Annisa Kesy Garside
@industri.umm.ac.id
Teknik Industri
Universitas Muhammadiyah Malang
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Annisa Kesy Garside, Leni Erlinda, and Ikhlasul Amallynda
AIP Publishing
Annisa Kesy Garside, Dana Marsetiya Utama, and Astrid Hilda Yunnia
AIP Publishing
Annisa Kesy Garside
AIP Publishing
Annisa Kesy Garside, Rara Putri Ayuning Tyas, and Rahmad Wisnu Wardana
Universitas Andalas
Indonesia is currently entering a new normal era; this requires people to adapt to the clean-living habit in accordance with health standards in order to carry out normal activities. At the same time, online transportation services have reopened for activity. The service quality provided by online ride-hailing companies (i.e., ojek) such as Gojek, Grab, and Maxim must now consider matters relating to user safety. This study proposes Multi Criteria Decision Making (MCDM) as a method for assessing the service quality of online transportation service providers and uses the Pandemic-SERVQUAL 4.0 model. Pandemi-SERVQUAL 4.0 model adds two new criteria, namely "pandemic" and "industry 4.0". The addition of two new criteria that are more relevant to the current circumstances will increase the accuracy of the research. This study aims to propose the integration of Interval Valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIF-AHP) to determine the criteria weight and Interval Valued Intuitionistic Fuzzy Weighted Aggregated Sum-Product Assessment (IVIF-WASPAS) to assess the service quality of several online transportation service providers based on the obtained criteria weights. From the results of the service quality assessment using the integration of IVIF-AHP and IVIF-WASPAS, the ranking of online transportation service providers during the new normal era were Grab-car, Go-car, and Maxim-car.
Dana Marsetiya Utama, Aminatul Yurifah, and Annisa Kesy Garside
International Journal of Technology
Rahmad Wisnu Wardana, Annisa Kesy Garside, and Adhitya Tri Anggara
AIP Publishing
Annisa Kesy Garside, Luki Trihardani, Baiq Nurul Izzah Farida Ramadhani, and Amelia Khoidir
AIP Publishing
Muhammad Alif Ihsan, Annisa Kesy Garside, and Rahmad Wisnu Wardana
Universitas Andalas
Supplier performance evaluation is one of the important factors in the supply chain because it is one of the company's strategies for increasing customer satisfaction and also maintaining the company's services in meeting consumer demand. This study proposes the integration of the Analytic Network Process (ANP) and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to evaluate supplier performance. The integration of the two methods is proposed to obtain more complex assessment results because the combination of the two methods considers various criteria derived from ANP and various preferences from PROMETHEE, so both methods are very good to use instead of using just ANP or PROMETHEE or other methods. ANP exhibit more complex relationships between criteria and levels in the decision hierarchy, while PROMETHEE provides decision-makers with flexible and straightforward outranking to analyze multi-criteria problems. In this study, ANP is used to weight the criteria, and PROMETHEE is used to rank suppliers in evaluating supplier performance. Integrating these two methods provides more objective and accurate results in multi-criteria decision-making. The proposed method is validated by solving an industrial case of supplier evaluation problem using the real data from the skewer industry. Finally, some useful implications for managerial decision-making are discussed.
Dana Marsetiya Utama, Triani Aulya Fitria, and Annisa Kesy Garside
IOP Publishing
Abstract Determining the route of vehicles in logistics has a vital function for logistics companies to reduce the total cost of distribution costs. One of the exciting problems in determining vehicle routes is the green vehicle routing problem with time windows (GVRPTW). This study aims to propose the Artificial Bee Colony (ABC) algorithm to solve the GVRPTW problem. This problem’s objective function is to minimize the total cost of distribution, which involves the total cost of fuel and late delivery costs. The ABC algorithm is applied to a case study of an Indonesian distribution company. Thirty customers and one distribution center were considered in this study. The results show that the ABC algorithm can minimize the total cost of distribution. In addition, the proposed algorithm produces a competitive total distribution cost compared to the nearest neighbor procedure.
Baiq Nurul Izzah Farida Ramadhani and Annisa Kesy Garside
Universitas Andalas
The Conventional Vehicle Routing Problem (VRP) has the objective function of minimizing the total vehicles’ traveling distance. Since the fuel cost is a relatively high component of transportation costs, in this study, the objective function of VRP has been extended by considering fuel consumption minimization in the situation wherein the loading weight and traveling time are restricted. Based on these assumptions, we proposed to extend the route division procedure proposed by Kuo and Wang [4] such that when one of the restrictions can not be met the routing division continues to create a new sub-route to find an acceptable solution. To solve the formulated problem, the Particle Swarm Optimization (PSO) algorithm is proposed to optimize the vehicle routing plan. The proposed methodology is validated by solving the problem by taking a particular day data from a bottled drinking water distribution company. It was revealed that the saving of at best 13% can be obtained from the actual routes applied by the company.
A K Garside, U B Farida, and I Masudin
IOP Publishing
Abstract Malang city government created Bank Sampah Malang (BSM) that offers 3R concept (reduce, reuse, and recycle). BSM is the institution which is oriented toward reducing plastic waste, but their recycling process still has not considered the environmental aspect yet. In this paper, we developed a reverse logistics model for plastic bottle recycling in Bank Sampah Malang (BSM) with consideration minimizing the costs of reverse logistics and environmental impacts. The proposed model takes into account various parameters, including kinds of costs, availability of used plastic bottle, storage capacity, disassembling capacity, vehicle capacity, and demand of chopped plastic. Goal programming is used to formulate mathematical model and LINGO is applied to solve the model. Lingo results indicate the two goals have been achieved because the value of the positive deviation variable associated with the two goals are equal to zero. In addition, we got several decision variable solutions including: number of plastic bottles purchased from depot, number of bottle bodies moved to recycling area, number of bottle bodies recycled, and amount of chopped plastics delivered to firm.
Dana Marsetiya Utama, Leo Rizki Ardiansyah, and Annisa Kesy Garside
Universitas Andalas
Flow shop scheduling problems much studied by several researchers. One problem with scheduling is the tardiness. Total tardiness is the performance to minimize tardiness jobs. it is the right performance if there is a due date. This study proposes the Cross-Entropy Genetic Algorithm (CEGA) method to minimize the mean tardiness in the flow shop problem. In some literature, the CEGA algorithm is used in the case of minimizing the makespan. However, CEGA not used in the case of minimizing total tardiness. CEGA algorithm is a combination of the Cross-Entropy Algorithm which has a function to provide optimal sampling distribution and Genetic Algorithms that have functions to get new solutions. In some numeric experiments, the proposed algorithm provides better performance than some algorithms. For computing time, it is affected by the number of iterations. The higher the iteration, computing requires high time.
Annisa Kesy Garside and Thomy Eko Saputro
Author(s)
Annisa Kesy Garside, Galih Wasis Wicaksono, and Wahyu Andhika Kusuma
IEEE
Annisa Kesy Garside
IEEE
Annisa Kesy Garside and Ahmad Rusdiansyah
Inderscience Publishers