A Fuzzy Inference Model for Social-Sustainability Production Planning

2021 ◽  
pp. 133-140
Author(s):  
Maximilian Zarte ◽  
Agnes Pechmann ◽  
Isabel L. Nunes
Keyword(s):  
Production Planning ◽  
Fuzzy Inference ◽  
Social Sustainability ◽  
Inference Model

scholarly journals Fuzzy Inference Model for Decision Support in Sustainable Production Planning Processes—A Case Study

2021 ◽  
Vol 13 (3) ◽  
pp. 1355
Author(s):  
Maximilian Zarte ◽  
Agnes Pechmann ◽  
Isabel L. Nunes
Keyword(s):  
Production Planning ◽  
Fuzzy Inference ◽  
Production Systems ◽  
Single Case ◽  
Inference Model ◽  
Health Crisis ◽  
Customer Expectations ◽  
Planning Processes ◽  
Climate Crisis

Due to crises (e.g., climate crisis, extinction of species, shortage of natural resources, human health crisis), customer requirements for conventionally produced products shift to more sustainably produced products, reducing and avoiding negative environmental and social impacts. Circular thinking in production systems offers new opportunities to meet these new customer expectations. However, it enlarges new challenges for production planning too. Research gaps exist in production planning approaches, considering all three sustainability aspects (economic, environmental, and social) simultaneously. This paper presents a concept of a fuzzy inference model (FIM) to assess the sustainability of production programs. The FIM concept is demonstrated and tested using a single case study considering lab production schedules. The model’s outcome indicates the most significant opportunities to improve production programs’ sustainability using experts’ knowledge.

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

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