International Journal of Engineering Science Technologies

SMART EOQ MODELS: INCORPORATING AI AND MACHINE LEARNING FOR INVENTORY OPTIMIZATION

Patel Nirmal Rajnikant — 2025-07-03

Traditional Economic Order Quantity (EOQ) models rely on static assumptions (e.g., constant demand ????????, fixed holding cost ℎ), failing in volatile environments. This research advances dynamic inventory control through an AI-driven framework where:
1) Demand Forecasting: Machine learning (LSTM/GBRT) estimates time-varying demand:
????????ₜ = ???????? (????????ₜ; ????????) + ????????ₜ
(????????ₜ: covariates like promotions, seasonality; ????????ₜ: residuals)
2) Adaptive EOQ Optimization
Reinforcement Learning (RL) dynamically solves the following optimization problem: min????????????????,???????????????? ???????????????? ???????? (ℎ⋅????????????????++????????⋅????????????????−+????????⋅????????(????????????????))????
Subject to: ????????????????=????????????????−1+????????????????−????????????????
Where:
• Q_t: Order quantity at time t
• s_t: Reorder point at time t
• h: Holding cost per unit
• b: Backorder (shortage) cost per unit
• k: Fixed ordering cost
• δ(Q_t): Indicator function (1 if Q_t>0, else 0)
• I_t^+: Inventory on hand (positive part of I_t)
• I_t^-: Backordered inventory (negative part of I_t)
• D_t: Demand at time t
Validation was performed using sector-specific case studies.
• Pharma: Perishability constraint ????????ₜ⁺ ≤ ???????? (????????: shelf-life) reduced waste by 27.3%
• Retail: Promotion-driven demand volatility (????????²(????????ₜ) ↑ 58%) mitigated, cutting stockouts by 34.8%
Automotive: RL optimized multi-echelon coordination, reducing shortage costs by 31.5%.
The framework reduced total costs by 24.9% versus stochastic EOQ benchmarks. Key innovation: closed-loop control where ????????ₜ = RL(????????????????????????????????????????ₜ) adapts to real-time supply-chain states.

BEHAVIORAL CHANGES OF SPILLED OIL IN THE MARINE ECOSYSTEM

Etuk Etiese Akpan — 2025-07-09

When oil is spilled at sea it normally spreads out and moves on the sea surface under the influence of the wind and current while undergoing a number of chemical and physical changes. These processes are collectively termed weathering and determine the behavior of the oil. An oil spill is the release of the liquid petroleum hydrocarbon into the environment, especially marine areas, as a result of human activity and other factors as equipment failures, human errors, willful damage to equipment and oil installations etc. Oil is the most common pollutant of the marine environment. The behavior of spilled oil depends on the oil properties and the environmental conditions. It is very important to recognize the dynamic nature of spilled oil because the properties of spilled oil can change over time. It is important to monitor the continuous changes in the properties of the spilled oil, as response strategies may have to be modified to suit the current changes in oil behavior. The properties of crude or refined oils vary in their physical and chemical characteristics. These characteristics affect their volatility, toxicity, weathering rate and persistency. Oil spills have a devastating and long term impact on waterways and coastal areas around the world. Seabirds are frequently affected by offshore oil spills. Spills can severely harm turtle eggs and damage fish larvae, causing deformities. Shellfish and corals are particularly at risk since they cannot escape the runaway slick. Oil spills are also responsible for tainting algae, which perform a vital role in water ecosystems. Oil spills can be partially controlled by chemical dispersion, combustion, mechanical containment and adsorption. As the world advances technologically, unfortunately accidents do happen and spills reoccur more frequently than we would like. A good knowledge and understanding of the types of oil, oil properties and the changes in the behavior of oil is very critical in effective response planning, strategies choices, execution and overall oil spill emergency response management in the marine ecosystems.

CONSTRUCTION 5.0 IS ON THE WAY TO BECOMING THE STRONGEST INDUSTRY THAT ADVANCES SOCIAL SUSTAINABILITY IN THE DIGITAL AGE. A MATURITY ASSESSMENT

Bianca Weber-Lewerenz — 2025-08-31

Convincing use cases and reliable empirical values are essential for the construction industry and its decision-makers to face the questions and challenges of digital transformation and to shape their own digital strategy. In fact, a connection has been identified between the increasing acceptance of digitization in construction companies and the innovative creation of use cases. Therefore, the aim of this study is to examine the evidence that will greatly facilitate the development of such an innovation scenario and ensure even wider adoption. The study critically questions whether the perception of the construction industry as one that is lagging behind digitization and Artificial Intelligence (AI) is realistic or not, or whether the industry is rather a driver of a sustainable digital age.
The study used structured literature analysis and interviews with corporate best practices. The analysis and evaluation of the database follow the Hermeneutic Approach. Overall, this research led to the new aspects that underpin a convincing innovation scenario contributing to the broader use of digital methods and AI. With new findings, this study adds value to scientific literature and is evidence of the industry's leadership.
Motivation/Background: The holistic approach from the perspective of a Civil Engineer, examining Construction 5.0, its related success factor and compass for socially sustainable construction and fulfillment of sustainability goals and ESG, may add value to your academic reading community. This manuscript is part of the author`s larger scientific research work “CDR in Construction in-line with ESG and SDGs”.
Method: The study for this original article applies the so-called “Next Generation”-Method. It recognizes both challenges and limitations of digital transformation and the related human factors followed by normative questions. But it also deepens the holistic understanding of the explosive power and potential of new technologies such as AI catalyzing the construction branch into a more resilient, innovative one.
Results: The aim of this work is to assess such an innovative scenario and ensure even wider adoption. The study critically questions whether the perception of the construction industry as one that is lagging behind digitization and AI is realistic or not, or whether the industry is rather a driver of a sustainable digital age.
Conclusions: The paper´s discussion offers new aspects of this new scientific niche, to add value to scientific research. Furthermore, this paper aims to fully capture a new approach to assess the construction branch´s innovation strategy with a practicable framework for increasing both societal and corporate sustainability.
The paper consists in a connection between a discovery, new scientific findings and its practical application.