Scheduling for sustainable manufacturing: A review

• Published in: Journal of cleaner production 2018-12, Vol.205, p.866-883
• Main Authors: Akbar, Muhammad, Irohara, Takashi
• Format: Article
• Language: English
• Subjects: Framework; Manufacturing; Scheduling; Sustainable; The triple bottom line
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2018.09.100

Most of the sustainable scheduling research deals with some environmental issues such as electricity consumption or carbon emissions although there are many other environmental and social indicators. Whereas, more and more manufactures are executing many solutions not only via implementation of environmentally-oriented but also socially responsible economically sound and management program. This proposed research has an objective to get a comprehensive view of the state and progress of sustainable scheduling by considering more sustainable indicators to assist many manufacturers to apply environmentally sound technological solutions. To achieve this objective, we, first, create a pool of sustainable manufacturing indicators that refers to some previous research that focus on applicable-proven indicators. Then, we employ an iterative process that is adapted from the general guidelines for literature review. We conceptualize, search, evaluate, analyze, and synthesize all 50 relevant papers resulting a completely new research framework to characterize the research. We use mathematical model components, i.e. manufacturing model, the system of objectives, objective function, constraints, model type, and optimization method, as the dimensions to classify the papers that were included in our analysis. We also apply the triple bottom line pillars – economic, environmental, and social to specify attributes and categories for the objective function and constraints. In literature classification step, the framework seems very sensitive since it can identify 49 different sustainable scheduling configurations from 50 reviewed papers. It means the framework can easily grasp the contributions of each paper. Then, in the analyses phase, we use ‘sustainable link’ to indicate if one research can be classified as sustainable scheduling research. The results show that energy cost and greenhouse gas indicators become the most frequently used indicators in sustainable scheduling. On the other hand, the mapping shows that some links have been rarely or never included, indicating potential areas for further research. We also list four main directions for future research which are: implementing other optimization methods; adding sustainability indicators; extending the model to a larger scale of manufacturing system; and loosening some assumptions.