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Design science research (DSR) is a structured approach for solving complex ill-structured problems in organizations through the development of an artefact followed by its validation. This paper aims to evaluate existing DSR methodology and propose specific accents to promote DSR for environmental, social and governance (ESG)-oriented operational excellence (OPEX) initiatives within organizations.

This commentary paper is based on an abductive reasoning approach to evaluate and understand DSR and assess its effectiveness for developing solutions to typical ESG-oriented OPEX-based problems within organizations.

Existing literature on DSR is reviewed, after which it is evaluated on its ability to contribute to the implementation of sustainable solutions for ESG-oriented OPEX-based problems. Based on the review, specific DSR methodological accents are proposed for the development of ESG-oriented OPEX-based solutions in organizations.

This conceptual paper contributes to the conceptual understanding of the applicability, limitations and contextual preconditions for applying DSR. This paper proposes an explicit and, in some ways, alternative view on DSR research for OPEX researchers to apply and further the body of knowledge on matters of sustainability (ESG) in operations management.

Currently, there is limited understanding and application of the DSR methodology for OPEX-based problem-solving initiatives, as appears in the scant literature on DSR applied for the implementation of OPEX based initiatives for ESG purposes. This paper aims to challenge and provide accents for DSR applied to OPEX-related problems by means of a DSR framework and thereby promotes intervention-based studies among researchers.

The proposed step-by-step methodology contains novel elements and is expected to be of help for OPEX-oriented academicians and practitioners in implementing DSR methodology for practical related problems which need research interventions from academics from Higher Education Institutions.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

The purpose of this paper is to solve commitment problem of generating units in thermal power plants and to find the optimal dispatches of the committed units.

The unit commitment (UC) problem has been solved in two stages. In the first stage, the optimal units are identified using contribution factor. Initially, the generating units to be committed for each interval in the time horizon are obtained without considering the unit operational constraints such as minimum up time, minimum down time and initial state. Then the unit operational constraints are enforced and the optimal UC schedule is obtained. In the second stage, sequential approach with a matrix framework has been proposed to obtain the optimal dispatches of the committed units.

The simple methodologies have been developed for unit selection and to find the optimal dispatches of the committed units. The results of proposed methodology illustrate an improvement in the savings of total cost. The proposed approach is computationally efficient for solving large‐scale systems and successive UC problems.

UC has a major role in electric thermal power plant operation. The problem with one day and one week scheduling horizon has a large potential of use, especially for small‐ and medium‐scale power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation. The techniques developed for UC problem will provide a support to electric power companies for their economic operation and the concepts presented are useful in both graduate teaching and research to understand the UC problem.

The contribution of the paper is the simple methodologies which have been developed for unit selection and economic dispatch.

The purpose of this paper is to introduce a novel methodology that has the capability of finding symmetrical and nonsymmetrical solutions in complex design domains without additional tuning when changing the design domain. These go from an academic design domain to a practical one.

Various crossovers operators are applied over the same representation using a genetic algorithm for truss structural optimization cases where literature solutions have a tendency to forced symmetry in order to find an optimal design with fewer iterations. Continuous‐discrete representations were cross‐bred by a uniform‐sbx simultaneous crossover, called zygote crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain.

Design solutions found were lighter and stiffer when comparing against cases reported in current literature and in engineering practice. Also these solutions were found in fewer iterations.

The cases solved herein are complex and are a challenge for any optimization routine however practical design limitations are observed in the sense of out plane stability. Further comparisons cases are required in order to generate a less adjusted design, this is because the greenhouse solution had to be stiffened with out of plane bars to give it enough lateral stability.

Continuous‐discrete representations were cross‐bred by a uniform‐sbx simultaneous crossover, called natural crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain. This scheme along with a less restrictive environment allows a wider exploration of search space.

The purpose of this paper is to investigate and analyze the challenges of quality skill development in complex and large economies like India and develop innovative processes of improving employability.

The problem areas and gaps have been identified through literature survey and published reports by governmental agencies on employability and quality skill development in India. The research focuses on prevalent challenges for large-scale skill development and utilizes TRIZ (Russian acronym for “Theory of Inventive Problem Solving”) for finding innovative solutions to the grand challenge of employability.

The applied research methodology in the paper leads to a model for the “Innovation driven ecosystem for quality skill development” and also defines the role and responsibilities of each stakeholders in the ecosystem.

Solutions derived through TRIZ are qualitative in nature. The actual implication of solution needs to be tested after implementation. Further, intangible costs incurred, and harmful and useful effects cannot be easily quantified.

The parameter mapping for the TRIZ matrix was undertaken in this paper and this methodology when applied to other problem statements renders an organized process for improving total quality and innovative process management. The inventive principles were applied to find solution to contradictions and arrive at an integrated ecosystem which binds all stakeholders efficiently, to generate higher employability. The innovative solutions derived through the process are applicable to policy makers, researchers and practitioners.

The process of improving employability through quality skill development, benchmarked by the TRIZ methodology can have far reaching social implications.

The research extends the body of knowledge of TRIZ modeling concepts in areas other than engineering, and depicts a unique total quality methodology which can be easily applied for other problem-solving contexts. The contribution can serve as a reference technique/tool for improving reliability and quality through a methodical process of working out innovative solutions to solve operational problems.

This paper aims to present an evolutionary algorithm (EA) to accelerate the convergence for the radiative transfer equation (RTE) numerical solution using high-order and high-resolution schemes by the relaxation coefficients optimization.

The objective function minimizes the residual value difference between iterations in each control volume until its difference is lower than the convergence criterion. The EA approach is evaluated in two configurations, a two-dimensional cavity with scattering media and absorbing media.

Experimental results show the capacity to obtain the numerical solution for both cases on all interpolation schemes tested by the EA approach. The EA approach reduces CPU time for the RTE numerical solution using SUPERBEE, SWEBY and MUSCL schemes until 97% and 135% in scattering and absorbing media cases, respectively. The relaxation coefficients optimized every two numerical solution iterations achieve a significant reduction of the CPU time compared to the deferred correction procedure with fixed relaxation coefficients.

The proposed EA approach for the RTE numerical solution effectively reduces the CPU time compared to the DC procedure with fixed relaxation coefficients.

In this chapter, an air cargo shipment planning problem is considered by including individual risk factors of any sub-contracted agents. Due to competitive market conditions, air cargo forwarders are advised to remain flexible in operations. A mixed integer linear programming formulation including the potential for divisible activities is developed to model the shipment planning problem. To solve this complicated problem, we employ an ant colony optimization (ACO) methodology. Numerical examples are generated using data from both the extant literature and from a global air cargo company, allowing investigation of the viability of the novel methodology. We find that the algorithm/methodology provides effective solutions for small problem sizes.

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

The purpose of this study is to understand the functional power of frequency from-to chart (FFTC) as an independent solution-key for generation optimal (exact) facilities sequences with an equal distance of straight-line flow patterns. The paper will propose a bi-objective function model based on the Torque Method then will turn it into a computer-based technique with a permutative manner using the full enumeration method. This model aims to figure out if there is a difference between the moment minimization and backtracking treatment. Furthermore, the proposed technique will measure the performance of related works from literature to numerically highlight their limitations.

The literature of related works provided two-principles assumed mastering material flow sequences. The researchers gathered and analyzed the three methods – used FFTC as an independent technique – mentioned in the literature then measured their performance with the proposed technique. The proposed technique is based on the computation of torque value using an enhancement of bi-objective function model then application a permutative approach with full enumeration methodology. The bi-objective function model used once to mimic the grand moment value of FFTC and again to study the reflection of minimizing the congestion of backtracking movements on the minimization of total transportation cost.

Based on the analysis of literature and comparative results of its three case studies using the proposed technique, it is found that: there are optimum facilities sequences with rich opportunities of exact pathway selection. Reduction methodology is an inefficient way to generate exact results. There is a gap between combining the minimization of the grand moment and the treatment of the backtracking problem.

This study is one of the first contributions that discusses the assumption of integration between optimization moment value and its relation to treatment backtracking problem. Also, the illness of reduction methodology to reach optimal solutions. The further direction of this research will highlight the conjecture of searching the exact results for small size problems, analyzing the given data and its logical dimensions, developing logical rules for solving and verifying large size problems based on the exact results (The conjecture of P = NP).

This paper provides a detailed numerical analysis of the most common problems generally faced facility layout problems through understanding the lack of integration between moment minimization and backtracking minimization. Also, the inefficiency of reliance on reduction methodology either in scores of frequencies between facilities with weak relation or the number of permutations. Based on those findings, further study will search the logical philosophy exactly optimizing FFTC manually or without having to deal with a permutative approach for large size problems – which considered non-deterministic polynomial-time problem.

To provide a new proof of convergence of the Adomian decomposition series for solving nonlinear ordinary and partial differential equations based upon a thorough examination of the historical milieu preceding the Adomian decomposition method.

Develops a theoretical background of the Adomian decomposition method under the auspices of the Cauchy‐Kovalevskaya theorem of existence and uniqueness for solution of differential equations. Beginning from the concepts of a parametrized Taylor expansion series as previously introduced in the Murray‐Miller theorem based on analytic parameters, and the Banach‐space analog of the Taylor expansion series about a function instead of a constant as briefly discussed by Cherruault et al., the Adomian decompositions series and the series of Adomian polynomials are found to be a uniformly convergent series of analytic functions for the solution u and the nonlinear composite function f(u). To derive the unifying formula for the family of classes of Adomian polynomials, the author develops the novel notion of a sequence of parametrized partial sums as defined by truncation operators, acting upon infinite series, which induce these parametrized sums for simple discard rules and appropriate decomposition parameters. Thus, the defining algorithm of the Adomian polynomials is the difference of these consecutive parametrized partial sums.

The four classes of Adomian polynomials are shown to belong to a common family of decomposition series, which admit solution by recursion, and are derived from one unifying formula. The series of Adomian polynomials and hence the solution as computed as an Adomian decomposition series are shown to be uniformly convergent. Furthermore, the limiting value of the mth Adomian polynomial approaches zero as the index m approaches infinity for the prerequisites of the Cauchy‐Kovalevskaya theorem. The novel truncation operators as governed by discard rules are analogous to an ideal low‐pass filter, where the decomposition parameters represent the cut‐off frequency for rearranging a uniformly convergent series so as to induce the parametrized partial sums.

This paper unifies the notion of the family of Adomian polynomials for solving nonlinear differential equations. Further it presents the new notion of parametrized partial sums as a tool for rearranging a uniformly convergent series. It offers a deeper understanding of the elegant and powerful Adomian decomposition method for solving nonlinear ordinary and partial differential equations, which are of paramount importance in modeling natural phenomena and man‐made device performance parameters.