Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree ...of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.
The long-term viability of an organization hinges on social, environmental, and economic measures. However, based on extensive review of the literature, we have observed that measuring and improving ...the sustainable performance of supply chains is complex. We have grounded our theoretical framework in institutional theory and resource-based view and drawn thirteen hypotheses. We developed our instrument scientifically to validate our model and test our research hypotheses. The data was collected from the Indian auto components industry following Dillman’s total design test method. We gathered 205 usable responses. Following Peng and Lai’s (J Oper Manag 30(6):467–480,
2012
) arguments, we have tested our model using variance-based structural equation modeling (PLS-SEM). We found that the constructs used for building our theoretical model possess construct validity and further satisfy the specified criteria for goodness of fit. The hypotheses test further suggests that coercive pressures under the mediation effect of top management belief and participation have significant influence on resource selection (i.e. supply chain connectivity and supply chain information sharing). The supply chain connectivity and supply chain information sharing have significant influence on environmental performance. Contrary to our belief, the normative and mimetic pressures have no significant influence on top management participation. The managerial implications of the findings are also discussed.
Abstract
India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages ...(Lockdown 2.0–4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% 95% confidence interval (CI) = 6–9% reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2–3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9–12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to ‘flatten the curve’ and buy additional time for pandemic preparedness, response and recovery.
With the rapid growth of electronics and telecommunication industries, electromagnetic pollution is a serious concern to be addressed because it not only affects the sensitivity and performance of ...the devices but also affects human's health. Here, we report lightweight hybrid composite mats, having porosity around 40%, composed of amorphous carbon, zinc oxide nanorods and nickel zinc ferrite for excellent electromagnetic interference (EMI) shielding in the X-band (8.2–12.4 GHz). The vibrating sample magnetometer measurement confirmed that the saturation magnetization value (Ms) of the composite materials enhances with the weight percentage of zinc oxide nanorods-nickel zinc ferrite (ZNF) powder, which leads to enhanced magnetic loss of the electromagnetic waves. With the thickness of 1.0 mm, the total EMI shielding effectiveness of the amorphous carbon composite was measured to be 25.70 dB which was further enhanced to 53 dB by the incorporation of the ZNF powder. Such high increment is attributed to the enhanced magnetic properties, interfacial polarization and dielectric properties of the composite. The synergistic combination of the materials results in the high reflection coefficient and absorption coefficient of the composites which were measured to be ∼0.916 and ∼0.083, respectively. Thus, the composites can shield up to 99.999% power of the electromagnetic waves which is shared by the 8.394% reflection and 91.605% absorption. Moreover, the magnetic, electrical and EMI shielding properties of the composites can be tuned by controlling the amount of ZNF powder in composites. Hence, the composite mats can be suitable for applications in defense and telecommunication.
Display omitted
Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in ...the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios-low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)-demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.
Owing to the availability of high computing servers and clusters to process the data, factors such as data skewness, class imbalance, and scalability in big data cause slow processing performance. ...This study proposes a framework for load balancing in the Apache Spark cluster that makes efficient use of cluster resources and improves overall processing performance. The proposed method configures the Apache Spark cluster initially, to fix the optimal number of CPU cores and memory for each executor. The proposed scheme explores the trade-off between workload balance and communication efficiency, while for dynamic task allocation, coarse-grained and fine-grained data placement strategies are being used. A coarse-grained strategy handles the execution of datasets with larger partitions, but comparatively few executors, by transforming resilient distributed datasets (RDDs) into smaller datasets. Fine-grained strategy handles the execution of datasets with a large number of executors in comparison with partitions; the number of executors is considered equivalent to the number of knapsacks, and the resulting multidimensional knapsack problem is solved using particle swarm optimization to join the data partitions. The partitions were transformed into RDDs equivalent to the number of executors. All experiments were carried out using a large-scale dataset comprised of Amazon product reviews data in JSON format. The fine-grained and coarse-grained data placement strategies were found to be 26.12% and 42% faster in terms of execution time compared to the default data placement approach.
This work develops a unique layered structural composite to prevent the device from internal electromagnetic (EM) waves’ self-disturbance and external EM wave interference. The composite is composed ...of two layers, the inner layer is a hybrid composite, using MWCNTs and NdFeB powders as fillers and epoxy as matrix, which is designed as absorbent layer, and the outer is MWCNT-coated non-woven fabric, which is treated as reflection layer. The inner layer containing MWCNTs and magnetic powder of NdFeB can firstly absorb parts of EM waves generating from inside devices, and when the rest EM waves transmit through the inner layer and impinge to the outer layer, almost all of the waves will be reflected back. The reflected waves will go through the inner layer and be absorbed again. In the meanwhile, as the external EM waves impinge to the outer layer, they will be reflected immediately. The optimal combination of this study is able to achieve over 92% (−23.09 dB) absorption and leave least than 8% (−11 dB) reflection of internal EM waves, and over 99.99% reflection of external EM waves.
Display omitted
Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user ...interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.
PDF
