A multi-objective model for cleaner production-transportation planning in manufacturing plants via fuzzy goal programming. (July 2017)

• Record Type: Journal Article
• Authors: Mokhtari, Hadi; Hasani, Aliakbar
• Is Part Of: Journal of manufacturing systems. Volume 44:Part 1(2017)
• Page Start: 230
• Page End: 242
• Publication Date: 2017-07
• Subjects: Manufacturing systems -- Environmental effects -- Supply chain management -- Energy saving; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2017.06.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: We propose clean manufacturing by considering wastes, gas emissions, noise disturbance, workers' damage and energy consumption. An integrated production-transportation planning is considered. A multi-objective optimization model is developed. The fuzzy goal programming approach is applied. Extensive computational experiments as well as real case studies are considered to evaluate the quality of algorithm. Abstract: In traditional supply chain management, manufacturers aim solely to reach a cost-effective way to meet customers' demands. Nowadays, increased social awareness together with new governmental regulations are forcing manufacturers to reconsider their production and transportation plans with respect to environmental issues. This paper proposes a multi-objective optimization model for a cleaner production-transportation planning (CPTP) problem in manufacturing plants. When a manufacturer is planning for production-transportation of mid-term future, the aim is to determine optimal production level, inventory level, back order level, workforce level, transportation mode, overtime, and subcontracted products, while attempting to minimize production and transportation costs as well as environmental effects (e.g. generated wastes, gas emissions, noise disturbance, workers injuries, and energy consumption). To handle the original optimization problem, fuzzy goal programming is adopted and heuristic algorithms are designed. Moreover, extensive computational experiments as well as real case studies are considered to evaluate the quality of algorithms and applicability of the proposed model.