Analysis of queue-level dynamics reveals more regular behavior in the evaluated scenarios, with reduced fluctuations in queue lengths, batch waiting, and minimum queue entropy over time, indicating that the proposed ABC-based approach can improve observed predictability at the queue level.
Abstract
This paper addresses the Job-Shop Scheduling Problem in dynamic semiconductor manufacturing environments by proposing a decentralized, bottom-up Artificial Bee Colony (ABC) scheduling algorithm. Machines and lots are modeled as autonomous agents whose local interactions give rise to system-level scheduling behavior. Alongside classical scheduling objectives, the proposed approach focuses on regulating production dynamics by maintaining sufficient diversity in machine queues, formalized through entropy-based measures. Bottlenecks are treated not only as a consequence of static capacity constraints relative to work in progress, but also as emergent effects of short-term demand concentration, where multiple lots converge toward the same resources within limited time horizons. To manage these effects, a fitness formulation is introduced that promotes balanced queue states through local decision-making. Scheduling foresight is incorporated via a Look-Ahead Window, while uncertainty in distant future routing is accounted for using a decay factor, jointly enabling adaptive prioritization under bounded computational effort. Simulation-based evaluation across fabrication scenarios of increasing scale shows that the method achieves modest improvements in Flow Factor and Tardiness, while inducing an expected trade-off in Makespan under higher load conditions. More importantly, analysis of queue-level dynamics reveals more regular behavior in the evaluated scenarios, with reduced fluctuations in queue lengths, batch waiting, and minimum queue entropy over time. These results indicate that the proposed ABC-based approach can improve observed predictability at the queue level, offering a complementary perspective to performance-driven scheduling in highly dynamic environments.
A memory-guided adaptive feature genetic programming algorithm that evolves interpretable heuristic dispatching rules while handling stochastic arrivals and iterative grey-time updating efficiently and can reduce the simulation budget needed for rule evolution, support managerial inspection of scheduling logic and reduce reliance on expert-designed heuristics.
Nanlei Chen, Bentao Su· Grey Systems Theory and Appl...· 0 citations
This study addresses a distributed flexible job shop scheduling problem with machine degradation and state-driven imperfect preventive maintenance, denoted as MD-SDIPM-DFJSP. A bi-objective model is developed to minimize makespan and total energy consumption by jointly optimizing job assignment, operation sequencing, machine selection, and processing speed. The model links processing speed with processing time, power consumption, and degradation increment, and uses a unified degradation bound to represent both degradation and reliability constraints. Preventive maintenance is treated as an imperfect recovery action and is generated according to machine states and idle-window conditions. To solve the problem, a degradation-aware multi-objective memetic algorithm (DMA) is proposed, incorporating four-layer encoding, state-driven decoding, hybrid initialization, knowledge-guided neighborhood search, and a speed-based adjustment operator. Numerical experiments show that Gurobi solved the small instance to optimality with a 0% optimality gap, and the resulting schedule satisfied the modeled production, maintenance, degradation, reliability, and energy accounting requirements. Across 84 combinations of instances and factory sizes, DMA achieved the highest HV in 72 cases and the lowest IGD in 62 cases. The Wilcoxon tests further confirmed its overall advantages over the three comparison algorithms in terms of both HV and IGD.
Li Liu, Chenhao Gu, Kaifeng Geng· Computers· 0 citations
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
: This paper presents a hybrid scheduling framework for Reconfigurable Production Systems (RPS) that integrates Timed Petri Nets (TPNs) with three heuristic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). TPNs are employed to formally model system dynamics, resource constraints, and task dependencies, while heuristic algorithms optimize scheduling decisions to minimize makespan, reduce tardiness, and improve resource utilization. By combining formal system modeling with adaptive optimization, the proposed approach ensures both schedule feasibility and operational efficiency in dynamic manufacturing environments. Experimental results demonstrate that the framework outperforms traditional scheduling rules, including FCFS, SPT, and EDD, achieving a 25 – 30% reduction in makespan and more than 20% improvement in resource utilization. Furthermore, the integration of Petri-net state feedback with heuristic search enhances adaptability and robustness under reconfiguration events. The proposed framework provides an effective decision-support solution for intelligent production scheduling and contributes to the development of flexible and efficient Industry 4.0 manufacturing systems.
Salah Hammedi, Haythem Chniti, L. Nabli· International Conference on...· 0 citations
This work addresses the resulting Multi-Objective Flexible Job Shop Scheduling Problem by proposing a deep reinforcement learning framework that jointly minimizes makespan and energy cost, and evaluates the approach against NSGA-II and Joined Heuristics on synthetic instances.
Dustin Moreira Simoes, Marvin Brune, Mehmet Ulrich et al.· Applied Sciences· 0 citations
Green manufacturing increasingly requires production systems to balance operational performance with energy efficiency and cost-effectiveness. This paper addresses an energy-aware flexible flow shop scheduling problem in which a factory operates under a time-of-use electricity tariff, participates in a demand response reward program, exploits on-site photovoltaic generation, battery energy storage, and applies an on/off machine strategy to reduce idle power consumption. Two objectives are optimized in lexicographic order: makespan as the primary criterion and net energy cost, electricity expenditure minus demand response rewards collected, as the secondary one. A Genetic Algorithm is proposed, combining an order-crossover permutation encoding, an earliest-finish-time decoder, and an energy management heuristic. Results show that the approach produces compact, energy-efficient schedules, while a sensitivity analysis highlights the strong impact of demand response incentives on reducing net energy costs without affecting production throughput.
J. Mhanna, H. Nouinou, S. Caillard et al.· International Conference on...· 0 citations
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