Nagesh Shukla

Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.

A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration

Considering the need for more effective decision support in the context of distributed manufacturing, this paper develops an advanced analytics framework for configuring supply chain (SC) networks. The proposed framework utilises a distributed multi-agent system architecture to deploy fuzzy rough sets-based algorithms for knowledge elicitation and representation. A set of historical sales data, including network node-related information, is used together with the relevant details of product families to predict SC configurations capable of fulfilling desired customer orders. Multiple agents such as data retrieval agent, knowledge acquisition agent, knowledge representation agent, configuration predictor agent, evaluator agent and dispatching agent are used to help execute a broad spectrum of SC configuration decisions. The proposed framework considers multiple product variants and sourcing options at each network node, as well as multiple performance objectives. It also captures decisions that span the entire SC simultaneously and, by implication, represents multiple network links. Using an industry test case, the paper demonstrates the effectiveness of the proposed framework in terms of fulfilling customer orders with lower production and emissions costs, compared to the results generated using existing tools.

Key characteristics-based sensor distribution in multi-station assembly processes

This paper presents a novel approach for optimal key characteristics-based sensor distribution in a multi-station assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics and Key Control Characteristics and represent critical product and process design features, respectively. In particular, KCs have defined dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation faults. The proposed approach allows obtaining minimum required production system 6-sigma diagnosability. A feature-based procedure is proposed which includes Genetic Algorithm-based approach (allowing pre-defined KCs as the measurement points) and state-of-the-art approaches (unrestricted location of measurement points) to iteratively include arbitrary measurement points together with KCs in the final sensor layout. A case study of automotive assembly processes is used to illustrate the proposed feature-based approach.

Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach

In this paper a mathematical model for the batch sequencing problem in a multistage supply chain is developed by taking into account three practically important objectives, viz. minimization of lead time, blocking time and due date violation. Attribute dependent operation time, sequence dependent setup time, different due dates, different lot sizes for batches and variable time losses due to interaction among several stages like waiting, idling, and blocking are also considered in the model. The problem is combinatorial in nature and complete enumeration of all its possibilities is computationally prohibitive. Therefore, a metaheuristic, artificial immune system (AIS) is employed to find an optimal/near optimal solution. In order to test the efficacy of AIS in solving the problem, its implementation on four different problems has been studied. Further, the comparative analysis of the results obtained by implementing AIS, genetic algorithm (GA) and simulated annealing (SA) on the proposed model reveals tha...

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature.

A utility-driven approach to supplier evaluation and selection: empirical validation of an integrated solution framework

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model.

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted.

Optimal sensor distribution for multi-station assembly process using chaos-embedded fast-simulated annealing

This paper presents a novel methodology for the allocation of sensors in multi-station assembly processes. It resolves two core issues pertaining to the determination of an optimal number of sensors to be employed and their best locations. To make the traditional approach more effective, the effect of noise on sensor placement is minimized by maximizing the determinant of the Fischer information matrix. A state-space approach is adopted to model the variation propagation pertaining to the transfer of parts in a given multi-station assembly process. Further, the objective function conceived is significant over other contributions with respect to adding the effect of noise coupled with the sensors. Moreover, a new algorithm is developed to optimize the newly formulated objective function. The proposed algorithm combines chaotic sequences with traditional evolutionary fast simulated annealing (EFSA), hence it is termed chaos-embedded fast-simulated annealing (CEFSA). It can find the optimal sensor distribution with the minimum effect of noise in the sensor data. This paper reports on conceptual work, which underpins the research, and also presents details of a numerical example carried out in an industrial context to test the efficacy of the proposed algorithm. Further analysis reveals that the proposed approach obtains optimal distribution of sensors and offers more generic results compared with previously concluded analysis.

Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization

This paper addresses a reliability optimization problem, where the motive is to select the best components for series and series-parallel systems such that system reliability becomes maximized while simultaneously minimizing the cost, weight, and volume. Previous formulation of the problem has implicit restrictions, i.e. it either maximizes system reliability or minimizes the cost. Thus, in order to give a realistic view to the model, a comprehensive objective function has been formulated by combining the normalized values of reliability, cost, weight, and volume. In this paper, a chaos-embedded hierarchical particle swarm optimization (CE-HPSO) algorithm has been proposed to solve the problems arising in the optimization of system reliability using redundancy. The salient features of the proposed algorithm are the use of chaotic sequences and time-varying acceleration coefficients which are responsible for diversifying the search space. Moreover, to restrict the premature convergence, a hierarchical particle swarm optimizer has been used in the proposed algorithm. The performance of the CE-HPSO algorithm has been tested on three benchmark problems and the comparisons are made with genetic algorithm results. In order to check the scalability of the proposed solution methodology, small and large problems are also considered. The results demonstrate the benefits of the proposed algorithm for solving this type of problem.

Applying a novel combination of techniques to develop a predictive model for diabetes complications

Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models.