Operations Research - Contemporary Role in Managerial Decision Making

As the global environment turns out to be furiously focused, Operations Research has picked up criticalness in applications like world-class Manufacturing systems (WCM), Lean generation, and Six-sigma quality administration, Bench marking, Just-in-time (JIT) inventory techniques. The development of worldwide markets and the subsequent increment in rivalry have highlighted the requirement for Operation Research. To survive and lead the todays very focused and request driven market, weight is on administration to settle on conservative choices. One of the key administrative aptitudes is capacity to distribute and use assets fittingly in the endeavors of accomplishing the ideal execution productively. Now and again, for example, little scale low many-sided quality environment; choice in light of instinct with insignificant quantitative premise might be sensibly satisfactory and viable in accomplishing the objective of the association. Be that as it may, for a substantial scale framework, both quantitative and subjective (i.e. instinct, experience, sound judgment) investigations are required to settle on the most practical choices. Utilizing Operations Research techniques including Linear Programming, Discrete Event Simulation and Queuing Theory, association pioneers can settle on top notch choices. Present paper is an endeavor to study the importance of Operation research and different techniques used to improve the operational efficiency of the association

Debt Forgiveness and Debt Relief for Covid-19 Economic Recovery Financed through GDP-Linked Sukuk

This paper proposes alternatives for governments to deal with the current pandemic crisis today. It suggests ways to deal with the increasing debt levels as a result of the fiscal stimulus issued to cushion the effects of a tremendous shock to the economy.Firstly, the paper proposes to protect the vulnerable group (based on debt-to-income ratio or its debt-servicing ability) through debt forgiveness and help SMEs through debt relief via debt restructuring for their outstanding loans. To finance this, we propose to convert the increased public debt from these initiatives into equity through a GDP-linked sukuk to stabilise a sovereign’s debt to GDP ratio. The repayment on these sukuk will be in proportion to the country’s GDP whereby the repayment automatically declines when growth is weak and increases when GDP is strong. In doing so, an anticipated deep recession caused by the global pandemic slowdown will makes it less likely to trigger a sovereign debt crisis. Secondly, such a strategy would provide the issuing government with economic reprieve when growth weakens and tax receipts decline. At the same time, investors can view these sukuk as an alternative asset class through exposure to the real economy, given the low interest rate environment. Both sides are incentivized by the debt-stabilising effects of issuance that would make sovereign defaults less likely and balance risk-taking

Inhibition of mild steel corrosion in HCl solution using amino acid L-tryptophan

The corrosion inhibition characteristics of nitrogen containing amino acid L-tryptophan on mild steel in 0.1 M HCl solution was studied by weight loss and potentiodynamic polarization measurements. L-tryptophan significantly reduces the corrosion rates of mild steel; the maximum inhibition efficiency being 83% at 50 oC in presence of inhibition concentration of 500 ppm. The adsorption of inhibitors on mild steel surface obeyed Langmuir’s adsorption isotherm. The calculated thermodynamic parameters for adsorption reveal a strong interaction between the inhibitors and the mild steel surface. The results obtained by electrochemical studies are consistent with the results of the weight loss measurement. L-tryptophan acts more anodic than cathodic inhibitor

Effect of Market Orientation on Channel Strategy- An Empirical Analysis of Pesticide Industry

Research in market orientation has overlooked the importance of its impact on various aspects of marketing strategy, especially on distribution channel strategy. Using Kohli & Jaworski's framework of measuring market orientation (MO) and pesticide industry of Pakistan as a context, this article explores the relationship between various constructs of MO with channel strategy. This paper draws survey data from the pesticide industry in Pakistan. Given the low response rate, a norm in developing countries, bootstrapping technique is employed and tests are run. The results reveal that level MO has an impact on how channel strategies are formulated. Findings of the research indicate that higher level of MO is associated with selective channel strategy involving low intensity of distribution and higher channel control. The results also suggest that the right channel strategy helps an organization to create differentiation and to improve performance in a commodity marke

Online semi-supervised learning in non-stationary environments

Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and balanced data, immediately or after some delay, to extract worthwhile knowledge from the continuous and rapid data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet. Manual labelling of these data streams is not practical due to time consumption and the need for domain expertise. Another challenge is learning under Non-Stationary Environments (NSEs), which occurs due to changes in the data distributions in a set of input variables and/or class labels. The problem of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms have no access to the true class labels directly when the concept evolves. Several approaches exist that deal with NSE and EVL in isolation. However, few algorithms address both issues simultaneously. This research directly responds to ILNSE’s challenge in proposing two novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label scarcity issues in online machine learning. The key capabilities of PSDSL include learning from a small amount of labelled data in an incremental or online manner and being available to predict at any time. To achieve this, PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it continuously learns from incoming data and updates the model as new labelled or unlabelled data becomes available over time. Furthermore, it can predict under NSE conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch and adapt to the conditions. The PSDSL adapts to learning states between self-learning, micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of the data stream. HDWM makes use of “seed” learners of different types in an ensemble to maintain its diversity. The ensembles are simply the combination of predictive models grouped to improve the predictive performance of a single classifier. PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than existing approaches on most real-time data streams including randomised data instances. PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is trained with the first examples in the data streams. When applied to MOA-generated data streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, while SCARGC performed the same as the Static. PSDSL achieved better average prediction accuracies in a short time than SCARGC. The HDWM algorithm is evaluated on artificial and real-world data streams against existing well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic DWM algorithm. The results showed that HDWM performed significantly better than WMA and DWM. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM. In both drift and real-world streams, significance tests and post hoc comparisons found significant differences between algorithms, HDWM performed significantly better than DWM and WMA when applied to MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms benefit from the use of both forgetting and retaining the models. The algorithm also provides the independence of selecting the optimal base classifier in its ensemble depending on the problem. A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts during the cluster labelling process. In this process, PSDSL transforms the centroids’ information of micro-clusters into micro-instances and generates new clusters called Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and successfully guide the cluster labelling process after the concept drifts in the absence of true class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC (81.6%), while the Static (49.0%) significantly degrades the performance due to changes in the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’ (number of clusters), while PSDSL automatically determine the best values for this parameter

Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal

Irrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal

A heterogeneous online learning ensemble for non-stationary environments

Learning in non-stationary environments is a challenging task which requires the updating of predictive models to deal with changes in the underlying probability distribution of the problem, i.e., dealing with concept drift. Most work in this area is concerned with updating the learning system so that it can quickly recover from concept drift, while little work has been dedicated to investigating what type of predictive model is most suitable at any given time. This paper aims to investigate the benefits of online model selection for predictive modelling in non-stationary environments. A novel heterogeneous ensemble approach is proposed to intelligently switch between different types of base models in an ensemble to increase the predictive performance of online learning in non-stationary environments. This approach is Heterogeneous Dynamic Weighted Majority (HDWM). It makes use of “seed” learners of different types to maintain ensemble diversity, overcoming problems of existing dynamic ensembles that may undergo loss of diversity due to the exclusion of base learners. The algorithm has been evaluated on artificial and real-world data streams against existing well-known approaches such as a heterogeneous Weighted Majority Algorithm (WMA) and a homogeneous Dynamic Weighted Majority (DWM). The results show that HDWM performed significantly better than WMA in non-stationary environments. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM