Operations Research, Systems Engineering and Industrial Engineering Commons^™

Application Of Quantum Algorithms In Optimization Of Cognitive Decision-Making Systems, Noilakhon Yakubova

Chemical Technology, Control and Management

The article analyzes the potential of quantum algorithms for optimizing cognitive decision-making systems in production processes in the energy sector. Compared with traditional algorithms, quantum algorithms allow high-speed processing of large amounts of variable data in real time and solving multiparameter problems based on an integrated approach. The study considers uncertainties and issues of increasing efficiency in the processes of production and distribution of electricity using a cognitive model. The integration of quantum algorithms into this model allows developing optimal strategies for using resources in production. A heating boiler was taken as a control object, and taking into account its …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Influence Of Substrate Temperature On The Properties Of Reactive Dc Magnetron Co-Sputtered Ag-Doped Tio2 Thin Films, Hezekiah B. Sawa

Tanzania Journal of Engineering and Technology (TJET)

This study reports on the influence of substrate temperature on the properties of Ag-doped TiO2 thin films. The films were deposited by reactive DC co-sputtering of Ti and Ag targets at different substrate temperatures and Ag target sputtering powers. Grazing incident X-ray diffractometer confirmed that all films were polycrystalline with dominant peak oriented along (101) planes representing the anatase TiO2 phase. The average grain size of the samples improved with increase in deposition temperature. At a substrate temperature of 450 ℃, the samples had a dominant peak representing the rutile phase, suggesting partial transformation from anatase to rutile phase. The …

A Critical Realist Erp Implementation In Zimbabwean Mining Industry Organisations, Jairos Mukwenha

Tanzania Journal of Engineering and Technology (TJET)

This research uses a critical realist framework to examine the factors influencing the success of enterprise resource planning (ERP) system implementation in Zimbabwean mining industry organisations. From the perspective of critical realism, the mining industry in Zimbabwe faces a complex interplay of opportunities and obstacles while implementing ERP systems. The deployment of ERP in mining firms is critically examined in this paper, emphasising how these systems might improve operational efficiency while considering Zimbabwe's particular socioeconomic circumstances. By exploring underlying structures, mechanisms, and outcomes, the research aims to identify critical challenges and opportunities and develop practical recommendations for improving ERP adoption …

Cellulose Recovery From Waste Denim Fabrics Through Indigo Vat Dye Reduction And Spandex Dissolution, Tito N. Venance

Tanzania Journal of Engineering and Technology (TJET)

This study addresses environmental conservation by tackling end-of-life management for post-consumer denim garments through the recycling of cotton fibres from waste denim. The purification process involved dithionite reduction of vat dyes in the presence of an alkali followed by selective dissolution of spandex using N,N-dimethylformamide (DMF). Optimal conditions for dye removal were determined at 90°C for 120 minutes with sodium hydroxide at 25 g/L, sodium dithionite at 6 g/L and PVP at 4.5 g/L, while spandex extraction was achieved using a 5% DMF solution at 70°C for 4 hours. Ultraviolet (UV)-vis spectrophotometric analysis indicated a significant increase in whiteness (DL*). …

Assessment Of Factors Contributing On Early Rotting Of Chromated Copper Arsenate Treated Utility Power Distribution Wood Poles In Tanzania, Innocent J. Macha

Tanzania Journal of Engineering and Technology (TJET)

The study aimed to assess the factors contributing to the premature decay of Chromated Copper Arsenate (CCA) treated utility power distribution wooden poles in Tanzania. Deteriorated poles samples from various regions in Tanzania were analyzed using handheld X-ray spectrometry to quantify the retention of CCA preservative chemicals. The ages of these poles were estimated based on annual growth rings. The findings indicated that premature failure of CCA treated wooden poles is attributed to an imbalance in the chemical composition of the preservative solution. The study found that CuO retention in most samples ranged between 10-18 kg/m3, significant lower that the …

Developing Predictive Mathematical Model For Optimizing Coating Weight Variation In Galvalume Production: A Case Study Of A Metal Industry, Victoria Mahabi

Tanzania Journal of Engineering and Technology (TJET)

Variations in coating weight for galvanized steel sheets can result in notable differences between batches. Such variations may cause various issues, such as diminished corrosion resistance, lower mechanical strength, and visual defects, which can ultimately drive-up costs, lead to customer dissatisfaction, and pose safety risks. Even with attempts to manage elements like air knife pressure and line speed, coating weight inconsistencies remain challenging. The research focuses on developing a predictive mathematical model designed to optimize variations in coating weight during Galvalume production. The critical parameters influencing coating weight variation were identified and analysed using a systematic literature review, primary data …

Design And Performance Analysis Of Fiber Bragg Grating Temperature Sensor For Industrial Processes Sensing Applications, Paul Stone Stone Brown Macheso S.B.

Tanzania Journal of Engineering and Technology (TJET)

The Fiber Bragg Grating (FBG) sensor has become a widespread sensing device because of its small size, passive design, immunity to electromagnetic interference, and direct ability to measure physical properties like temperature and strain. Recently, femtosecond infrared laser processing and regeneration techniques have resulted in the development of stable high-temperature gratings, which are a powerful tool in smart factories, an aspect of the fourth Industrial Revolution (4IR), and show promise for application in harsh environments like high pressure, high temperature, or ionizing radiation. The development of stable high-temperature gratings that can withstand harsh environmental factors like high temperatures, pressures, and …

Application Of Artificial Neural Network Models For Predicting Diesel And Petrol Prices In The Geographically Sparsed Regions In Tanzania, John M. Kafuku

Tanzania Journal of Engineering and Technology (TJET)

Fuel consumption in Tanzania, mainly diesel and petrol, accounts for 82 percent of the energy consumption in the country, with significant price volatility affecting market stability, availability of fuel, and investment decisions. This study uses an artificial neural network (ANN) with a backpropagating algorithm to predict fuel prices in four regions of Tanzania. Key input parameters include the currency inflation rate (CIR), the petrol fuel inventory (PFI), the diesel fuel inventory (DFI), and the fuel transport costs (FTC). The study selected the 6-10-10-2 ANN structures for Sumbawanga-Rukwa, Mpanda-Katavi, and Mbeya-Mbeya as well as 6-10-9-2 for the Songea-Ruvuma region. The results …

Production Of Potassium-Rich Biofertilizer From Composted Banana Peels And Watermelon Rinds, Neema O. Msuya

Tanzania Journal of Engineering and Technology (TJET)

This study investigated the production of biofertilizer from composted banana peels and watermelon rinds, focusing on the mineral content and its impact on plant growth. The process involved characterizing raw materials, producing biofertilizer, analyzing mineral concentrations (potassium, nitrogen and phosphorus), and evaluating its quality through Spiny Amaranth seed growth. A compost bin with three compartments was designed, testing three composting ratios of banana peels to watermelon rinds (2:1, 1:2 and 1:1). Composting was monitored on moisture content, pH, organic matter, and temperature for 37 days. The 1:1 ratio had the lowest temperature (33.45°C) and highest pH (7.08), while the 2:1 …

A Fuzzy Based Framework For Sustainable Technology Selection In Small-Scale Gold Mining Operations, John M. Kafuku

Tanzania Journal of Engineering and Technology (TJET)

Small-scale gold mining (SSGM) operations in Tanzania has been operating inefficiently due to inadequate mining processing technologies, poor working tools, lack of enough capital, and insufficient electricity. Despite the efforts made by different stakeholders in boosting the sustainability of SSGM yet the sector has not reached the expected goal. This paper proposes a framework for appropriate technology selection to help small scale gold miners in evaluating various gold mineral processing technologies. The framework utilizes the fuzzy logic set theory for technology evaluation and selection. The developed framework for technology selection upon validation provided results that technology adequacy of more than …

Factors Affecting Adoption Of Industry 4.0 Predictive Maintenance By Manufacturing Industries: Tanzania Food And Beverage Manufacturing Industries, Fred E. Peter

Tanzania Journal of Engineering and Technology (TJET)

Industry 4.0 has increasingly become a focal point of scholarly inquiry. Nonetheless, there remains a noticeable lack of comprehensive research on the diverse and systematic factors that influence the adoption of Industry 4.0 Predictive Maintenance (PdM 4.0) in manufacturing sectors within developing regions, particularly in East Africa. This study seeks to bridge this research gap by investigating the key determinants affecting the actual implementation of PdM 4.0 in Tanzania’s food and beverage manufacturing industries. To achieve this objective, a mixed-methods research design was adopted, combining both qualitative and quantitative approaches. Qualitative data were obtained through in-depth interviews with ten industry …

Microwave Ore Pre-Treatment Review And Process Flowsheet Conceptualization, Baker F. Giyani

Tanzania Journal of Engineering and Technology (TJET)

The global demand for mineral commodities has increased dramatically over the years, and this is largely due to several factors, including technological advancements, industrialization, and a rise in population. Further, the demand keeps increasing because the ore being mined is becoming more complex, and the feed grade is declining over the years, prompting the need for innovative technologies to treat such ores more efficiently. Microwave ore pre-treatment is one of the potential technologies that can be employed to improve process performance. This is possible because ores are composed of both good-microwave heaters and poor microwave heaters, which creates thermal stresses …

Kennesaw State University Student Managed Investment Fund Sector Sensitivity Analysis, John Kiersznowski, Joe Johnson, Kyler Howell, Geranger Lewis

Senior Design Project For Engineers

The Kennesaw State University Student Managed Investment Fund (SMIF) Sector Sensitivity Analysis focuses on improving the fund’s decision-making and performance through data science. The SMIF is a diversified index fund designed to outperform indices like the S&P 500. This project investigates how macroeconomic variables—such as GDP growth, inflation, interest rates, and commodity prices—impact sector performance. By structuring data, developing a sustainable data pipeline, and leveraging advanced statistical techniques and predictive modeling, our team was able to provide the framework and proof of actionable insights that enhance the fund's ability to manage risks and optimize returns.

Compression Strength Prediction Of Regular-Slotted-Container Corrugated Fiberboard Boxes Based On Artificial Neural Network Using Multiple Materials And Design Parameters, Tita Archaviboonyobul, Ravipim Chaveesuk, Jay Singh, Tunyarut Jinkarn

Industrial Technology and Packaging

Importance of the work: An accurate compression strength prediction model of RSC corrugated boxes could be used as an effective packaging design tool for industry.

Objectives: This research applied the material and design parameters for RSC corrugated box strength prediction using artificial backpropagation neural network modelling (BPN).

Materials and Methods: Total of 17 material and design factors from 630 commercially corrugated box samples in Thailand were recorded as input parameters along with their box compression test (BCT) values as output parameter. Data was randomly grouped as 80:10:10 during the model development for training set, testing set and validating set, respectively. …

Carbon Accountability Scores: A Process-Oriented Approach For Carbon Offsetting Using Ai Agents, Joshit Mohanty, Vaishali Vaishali

Graduate Student Government Association Research Conference

Conventional carbon offset programs rely on quantified emissions to determine balancing requirements. While this approach offers a standardized means of measuring carbon output, it often provides industries with a loophole—allowing them to offset their emissions by purchasing equivalent credits for activities such as tree planting rather than tackling inefficiencies at the source. This research proposes a process-based framework called Carbon Accountability Scores (CA scores) to offer a proactive strategy for assessing and reducing carbon footprints. Instead of focusing on the mere balancing of emitted and sequestered carbon, CA scores integrate an organization’s operational processes into the calculation, thereby offering the …

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Tamos: Task-Aware Multi-Agent Orchestrator System, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Survey On Intelligent Planning Methods From Large Language Models Perspective, Yanzhong Zhou, Junren Luo, Xueqiang Gu, Wanpeng Zhang

Journal of System Simulation

Abstract: Starting from the perspective of large language models, this paper gives an overview of the definition and development of intelligent planning, and briefly introduces the traditional methods of intelligent planning; based on the close relationship between large language model intelligent agents and intelligent planning, introduces the architecture of large language models and typical large model intelligent agents; focusing on the intelligent planning for large language models, combs through the learning of planning languages, chain of thought, feedback optimization, and process automation; combining with the current challenges and difficulties, introduces the outlook of cutting-edge research on intelligent planning with large …

Simulation Environment Construction Of Track Segment Association And Algorithm Performance Evaluation, Dian Ding, Guangfen Wei, Zheng Cao, Shaohui Wen

Journal of System Simulation

Abstract: In order to study the applicability of Track Segment Association (TSA) algorithms in actual radar working environment , a TSA simulation environment which can simulate the real movement of the target is constructed. By constructing a rich set of target motion sets, the state switching process of target motion is described based on Markov state transition matrix, and the density is flexibly controlled through track translation. The simulation results show that this environment can evaluate the performance of the current classical TSA algorithms. The evaluation results provide a good reference for the practical engineering application of interrupted track association.

A Method For Road Extraction Using Masked Image Modeling And Contrastive Learning, Jiangjiang Wu, Zhenghong Li, Zhichao Sha, Hao Chen, Shuang Peng, Chun Du, Jun Li

Journal of System Simulation

Abstract: Aiming at the occlusion problem of road extraction from remote sensing images, a road extraction method combining MIM and CL is proposed, the model training process includes a masked pretraining stage and a contrast training stage. The masked pre-training stage mainly carries out mask image reconstruction, and trains the model to recover the whole image from some areas that are randomly occluded. The comparison training stage is mainly for the prediction error and low confidence regions to learn the comparison, to narrow the distance between the features of the same category and increase the distance between the features of …

A Transfer Learning-Based Hybrid Model For Pm2.5 Concentration Prediction, Xinbiao Lu, Chunlin Ye, Yisen Chen, Wen Wu, Yudan Chen

Journal of System Simulation

Abstract: In order to solve the problems of increased computational cost due to irrelevant features and decreased prediction accuracy due to the difference in probability distribution caused by the change of data distribution over time in PM2.5 concentration prediction, this paper constructs a hybrid deep learning model TraTCN-LSTM-BiGRU based on migration learning. The meteorological factors related to PM2.5 concentration are selected as the model input using the mean-value heat map algorithm features; the source domain data and target domain data are divided by KL scatter and an adaptive layer is introduced into the model to achieve inter-domain distribution adaptation; the …

An Event Ontology And Dataset Construction Method For Strategic Operations Analysis, Quanlin Chen, Jun Jia

Journal of System Simulation

Abstract: Aiming at the lack of professional datasets for information extraction technology research in the field of strategic operations research analysis, this paper proposes an event ontology and dataset construction method for strategic operations research analysis. The method proposes an event ontology model for strategic operations research analysis according to the needs of situation judgment in strategic operations research analysis, and uses the method of "a small amount of manual annotation + fine-tuned large language model annotation" to construct the event dataset EfSOA for strategic operations research analysis. The dataset construction method proposed in this paper and the constructed dataset …

Trajectory Planning Of Quadruped Robot Over Obstacle With Single Leg Based On Deep Reinforcement Learning, Min Li, Sen Zhang, Xiangguang Zeng, Gang Wang, Tongwei Zhang, Dijie Xie, Wenzhe Ren, Tao Zhang

Journal of System Simulation

Abstract: Aiming at the problems of joint vibration and high energy consumption of quadruped robot in the process of walking over obstacles, a foot trajectory planning method of quadruped robot based on deep reinforcement learning SAC algorithm is proposed. Based on robot kinematics and Monte Carlo method, the motion space of the single-legged foot of quadruped robot is analyzed. A compound seventhdegree polynomial trajectory of the quadruped robot is planned. The SAC algorithm is used to train and obtain the low energy consumption obstacle crossing strategy of four-legged robot under different obstacle environment. The simulation results show that the compound …

Research On The Resilience Of Integrated Urban Passenger Transport Network In Urban Agglomerations Considering The Intra-Urban Service Network, Shida Nie, Chengbing Li, Bowei He, Xintao Li

Journal of System Simulation

Abstract: In order to solve the problem of insufficient comprehensiveness and refinement of the urban agglomeration passenger transport network model, Space L modelling method in complex network theory is adopted to construct a comprehensive urban passenger transport network model considering the urban internal service network. A comprehensive urban passenger transport network composed of intercity networks and intra-city service networks is built, and time-dependent edge weights in the network is considered. A time-weighted network efficiency model is proposed to evaluate the network resilience. The results show that under a random attack strategy, the relative time efficiency of the network fluctuates less, …

A Multi-Robot Collaborative Path Planning Algorithm With Chain Working Mode, Zhigang He, Dayan Li, Niya Wang, Jianlin Mao, Ning Wang

Journal of System Simulation

Abstract: In order to solve the problem that the traditional MAPF algorithms can lead to a large number of repeated paths and thus non-essential energy loss in application scenarios where multiple robots have a common goal point, a multi-robot chain work mode with a tractor is proposed, which divides the robots with common target points into subgroups for multi robot collaborative path planning, and a collaborative dynamic priority SIPP with tractor (Co-DPtSIPP) algorithm is given. The polygonal Fermat point principle and other methods are used to obtain the serial connection areas of each collaborative group robot; considering the sequence of …

Traffic Signal Detection Based On Improved Yolov7, Lanyue Zheng, Yujie Zhang

Journal of System Simulation

Abstract: An improved YOLOv7 is proposed to address the problem of low recognition accuracy in general object detection algorithms for traffic signal detection. The algorithm removes the 20×20 detection scale and adds a 160×160 detection scale to increase shallow features while making the model lightweight. It combines the bi-level routing attention (BRA) proposed in BiFormer with axial attention, and innovatively proposes axially-guided BRA (ABRA). This module is specifically designed for the characteristics of traffic signal positions. To address the issue of object size sensitivity to the IoU metric, the normalized wasserstein distance (NWD) measurement is introduced to improve object location …

Research On Dual-Layer Path Planning Method For Lunar Rover Based On Slip Prediction, Zhang Xingyu, Baolei Wu, Jun Wang, Miaoying Hong, Jiahui Wang, Yongqiang Qi

Journal of System Simulation

Abstract: In response to the challenges faced by lunar rovers in the process of path planning, such as safe obstacle avoidance and target deviation caused by complex terrain, a dual-layer path planning based on slip prediction is proposed. In this approach, flat terrain is adaptively selected to reduce the wheel slip of the lunar rover. The overall complexity of the terrain is calculated using digital elevation information, and a Q-learning algorithm with a three-level reward mechanism is designed to navigate around highslip areas, achieving global path planning. A depth camera is used to perceive obstacles, a dynamic window method based …