Green–lean supply chain management (GLSCM) refers to strategically adopting and coordinating environmentally sustainable practices and lean concepts in supply chain operations. A considerable set of factors needs to be identified to implement GLSCM successfully. This study examined the factors influencing green lean supply chain management implementation in the Readymade Garments Industries of Bangladesh through a literature review and discussions with field experts. The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) approach is employed to analyze these factors to implement GLSCM effectively. This research identifies capacity utilization, green purchasing, and demand variation as the most influential factors in GLSCM, while quality improvement and the Kanban system are considered the least important factors. This study explored categorizing factors into the cause-and-effect group, the degree of interaction, and the interrelationship of the factors under consideration. The findings of this study may help managers develop an effective GLSCM system, hence increasing an organization’s total profitability.

Pavement network conditions deteriorate over the years of use. To keep pavement conditions at acceptable levels, highway agencies plan pavement maintenance and rehabilitation (M&R) programs and perform accordingly. Highway agencies usually face budget variability for pavement M&R activities because of limited resources, economic conditions, and changes in policies. The situation makes it difficult for highway agencies to keep an acceptable pavement condition at the network level. Therefore, it is important for highway agencies to adopt M&R policies that can maximize the network condition as well as handle the deviation of the network condition considering the available maintenance funds. In this paper, a multi-period multi-objective linear integer programming model is proposed. Two objectives, maximization of the average network condition and minimization of deviation of the network condition from an idealized network condition trend, are considered in the formulation. The model is formulated for fixed M&R budgets, as well as for variable M&R budgets. The proposed model provides an M&R program for the pavement network that helps decision makers to manage pavement maintenance programs considering budgetary constraints. A case study examining a network of 45 pavement sections is conducted. The solutions of the fixed-budget and variable-budget model are presented. In addition, the values of the system to the decision maker are discussed. Results show that the proposed model is an attractive way to manage pavement maintenance programs at the network level.

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