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This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines. [ABSTRACT FROM AUTHOR]

A methodology based on a combinatorial mathematics-based decision-making method is proposed for evaluation of alternative flexible manufacturing systems. The methodology is developed to judge the relative merits of different flexible manufacturing systems for the industrial application considered. The proposed 'FMS selection index' evaluates and ranks flexible manufacturing systems for a given FMS selection problem. Two examples are included to illustrate the approach. [ABSTRACT FROM AUTHOR]

Byline: R. Venkata Rao, P.J. Pawar, J. Paulo Davim The optimum selection of process parameters is essential for advanced machining processes as these processes incur high initial investment, tooling cost, operating and maintenance cost. This paper presents the results of optimisation of process parameters of mechanical type advanced machining processes using a simulated annealing algorithm. The results obtained are then compared with those obtained using a genetic algorithm. It is observed that simulated annealing algorithm has outperformed the genetic algorithm in the present work.

Byline: R. Venkata Rao A Flexible Manufacturing System (FMS) is designed to combine the efficiency of a mass-production line and the flexibility of a job shop to produce a variety of products on a group of machines. Even though a number of studies were proposed in the past to address the issue of evaluation and selection of FMSs, there is a need for a simple and systematic method to guide user organisations in taking a proper decision. This is considered in this paper using an improved compromise ranking method in conjunction with Analytic Hierarchy Process (AHP). The proposed 'FMS Selection Index' (FMS-SI) evaluates and ranks FMSs for a given FMS selection problem. Two examples are included to illustrate the approach.

Byline: R. Venkata Rao The performance of the vendor is a key element in a company's success or failure. In order to attain the goals of low cost, consistent high quality, flexibility and quick response, companies have increasingly considered better vendor selection approaches. The overall goal of selection is to identify high-potential vendors and their quota allocations. An effective and appropriate vendor selection method is therefore very crucial to the competitiveness of companies. This paper presents a logical procedure for solving the vendor selection problem in a supply chain environment with multiple objectives. The procedure is based on a combined Analytic Hierarchy Process (AHP) and a Genetic Algorithm (GA) method. The proposed procedure has the capability to handle realistic situations and provides a decision-making tool for the vendor selection decision in a supply chain. An example is included to illustrate the approach.

Byline: R. Venkata Rao Flexible Manufacturing Cells (FMCs) represent a class of highly automated systems. The increased importance of these highly automated manufacturing systems to the survival of modern industries has resulted in increasing research efforts that address the many issues inherent in flexible manufacturing. One of the key issues is the problem of machine group selection in a FMC. Even though precision-based methods such as Multi-Attribute Decision-Making (MADM) methods, expert systems, neural networks, goal-programming methods, fuzzy algorithms, genetic algorithms, simulated annealing, etc. had been proposed in the past, these methods are knowledge-intensive, complicated, require more computation and may go beyond the capabilities of the real decision maker (i.e. user organisation). Hence, this paper presents a simple, systematic and logical methodology for machine group selection in a FMC using digraph and matrix methods. A Machine Group Selection Index (MGSI) is proposed, which evaluates and ranks machine groups for a given machine group selection problem. A step-by-step procedure for evaluation of MGSI is suggested. The unique feature of the proposed methodology is that it offers a general procedure that can be used for any type of selection problem involving any number of selection attributes. An example is included to illustrate the approach.

Byline: G. Karuppusami, R. Gandhinathan, R. Venkata Rao This paper presents a methodology to benchmark the quality-related action programmes and critical success factors of Strategic Quality Management (SQM) for manufacturing industries using the Quality Function Deployment (QFD) approach. The expert committee constituted for this research project identified 27 organisation performance requirements. The required quality-related action programmes to achieve the identified organisation performance requirements were obtained by adopting QFD methodology. Eleven SQM critical success factors were derived by grouping the similar quality-related action programmes. The QFD matrix was constructed with the organisation performance requirements as 'Whats' and the quality-related action programmes as 'Hows'. Finally, the critical success factors and the quality-related action programmes were benchmarked by their corresponding importance weight factors. A case study was presented to illustrate the methodology.

This study proposes an elitist-based self-adaptive multi-population (SAMPE) Jaya algorithm to solve the constrained and unconstrained problems related to numerical and engineering optimization. The Jaya algorithm is a newly developed metaheuristic-based optimization algorithm, and it does not require any algorithmic-specific parameters to be set other than the common control parameters of number of iterations and population size. The search mechanism of the Jaya algorithm is improved in this work by using the subpopulation search scheme with elitism. It uses an adaptive scheme for dividing the population into subpopulations. The effectiveness of the proposed SAMPE-Jaya algorithm is verified on CEC 2015 benchmark problems in addition to fifteen unconstrained, six constrained standard benchmark problems and four constrained mechanical design optimization problems considered from the literature. The Friedman rank test is also done for comparing the performance of the SAMPE-Jaya algorithm with other algorithms. It is also tested on three large-scale problems with the dimensions of 100, 500 and 1000. Furthermore, the proposed SAMPE-Jaya algorithm is used for solving a case study of design optimization of a micro-channel heat sink. The computational experiments have proved the effectiveness of the proposed SAMPE-Jaya algorithm. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive multi-team perturbation guiding Jaya (AMTPG Jaya) algorithm. The proposed approach uses multiple teams to explore the search space. Depending on the percentage of consumed function evaluations, the AMTPG Jaya algorithm adjusts the number of teams in the search. Furthermore, all teams move from a single population set and are simultaneously guided by different perturbation equations to different regions of search space. As each team has a different perturbation scheme, the set of moves to new positions by each team is unique. During the search process, the perturbation equations are exchanged depending on the quality of the solutions produced by them. The proposed algorithm employs dominance principles and the crowding distance estimation method to handle the multiple objectives simultaneously. The proposed algorithm is examined using two multi-objective optimization case studies of a solar dish Stirling heat engine system and a multi-objective optimization case study of the Stirling heat pump. Also, the Technique for Order of Preference by Similarity to Ideal Solution decision-making method is employed for identifying an optimal solution. The computational results obtained by the proposed AMTPG Jaya algorithm are superior to those achieved by the other algorithms presented in this work. [ABSTRACT FROM AUTHOR]

Welding processes are characterised as a multi-input processes. Selection of optimum combination of process parameters of a welding process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to improve the performance of submerged arc welding (SAW) process, electron beam welding (EBW) process, friction stir welding (FSW) process and gas tungsten arc welding (GTAW) process through parameter optimisation using a variant of Jaya algorithm, which is fast, robust and convenient. This variant is named as ‘Quasi-oppositional-based Jaya algorithm’ (QO-Jaya). The optimisation case studies for each of the above-mentioned welding processes are considered and the results obtained by the QO-Jaya algorithm are compared with the results obtained by well-known optimisation algorithms such as genetic algorithm (GA), simulated annealing (SA), teaching-learning-based optimisation (TLBO) and basic Jaya algorithm. [ABSTRACT FROM PUBLISHER]