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A genetic algorithm approach to green vehicle routing: Optimizing vehicle allocation and route planning for perishable products Asih, Hayati Mukti; Leuveano, Raden Achmad Chairdino; Dharmawan, Dhimas Arief; Ardiansyah, Ardiansyah
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1784

Abstract

This paper introduces a novel approach to the Green Vehicle Routing Problem (GVRP) by integrating multiple trips, heterogeneous vehicles, and time windows, specifically applied to the distribution of bakery products. The primary objective of the proposed model is to optimize route planning and vehicle allocation, aiming to minimize transportation costs and carbon emissions while maximizing product quality upon delivery to retailers. Utilizing a Genetic Algorithm (GA), the model demonstrates its effectiveness in achieving near-optimal solutions that balance economic, environmental, and quality-focused goals. Empirical results reveal a total transportation cost of Rp. 856,458.12, carbon emissions of 365.43 kgCO2e, and an impressive average product quality of 99.90% across all vehicle trips. These findings underscore the capability of the model to efficiently navigate the complexities of real-world logistics while maintaining high standards of product delivery. The proposed GVRP model serves as a valuable tool for industries seeking sustainable and cost-effective distribution strategies, with implications for broader advancements in supply chain management.
EDUKASI PENGOLAHAN DAN PEMILAHAN SAMPAH PLASTIK GUNA PENINGKATAN EKOLITERASI ORANG TUA SISWA TK CAHYA MENTARI Hakika, Dhias Cahya; Asih, Hayati Mukti; Biddinika, Muhammad Kunta; Yuwantina, Anissa; Safitri, Anggi; Sugianti, Ardina Fitri
Jurnal Abdi Insani Vol 11 No 4 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i4.2013

Abstract

Garbage is identified as objects or materials that are no longer deemed necessary, useful, or wanted, and are often disposed of by individuals or communities. Various sources and categories of garbage and waste exist, which, without proper management, could contaminate soil, water, and air. Implementing educational programs on waste management is crucial to boost community awareness and involvement in mitigating the detrimental effects on environmental and public health. Particularly, enhancing parental awareness of waste is essential for cultivating environmental stewardship in young children. This community service initiative aims to educate parents, especially those of students at TK Cahya Mentari in Semarang District, on the processing and sorting of primarily plastic waste to improve their eco-literacy. The program includes a series of educational and technical guidance sessions that introduce fundamental waste management principles, effective sorting techniques, and the adoption of eco-friendly practices in daily life. This intervention is intended to enable parents to incorporate these practices into domestic routines and to instil environmental consciousness in their children. This program resulted in a significant increase in knowledge, with parents’ understanding improving from 53% to 91.33%. The results from this initiative indicate that the educational efforts have successfully enhanced parental understanding of waste management, thereby contributing to the development of environmental awareness among children at TK Cahya Mentari.
A Comprehensive Review of Optimization Techniques in Industrial Applications: Trends, Classifications, and Future Directions Asih, Hayati Mukti; Mohamad, Effendi; Irianto, Irianto; Ma’arif, Alfian
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13261

Abstract

In recent years, optimization techniques have played a central role in enhancing operational efficiency and decision-making across diverse industrial sectors, including manufacturing, logistics, and transportation, energy, healthcare, and agriculture. These sectors face complex, large-scale, and often nonlinear challenges that demand both precision and adaptability. The research contribution of this review is to provide a structured classification of optimization methods—namely exact algorithms, heuristics, metaheuristics, and AI-integrated hybrid models—and to critically evaluate their practical applications, limitations, and emerging trends across industries. This study adopts a review approach to identify and compare those techniques in solving various optimization problems. Through a detailed analysis of over 30 recent publications for last four years, the review highlights how these techniques are being applied in real-world industrial environments, including cold chain logistics, smart energy systems, precision agriculture, and healthcare scheduling. The results indicate a growing reliance on hybrid and AI-enhanced models due to their superior scalability, adaptability, and potential alignment with Industry 4.0 and Sustainable Development Goals (SDGs). However, challenges remain in areas such as computational efficiency, model interpretability, and real-time data integration. In conclusion, this study provides valuable insights for both researchers and practitioners seeking to apply optimization techniques more effectively in industrial systems, while also identifying critical research gaps for future exploration by addressing the growing complexity and sustainability demands of modern industry.
From Awareness to Action: How Green Marketing Shapes Purchase Intentions for Sustainable Cosmetics Jamal, Fauziyah Nur; Yuniarti, Dini; Asih, Hayati Mukti; Jumbri, Isma Addi; Wibowo, Shaneva Fitria Desta
Jurnal Minds: Manajemen Ide dan Inspirasi Vol 12 No 1 (2025): June
Publisher : Management Department, Universitas Islam Negeri Alauddin Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/minds.v12i1.55786

Abstract

Green marketing has intensified as cosmetic companies compete to project environmentally responsible identities, yet converting awareness into actual purchase remains uneven. This study examines the influence of environmental attitude, green brand image, and green perceived value on green purchase intention, drawing on survey data from 150 Body Shop consumers in Indonesia and analyzed with SEM-PLS. Results show that environmental attitude does not significantly predict intention, while both green brand image and green perceived value exert positive effects. These findings indicate that credible brand signals and tangible value perceptions act as more immediate drivers of intention than generalized attitudes, refining how the attitude–behavior gap is understood in sustainable consumption. The study contributes by highlighting the primacy of image and value in shaping intention and provides managerial implications for firms to emphasize credible positioning and consumer value rather than relying solely on environmental appeals to stimulate sustainable purchasing.
Optimizing lot sizing model for perishable bread products using genetic algorithm Asih, Hayati Mukti; Leuveano, Raden Achmad Chairdino; Dharmawan, Dhimas Arief
Jurnal Sistem dan Manajemen Industri Vol. 7 No. 2 (2023): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v7i2.7172

Abstract

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.