This study addresses the critical problem of identifying optimal business locations for small and medium enterprises (SMEs), a decision-making process by factors such as travel time uncertainty, natural disasters, and population density. Existing research in this area has not adequately addressed these complexities, leaving a knowledge gap that this study aims to fill. Our research employs two optimization methods, differential evolution (DE) and mixed integer programming (MIP), to maximize customer coverage. We present a comprehensive model that not only determines optimum and near-optimum business locations but also investigates the scalability of the algorithms with increasing facilities and their adaptability to different traffic scenarios. Key findings indicate that the DE algorithm, in particular, demonstrates superior coverage performance. This study contributes to the field by providing a robust and adaptable model for facility location problem-solving. The insights gained have practical applications for both academia and industry, aiding SMEs in making informed, strategic decisions about business location placement.
{"title":"Optimizing business location for small and medium enter¬prises considering travel time uncertainty, natural disasters, and density population: a study case in Jakarta","authors":"Herman Sjahruddin, Ahmad Faisal Dahlan","doi":"10.30656/jsmi.v8i1.8224","DOIUrl":"https://doi.org/10.30656/jsmi.v8i1.8224","url":null,"abstract":"This study addresses the critical problem of identifying optimal business locations for small and medium enterprises (SMEs), a decision-making process by factors such as travel time uncertainty, natural disasters, and population density. Existing research in this area has not adequately addressed these complexities, leaving a knowledge gap that this study aims to fill. Our research employs two optimization methods, differential evolution (DE) and mixed integer programming (MIP), to maximize customer coverage. We present a comprehensive model that not only determines optimum and near-optimum business locations but also investigates the scalability of the algorithms with increasing facilities and their adaptability to different traffic scenarios. Key findings indicate that the DE algorithm, in particular, demonstrates superior coverage performance. This study contributes to the field by providing a robust and adaptable model for facility location problem-solving. The insights gained have practical applications for both academia and industry, aiding SMEs in making informed, strategic decisions about business location placement.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"93 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AKA Farm is an urban agriculture-based silver catfish hatchery enterprise in Bogor Regency. AKA Farm has successfully met local demand for silver catfish fry production by utilizing limited space within vacant houses in Cihideung Ilir village. The comprehensive facilities, including electricity, wells, roads, and drainage channels, support the success of this operation. Challenges in the silver catfish hatchery are associated with low efficiency and responsiveness due to the complexity of the production process, resulting in suboptimal harvest outcomes. The primary contribution of this research lies in developing and implementing an innovative IoT-based monitoring and control system to address water quality conditions, as fluctuations in water temperature and pH significantly impact fish metabolism and survival. The main objective of this study is to improve efficiency and responsiveness in the hatchery process, aiming for optimal harvest outcomes. The integrated system utilizes the Blynk application for real-time monitoring and control. Another advantage of the system is its automation; when the temperature and pH are not optimal, the actuators automatically optimize the aquarium conditions according to applicable standards. The actuators control heating lamps and release acidic or basic solutions. The system performs real-time and remote monitoring and control, reducing delays in responding to changes in the aquarium environment ultimately substantially improving the survival and growth of silver catfish. Implications of this research include assisting farmers in saving time and energy while increasing the productivity of silver catfish hatcheries. The study also reinforces the system's ability to create reliable water quality, supporting the well-being of silver catfish and ultimately enhancing performance in urban farming.
{"title":"Implementation system monitoring and control temperature and pH in urban silver catfish hatchery to enhance efficiency and responsiveness based on IoT","authors":"Firda Amalia, Syarifuddin Nasution","doi":"10.30656/jsmi.v8i1.7544","DOIUrl":"https://doi.org/10.30656/jsmi.v8i1.7544","url":null,"abstract":"AKA Farm is an urban agriculture-based silver catfish hatchery enterprise in Bogor Regency. AKA Farm has successfully met local demand for silver catfish fry production by utilizing limited space within vacant houses in Cihideung Ilir village. The comprehensive facilities, including electricity, wells, roads, and drainage channels, support the success of this operation. Challenges in the silver catfish hatchery are associated with low efficiency and responsiveness due to the complexity of the production process, resulting in suboptimal harvest outcomes. The primary contribution of this research lies in developing and implementing an innovative IoT-based monitoring and control system to address water quality conditions, as fluctuations in water temperature and pH significantly impact fish metabolism and survival. The main objective of this study is to improve efficiency and responsiveness in the hatchery process, aiming for optimal harvest outcomes. The integrated system utilizes the Blynk application for real-time monitoring and control. Another advantage of the system is its automation; when the temperature and pH are not optimal, the actuators automatically optimize the aquarium conditions according to applicable standards. The actuators control heating lamps and release acidic or basic solutions. The system performs real-time and remote monitoring and control, reducing delays in responding to changes in the aquarium environment ultimately substantially improving the survival and growth of silver catfish. Implications of this research include assisting farmers in saving time and energy while increasing the productivity of silver catfish hatcheries. The study also reinforces the system's ability to create reliable water quality, supporting the well-being of silver catfish and ultimately enhancing performance in urban farming.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"235 1‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chusnul Aprilianti, A. Garside, Amelia Khoidir, Thomy Eko Saputro
This study proposes an economic production quantity (EPQ) model that comprehensively addresses scrap items, imperfect quality items, rework processes, and shortages. The model incorporates various types of defective items, including scrap, imperfect quality, and rework able items, and implements immediate rework processes upon the completion of regular production. Shortages are treated as lost sales, enhancing the accuracy of inventory cost estimations. Numerical experiments demonstrate the optimality of production lot sizes and underscore the impact of production and demand rate adjustments on overall inventory costs. Sensitivity analysis further elucidates the influence of imperfect quality items on inventory costs. This EPQ model offers a comprehensive approach to efficient and effective finished product inventory management by integrating considerations for scrap items, imperfect quality items, and rework processes. Additionally, a furniture manufacturing company case is presented to illustrate the practical application of the proposed model.
{"title":"Economic production quantity model with defective items, imperfect rework process, and lost sales","authors":"Chusnul Aprilianti, A. Garside, Amelia Khoidir, Thomy Eko Saputro","doi":"10.30656/jsmi.v8i1.7580","DOIUrl":"https://doi.org/10.30656/jsmi.v8i1.7580","url":null,"abstract":"This study proposes an economic production quantity (EPQ) model that comprehensively addresses scrap items, imperfect quality items, rework processes, and shortages. The model incorporates various types of defective items, including scrap, imperfect quality, and rework able items, and implements immediate rework processes upon the completion of regular production. Shortages are treated as lost sales, enhancing the accuracy of inventory cost estimations. Numerical experiments demonstrate the optimality of production lot sizes and underscore the impact of production and demand rate adjustments on overall inventory costs. Sensitivity analysis further elucidates the influence of imperfect quality items on inventory costs. This EPQ model offers a comprehensive approach to efficient and effective finished product inventory management by integrating considerations for scrap items, imperfect quality items, and rework processes. Additionally, a furniture manufacturing company case is presented to illustrate the practical application of the proposed model.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"195 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Ridwan Andi Purnomo, Imam Djati Widodo, Z. Zukhri
The primary objective of conventional manufacturing cell formation typically uses grouping efficiency and efficacy measurement to reduce voids and exceptional parts. This objective frequently leads to extreme solutions, such as the persistently significant workload disparity among the manufacturing cells. It will have a detrimental psychological impact on operators who work in each formed manufacturing cell. The complexity of the problem increases when there is a requirement to finish all parts before the midday break, at which point the formed manufacturing cells can proceed with the following production batch after the break. This research examines the formation of manufacturing cells using two widely recognized intelligent optimization techniques: genetic algorithm (G.A.) and particle swarm optimisation (PSO). The discussed manufacturing system has flexible machines, allowing each part to have multiple production routing options. The optimisation process involved addressing four simultaneous objectives: enhancing the efficiency and efficacy of the manufacturing cells, minimizing the deviation of manufacturing cells working time with the allocated working hours, which is prior to the midday break, and ensuring a balanced workload for the formed manufacturing cells. The optimisation results demonstrate that the G.A. outperforms the PSO method and is capable of providing manufacturing cell formation solutions with an efficiency level of 0.86, efficacy level as high as 0.64, achieving a minimum lateness of only 24 minutes from the completion target before midday break and a maximum difference in workload as low as 49 minutes.
{"title":"Intelligent optimisation for multi-objectives flexible manufacturing cells formation","authors":"Muhammad Ridwan Andi Purnomo, Imam Djati Widodo, Z. Zukhri","doi":"10.30656/jsmi.v8i1.7974","DOIUrl":"https://doi.org/10.30656/jsmi.v8i1.7974","url":null,"abstract":"The primary objective of conventional manufacturing cell formation typically uses grouping efficiency and efficacy measurement to reduce voids and exceptional parts. This objective frequently leads to extreme solutions, such as the persistently significant workload disparity among the manufacturing cells. It will have a detrimental psychological impact on operators who work in each formed manufacturing cell. The complexity of the problem increases when there is a requirement to finish all parts before the midday break, at which point the formed manufacturing cells can proceed with the following production batch after the break. This research examines the formation of manufacturing cells using two widely recognized intelligent optimization techniques: genetic algorithm (G.A.) and particle swarm optimisation (PSO). The discussed manufacturing system has flexible machines, allowing each part to have multiple production routing options. The optimisation process involved addressing four simultaneous objectives: enhancing the efficiency and efficacy of the manufacturing cells, minimizing the deviation of manufacturing cells working time with the allocated working hours, which is prior to the midday break, and ensuring a balanced workload for the formed manufacturing cells. The optimisation results demonstrate that the G.A. outperforms the PSO method and is capable of providing manufacturing cell formation solutions with an efficiency level of 0.86, efficacy level as high as 0.64, achieving a minimum lateness of only 24 minutes from the completion target before midday break and a maximum difference in workload as low as 49 minutes.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"22 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141388130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fertilizer industry plays a crucial role in assuring the food security of a nation, but it also faces significant environmental obstacles. These problems often contribute to decreased supply chain efficiency and overall industrial productivity. The industry's focus on profit maximization hinders adopting green supply chain strategies. This paper examines company q's adoption of green supply chain management (GSCM) practices. This study evaluates its performance using the green supply chain operations reference (Green SCOR) model, scoring 73.54 out of 100, classifying it as 'good.' However, there is room for improvement, especially concerning key performance indicators (KPIs). This paper identifies six KPIs that fall below satisfactory levels and offers specific recommendations for improvement. This study significantly contributes to the fertilizer industry by providing actionable insights for practitioners and advancing theoretical understanding by highlighting key overlooked indicators. Furthermore, this research also emphasizes the crucial role of government policies in stimulating the implementation of sustainable supply chain practices.
{"title":"Supply chain performance measurement incorporating green factors using the supply chain operations reference on a fertilizer company","authors":"Putri Jasmine Solekha, Qurtubi Qurtubi, Haswika Haswika, Danang Setiawan","doi":"10.30656/jsmi.v8i1.7499","DOIUrl":"https://doi.org/10.30656/jsmi.v8i1.7499","url":null,"abstract":"The fertilizer industry plays a crucial role in assuring the food security of a nation, but it also faces significant environmental obstacles. These problems often contribute to decreased supply chain efficiency and overall industrial productivity. The industry's focus on profit maximization hinders adopting green supply chain strategies. This paper examines company q's adoption of green supply chain management (GSCM) practices. This study evaluates its performance using the green supply chain operations reference (Green SCOR) model, scoring 73.54 out of 100, classifying it as 'good.' However, there is room for improvement, especially concerning key performance indicators (KPIs). This paper identifies six KPIs that fall below satisfactory levels and offers specific recommendations for improvement. This study significantly contributes to the fertilizer industry by providing actionable insights for practitioners and advancing theoretical understanding by highlighting key overlooked indicators. Furthermore, this research also emphasizes the crucial role of government policies in stimulating the implementation of sustainable supply chain practices.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"42 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. A. Soenandi, Lamto Widodo, Budi Harsono, Isnia Oktavera, Vera Lusiana
Work fatigue is an important aspect and is very influential in determining the level of accidents, especially motorbike accidents. According to WHO, almost 30% of all deaths due to road accidents involve two- and three-wheeled motorized vehicles, such as motorbikes, mopeds, scooters and electric bicycles (e-bikes), and the number continues to increase. Motorcycles dominate road deaths in many low- and middle-income countries, where nine out of ten traffic accident deaths occur among motorcyclists, as in Indonesia. However, until now, in Indonesia, there has been no monitoring system capable of identifying fatigue in motorbike drivers in the transportation sector. This research aims to determine fatigue patterns based on driver working hours and create a sensor system to monitor fatigue measurements in real-time to reduce the number of accidents. The research began with processing questionnaire data with Pearson correlation, which showed a close relationship between driver fatigue and driving time and a close relationship between fatigue and increased heart rate and sweating levels. From calibration tests with an error of 3% and direct measurements of working conditions, it was found that two-wheeled vehicle driver fatigue occurs after 2-3 hours of work. With a measurement system using the Box Whiskers analysis method, respondents' working conditions can also be determined, which are divided into 4 zones, namely zone 1 (initial condition or good condition), zone 2 a declining condition, zone 3 a tired condition and zone 4 is a resting condition. Hopefully, this research will identify fatigue zones correctly and reduce the number of accidents because it can identify tired drivers so they do not have to force themselves to continue working and driving their motorbikes. As a conclusion from this research, a measurement system using two sensors, such as ECG and GSR can identify work fatigue zones well and is expected to reduce the number of accidents due to work fatigue.
{"title":"Fatigue analysis and design of a motorcycle online driver measurement tool using real-time sensors","authors":"I. A. Soenandi, Lamto Widodo, Budi Harsono, Isnia Oktavera, Vera Lusiana","doi":"10.30656/jsmi.v7i2.7500","DOIUrl":"https://doi.org/10.30656/jsmi.v7i2.7500","url":null,"abstract":"Work fatigue is an important aspect and is very influential in determining the level of accidents, especially motorbike accidents. According to WHO, almost 30% of all deaths due to road accidents involve two- and three-wheeled motorized vehicles, such as motorbikes, mopeds, scooters and electric bicycles (e-bikes), and the number continues to increase. Motorcycles dominate road deaths in many low- and middle-income countries, where nine out of ten traffic accident deaths occur among motorcyclists, as in Indonesia. However, until now, in Indonesia, there has been no monitoring system capable of identifying fatigue in motorbike drivers in the transportation sector. This research aims to determine fatigue patterns based on driver working hours and create a sensor system to monitor fatigue measurements in real-time to reduce the number of accidents. The research began with processing questionnaire data with Pearson correlation, which showed a close relationship between driver fatigue and driving time and a close relationship between fatigue and increased heart rate and sweating levels. From calibration tests with an error of 3% and direct measurements of working conditions, it was found that two-wheeled vehicle driver fatigue occurs after 2-3 hours of work. With a measurement system using the Box Whiskers analysis method, respondents' working conditions can also be determined, which are divided into 4 zones, namely zone 1 (initial condition or good condition), zone 2 a declining condition, zone 3 a tired condition and zone 4 is a resting condition. Hopefully, this research will identify fatigue zones correctly and reduce the number of accidents because it can identify tired drivers so they do not have to force themselves to continue working and driving their motorbikes. As a conclusion from this research, a measurement system using two sensors, such as ECG and GSR can identify work fatigue zones well and is expected to reduce the number of accidents due to work fatigue.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":" 586","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Optimizing lot sizing model for perishable bread products using genetic algorithm","authors":"H. M. Asih, R. Leuveano, Dhimas Arief Dharmawan","doi":"10.30656/jsmi.v7i2.7172","DOIUrl":"https://doi.org/10.30656/jsmi.v7i2.7172","url":null,"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.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"114 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138607518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Increasing energy consumption has faced challenges and pressures for modern manufacturing operations. The production sector accounts for half of the world's total energy consumption. Reducing idle machine time by employing No-Idle Permutation Flow Shop Scheduling (NIPFSP) is one of the best decisions for reducing energy consumption. This article modifies one of the energy consumption-solving algorithms, the Aquila Optimizer (AO) algorithm. This research contributes by 1) proposing novel AO procedures for solving energy consumption problems with NIPFSP and 2) expanding the literature on metaheuristic algorithms that can solve energy consumption problems with NIPFSP. To analyze whether the AO algorithm is optimal, we compared by using the Grey Wolf Optimizer (GWO) algorithm. It compares these two algorithms to tackle the problem of energy consumption by testing four distinct problems. Comparison of the AO and GWO algorithm is thirty times for each case for each population and iteration. The outcome of comparing the two algorithms is using a t-test on independent samples and ECR. In all case studies, the results demonstrate that the AO algorithm has a lower energy consumption value than GWO. The AO algorithm is therefore recommended for minimizing energy consumption because it can produce more optimal results than the comparison algorithm.
{"title":"A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem","authors":"D. M. Utama, Nabilah Sanafa","doi":"10.30656/jsmi.v7i2.6446","DOIUrl":"https://doi.org/10.30656/jsmi.v7i2.6446","url":null,"abstract":"Increasing energy consumption has faced challenges and pressures for modern manufacturing operations. The production sector accounts for half of the world's total energy consumption. Reducing idle machine time by employing No-Idle Permutation Flow Shop Scheduling (NIPFSP) is one of the best decisions for reducing energy consumption. This article modifies one of the energy consumption-solving algorithms, the Aquila Optimizer (AO) algorithm. This research contributes by 1) proposing novel AO procedures for solving energy consumption problems with NIPFSP and 2) expanding the literature on metaheuristic algorithms that can solve energy consumption problems with NIPFSP. To analyze whether the AO algorithm is optimal, we compared by using the Grey Wolf Optimizer (GWO) algorithm. It compares these two algorithms to tackle the problem of energy consumption by testing four distinct problems. Comparison of the AO and GWO algorithm is thirty times for each case for each population and iteration. The outcome of comparing the two algorithms is using a t-test on independent samples and ECR. In all case studies, the results demonstrate that the AO algorithm has a lower energy consumption value than GWO. The AO algorithm is therefore recommended for minimizing energy consumption because it can produce more optimal results than the comparison algorithm.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138626862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.
{"title":"Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment","authors":"Muhammad Ridwan Andi Purnomo","doi":"10.30656/jsmi.v7i2.6495","DOIUrl":"https://doi.org/10.30656/jsmi.v7i2.6495","url":null,"abstract":"Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inventory control is critical because the inability to overcome inventory problems causes unpreparedness to meet consumer demand. MSMEs Bekawan Agro Coconut Sugar, independently around 35% -70%, cannot meet consumers' demand for coconut sugar, so an inventory control model is needed. Inventory control models must integrate with demand forecasting as an inventory control input. This study aims to integrate the demand forecasting model with the inventory control model. The method used for demand forecasting is ARIMA. The inventory control model uses a modified EOQ hybrid method because coconut sugar products have a shelf life; they also use coconut sap as raw material, which must be processed to prevent fermentation. The research results show that demand forecasting for one year ahead is a total of 10,310.82 Kilograms with an economic lot size of 120 Kilograms and a reorder point when the inventory position is 30 Kilograms. Daily production of 30 kilograms requires 210 litres of coconut sap/per day. The amount of sap needed requires 105 coconut trees / per day. Arrival time of coconut sugar at the storage warehouse every five days. The resulting model can be a solution for sustainable MSMEs.
{"title":"Integration models of demand forecasting and inventory control for coconut sugar using the ARIMA and EOQ modification methods","authors":"Siti Wardah, Nunung Nurhasanah, Wiwik Sudarwati","doi":"10.30656/jsmi.v7i2.6500","DOIUrl":"https://doi.org/10.30656/jsmi.v7i2.6500","url":null,"abstract":"Inventory control is critical because the inability to overcome inventory problems causes unpreparedness to meet consumer demand. MSMEs Bekawan Agro Coconut Sugar, independently around 35% -70%, cannot meet consumers' demand for coconut sugar, so an inventory control model is needed. Inventory control models must integrate with demand forecasting as an inventory control input. This study aims to integrate the demand forecasting model with the inventory control model. The method used for demand forecasting is ARIMA. The inventory control model uses a modified EOQ hybrid method because coconut sugar products have a shelf life; they also use coconut sap as raw material, which must be processed to prevent fermentation. The research results show that demand forecasting for one year ahead is a total of 10,310.82 Kilograms with an economic lot size of 120 Kilograms and a reorder point when the inventory position is 30 Kilograms. Daily production of 30 kilograms requires 210 litres of coconut sap/per day. The amount of sap needed requires 105 coconut trees / per day. Arrival time of coconut sugar at the storage warehouse every five days. The resulting model can be a solution for sustainable MSMEs.","PeriodicalId":53057,"journal":{"name":"Jurnal Sistem dan Manajemen Industri","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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