Companies are frequently confronted with the need to order different types of items from a single supplier or to manufacture the items in a production line. Indeed, coordinated ordering of multiple items may lead to important savings whenever a family of items can be ordered from a common supplier, produced in a common facility, or use a common mode of transportation. The Joint Replenishment Problem (JRP) tackles the coordinated replenishment of multiple items by minimizing the total cost, composed of ordering (or setup) costs and holding costs, while satisfying the demand. On the other hand, when items are subject to obsolescence, they may face an abrupt decline in demand as they are no longer needed. This decline can be caused by reasons such as rapid advancements in technology, going out of fashion, or ceasing to be economically viable. The present article develops an extension of the JRP where the items may suddenly become obsolete during an infinite planning horizon. The point at which an item becomes obsolete is uncertain. The lifetimes of the items are assumed to follow independent negative exponential distributions. A model is proposed by using the total expected discounted cost as the minimization criterion. The time value of money is considered through an appropriate discount rate. Extensive tests were performed to assess the impact of obsolescence rates and discount rates on the ordering policies. The progressive increase of the obsolescence rates determines smaller periods between successive replenishments, while the progressive increase of the discount rate determines smaller lot sizes.

This paper considers an unrelated parallel machine scheduling problem with variable maintenance based on machine reliability to minimize the maximum completion time. To obtain the optimal solution of small-scale problems, we firstly establish a mixed integer programming model. To solve the medium and large-scale problems efficiently and effectively, we develop a hybrid discrete spider monkey optimization algorithm (HDSMO), which combines discrete spider monkey optimization (DSMO) with genetic algorithm (GA). A few additional features are embedded in the HDSMO: a three-phase constructive heuristic is proposed to generate better initial solution, and an individual updating method considering the inertia weight is used to balance the exploration and exploitation capabilities. Moreover, a problem-oriented neighborhood search method is designed to improve the search efficiency. Experiments are conducted on a set of randomly generated instances. The performance of the proposed HDSMO algorithm is investigated and compared with that of other existing algorithms. The detailed results show that the proposed HDSMO algorithm can obtain significantly better solutions than the DSMO and GA algorithms.

Operations with Electric Vehicles (EVs) on logistic companies and power utilities are increasingly related due to the charging stations representing the point of standard coupling between transportation and power networks. From this perspective, the Multi-depot Electric Vehicle Routing Problem (MDEVRP) is addressed in this research, considering a novel hybrid matheheuristic approach combining exact approaches and a Chu-Beasley Genetic Algorithm. An existing conflict is shown in three objectives handled through the experimentations: routing cost, cost of charging stations, and increased cost due to energy losses. EVs driving range is chosen as the parameter to perform the sensitivity analysis of the proposed MDEVRP. A 25-customer transportation network conforms to a newly designed test instance for methodology validation, spatially combined with a 33 nodes power distribution system.

Reverse auction is an effective tool for a 4PL to purchase transportation services. This paper investigated a new transportation services procurement problem for 4PL, which involves three features: the 4PL’s loss-averse behavior, price and non-price attributes, and multiple transportation requests. An efficient multi-attribute multi-item reverse auction mechanism considering the 4PL ex-ante and ex-post satisfaction (EES-MMRA) is proposed to purchase transportation services for the 4PL. In the EES-MMRA, integrating the allocation rule with the 4PL ex-ante satisfaction, a 0-1 programming model is constructed to determine winning 3PLs and obtain efficient allocations. Then, a payment rule considering the 4PL ex-post satisfaction is established to ensure truthful bidding of 3PLs. And we discuss some desirable properties (e.g., incentive compatibility, individual rationality, efficiency, and budget balance properties) to justify the EES-MMRA mechanism, subsequently. Next, several numerical experiments are conducted to demonstrate the effectiveness and applicability of the EES-MMRA mechanism. Furthermore, sensitivity analysis presents the influences of the weights of the non-price attributes, risk attitude coefficients, and loss aversion coefficients. Finally, we conduct comparison analysis to show the advantages of the EES-MMRA mechanism over the known Vickrey–Clark–Groves (P-VCG) mechanism.

Training and productivity have been found positively correlated in theory and practice. The pattern of the relationship, however, was not explored in pre - and post-regulation in the conceived sector. This paper aims to observe the effects of regulation and training expenses along with other covariates on employee productivity in the Microfinance sector of Bangladesh. Using annual data of MRA-licensed MFIs, we have estimated both panel and cross-sectional regression models. The regression results confirm the theoretical relationship between training expenses and employee productivity. The regulation also worked positively in enhancing employee productivity. However, in the early stage of regulation, the average productivity gain due to regulation was substantial and was showing an increasing trend but then it declined and reached a constant level of about 4% - 5% each year. Between 2008 and 2011, both regulation and training positively contributed to the gains in average productivity of the employees. After 2012, there was a positive trend of average productivity elasticity of training expenses but there was a flat effect of regulation after 2012. Regulation was found to short-run shifter in the average productivity of employees while training expenses had a positive trend effect.

The main focus of this research paper is to shed light on the challenges encountered by the chair production department employees when it comes to identifying and locating glue residue after the assembly process. This issue arises due to the transparent nature of the glue, making it difficult to detect and subsequently negatively impacting the overall quality of the chairs in the polishing department. To tackle this problem, various experiments and tests were conducted, which are thoroughly discussed in this paper. The findings of this study are not only applicable to the specific manufacturing company under investigation but also to other companies within the industry that face similar difficulties in detecting defects caused by transparent glue. As a result, a new method for detecting glue defects is proposed, which can be adopted by various industries encountering similar challenges.

Effective application of tools and techniques is critical for successful Total Quality Management (TQM) implementation. The influence of organization profile on the application of TQM tools and techniques and outcomes is investigated in Indian organizations. Correlation analysis and ANalysis Of VAriance (ANOVA) are utilized for the research. Tools and techniques are found having significant relation to the outcomes of implementation. Organization’s age, size, and turnover significantly influenced the TQM results. Organization’s size and turnover significantly influenced the application of tools and techniques. The study suggests taking the organization profile into consideration for effective implementation. Further research can be conducted in other countries.

The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

This study explores the impacts of participatory design (PD) on data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a dearth of research in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The paper provides a research agenda for future researchers as well as discussing best practices for organizations seeking to optimise their data driven decision-making processes in a participatory manner. The research also discussed the ethical implications of data-driven decision-making. Ultimately, this research advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.

Most small and medium scale enterprises in Ghana neglect very critical performance indicators in their bossiness. As a result most SMEs do not survive till their fifth anniversary. The purpose of this study is to examine the relationship between prevention focus, promotion focus, resilience, and SME performance in Ghana. Furthermore, we investigated the moderation effect of entrepreneurial learning and Entrepreneurial orientation on the relationship between entrepreneurial resilience and SME performance in Ghana. Our results show that both promotion- and prevention-focused increase entrepreneurial resilience and SME performance significantly. This is an indication that there is a strong positive correlation between entrepreneurial resilience and success in both the individual and firm levels. This inference can therefore assist policy makers, politicians and various industry players in designing training programs on how to improve the resilience of SME owners in Ghana. Statistically our analysis shows that prevention focus (PreV) has a positive effect on entrepreneurial resilience (EntR) (β = 0.250; p<0.001), and promotion focus (PreM) has a positive effect on entrepreneurial resilience (EntR) (β = 0.268; p<0.001). Additionally, entrepreneurial resilience (EntR) has a positive effect on innovative performance (IP) (β = 0.547; p<0.001). Furthermore, the mediation results has shown that entrepreneurial resilience (EntR) mediates the relationship between prevention focus (PreV) and innovative performance (IP) (β = 0.136; p<0.001); as well as that of promotion focus (PreM) and firm performance (IP) (β = 0.146; p<0.001). Finally, the results with regards to moderation effects has shown that entrepreneurial orientation (EntO) partially moderates the relationship between entrepreneurial resilience (EntR) and innovative performance (IP) (β = 0.109; p<0.05), however, the result established that entrepreneurial learning (EntL) does not moderate the relationship between entrepreneurial resilience (EntR) and firm performance (IP) (β = -0.031; p<0.05). Using the Resource Based View theory, we concluded that, SME’s needs to adopted prevention (vigilant/avoidant strategies) by minimizing losses, and taking calculated risks with motivation from mentors to avoid loss as this would achieve resilience as well as promotion of strategies (eager/approach strategies) to achieve gains for their SMEs during economic disruption and economic downturn such as COVID-19. We concluded that SME’s could also achieve resilience when managers or owners of SMEs inspire the employees to work harder, and think outside the box to identify latent ideas and opportunities within their SME business environment.