The researchers formalized their challenge as a dynamic multi-objective fuzzy flexible job-shop scheduling problem, abbreviated DMFFJSP. In a flexible job shop, each job consists of a sequence of operations, and each operation can be assigned to one of several machines, giving the scheduler a vast space of possible assignments and sequences. The “fuzzy” element reflects reality: processing times are not exact numbers but uncertain ranges, because tool wear, operator variability and material differences conspire to blur any estimate. The “dynamic” element captures the fact that events, including new orders, changed workshop processing information, deviations in job processing times and insufficient energy supply, strike while the schedule is running, invalidating whatever optimal plan existed before the disruption.

To handle this, the team built a model they call RE-MOFFJSP, a multi-objective fuzzy flexible job-shop scheduling model wrapped in a rescheduling mechanism. The model explicitly integrates a microgrid energy system into the scheduling framework, so that the optimizer sees not only machines and jobs but also the fluctuating availability and cost of electricity. Four optimization objectives are pursued simultaneously, spanning production efficiency and the energy-related costs that green manufacturing demands. Because these objectives can conflict, for example, finishing jobs faster may require running machines during expensive peak-power periods, the problem is treated as multi-objective, seeking a set of balanced compromise solutions rather than a single answer.

The rescheduling mechanism is the framework’s nervous system. When a dynamic event occurs, heuristic rules decide how to respond: whether the disturbance is small enough to absorb with minor adjustments or severe enough to warrant rebuilding the schedule from scratch. This distinction matters because wholesale rescheduling is expensive in both computational effort and practical disruption, forcing machines and operators to abandon their current rhythm. The heuristic approach filters events by their impact, preserving stability when possible while guaranteeing adaptability when the situation genuinely demands it. The researchers also introduced an energy-cost-driven machine restart strategy, which powers machines down and back up in response to energy conditions, trimming both production costs and wasted resources rather than letting equipment idle and burn electricity.

Solving the resulting optimization problem requires serious algorithmic muscle, and here the authors turned to nature for inspiration. They designed an improved golden jackal optimization algorithm, a metaheuristic that mimics the cooperative hunting behavior of golden jackals. The improved version, called IGJO, layers three classic multi-objective optimization techniques onto the base algorithm: non-dominated sorting to rank solutions by Pareto dominance, crowding distance computation to maintain a well-spread front of trade-off solutions, and tournament selection to drive the population toward better regions of the search space. This combination pushes the algorithm to explore widely across the enormous combinatorial landscape of machine assignments, operation sequences and energy decisions while converging on solutions that balance all four objectives.

Why a jackal? Golden jackal optimization, introduced relatively recently, has shown competitive performance on complex continuous and discrete problems, and variants of it have already been applied to tasks from image matching to reliability optimization of hydraulic systems. The Guizhou team’s adaptation is notable because it converts the algorithm to the discrete, constrained world of job-shop scheduling, where solutions must respect precedence relations between operations, machine capacity limits and the fuzzy timing constraints of the model. The non-dominated sorting framework means the output is not one schedule but a Pareto front: a menu of schedules, each representing a different compromise between completion time, energy cost and the other objectives, from which a plant manager can choose according to current priorities.

To test the framework, the researchers ran a case study of an equipment manufacturing plant, comparing RE-MOFFJSP against other scheduling methods under the same dynamic conditions of fluctuating energy supply and production disturbances. The results showed significant advantages across all four optimization objectives, confirming both the efficacy of the model and the superiority of the improved algorithm. In practical terms, the method produced schedules that kept jobs moving despite processing-time errors, adapted smoothly to order changes, and exploited moments of abundant or cheap renewable energy, exactly the kind of behavior a factory needs when its power comes from a microgrid rather than a steady utility feed.

The broader context makes this work timely. A large body of recent research has attacked pieces of this puzzle separately: some studies optimize job-shop schedules under time-of-use electricity tariffs, shifting heavy operations to cheap hours; others handle machine breakdowns with reactive or predictive-reactive strategies; still others tackle fuzzy processing times or energy-efficient flexible job shops. The Chinese team’s contribution is integration, combining energy fluctuations, multiple kinds of production disturbances, fuzzy timing and a formal rescheduling mechanism in a single framework, rather than treating each uncertainty as an isolated problem. The authors note that enterprises pursuing green, low-carbon manufacturing are often constrained by the fluctuating characteristics of renewable energy, and that adapting internal workshop tasks flexibly to these dynamics has become a hot research issue.

The work was supported by the Graduate Research Fund of Guizhou Province, the Guizhou Provincial Basic Research Program and the National Natural Science Foundation of China, and the authors declare no competing financial interests. For an industry watching energy prices swing and climate commitments tighten, the message is striking: the smart factory of the near future will not simply schedule work, it will negotiate continuously with an unpredictable grid, powered by algorithms that watch both the weather above the solar panels and the wear on the machine tools below. If frameworks like RE-MOFFJSP move from the journal page to the plant floor, the phrase “production schedule” may soon mean something far more alive than the printed sheet taped to a workshop wall.

Subject of Research: Dynamic multi-objective fuzzy flexible job-shop scheduling under fluctuating energy supply and production disturbances

Article Title: A self-adaptive scheduling method with rescheduling mechanism for multi-objective fuzzy flexible job-shop scheduling problem in dynamic energy and production environments

Article References: Wang, X., Lu, Y., Ma, Q., Tian, C., Yao, L., & Zhang, T. (2026). A self-adaptive scheduling method with rescheduling mechanism for multi-objective fuzzy flexible job-shop scheduling problem in dynamic energy and production environments. Cluster Computing, 29(13), Article 782. https://doi.org/10.1007/s10586-026-06549-0

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06549-0

Keywords: flexible job-shop scheduling, rescheduling mechanism, golden jackal optimization, microgrid energy, fuzzy processing times, multi-objective optimization, green manufacturing, dynamic scheduling, energy costs, metaheuristics, smart manufacturing, non-dominated sorting