With increasing concerns and challenges to climate change in recent years, green operations management (GOM) has gained significant attention from society for achieving sustainable growth. GOM is a set of practices that can be applied in production processes to produce goods with improved productivity and significantly reduced threats of carbon emission to the environment and Mother Nature. GOM mainly includes green manufacturing, green design, green logistics, and green purchases. In the paper, fuzzy best-worst method (FBWM) is used to determine the best and worst criteria affected by GOM practices. Thus, the paper attempts to explicitly analyze and highlight the significance of GOM in preserving the environment and manage the triple bottom line for achieving overall sustainable business operations.

The increasing demand for good and improved polymeric composites has led to a surge in the number of researches on hybrid composites, strengthened and enforced with the natural fibres. This paper mainly analyses and presents the attributes of hybrid composites made from natural fibres and carbon nano-tube (CNT) nanoparticles. A novel hybrid composite considered in this research includes kenaf and coir fibres with CNT nanoparticles embedded in an epoxy matrix. The proposed hybrid nanocomposite’s elastic features are calculated by using different analytical models like Chamis, Mori-Tanaka, Nielson elastic models etc and also with the help of Representative Volume Element Analysis (RVE). The content of fibre volume is varied in four different samples and it is found that upon varying the content of fibre volume, the mechanical properties like longitudinal modulus and transverse modulus got affected. The results evaluated from different analytical models are observed to be in good agreement with each other and also with the results of RVE analysis.

This paper compares and evaluates the performance of five different conventional job selection dis-patching rules for scheduling of multi-load automatic guided vehicles (AGVs) serving for material handling operations in variable sized flexible manufacturing system (FMS) layout. Four sizes of FMS layout are considered for the performance evaluation of the five types of conventional job se-lection dispatching rules. The FMS layouts under consideration are served by the two multi-load AGVs. The multi-load AGVs cruises under machine initiated the nearest vehicle (NV) dispatching rule for the material handling activities at all work centers (WCs) for all four sizes of FMS layout. Four sizes of FMS layout produce five different types of parts and consist of three, six, nine and twelve work centers and loading-unloading centers, respectively. In the simulation test, it is found that the identical destination first (IDF) job selection rule having selection criterion based on the destination similarity of two picked up jobs outperforms all other job selection dispatching rules for an overall production rate of the FMS (parts/hr) in all four FMS layouts.

Automatic guided vehicle system (AGVs) plays a vital role in material handling operations for a flexible manufacturing system (FMS).Optimum AGVs fleet size selection is one of the most sig-nificant decisions in effective design and control of automated material handling system. The fleet size estimation and optimization of AGVs requires an in-depth understanding of the various factors that AGVs in the FMS relies on. In this paper, an investigation for fleet size optimization of AGVs in different layouts of FMS by application of the analytical method and grey wolf optimization al-gorithm (GWO) is carried out. Layout design is one of the significant factors for optimization of AGV’s fleet size in any FMS. Results yield from analytical and grey wolf optimization algorithm are compared and validated for the different sizes of FMS layouts by computational experiments.

The application of material handling robots (MHRs) has been commonly observed in flexible manufacturing systems (FMS) for efficient material handling activities. In order to gain maximum throughput, minimum tardiness from the minimum investment of funds for the material handling activities, it is important to determine the optimum numbers of MHRs required for efficient production of jobs in the FMS. In the present work, the requirement of MHRs is optimized for different FMS layouts by using a heuristic procedure. Initially, a mathematical model is proposed to identify the MHRs requirement to perform the material handling activities in the FMS, later on, the model is optimized by simulating a novel heuristic procedure to find the required optimum number of MHRs in the FMS. The proposed methodology is found to be generic enough and can also be applied in various industries employing the MHRs.

The simultaneous scheduling decisions between production systems and material handling systems are highly significant for a substantial reduction in makespan and improvement in throughput of flexible manufacturing system resources. In the absence of appropriate scheduling of production resources, the optimum utilization of FMS resources is not harnessed which turns into wastage of resources. In the present study, investigations are carried out for the sustainable multi-objective scheduling of automatic guided vehicle and flexible manufacturing system by the application of a grey wolf optimization algorithm (GWO). Initially the Giffler and Thompson (GT) algorithm [Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations research, 8(4), 487-503.] along with four different priority hybrid dispatching rules (PHDRs) are applied for the development of the production center schedule thereafter the grey wolf optimization algorithm is applied for the yield of the sustainable multi-objective schedul-ing of automatic guided vehicles (AGVs) and the FMS together with an objective to minimize the total distance travel and number of backtracking of cruising automatic guided vehicle in the U type flexible manufacturing system facility. The applied methodology is evaluated by conducting computational experiments on a benchmark flexible manufacturing system configuration considered from the literature. The results obtained from the computational experiments clearly show that the proposed application of grey wolf optimization algorithm outperforms the other applied procedures in the literature.