Problem
Research questions and friction points this paper is trying to address.
Job-shop Scheduling Problem (JSP) optimization
Decomposition into time windows
Multi-shot Answer Set Programming (ASP) solving
Decomposition Strategies and Multi-shot ASP Solving for Job-shop Scheduling
🤖 AI Summary
For the NP-hard Job Shop Scheduling Problem (JSP), this paper proposes a decomposition-and-coordination framework based on dynamic time-window partitioning and multi-round Answer Set Programming (ASP) solving. The method decomposes the global scheduling task into temporally coupled subproblems via multi-granularity time-window segmentation, then iteratively refines solutions using ASP. Its key contributions are: (i) the first introduction of overlapping and adaptive time-window compression mechanisms; and (ii) integration of operation-selection heuristics with window-based decomposition to mitigate local optima arising from sequential optimization. Experimental evaluation on standard JSP benchmarks demonstrates that, compared to single-shot full-scale ASP solving, the approach significantly improves both solution efficiency and optimality under tight time limits. Moreover, applying window-based refinement to heuristic-initial solutions reduces average makespan by 12.7%.
📝 Abstract
The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we investigate problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations such that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract optimization limitations due to the restriction to window-wise partial schedules. Our experiments on different JSP benchmark sets show that successive optimization by multi-shot ASP solving leads to substantially better schedules within tight runtime limits than single-shot optimization on the full problem. In particular, we find that decomposing initial solutions obtained with proficient heuristic methods into time windows leads to improved solution quality.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decomposition into time windows
Multi-shot ASP solving
Time window overlapping techniques
🔎 Similar Papers
No similar papers found.
