Continuously increased order and variability of the inventory in the uppermost level of the supply chain node create the Bullwhip effect. In the context of closed-loop supply chains, this dynamic phenomenon is still little understood despite modern nations' increasing interest in exploring the potential for a circular economy. The problem-specific literature has produced results that are a little bit contradictory. I derive formulas in four archetypes for computing inventory order and variance amplification with different information transparency structures to better understand the Bullwhip Effect in the closed-loop structure. It’s interesting to note that the visibility of the supply chain's degree significantly influences how lead time and return rate affect the performance of that system. From this vantage point, I may review differences from earlier studies. Later on, I switched the perspective of the study from operational to economic. Here, the ideal return rate was established, and the four closed-loop supply chain (CLSC) archetypes where it might be expressed were provided. I demonstrate that the lead times, demand unpredictability, and the cost structure of all nodes affect the ideal rate of return. In this study, I also address pertinent management implications and the properties of various closed-loop systems from the perspective of Bangladesh.

A multi-objective Flexible Job-shop Scheduling technique for hospitals is proposed using DPSO-AIW i.e. discrete particle swarm optimization with adaptive inertia weight method. The approach encodes the layer of the chromosomes using an operation sequence (OS) and machine assignment (MA) which is a two-layer coding structure. Global selection based on the operation (GSO) of MA and random selection of OS are coupled in the initial population. Rapid non-dominated sorting yields fronts of non-domination, which are necessary for getting the Pareto optimum solution. The diversity of the population is increased during the evolution process by adaptive adjustment of the variation of the weight of inertia, expressed by ω. Then, the Pareto optimal solution found during the process is kept in the Pareto optimal solution set (POS). The discrete particle swarm optimization algorithm is utilized to solve the values of the next generation chromosomes in the discrete domain directly. Lastly, comparisons with certain current techniques and numerical simulation based on two sets of international standard examples are performed, which are already established. The findings from the comparison show that the suggested DPSO-AIW is practical, effective, and more feasible for solving the problem related to the Multi-objective Flexible Job-shop Scheduling Problem.

The footwear industry is booming with the increasing demand for leather-based products and manufacturers from the Asia region are leading the market. In the meantime, the negative impact on the environment of footwear goods and manufacturing processes of footwear-based products concerns the buyers and consumers as the global environmental situation is worsening day by day. For this reason, footwear industries are bound to use environmentally friendly production techniques to cope with the conditions as well as demands of the buyers otherwise the manufacturer can face the drop of order which can be affected financially. The Bangladeshi footwear industry is most unlikely to adopt sustainable manufacturing and thus lagging in the competitive market containing manufacturers from other Asian countries. The reason behind not implementing sustainable manufacturing practices should be explored to ensure this practice. In the study, Graph Theory and Matrix Approach (GTMA) was utilized along with the best and worst matrix using the weighted values as the diagonal and off-diagonal elements which was found from the two surveys after consultation with experts from the Bangladeshi footwear industry to gather responsible barriers for both large-scale and small-medium sized enterprises. Ranking based on impact was developed for two scales and an index value was found from the calculations of the matrix formed by permanent functional value found for each of the barriers and their best-worst values also which was another determinant of this study for expressing the fitness level for this certain adaptation. Similarity coefficient was another factor to be determined for the decision making required for the first objective of this research.