In recent years, issues on global warming and climate change have received public attention. One of the causes of this problem is carbon emissions in the transportation sector. Therefore, a proper route can reduce this problem. This article aims to develop the Hybrid Whale Optimization Algorithm (HWOA) to minimize the distribution cost of the Green Vehicle Routing Problem (GVRP). The proposed algorithm is based on the Whale Optimization Algorithm (WOA) algorithm combined with the Tabu search algorithm and local search procedures. In the proposed HWOA algorithm, 10% of the whale search agents were adjusted according to the Tabu Search heuristic procedure. A local search was applied to improve the algorithm's quality. Furthermore, a numerical experiment is carried out to test the proposed algorithm. The experiments are conducted to determine the effect of parameters and various speeds on total distribution cost and computation time. Moreover, The proposed algorithm is also compared to some previous algorithms. The findings showed that HWOA is more competitive in minimizing the total distribution cost of GVRP as compared to other algorithms.

In the industrial sector, transportation plays an essential role in distribution. This activity impacts climate change and global warming. One of the critical problems in distribution is the green vehicle routing problem (G-VRP). This study focuses on G-VRP for a single distribution center. The objective function is to minimize the distribution costs by considering fuel costs, carbon costs, and vehicle use costs. This research aims to develop the hybrid butterfly optimization algorithm (HBOA) to minimize the distribution costs on G-VRP. It was inspired by the butterfly optimization algorithm (BOA), which was by combining the tabu search (TS) algorithm and local search swap and flip strategies. BOA is a new metaheuristic algorithm that has been successfully applied in various engineering fields. Experiments were carried out to test the parameters of the proposed algorithm and vary the speed of vehicles. The proposed algorithm was also compared with several procedures of prior study. The experimental results proved that the HBOA could minimize the total distribution cost compared to other algorithms. Moreover, the computation time is also included in the analysis.

This study aims to review the literature in reducing carbon emission from supply chain system for the past few years and presenting drivers, barrier, performance indicator, and practice. The issue of reducing carbon emission become frequently discussed since unplanned and irresponsible actions by industries are potential threats to sustainability. Companies are now trying to minimize environmental impacts by integrating environmental concerns into their supply chain operations. In the aim of reducing carbon emissions, the industry is faced with different drivers, barriers, and performance indicator. With a variety of problems faced, the practices taken to reduce carbon emissions are very diverse. Thirty papers were collected from previous studies and linked to discussion points. The result showed that good coordination with various media in the supply chain system would be able to achieve common goals in reducing gas emissions.

An adequate sustainable production inventory model is expected to represent complex real-life cases involving fuel, emissions, and electricity costs as well as multi-materials, quality degradation, and probabilistic demand. Therefore, this study was conducted to develop this kind of model to determine the number of raw material shipments (), production cycle time (), and the number of finished goods delivered (n) to maximize the Expected Total Profit (ETP). The proposed model is based on a bibliometric literature analysis of the sustainable production-inventory problem which is visualized using the VOSviewer. Moreover, a sophisticated Harris-Hawks Optimization (HHO) algorithm was proposed to solve the problems identified in the sustainable production inventory model optimization. It is also important to note that three numerical cases were provided to evaluate the performance of the algorithm. The findings showed that the suggested HHO method outperforms the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in maximizing ETP and this means it is better for ETP optimization. It was also discovered from the sensitivity analysis that an increase in the rate of quality degradation (k) led to a reduction in both the ETP and T.

Scheduling problems should not only focus on minimizing completion time, but other performances, such as minimizing energy consumption, are urgent to investigate. Many companies and researchers are competing to develop various methods to solve this problem. One problem that has received much attention lately is the energy-efficient Hybrid Flow Shop Scheduling Problem (HFSP). The HFSP problem is also popularly called the flexible flow shop problem. This research provides a comprehensive review of articles that discuss energy-efficient HFSP scheduling to determine the trend of energy-efficient HFSP research and research gaps for future research interests. This review uses a systematic literature review method. The total articles reviewed in this study are 90 articles from January 2008 - December 2022. The articles are classified by year, country, journal/conferences, publisher, objective function, and method. The analysis results show that the trend of energy-efficient HFSP research continues to increase. In addition, the multi-objective problem is the dominating problem in energy-efficient HFSP scheduling problems. The study also presents a gap research analysis and a review of future research directions.