A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a three-layer gene coding method, hierarchical crossover and mutation techniques, and the development of an adaptive operator that considered gene similarity and chromosome fitness values. The optimal and worst individuals were exchanged between the two subpopulations to improve the exploration ability of the algorithm. An orthogonal experiment was performed to obtain the optimal horizontal parameter set of the algorithm. Furthermore, an experiment was conducted to compare the proposed algorithm with a basic genetic algorithm, particle swarm optimization algorithm, and ant colony optimization, which were all performed on the same scale. The experimental results show that the fitness value of the proposed algorithm is above 15% stronger than the other 4 algorithms on a small scale, and was more than 10% stronger than the other 4 algorithms on a medium and large scale. Under the condition close to the actual scale, the results of ten repeated calculations showed that the proposed algorithm had higher robustness.

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%–6% and the utilization ratio of floor shop is increased by 2%–7%.

The Multi-load Automated Guided Vehicle (M-AGV) has emerged as a key enabling technology for intelligent and sustainable workshop logistics owing to its potential to enhance transportation efficiency and reduce system costs. To address the limitations in energy optimization caused by simplified AGV speed and payload modeling in existing scheduling models, this study develops a multi-factor coupled energy consumption model—integrating vehicle speed, travel distance, and dynamic payload—to minimize the total energy consumption of M-AGV systems. To effectively solve the model, a hybrid optimization algorithm that combines the State–Action–Reward–State–Action (SARSA) learning algorithm with the Triangulation Topology Aggregation Optimizer (TTAO), complemented by a similarity-based individual generation strategy, is designed to jointly enhance the algorithm’s exploration and exploitation capabilities. Comparative experiments were conducted across task scenarios involving three different handling task scales and three levels of M-AGV fleet heterogeneity, demonstrating that the proposed SARSA-TTAO algorithm outperforms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Hybrid Genetic Algorithm with Large Neighborhood Search (GA-LNS) in terms of solution accuracy and convergence performance. The study also reveals the differences between homogeneous and heterogeneous M-AGV fleets in task allocation and resource utilization under energy-optimal conditions.

Facility layout is not only the premise of production, but also a breakthrough for manufacturing industry to realize energy saving, environmental protection, and low entropy development. On the one hand, considering the interaction between product process routes and facility layout, a joint optimization model is proposed. The model aims to minimize the total logistics cost and consider the global optimization of facility layout and process route planning. On the other hand, considering the application of low entropy concept in facility layout, the analytic network process (ANP) is used to evaluate the low entropy layout. In the choice of the final facility layout, the algorithm results and expert knowledge are considered comprehensively to make up for the shortcomings of the model in the design of qualitative indicators. The algorithm innovation of this paper is to use genetic algorithm (GA) and particle swarm optimization (PSO) to search the solution of product process routes and facility layout simultaneously, to ensure the overall optimal solution of the two decision variables. Finally, an example is given to compare the joint optimization results with the independent optimization results, and the effectiveness of the joint optimization method is verified.

In this paper, the problem of integration and scheduling of automated guided vehicles (AGVs) and inspection machines in medical sample-testing laboratories is addressed with the objective of minimizing the maximum turnaround time (TAT). A genetic algorithm (GA) with a sliding time window (STW) heuristic algorithm (GA-STW) is proposed for the studied problem while incorporating the vehicle assignment algorithm (VAA) for decoding operations. A mathematical model for AGV and inspection machine integration scheduling is established, and a dual-coding method for the inspection process and machine is used to solve the model. VAA was used to assign suitable AGVs to each transportation task during the decoding stage. The STW heuristic algorithm was used to further optimize the initial solution and allocate samples to appropriate inspection machines while scheduling the shortest transportation time for AGVs. The crossover operators, mutation operators, and elite numbers were experimentally optimized. A numerical simulation of standard examples demonstrated the superiority of the proposed algorithm.