In this study, we discuss the problem of permutation flowshop scheduling problem (PFSP) to reduce total energy consumption (TEC). We offer a new hybrid meta-heuristic algorithm for solving the problem. The paper aims to combine the cross entropy and genetic algorithm (CEGA) with the simulated annealing (SA) algorithm. The CEGA is applied to find the best initial solution inside the SA algorithm and the proposed algorithm is compared to previous tests of the famous NSGA-II and GA-SA algorithm. During study of the numerical test, the proposed algorithm genuinely useful is compared certain efficient algorithms of the from previous research.
This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.
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.
Recently, The industrial sector produces about half of the worlds total energy consumption. Manufacturing companies are required to reduce energy consumption. This article aims to develop a Hybrid Whale Optimization Algorithm (HWOA). We use the objective function of minimizing energy consumption. It solves the problem with permutation flow scheduling problems (PFSSP). Dependent sequence setup is a PFSSP problem with setups that depend on schedule sequence. We offer HWOA with local search strategies. The solution in each HWOA iteration is improved using flip and swap mutations. Furthermore, HWOA is compared with several algorithms. We use numerical experiments to show the performance of the proposed algorithm. Comparative analysis with several algorithms has previously been carried out with ten variations of PFSSP problems. Based on numerical experiments, HWOA proved to be competitive compared to other algorithms.
The energy crisis has become an environmental problem, and this has received much attention from researchers. The manufacturing sector is the most significant contributor to energy consumption in the world. One of the significant efforts made in the manufacturing industry to reduce energy consumption is through proper scheduling. Energy-efficient scheduling (EES) is a problem in scheduling to reduce energy consumption. One of the EES problems is in a flow shop scheduling problem (FSSP). This article intends to develop a new approach to solving an EES in the FSSP problem. Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to resolve the EES issue on FSSP by considering the sequence-dependent setup. Swap and flip procedures are suggested to improve HHO performance. Furthermore, several procedures were used as a comparison to assess hybrid HHO performance. Ten tests were exercised to exhibit the hybrid HHO accomplishment. Based on numerical experimental results, hybrid HHO can solve EES problems. Furthermore, HHO was proven more competitive than other algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.