Applications of Operations Research in Retail Management
Retailers face growing pressure to make faster, data-driven decisions on inventory, logistics, pricing and demand, and the shift to e-commerce and omni-channel retailing since the COVID-19 pandemic has made these problems more complex. This study uses a bibliometric approach to map the literature on the application of Operations Research (OR) in retail management. Using the Scopus database, 316 peer-reviewed journal articles published between 2010 and 2026 were identified through a PRISMA-guided screening process and analysed with Biblioshiny and Microsoft Excel. The analysis covers publication trends, leading sources, keyword co-occurrence, thematic mapping and multiple correspondence analysis. The results show a sharp rise in output after 2020, peaking at 41 publications in 2025. The literature is built mainly around inventory management, forecasting and distribution, with emerging themes in artificial intelligence, omni-channel retailing and sustainability. However, traditional OR methods and newer computational approaches remain largely disconnected. The study also finds that omni-channel research is mostly descriptive, sustainability is rarely built into optimisation models, food-waste research is still exploratory, and few studies are validated with real retail data. Based on these gaps, the paper proposes a future research agenda covering hybrid AI-OR models, omni-channel optimisation, sustainability as a core constraint, perishable-inventory modelling, customer-centric optimisation and field validation with retailers. The findings help researchers position future work and give practitioners a view of which methods are mature and which still need testing.
Published by: Dr. Richa Saxena, Sandhini Gangurde, Rudraksh Garg, Sahasra Chilakamarthi, Sehajdeep Singh, Saatvic Karnawat
Author: Dr. Richa Saxena
Paper ID: V12I5-1233
Paper Status: published
Published: October 25, 2026
Applications of Operations Research Consumer Behaviour
This review is a literature/bibliometric analysis of Consumer Behaviour to understand and scale the field in a holistic manner. Through this review, the authors have tried to find dominant, important themes in the field of Consumer Behaviour and the importance of Operational Research in the Consumer Behaviour sector by making it more reliable. The data for this review is procured from Elseiver’s Scopus database by using important keywords. The final database uses 929 articles and reviews. It was found that recent shifts in the ecosystem have begun dominating and shaping this study.
Published by: Dr. Richa Kalpesh Saxena, Vedang Shringarpure, Vatsala Jain, Zayaan Shaikh, Yash Chhajed, Viransh Shah
Author: Dr. Richa Kalpesh Saxena
Paper ID: V12I5-1207
Paper Status: published
Published: October 22, 2026
Applications of Operations Research in AI for Optimization: A Bibliometric Review
Artificial intelligence (AI) is increasingly combined with Operations Research (OR) to solve optimisation problems across sectors, yet existing reviews study each sector in isolation. This study presents a cross-sector bibliometric review of 1,550 Scopus-indexed articles and reviews published between 2016 and 2026, analysed using Bibliometrix, Biblioshiny and VOSviewer. Publications have grown rapidly, with 88.3% appearing since 2024, and the field is highly collaborative and spread across many journals. Thematic mapping identifies predictive analytics and decision-making as the motor theme, machine learning and optimisation as basic themes, and large language models as an emerging theme. The review finds five gaps: prediction dominates prescription, real-world validation is limited, sectors are weakly integrated, adoption barriers are rarely examined, and fairness and AI costs get little attention. A six-priority research agenda is proposed to address them.
Published by: Dr Richa Saxena, Dhruv Jain, Dhrushank Thakker, Deepti Soneji, Dev Udeshi, Deshna Malu
Author: Dr Richa Saxena
Paper ID: V12I5-1228
Paper Status: published
Published: October 22, 2026
A Zero-Infrastructure, Browser-Based Architecture for Automated Video Interview Recording Using Client-Side Media Capture and Serverless Cloud-Storage Integration
Automated asynchronous video interviewing has become a common early-stage recruitment tool, but existing solutions are almost exclusively delivered as paid, proprietary software-as-a-service platforms, creating a cost and integration barrier for individual recruiters, small organizations, and academic institutions. This paper presents the design and implementation of a video interview recording system built entirely from freely available, standards-based browser APIs and a serverless scripting platform, requiring no paid infrastructure, no traditional backend server, and no database. The client captures a continuous audio-video recording across a sequence of timed, operator-defined questions using the W3C MediaStream Recording API, and transmits the result to a serverless integration layer via a custom chunked-upload protocol designed to remove the request-payload ceilings typical of serverless platforms. The integration layer, implemented in Google Apps Script, executes under the operator’s own cloud identity, allowing candidates to submit recordings without a Google sign-in of their own while ensuring recordings are persisted directly to the operator’s Google Drive. We describe the system architecture, the chunked-transport protocol and its rationale, the access-control model, and a set of controlled technical micro-benchmarks characterizing encoding overhead and payload scaling. We further report on an iterative, real-world debugging process that surfaced two non-obvious browser/platform interoperability failure modes, both resolved and documented here to inform future implementers. We conclude by identifying the system’s limitations — principally the absence of a controlled multi-participant usability study — and outline the empirical evaluation required to substantiate broader claims of practical utility.
Published by: Aditya Kumar
Author: Aditya Kumar
Paper ID: V12I5-1150
Paper Status: published
Published: October 22, 2026
From Marital Infidelity to Homicide: A Criticism Analysis of Motive, Criminal Lability and Spousal Killing under Indian Criminal Law
Spousal homicide is a complex crime involving a criminal and victim that are connected not only by crime but also emotional threads. The problem of intimate killings is linked with the power dynamics between genders. Such killings are different from those crimes of passion that are committed by strangers because in the spouse killing, the relationship between the victim and killer is built on a long and close family relations. The central focus in spousal homicide is the element of motive which can arise from allegations of partner’s straying or which can arise from allegations of a partner’s straying. Under Indian criminal law, which has been traditionally guided by the Indian Penal Code 1860 (IPC) and now transitioning to Bharatiya Nyaya Sanhita 2023 (BNS), courts are tasked with the precarious responsibility of distinguishing between pre-panned murder and acts of passion stemming from an overwhelming loss of self-control. This paper attempts to critically examine the evidence and substantive role of the element of motive in the context of spousal homicides, and the various ways Indian courts have interpreted the concept of provocation in the wake of accusations of marital unfaithfulness. The paper further attempts to analyse the statutory distinction between Murder ( IPC Section 300 / BNS Section 101 ) and Culpable Homicide not amounting to Murder ( IPC Section 299 / BNS 100 ) through the less of exception 1 (grave and sudden provocation). A gendered analysis of the differing criminal justice responses to male-initiated homicides on the grounds of marital unfaithfulness and female-initiated homicides on the grounds of battered woman syndrome is undertaken. More over the paper aims to critically evaluate the Joseph Shine jurisprudence, as wells as recommend potential statutory amendments under the new criminal law framework for a more equitable adjudication of crimes of passion in light of the newly transitioned BNS.
Published by: Saloni Gupta, Ms. Ritu Malik
Author: Saloni Gupta
Paper ID: V12I5-1216
Paper Status: published
Published: October 21, 2026
Design and Development of an AI-Driven Framework to Secure IoT Networks
The rapid proliferation of Internet of Things (IoT) devices across industrial, domestic, and critical-infrastructure environments has created a correspondingly rapid expansion of cyber-attack surfaces, while the heterogeneity, resource constraints, and high-volume dynamic traffic of IoT networks render conventional signature- and rule-based security mechanisms increasingly inadequate. This article presents a comprehensive review of twelve recent studies (2020–2026) on artificial intelligence (AI)-driven approaches to securing IoT networks, spanning machine learning and deep learning intrusion detection systems (IDS), federated and blockchain-assisted learning frameworks, 5G software-defined network (SDN) security architectures, service-oriented edge-layer architectures, and large-scale systematic and taxonomic reviews. Reported detection accuracies across the empirical studies range from approximately 82% to 98.25%, with federated and hybrid blockchain–AI approaches offering the strongest privacy–accuracy trade-offs and 5G-SDN frameworks achieving the lowest detection latencies (as low as 15 milliseconds). Despite these advances, the literature consistently reports unresolved research gaps, including limited adaptability to evolving traffic patterns, elevated false-positive rates, insufficient attention to the computational and energy constraints of IoT endpoints, dependence on single machine learning or deep learning algorithms rather than integrated pipelines, and an absence of unified, lightweight, and quantum-resistant frameworks validated on real hardware. Building on this synthesis, the review articulates a research problem, four research objectives, and a proposed seven-stage AI-driven security framework — spanning data collection, preprocessing, feature extraction and selection, AI-based threat detection, attack classification, adaptive security response, and continuous monitoring — intended for validation on benchmark datasets (CICIoT2023, TON_IoT, BoT-IoT/N-BaIoT) and on constrained hardware platforms (Raspberry Pi, ESP32). The proposed research direction aims to deliver a lightweight, adaptive, and empirically validated AI-driven security solution suited to resource-constrained IoT deployments.
Published by: Afza Firdous, Dr. Md. Faizan Farooqui
Author: Afza Firdous
Paper ID: V12I5-1221
Paper Status: published
Published: October 21, 2026
