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The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

To respond to customer needs and achieve customized manufacturing, the manufacturing industry, as represented by electronics assembly companies, has embarked on a path of business model transformation (customer to manufacturer [C2M]). The purpose of this paper is to examine the practical application of assembly line-Seru conversion in a Chinese electronics assembly company during the C2M transition.

To begin with, this paper proposed a production line improvement scheme suitable for the conversion of C2M manufacturing enterprise assembly line-Seru based on an analysis of the difficulties encountered in the existing production line of A company in China. Then, a mathematical model was presented for the minimum value of the makespan and the maximum workers’ expenditure between Serus. Finally, the SA-NSGA-II algorithm and the entropy-weight TOPSIS approach were used to determine the optimal scheme for Seru unit, batch, product type and worker distribution.

Seru production and multiskilled workers are more suited to the C2M business model. The most effective strategy for worker allocation can reduce the number of employees and makespan in Serus. Additionally, the performance of the SA-NSGA-II algorithm and the method of selecting the optimal solution from the Pareto solution by the entropy-weighted TOPSIS method is also demonstrated.

Through a detailed study of how to transform the production line, other companies can apply the methods outlined in this article to shorten the delivery time, make full use of the abilities of workers and assign workers to specific positions, thereby reducing the number of workers, workers’ expenditure and improving the balance rate of production lines.

Given the scarcity of studies on the production method of C2M-type firms in the prior literature, this paper examined the assembly line-Seru conversion problem with the goal of minimizing the makespan and worker expenditure. To address the NSGA-II algorithm’s insufficient convergence, the simulated annealing process is incorporated into the method, which improves the optimization performance.

The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems.

This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population.

The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm.

The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper.

The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.

This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).

Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.

Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.

SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.

The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

The purpose of this study which resulted in this work is to propose an optimization method of sensors distribution for structural impact localization.

This paper presents a multi-objective optimization study of a novel sensors distribution technique, where two optimization objective functions are considered: sensors number and sensors location optimization performance index. In addition, the finite element analysis, the time-frequency transform and the principal component analysis are combined to quantize the above objective functions. The non-dominated sorting genetic algorithm II (NSGA-II) is used to acquire Pareto solutions.

The effectiveness of this method is validated through a prototype laboratory called the piezoelectric intelligent structure where promising results are obtained.

An optimization method of this novel sensors distribution technique is built and produced a set of efficiency solutions for the real-world problem of impact localization where two conflicting objectives are involved.

This paper consolidates and presents the results of a work conducted to fabricate micro-channels on titanium grade-2 material by ultrasonic machining process (USM). In this research, the effects of important USM parameters, namely, kind of abrasives and its size, concentration of slurry, USM power rating and feed rate, have been probed on micro-channels quality for average surface roughness and process throughput in the form of material removal rate.

Multiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool based USM process. Taguchi-based L18 (mixed level) OA has been selected for experimental design. Analysis of variance (ANOVA) study and regression modeling have also been done. Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been used for process optimization to get optimum values of material removal rate (MRR) and surface roughness (SR).

The influence of important USM variables on SR and MRR have been investigated, and NSGA-II-based multi-response optimization has been done. The best surface roughness values obtained via NSGA-II solution for SiC and B₄C are 0.354 µm and 1.303 µm, respectively. Scanned electron microscopic investigation proves the fabrication of micro-channels with smooth surfaces, and minimum burrs and other defects. The material removed from the surface was due to ductile fractures.

Miniaturization is a modern trend these days to solve many precision, scientific and industrial problems. To manufacture precise micro-products, shapes and features, advanced and micro-machining processes can play a very prominent role. Micro-channels are typical micro-features required in micro-fluidic applications like micro heat exchangers and micro-pumps. Exhaustive review of existing research work indicated that precision micromachining of various materials can be effectively performed using USM, though not much work has been undertaken to explore the feasibility of multiple micro-channels in a single run using USM. The current work fulfills the gap, where multiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool-based USM process.

The purpose of this paper is to explain in detail the optimization of the sensitivity versus the power consumption of a pressure microsensor using multi-objective genetic algorithms.

The tradeoff between sensitivity and power consumption is analyzed and the Pareto frontier is identified by using NSGA-II, AMGA-II and ɛ-MOEA methods.

Comparison results demonstrate that NSGA-II provides optimal solutions over the entire design space for spread metric analysis, and AMGA-II is better for convergence metric analysis.

This paper provides a new multiobjective optimization tool for the designers of low power pressure microsensors.

The purpose of this paper is to present a new multi‐objective clonal selection algorithm (MCSA) for the solution of electromagnetic optimization problems.

The method performs the somatic hypermutation step using different probability distributions, balancing the local search in the algorithm. Furthermore, it includes a receptor editing operator that implicitly realizes a dynamic search over the landscape.

In order to illustrate the efficiency of MCSA, its performance is compared with the nondominated sorting genetic algorithm II (NSGA‐II) in some analytical problems and in the well‐known TEAM benchmark Problem 22. Three performance evaluation techniques are used in the comparison, and the effect of each operator of the MCSA in its accomplishment is estimated.

In the analytical problems, the MCSA enhanced both the extension and uniformity in its solutions, providing better Pareto‐optimal sets than the NSGA‐II. In the Problem 22, the MCSA also outperformed the NSGA‐II. The MCSA was not dominated by the NSGA‐II in the three variables case and clearly presented a better convergence speed in the eight variables problem.

This paper could be useful for researchers who deal with multi‐objective optimization problems involving high‐computational cost.

The new operators incorporated in the MCSA improved both the extension, uniformity and the convergence speed of the solutions, in terms of the number of function evaluations, representing a robust tool for real‐world optimization problems.