A pollution sensitive dense fuzzy economic production quantity model with cycle time dependent production rate. (15th June 2017)

• Record Type: Journal Article
• Authors: Karmakar, Snigdha; De, Sujit Kumar; Goswami, A.
• Is Part Of: Journal of cleaner production. Volume 154(2017)
• Page Start: 139
• Page End: 150
• Publication Date: 2017-06-15
• Subjects: Production inventory -- Backorder -- Deterioration -- Pollution -- Risk -- Preservation technology -- Triangular dense fuzzy number; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.03.080
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In recent times, environmental pollution is one of the most critical issues of any industrial set up throughout the globe. The excess of marginal pollution and risk for unsold goods (like chemical and plastic items etc.) and their decomposition play a vital role in any decision-making process. Also the uses of automation/technology in pollution control measures contributes to another kind of pollution drastically. Moreover, the waste products as well as factory outlet sewage water itself have some negative externalities on the environment. Keeping this fact in mind, we first define a new functional relation between environmental pollution rate and production rate. Then we describe an economic production quantity model with deterioration for cycle time dependent production rate. We include preservation technology cost to restrict the normal deterioration. Considering a real data set obtained from the case study of a sponge iron industry, we analyze the crisp model first, then we go for its fuzzification. Inspiring from the learning effects we have utilized a new fuzzy inference scheme based on the dense fuzzy rule. However, our findings suggest that to restrict pollution and financial loss, the decision-maker might have to control the gross production of the industry. Finally, sensitivity analysis and graphical illustrations are also provided to justify the model. Graphical abstract: Highlights: EPQ model with deterioration for cycle time dependent production rate with proper implementation of preservation technology. Functional relation between environmental pollution rate and production rate has been derived. Environmental risk factor due to excess production has been considered. Dense fuzzy theory (inspired from learning effect) in inventory has been introduced.