Development of Green-Affective Work System for Food SMEs

Ushada, Mirwan; Okayama, Takayuki

doi:10.18502/kls.v4i2.1660

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…Therefore, laborers needed sufficient rest periods under these circumstances. SMEs' work system was influenced by various factors and mainly by workplace environmental ergonomics (Ushada and Okayama, 2018). Determining the rest period could be adapted to reduce ergonomic risk at work (Tiacci, 2018).…”

Section: Introductionmentioning

confidence: 99%

Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics

Amalia¹,

Ushada²,

Pamungkas³

2023

IJTech

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Food SMEs (Small and Medium Enterprises) were examples of labor-intensive industry, which involved laborers in pursuing production activities. Food SMEs require complex processes in production activities. Support to increase work productivity and reduce ergonomic risks of the activities was needed. The study was conducted at Tofu SMEs. The determination of the rest period could be developed to give some recovery times to laborers. WBGT (Wet Bulb Globe Temperature) was estimated to determine the rest period. The rest period was determined by the workstation environment and workload labor. ANN (Artificial Neural Networks) model was carried out due to a nonlinear relationship. ANN was used to process the information from the data set and predict the amount of rest period and WBGT. ANN was trained using backpropagation. The backpropagation algorithm used the error value to change the weight with forward and backward propagation. The result showed that dry bulb temperature, heart rate, wet bulb temperature, and gender significantly impacted the rest period and WBGT. A total of 180 data sets from tofu SMEs were divided into training data (80%) and validation data (20%). The optimal ANN structure was determined by four input, four hidden, and two output neurons. The activation function was sigmoid for both layers. SSE (Sum of Squared Errors) was used to obtain the best structure. The value of R 2 was equal to above 0.900, which indicated that ANN could model the labor rest period based on environmental ergonomics.

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Section: Introductionmentioning

confidence: 99%

Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics

Amalia¹,

Ushada²,

Pamungkas³

2023

IJTech

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“…Ushada et al have developed the modelling of affective temperature control using Artificial Neural Network (ANN) (Ushada et al 2017b). Based on this modelling, hardware of Kansei Engineering-based Sensor for Agro-industry (KESAN) has designed to control environmental ergonomics (Ushada et al 2016). This sensor of KESAN completed the previous development of digital ergonomics to assess integrated workload (Ushada et al 2016) and (Ushada et al 2017c).…”

Section: Introductionmentioning

confidence: 99%

“…Based on this modelling, hardware of Kansei Engineering-based Sensor for Agro-industry (KESAN) has designed to control environmental ergonomics (Ushada et al 2016). This sensor of KESAN completed the previous development of digital ergonomics to assess integrated workload (Ushada et al 2016) and (Ushada et al 2017c). Ushada also concluded that digital ergonomic is expected to integrate the data of workload, supporting equipment, and environmental ergonomic for the sustainability of SMEs (Ushada 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Delgoshaei and Ali have combined hybrid Ant Colony Optimization and Tabu Search for location-allocation of skilled workers in systems of cellular manufacturing (Delgoshaei and Ali 2017). Ushada and Okayama have developed the tradeoff between green and affective work system for food SMEs (Ushada and Okayama 2016). Besides, Ushada and Okayama have used simulated annealing to optimize environmental parameter for the purpose of SME worker affective index (Ushada and Okayama 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Ushada and Okayama have developed the tradeoff between green and affective work system for food SMEs (Ushada and Okayama 2016). Besides, Ushada and Okayama have used simulated annealing to optimize environmental parameter for the purpose of SME worker affective index (Ushada and Okayama 2016). However, none of these research are applicable to assess environmental ergonomics in bioproduction of food SMEs.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs

et al. 2020

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Environmental ergonomics in bioproduction of food Small Medium-sized Enterprises (SMEs) become a concern and need to be optimized. An optimization model was developed using a Genetic Algorithm (GA). The weight of an Artificial Neural Network Model was used as a fitness function for GA. The research objectives were: 1) To design an environmental ergonomic assessment system for bioproduction of Food SMEs, 2) To develop an optimization model for environmental ergonomic assessment using a Genetic Algorithm. GA is utilized to search optimal set points of environmental ergonomics based on the predicted fitness values. Each chromosome of GA represents the environmental ergonomics value. The parameters were heart rate, bioproduction temperature, distribution of bioproduction relative humidity and light intensity. The target of the optimization model was the bioproduction temperature set points. The research result indicated the model generated optimum values of environmental ergonomics parameter in bioproduction of food SMEs. The parameters could be used to provide standard workplace environment for the sustainability of food SMEs.

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Development of Kansei Engineering-based System for Agro-industry (KESAN) for Worker Trust Assessment in Food Small Medium-sized Enterprises

Ushada

Sulistyo

Wijayanto

et al. 2020

Engineering in Agriculture, Environment and Food

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Trust influenced the attractiveness of ergonomic programs to Small Medium-sized Enterprises (SMEs) workers as individuals and groups as the collective. A framework of Kansei Engineering-based System for Agro-industry (KESAN) was proposed for worker trust assessment using a platform of Kansei engineering and artificial intelligence. A fuzzy inference model was used to process worker trust based on an input of ergonomic status against their mentality constraints of prior knowledge, familiarity, agreement, and preference. The research result indicated that fuzzy inference could simulate worker trust which was influenced by ergonomic status, mentality constraint, benefit agreement, and preference. This result indicated the possibility of using Kansei engineering and fuzzy inference to assess worker trust.

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Development of Green-Affective Work System for Food SMEs

Cited by 3 publications

References 10 publications

Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics

Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics

An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs

Development of Kansei Engineering-based System for Agro-industry (KESAN) for Worker Trust Assessment in Food Small Medium-sized Enterprises

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