2026· Journal of Engineering, Project, and Production Management· 0 citations
TL;DR
A Adaptive Genetic Algorithm (AGA) is designed to solve the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions.
Abstract
With the rise of the concept of green manufacturing, incorporating energy consumption-related objectives into scheduling problems has become an important research field. Combined with actual production scenarios, this study constructs a mathematical model for the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions. To address the limitation of the traditional Genetic Algorithm (GA) in terms of insufficient local search capability, an Adaptive Genetic Algorithm (AGA) is designed to solve the model. A population initialization method that integrates global and local load minimization is proposed to accelerate the elimination of inferior individuals; the elite retention and roulette wheel selection strategies are combined to prevent the algorithm from falling into local optima. Simulation tests based on standard benchmark instances show that the improved GA can effectively solve the MO-FJGSP, significantly improving both the solution speed and quality. This study provides a novel methodological approach to optimizing production scheduling in green manufacturing environments.
The Job Shop Scheduling Problem (JSP) is a core decision-making issue for improving production efficiency in discrete manufacturing industries. Traditional genetic algorithms (GAs) used to solve JSP suffer from bottlenecks such as a high number of invalid solutions and difficulty in balancing solution accuracy and convergence speed. To address large-scale JSP under dynamic machine fault disturbances, this study proposes an improved genetic algorithm integrating hybrid encoding and customized operators. Specifically, a hybrid encoding strategy combining job sequences and machine sequences is adopted to naturally satisfy the process and equipment constraints of JSP. The evolutionary process is optimized using tournament selection, Position-based Order Crossover (POX), and mutation within the valid domain, while a fault identification and machine switching mechanism is integrated to adapt to dynamic disturbance scenarios. Experimental results show that the improved algorithm achieves an optimal Makespan value of 190 in dynamic disturbance scenarios and exhibits strong robustness, providing an efficient and feasible solution for job shop scheduling in complex production environments.
Jianguo Du, Chengkun Li, Zijie Tang· ITM Web of Conferences· 0 citations
Aiming at the engineering pain points of difficult solving in practical production, slow convergence of traditional genetic algorithms, and easy getting stuck in local optima, an improved genetic algorithm with the goal of minimizing the maximum completion time has been designed. By improving coding, crossover, and introducing roulette wheel selection methods, the overall algorithm's global optimization capability is enhanced. The comparison of experimental results shows that the improved genetic algorithm is superior to the traditional genetic algorithm in optimizing the target solution.
Junlan Feng· International Journal of Edu...· 0 citations
The dynamic scheduling of ready-mixed concrete constitutes a critical bottleneck in construction automation. Following the design science paradigm and informed by a systematic literature review, this study develops the multistrategy ant colony optimization (MSACO) algorithm, which integrates three mechanisms: adaptive pheromone evaporation, elite ant guidance, and genetic mutation. Empirical validation based on the road network of a major Chinese city (involving four batching plants, six customer sites, and a fleet of seven fuel vehicles and five electric vehicles) demonstrates that MSACO significantly outperforms algorithms including the genetic algorithm, ant colony optimization, particle swarm optimization, and multistrategy adaptive ant colony optimization in terms of solution accuracy, convergence speed, and stability. The proposed algorithm achieves an average reduction in distribution costs of 7.06%, with advantages reaching 10.7% under highly constrained conditions (
p
<
10
−
7
). The main contributions are threefold: it proposes a triple adaptive mechanism tailored for dynamic scheduling scenarios; formulates a mathematical model incorporating plant capacity, load limits, and electric vehicle range; and provides a quantifiable basis for the digital transformation of construction logistics.
Yang Guan, Ge Shi, Jie Yang et al.· Journal of construction engi...· 0 citations
This paper investigates the energy-efficient hybrid flow shop scheduling problem considering deteriorating maintenance and transportation times (EHFSP-DMT), which is an NP-hard combinatorial optimization problem in complex manufacturing systems. To formulate this problem, a mixed-integer programming mathematical model is established. A computational intelligence approach, named the adaptive feedback multi-start variable neighborhood search (AFMS-VNS) algorithm, is proposed to simultaneously minimize the makespan and total energy consumption. In AFMS-VNS, a decoding strategy integrating conflict detection is designed to satisfy the mathematical constraints of equipment maintenance. A reinforcement learning (Q-learning) mechanism evaluates and adaptively selects search operators to enhance search efficiency. A search strategy based on the feedback of population distribution status is proposed to balance the optimization directions between the two conflicting objectives. An iterated greedy reconstruction strategy, guided by an elite external archive, is adopted to replace stagnant individuals. Experiments on 44 instances and Wilcoxon signed-rank tests show that AFMS-VNS outperforms five comparison algorithms in terms of inverted generational distance, hypervolume, and set coverage when solving the EHFSP-DMT.
Yan Wang, Yabo Wei, Huanli Zhao et al.· Mathematics· 0 citations
A hybrid algorithm combining two metaheuristics is proposed to solve the flow-shop scheduling problem, aiming to minimise the makespan (Cmax). This approach accounts for random machine failures and limited buffer capacity between machines. Since flow-shop scheduling problems are NP-hard, the metaheuristics could be used to solve them effectively. Researchers proved that the hybridisation of metaheuristics would improve the solution quality. Therefore, this study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm. The initial solutions are generated using a dynamic generation technique that relies on a set of constructive heuristics. The optimal solutions from the PIOA serve as input for the ABC algorithm. Various local search and variable neighbourhood search methods are also included to enhance solution quality. Extensive computational experiments, which focus on industrial scheduling scenarios and benchmark problem instances, are conducted to test the performance of the hybrid algorithm. Statistical analysis shows that the proposed algorithm outperforms other algorithms found in the existing literature.
M. K. Marichelvam, M. Geetha· Computers· 0 citations
The buffer allocation problem (BAP) is one of the most important problems in production line design and optimization. Under the constraint of total buffer size, find the optimal buffer configuration to maximize the Production Rate (PR) of unreliable production lines. BAP is an NP-hard combinatorial optimization problem, and its solution space grows exponentially with the size of the problem. Therefore, metaheuristic algorithms are widely used to solve BAP. In this study, we propose a hybrid adaptive genetic algorithm (AGA) and simulation-based approach to solve BAP, using a simulation model that simulates production line behavior to evaluate the applicability of each solution. Conduct numerical experiments on existing benchmark problems for unreliable production lines of different scales. The proposed method is compared against scenarios with no buffers, average buffer allocation, and existing methods from the literature. The purpose is to demonstrate the effectiveness of the optimal buffer configuration. For the four types of production lines, the PR of the proposed method are 0.87695, 0.50860, 0.65171, and 0.70492, respectively, which are 9.75%, 40.76%, 121.55%, and 137.92% higher than those without buffer zones, and are superior to the three benchmark methods. It can also enhance the utilization rate of the device, with improvement effects of 9.45%, 49.44%, 127.87%, and 140.14%, respectively. The AGA can shorten the optimization time, making up for the shortcomings of simulation methods. The simulation results show that our method is effective in maximizing PR.
Bin Huang, Chunhui Ji, Mingyang Tan· IEEE Access· 0 citations
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