10th International Conference of Managing Value and Supply Chains (MaVaS 2024)

July 20 ~ 21, 2024, Toronto, Canada

Accepted Papers


Stcanetviz: a Visual Analytics Framework for Optimizing an Ocean Currents Prediction Model Stcanet
ABSTRACT

STCANet is a spatio-temporal coupled attention deep network model for predicting ocean currents, which consists of a spatio- temporal module and an attention module , forming a complex nested/coupled system. Understanding and optimizing the STCANet model is challenging due to the complexity of the underlying modules influencing model predictions.In this work we present STCANetViz, a web- based visual analysis tool for understanding and optimizing STCANet models. We proposed an Inter-Module Interdependencies Computing Algorithm (IMDCA), which computes module specific interdependency metrics derived from module interactions to optimize the STCANet model. A modular inter-dependency graph is then designed for examining and analyzing the behavior of the model.The graph consists of a network layer visualization (for module-level exploration) and a heatmap-scatterplot (for module output analysis). We implement a scalable visualization technique incorporating cloud storage for dynamically extracting, seamlessly visualizing and analyzing the high-dimensional ensemble data generated by the STCANet model on ensemble force-directed diagrams (EFDs).

KEYWORDS

Nested / Coupled models, Scalability, Ensemble data, Visual Analytics.


Machine Learning Innovations in Supply Chain Management: Revolutionizing Predictive Modeling for Efficiency and Growth

Sarthak Pattnaik1, Natasya Liew1, Ali Ozcan Kures1, Kathleen Park2, and Eugene Pinsky1, 1Department of Computer Science, Metropolitan College, Boston University 1010 Commonwealth Avenue, Boston, MA 02215, 2Department of Administrative Sciences, Metropolitan College, Boston University 1010 Commonwealth Avenue, Boston, MA 02215, United States of America

ABSTRACT

Supply Chain Management (SCM) stands at the forefront of modern business practices, serving as the backbone of efficient operations in a globalized and interconnected world. This paper delves into the intricate web of activities that define SCM and explores the transformative impact of technology on optimizing the flow of goods, services, information, and finances across organizations. The evolution of SCM from a logistics-centric function to a multi-functional corporate endeavor underscores the complexity and criticality of managing the end-to-end supply chain processes. By integrating technology into SCM practices, organizations can streamline operations, enhance collaboration, and drive competitive advantage in today’s dynamic marketplace. Technology plays a pivotal role in reshaping traditional supply chain paradigms, offering innovative solutions to longstanding challenges and unlocking new opportunities for growth and efficiency. From advanced data analytics to artificial intelligence (AI) tools, organizations can harness the power of technology to gain real-time insights, forecast demand accurately, and optimize inventory levels. The advent of digital supply chain platforms has revolutionized information sharing and external collaboration, enabling organizations to adapt swiftly to changing market dynamics and customer preferences. Furthermore, technology facilitates the optimization of logistics and transportation processes, leading to reduced fuel consumption, lower carbon emissions, and enhanced sustainability in supply chain operations.

KEYWORDS

Supply Chain Management, Customer Satisfaction, Customer Loyalty, Innovation, Machine Learning, Predictive Models.


Proximal Policy Optimization for Efficient Product Transportation: A Reinforcement Learning Approach

Asharful Islam and Chuan Li, Department of Computer Science, Sichuan University, Chengdu, China

ABSTRACT

Optimizing the delivery of products from a central depot to multiple retail locations presents a multifaceted challenge, especially when considering factors such as minimizing costs while ensuring product availability for customers. Traditional approaches to this problem often rely on heuristic methods or mathematical optimization techniques. However, these approaches may struggle to adapt to dynamic real-world scenarios with complex, evolving conditions. This study pioneers the application of Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, to the domain of product transportation and inventory management. By creating a custom simulation environment, “ProductTransportEnv,” we delve into the complexities of supply chain logistics, demonstrating the significant potential of reinforcement learning to transform operational efficiencies. The “ProductTransportEnv” mimics real-world logistics scenarios, allowing for a detailed exploration of transportation routes, inventory levels, and demand fluctuations, providing a rigorous testing ground for the PPO algorithm.

KEYWORDS

Openai Gym-environment, “ProductTransportEve,” PPO, RL, DQN, Inventory management.


Identification of Bioactive Molecules Present in the Essential Oil of Melicope Madagascariensis (Rutaceae), an Endemic Plant of Madagascar, and Its Biological Activities

Razafindrakoto Fanoina Ny Riana1, 2, 1doctoral School of Process Engineering for Agricultural and Food Industrial Systems, University of Antananarivo, 2antananarivo Higher Polytechnic School, University of Antananarivo

ABSTRACT

The Big Island is one of the few countries in the world that boasts a specific biological megadiversity in terms of flora and fauna. With its exceptional biological diversity, we were guided to focus our study on the leaves of Melicope madagascariensis, belonging to the Rutaceae family, an endemic species, to discover its medicinal properties. The Rue family (Rutaceae) includes more than 125 species across 09 genera in Madagascar, most of which are woody shrubs and trees. The flowers are generally showy and fragrant, and many species have attractive aromatic foliage. Extensive research into the biological activities of the essential oil from the leaves of this plant has been carried out in the rainforest of Madagascars east coast. The essential oil of Melicope madagascariensis has a very specific chemotype. Various GPC and TLC analyses were carried out. The majority of essential oils contain (Z)-β- ocimene (12.7%) and (E)-β-ocimene (25.7%) belonging to the monoterpene family. The synergy of these various bioactive molecules, namely α-pinene (8.56 %); methyl chavicol (6.25 %); trans-piperitol (7.42 %); (E)-β-caryophyllene (3.21 %), gives it several biological activities and an aromatic profile that is unusual for Madagascar. The synergy of its bioactive molecules helps to eliminate free radicals and fungal germs. However, analyses of its toxicity have produced some interesting results. Therefore, the notion of the duality of "efficacity and toxicity" needs to be taken into account, as all active substances are potentially toxic. This is why a more in-depth toxicity study was carried out, using various in vitro tests, namely larval activity and acute toxicity, to determine the lethal dose (LD50) performed on Swiss Musculus L mice. Given its biological properties, the use of the essential oil of Melicope madagascariensis leaves offers a vast opportunity for scientific exploration.

KEYWORDS

Melicope madagascariensis, Endemic species, Essential oil, Biological activity, Toxicity.


The Role of Organic Fertilizers in Growing Maize and Improving the Properties of Saline Soil

Raji .A , AL- Awadi (Ph.D) Erciyes University,Turkey

ABSTRACT

The experiment was carried out on the farm of sayeed. Habib Al-Khatib in the city of Numaniyah, 45 km west of the city of Kut, during the 2024 agricultural season. To study 6 types of organic waste: cows, sheep, and poultry, then a mixture of cow and sheep waste, a mixture of cow and poultry waste, and a mixture of poultry and sheep waste, in an amount of 2 t.ha-1 for each of them, in addition to the comparison sample F0, were given the following symbols, respectively, F1,F2,F3, (F1+F2), (F1+F3), (F2+F3), to know the effect of these fertilizers on the growth and production of Maize plants and soil properties. The land was plowed, ground, and mixed, then the waste was added to the soil with the surface layer 2 t.ha-1, then the corn grains were planted and irrigation began until the end of the experiment. Agricultural operations and crop service were carried out according to the recommendations followed in the region. Soil, plant and yield measurements were taken at the end of the growing season and included. The research results showed that the level (F 2 + F3) of organic Fertilizer from sheep waste mixed with poultry waste resulted in a significant increase in plant height, root length, Ear length, leaf area, weight of 500 grains, yield, and biological yield compared to other levels of fertilizer and organic matter in the soil, while the PH and EC of the soil did not give a significant difference.

KEYWORDS

Maize ,Poultry, Fertilizer, Organic waste, Al-Khatib.


Quantum Reinforcement Learning for High Frequency Trading

Alexander Kirnasov, Head of Quant Dev in Znamenka Capital

ABSTRACT

We introduce a new approach of Reinforcement Learning Application for High Frequency Trading called Quantum Reinforcement Learning as our agent learns to react on ‘quantum’ individual events in Limit Order Book – single Limit Order Book updates and single trades (and optionally single Orders if provided by Exchange). We claim that such level of learning granularity allows our agent to find optimal trading strategies by on-line modeling of Market Microstructure with a maximum rate and precision.