. The domain of consideration now becomes the subdomain formed by subtracting the space contained within this hypersphere from the previous domain of interest. Minima are now sought within the remaining space, as before.This paper is derived from research performed by the author while employed by TRW Systems Group, Redondo Beach, California.The author acknowledges the helpful suggestions of Dr. G. Bekey, University of Southern California, and those of Mr. E. A. Quast, Dr. M. P. Scher, and Dr. R. J. Wiley, Dynamics Department, TRW Systems Group, Redondo Beach, California.     

An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem

In this paper, an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discrete differential evolution (DDE) algorithm, the destruction and construction (DC) procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator. The performance of the DDE algorithm is substantially affected by the parameters of DC procedure as well as the choice of crossover operator. In order to enable the DDE algorithm to make use of different parameter values and crossover operators simultaneously, we propose an ensemble of DDE (eDDE) algorithms where each parameter set and crossover operator is assigned to one of the parallel populations. Each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators. As an application area, the well-known generalized traveling salesman problem (GTSP) is chosen, where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster. The experimental results show that none of the single populated variants was effective in solving all the GTSP instances whereas the eDDE performed substantially better than the single populated variants on a set of problem instances. Furthermore, through the experimental analysis of results, the performance of the eDDE algorithm is also compared against the best performing algorithms from the literature. Ultimately, all of the best known averaged solutions for larger instances are further improved by the eDDE algorithm.     

A particle swarm algorithm for inspection optimization in serial multi-stage processes

Implementing efficient inspection policies is much important for the organizations to reduce quality related costs. In this paper, a particle swarm optimization (PSO) algorithm is proposed to determine the optimal inspection policy in serial multi-stage processes. The policy consists of three decision parameters to be optimized; i.e. the stages in which inspection occurs, tolerance of inspection, and size of sample to inspect. Total inspection cost is adopted as the performance measure of the algorithm. A numerical example is investigated in two phases, i.e. fixed sample size and sample size as a decision parameter, to ensure the practicality and validity of the proposed PSO algorithm. It is shown that PSO gives better results in comparison with two other algorithms proposed by earlier works.     

Extended optimal inspection policies for a system in storage

A system such as missiles and spare parts of aircraft has to perform a normal operation in a severe environment at any time when it is used. However, the system is in storage for a long time from the delivery to the usage and its reliability goes down with time. Thus, a system in storage should be inspected and maintained at periodic times to hold a higher reliability than is prespecified.The following inspection model is considered: A system has three types of units, where unit 1 is maintained, unit 21 is not maintained but is replaced and unit 22 is neither maintained nor replaced. The system is overhauled if its reliability becomes lower than a prespecified probability. The number of replacements and time until overhaul are derived. Using these results, the average cost is obtained and both an optimal inspection time and an optimal replacement time to minimize it are numerically discussed.     

A continuation method for solving symmetric Toeplitz systems

A fast algorithm is proposed for solving symmetric Toeplitz systems. This algorithm continuously transforms the identity matrix into the inverse of a given Toeplitz matrix T. The memory requirements for the algorithm are O(n), and its complexity is O(log κ(T)nlogn), where (T) is the condition number of T. Numerical results are presented that confirm the efficiency of the proposed algorithm.     

A model for determining machine requirements in a multistage manufacturing system with discretely distributed demand

A model is presented for resolving the problem of determining the optimum number of machines, and their discrete operating rates, required to meet discretely distributed production demands with a minimum total expected cost. The model is developed for a serial, multistage manufacturing system with both straight-time and overtime operating periods.     

A noniterative multiproduct multiperiod production planning method

Production planning for multiple products on a single production facility over a time horizon requires minimizing the sum of production costs (regular time, overtime and subcontracting) and inventory carrying costs for meeting the known demands of the products. It is shown that the problem can be formulated and solved by a simple and noniterative method of ‘column minima’ even for multiple product situations.     

A hybrid method for solving multi-objective global optimization problems

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of optimums, which constitute the so called Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, most problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. The objective of this work is to compare the performance of a new hybrid method here proposed, with several well-known multi-objective evolutionary algorithms (MOEA). The main attraction of these methods is the integration of selection and diversity maintenance. Since it is very difficult to describe exactly what a good approximation is in terms of a number of criteria, the performance is quantified with adequate metrics that evaluate the proximity to the global Pareto-front. In addition, this work is also one of the few empirical studies that solves three-objective optimization problems using the concept of global Pareto-optimality.     

A novel evolution strategy for multiobjective optimization problem

Recent literatures have suggested that multiobjective evolutionary algorithms (MOEAs) can serve as a more exploratory and effective tool in solving multiobjective optimization problems (MOPs) than traditional optimizers. In order to contain a good approximation of Pareto optimal set with wide diversity associated with the inherent characters and variability of MOPs, this paper proposes a new evolutionary approach—(μ, λ) multiobjective evolution strategy ((μ, λ)-MOES). Following the highlight of how to balance proximity and diversity of individuals in exploration and exploitation stages respectively, some cooperative techniques are devised. Firstly, a novel combinatorial exploration operator that develops strong points from Gaussian mutation of proximity exploration and from Cauchy mutation of diversity preservation is elaborately designed. Additionally, we employ a complete nondominance selection so as to ensure maximal pressure for proximity exploitation while a fitness assignment determined by dominance and population diversity information is simultaneous used to ensure maximal diversity preservation. Moreover, a dynamic external archive is introduced to store elitist individuals as well as relatively better individuals and exchange information with the current population when performing archive increase scheme and archive decrease scheme. By graphical presentation and examination of selected performance metrics on three prominent benchmark test functions, (μ, λ)-MOES is found to outperform SPEA-II to some extent in terms of finding a near-optimal, well-extended and uniformly diversified Pareto optimal front.     

Matrix-free iterative solution strategies for large dense linear systems

A purely algebric approach to solving very large general unstructured dense linear systems, in particular, those that arise in 3D boundary integral applications is suggested. We call this technique the matrix-free approach because it allows one to avoid the necessity of storing the whole coefficient matrix in any form, which provides significant memory and arithmetic savings. We propose to approximate a non-singular coefficient matrix A by a block low-rank matrix à and to use the latter when performing matrix–vector multiplications in iterative solution algorithms. Such approximations are shown to be easily computable, and a reliable a posteriori accuracy estimate of ‖A − Ã‖₂ is derived. We prove that block low-rank approximations are sufficiently accurate for some model cases. However, even in the absence of rigorous proof of the existence of accurate approximations, one can apply the algorithm proposed to compute a block low-rank approximation and then make a decision on its practical suitability. We present numerical examples for the 3D CEM and CFD integral applications, which show that, at least for some industrial applications, the matrix-free approach is robust and cost-effective. © 1997 John Wiley & Sons, Ltd.     

A new approach for solving stokes systems arising from a distributive relaxation method

The distributed relaxation method for the Stokes problem has been advertised as an adequate change of variables that leads to a lower triangular system with Laplace operators on the main diagonal for which multigrid methods are very efficient. We show that under high regularity of the Laplacian, the transformed system admits almost block‐lower triangular form. We analyze the distributed relaxation method and compare it with other iterative methods for solving the Stokes system. We also present numerical experiments illustrating the effectiveness of the transformation which is well established for certain finite difference discretizations of Stokes problems. © 2010 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 27: 898–914, 2011     

A robust and efficient algorithm for planar competitive location problems

In this paper we empirically analyze several algorithms for solving a Huff-like competitive location and design model for profit maximization in the plane. In particular, an exact interval branch-and-bound method and a multistart heuristic already proposed in the literature are compared with uego (Universal Evolutionary Global Optimizer), a recent evolutionary algorithm. Both the multistart heuristic and uego use a Weiszfeld-like algorithm as local search procedure. The computational study shows that uego is superior to the multistart heuristic, and that by properly fine-tuning its parameters it usually (in the computational study, always) find the global optimal solution, and this in much less time than the interval branch-and-bound method. Furthermore, uego can solve much larger problems than the interval method.     

A multistage differential transformation method for approximate solution of Hantavirus infection model

The goal of this study is presented a reliable algorithm based on the standard differential transformation method (DTM), which is called the multi-stage differential transformation method (MsDTM) for solving Hantavirus infection model. The results obtanied by using MsDTM are compared to those obtained by using the Runge-Kutta method (R-K-method). The proposed technique is a hopeful tool to solving for a long time intervals in this kind of systems.