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All Journal International Journal of Enterprise Modelling Jurnal Sipakatau
Ramson Rikson Maruwahal Sijabat
Politeknik Ganesha Medan, Indonesia
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Social Sciences Transportation Veterinary Other

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Machine learning-based multi-objective optimization for dynamic scheduling and routing of heterogeneous instant delivery orders and scheduling strategies with real-time adaptation Ramson Rikson Maruwahal Sijabat; Zhou Klapp Parodos
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.826 KB) | DOI: 10.35335/emod.v16i2.58

Abstract

This research develops a machine learning-based multi-objective optimization technique for dynamic scheduling and routing heterogeneous instant delivery orders. Instant delivery service providers confront issues improving their operations due to order characteristics, time windows, vehicle capabilities, and real-time adaption. Scheduling, routing, and optimization literature for immediate delivery services is reviewed to start the investigation. Based on gaps, a new mathematical formulation is proposed to model the problem. Machine learning allows adaptive and dynamic decision-making. The formulation is used to address the optimization problem utilizing a method. Machine learning algorithms use past data to anticipate, optimize, and schedule routes. Real-time adaption solutions address changing order characteristics and operating situations. Numerical examples and case studies evaluate the proposed approach. The optimization approach solves difficult scheduling and routing problems in these cases. The research improves operational efficiency, cost savings, and order satisfaction. This research introduces a machine learning-based multi-objective optimization framework for rapid delivery order scheduling and routing. The findings help immediate delivery service providers streamline operations, boost customer happiness, and maximize resource use. To create more comprehensive optimization models, future research can integrate traffic circumstances, environmental implications, and customer preferences
Socialization of Healthcare Service Innovation: Developing a Pharmacy Information System to Improve Access and Service Quality Dadang Muhammad Hasyim; Arief Budi Pratomo; Abdul Rosid; Novycha Auliafendri; Ramson Rikson Maruwahal Sijabat
Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Vol. 2 No. 2 (2025): Jurnal Sipakatau
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/jsipakatau.v2i2.257

Abstract

Effective pharmacy management is essential in ensuring drug availability in health facilities. This study aims to analyze the pharmaceutical management system in Garut Regency, focusing on aspects of recording, infrastructure, readiness of health workers, and local government policies. The research method used was a qualitative approach with interview and observation techniques in several health facilities. The results showed that many health facilities still use a manual recording system, which risks causing errors and delays in drug procurement. In addition, limited infrastructure and the readiness of health workers in adopting pharmaceutical information systems are the main obstacles in digitizing pharmaceutical management. In terms of policy, the absence of strong regulations and limited budget hamper the optimal implementation of digital systems. Therefore, it is necessary to improve infrastructure, train health workers, and support clearer policies to encourage the digitalization of the pharmaceutical system in Garut Regency.
Co-Authors Abdul Rosid Arief Budi Pratomo Dadang Muhammad Hasyim Novycha Auliafendri Zhou Klapp Parodos