Open research questions in Scheduling and Optimization Algorithms
87 gap statements mined from Scheduling and Optimization Algorithms papers in our 4.5M-paper local library, which holds 1,461 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
Future research could explore adaptive PSO strategies, stochastic disruptions, or integration with IoT and digital twin technologies to further improve responsiveness, robustness, and sustainability in dynamic, Industry 4.
Optimizing logistics and production flows for sustainability in green flexible job-shops · 2026 · DOIFuture research should focus on integrating advanced neural architectures, such as graph-based models and hierarchical learning frameworks, to further improve the robustness and efficiency of scheduling solutions in dynamic and complex manufacturing environments.
Lot Streaming Optimization in Flexible Job Shop Scheduling via Deep Reinforcement Learning · 2026 · DOIFuture research can focus on improving the computational capacity and time constraint of the proposed approach. Future research can explore the application of the proposed approach to other manufacturing operations. Future research can investigate the potential limitations of the proposed approach.
An engineering lot scheduling strategy for capacity ramp-up in real-world semiconductor manufacturing operations · 2026 · DOIThe current practice of scheduling engineering lots is mainly based on human experience, leading to variability and inefficiency. There is a need for a systemized approach to prioritize engineering lots. The paper identifies the gap in the current practice and proposes a new approach.
An engineering lot scheduling strategy for capacity ramp-up in real-world semiconductor manufacturing operations · 2026 · DOIThe paper does not provide a comprehensive comparison with other algorithms. The computational experiments are limited to instances with up to 500 jobs and 10 machines.
Heuristic approaches for solving a bilevel optimistic scheduling problem on parallel machines · 2026Future research should explore hybrid models that combine the mathematical precision of LP with the adaptive search capabilities of SA, potentially offering MSMEs a more robust framework for production planning under uncertainty.
The study does not suggest any future research directions. Potential future research directions could include evaluating the framework using real-world data or considering dynamic changes in the planning horizon.
MULTI-PERIOD STAFFING SCHEDULING OPTIMIZATION BASED ON COLUMN GENERATION AND NETWORK FLOW ALGORITHMS · 2026 · DOIFuture investigations may focus on the following areas: Artificial Intelligence-Driven Assignment Models Machine Learning and Artificial Intelligence can predict future workloads, resource availability, and operational costs, enabling assignment algorithms to generate more adaptive and intelligent solutions.
The need for more sustainable and robust operations research methods. The lack of integration between different methodological approaches, such as exact combinatorial optimisation and machine learning. The importance of considering real-world applicability in operations research.
Mathematical Methods and Operation Research in Planning, Scheduling and Supply Chain Operations Management · 2026 · DOIThe computation resources are limited. The number of samples is set to 20 due to limited computation resources.
Integrated green scheduling of quay cranes, internal vehicles, and yard cranes with uncertainties via learning-assisted evolutionary algorithms · 2026 · DOIFurther research can be done to improve the efficiency of the algorithm. The algorithm can be applied to other scheduling problems in container terminals.
Integrated green scheduling of quay cranes, internal vehicles, and yard cranes with uncertainties via learning-assisted evolutionary algorithms · 2026 · DOIFuture work could build on this study by incorporating practical elements like release dates or machine breakdowns. Additionally, adapting the proposed iterated greedy algorithm to other scheduling problems holds significant potential.
Iterated greedy with strategic reconstruction for parallel machine problem with weighted tardiness and setup times · 2026 · DOIFurther research can be conducted to improve the efficiency of the algorithms. The application of the results to other production systems can be explored.
Cyclic re-entrant hybrid flow shop scheduling with sequence-dependent setup times in semiconductor manufacturing · 2026 · DOIThe incorporation of sequence-dependent setup times in the CRHFS scheduling problem has not been investigated before. The existing algorithms are not efficient in solving the CRHFS scheduling problem with SDST.
Cyclic re-entrant hybrid flow shop scheduling with sequence-dependent setup times in semiconductor manufacturing · 2026 · DOIConsequently, processable-edge masking events within the early scheduling horizon are insufficiently represented during training, and the semantic-layer attention weights 𝛽𝑟 associated with processable edges may fail to converge to values that are optimal for early-stage machine failures.
Optimizing real-time rescheduling mechanism for flexible manufacturing systems considering equipment fault disturbances using HGNN-PPO cooperative policy learning algorithm · 2026 · DOIAlthough this study verifies the superiority of the PPO-based deep reinforcement learning scheduling model in warehouse order picking, several limitations remain to be addressed.
The paper does not discuss the scalability of the DD-based approach for very large instances. The computational experiments are limited to a specific set of instances.
A decision diagram approach for the parallel machine scheduling problem with chance constraints · 2026 · DOIFuture research can focus on improving the scalability of the DD-based approach for very large instances. The approach can be applied to other scheduling problems with uncertain processing times.
A decision diagram approach for the parallel machine scheduling problem with chance constraints · 2026 · DOIConclusion and managerial implications As global manufacturing faces the dual challenges of volatile energy prices and highly demanding supply chain schedules, cost-efficient production has become a critical operational requirement.
Cost-efficient job shop scheduling under strict deadline constraints: a hybrid seed-injected evolutionary search approach · 2026 · DOIFuture work will extend the model to dynamic production settings with uncertainty and additional energy-saving mechanisms.
A hybrid memetic algorithm for an energy-aware distributed blocking hybrid flowshop scheduling problem with transportation time · 2026 · DOILarge language models (LLMs) provide a natural-language interface for translating shop-floor updates into executable scheduling models, but final objective-value checking is insufficient: an LLM may omit tool, worker, transport, or calendar constraints while still obtaining an accidentally correct makespan on a particular instance.
FJSP-E2E: A process-verified framework for automatic scheduling-model reconfiguration in smart manufacturing · 2026 · DOIWe outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts.
The study identifies a combinatorial optimization problem that requires a structured algorithm. The study notes that direct enumeration becomes computationally difficult due to the large number of possible schedules.
MULTI-PERIOD STAFFING SCHEDULING OPTIMIZATION BASED ON COLUMN GENERATION AND NETWORK FLOW ALGORITHMS · 2026 · DOIExact methods have high computational cost and are usually only applicable to small-scale problems, - heuristic and meta-heuristic algorithms exhibit limited adaptability to dynamic scheduling problems, - the solution efficiency of exact methods declines as the size of the workshop…
A Competition-Aware Deep Reinforcement Learning Framework for Practical Flexible Job Shop Scheduling · 2026 · DOIFurther improvement of the proposed method, - application of the method to other scheduling problems, - investigation of other deep reinforcement learning…
A Competition-Aware Deep Reinforcement Learning Framework for Practical Flexible Job Shop Scheduling · 2026 · DOI
Most-cited papers in Scheduling and Optimization Algorithms
- The Shifting Bottleneck Procedure for Job Shop Scheduling · Management Science · 1988 · 1,198 citations
- A Survey of Scheduling Rules · Operations Research · 1977 · 953 citations
- Job Shop Scheduling by Simulated Annealing · Operations Research · 1992 · 824 citations
- Sequencing with Earliness and Tardiness Penalties: A Review · Operations Research · 1990 · 748 citations
- A Fast Taboo Search Algorithm for the Job Shop Problem · Management Science · 1996 · 707 citations
- A Survey of Machine Scheduling Problems with Blocking and No-Wait in Process · Operations Research · 1996 · 694 citations
- Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey · IEEE Access · 2025 · 691 citations
- A Heuristic Algorithm for the n Job, m Machine Sequencing Problem · Management Science · 1970 · 656 citations
- Scheduling with Deadlines and Loss Functions · Management Science · 1959 · 637 citations
- Parallel Sequencing and Assembly Line Problems · Operations Research · 1961 · 617 citations
Most recent work
- Constraint Programming for AGV and Machine Integrated Scheduling Problem in Flexible Manufacturing System · IEEE Transactions on Automation Science and Engineering · 2026
- A Heterogeneous Graph Reinforcement Learning Framework With Question-Aware Neighborhood Aggregation and Interoption Prompt Attention for Dynamic Flexible Job Shop Scheduling Problem · IEEE Transactions on Industrial Informatics · 2026
- Automatic Programming via Large Language Models With Population Self-Evolution for Dynamic Fuzzy Job Shop Scheduling Problem · IEEE Transactions on Fuzzy Systems · 2026
- Hybrid agentic AI and multi-agent systems in smart manufacturing · Journal of Manufacturing Systems · 2026
- A literature review on deep reinforcement learning for machine scheduling problems · Journal of Manufacturing Systems · 2026
- Multi-relational graph reinforcement learning for dynamic flexible job-shop scheduling under machine breakdowns · Applied Soft Computing · 2026
- Large language model-empowered dynamic scheduling for intelligent hybrid flow shop using multi-agent deep reinforcement learning · Advanced Engineering Informatics · 2026
- Digital-twin-based reinforcement learning method for flexible assembly job-shop scheduling with dynamic material kitting · Computers & Industrial Engineering · 2026
- Adaptive active decoding and novel disjunctive graph-based improved genetic algorithm for multi-type machine robot cell scheduling in mass customization · Robotics and Computer-Integrated Manufacturing · 2026
- Dual-Resource constrained flexible job-shop scheduling with ergonomic considerations in conventional and human-robot systems using an enhanced NSGA-II with teaching-learning effect · Robotics and Computer-Integrated Manufacturing · 2026
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