Advances in Operations Research

This article describes an efficient and effective way to apply the PDCA (Plan-Do-Check-Act) method in the design process to meet quality and stakeholders’ expectations. Through the case study of developing a smart workstation to train workers in the assembly process with a target to reduce the defects and improve the management task, the paper explores the main barriers and success factors for the PDCA cycle implemented in complex quality improvement projects. A prototype of the new workstation design is tested and shows significant benefits not only in defect reduction and management efficiency but also in newcomers’ learning process. This research can be used as a benchmark application of PDCA in quality improvement and engineering design processes with systematic and comprehensible guidance of the cycle.

Blanket factory as a textile industry is one of the manufacturing sectors in Ethiopia; however, the sector productivity is the main issue of the business owners. For the reason of improving the productivity of the sector, factors affecting productivity should be identified and prioritized since improvement is capital intensive measurement. In this research, a FAHP methodology has been developed to prioritize the identified productivity-affecting factors of the blanket factory. Productivity problem is sourced from different factors. However, the concept of productivity-affecting factors has been considered in previous literature, its integration with productivity of the blanket factory and the FAHP methodology has not been studied. For the sake of filling this gap, this research has been conducted using the following main steps: at the beginning, productivity-affecting factors have been identified from previous literature. Then, as there are many productivity-affecting factors in different manufacturing sectors, the list of potential productivity-affecting factors has been investigated to check which factors are most common in the blanket factory. Finally, a FAHP model has been applied to prioritize productivity-affecting factors. According to this model, the result showed that skilled employee and on and off job training, production process line balancing, and better technology and manufacturing system are the most important factors of productivity problem in the blanket factory. Based on the normalized weight, these factors scored 35.92%, 22.94%, and 17.06%, respectively. As the main implications, the research procedure and obtained results using the developed methodology can help industry managers, operation managers and practitioners, business owners, academicians, and researchers to determine productivity-affecting factors so that they can provide possible solutions to the blanket factory.

The primary motive of each and every organization or company is to sustain, strengthen, upgrade, and improve its position and standard in the highly dynamic, constantly changeable, aggressive, and competitive environment. There is a very urgent requirement to develop a framework for green and sustainable vendor selection in the organizations. The main aim of this research is to explore, identify, examine, and evaluate the important, applicable, green, and sustainable vendor selection attributes and to analyze and determine their interactions or relationships in the area of green and sustainable vendor selection. A total of ten important attributes have been determined through review reports and by the assessments of the group of professionals belonging to various organizations. A questionnaire has been prepared for these ten attributes and feedback was maintained from the judgments of the group of professionals in dairy industries. In this paper, a multicriteria decision-making technique namely interpretive structural modeling is implemented to look over the interactions and connections among various attributes and to put forward a constructional representation or digraph. Matriced Impacts Croise’s Multiplication Appliqúe and Classment inspection is used to identify which attributes are autonomous, dependent, independent, influencing or not influencing, and importance of one criterion over another according to their driving and dependence powers. It is analyzed numerically from this model that the cost attribute is a very remarkable attribute as it exists in the bottom or sixth level and the attributes economic growth, financial capacity, and research ability are at first or topmost level of this model. The interpretive structural modeling hierarchy constructional representation and Matriced Impacts Croise’s Multiplication Appliqúe and Classment inspection will support the company’s owner or decision maker for attaining a best decision.

Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.

In the present study, the group acceptance plan is examined when the lifetime of an item follows the odd Perks exponential distribution, and a large number of items regarded as a group are evaluated simultaneously. The crucial parameters are derived from the consumer risk and the test termination period. The operating characteristics function values are generated for various quality levels. An optimized group acceptance plan and comparison of group acceptance sampling plan with the ordinary sampling plan are also presented. Additionally, a graphical illustration of operating characteristics for diverse groups and parametric values is provided. The minimum ratios of the actual average life to the stipulated average life are likewise computed at the prescribed producer’s risk. Examples are used to illustrate the outcomes via our algorithm under the odd Perks exponential distribution setting. It is explained using a quality control dataset to establish its practical versatility.

Uncertain data and undesirable outputs are two challenging issues in traditional data envelopment analysis (DEA) models while dealing with the environmental efficiency estimation of decision-making units (DMUs). This study considers Stackelberg and the centralized game theory approach in a two-stage DEA model for evaluating DMUs in the presence of uncertainty and undesirable outputs simultaneously. To tackle the uncertainty, we apply the p-robust technique and assume that undesirable outputs are weakly disposable. The proposed fractional models are linearized using the Charnes and Cooper transformation. We utilize the new models for a real dataset drawn from 11 oil generation ports in the Persian Gulf region consisting of two stages: an oil production stage and a wastewater treatment stage. The results revealed that the managers should take different strategies in environmental efficiency evaluation including undesirable impacts and also efficiency improvement in increasing oil generation. Further, the empirical results showed that the stochastic p-robust approach for controlling the conservatism level leads to a more conservative solution, and policymakers could recognize the significant steps that should be followed to improve each oil generation unit’s environmental performance. Also, to show the reliability and accuracy of the results and the effect of the decision-maker’s preference, a detailed sensitivity analysis is performed.