Decomposition-Based Job-Shop Scheduling with Constrained Clustering

El-Kholany, Mohammed M. S.; Schekotihin, Konstantin; Gebser, Martin

doi:10.1007/978-3-030-94479-7_11

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…While Machine-based decomposition turned out to be helpful for finding better schedules, the strategies presented in Section 3.2 can be regarded as naive heuristics based on straightforward instance properties, and incorporating machine-learning approaches (El-Kholany et al . 2022; Tassel et al . 2021) for more adept partitioning is another promising direction for future work.…”

Section: Discussionmentioning

confidence: 99%

“…deadlines, which are crucial in many application areas. While Machine-based decomposition turned out to be helpful for finding better schedules, the strategies presented in Section 3.2 can be regarded as naive heuristics based on straightforward instance properties, and incorporating machine-learning approaches (El-Kholany et al 2022;Tassel et al 2021) for more adept partitioning is another promising direction for future work. Provided the adjustment of problem decomposition strategies, our multi-shot solving methods can also be applied to the Flow-shop and Open-shop Scheduling Problems (Taillard 1993).…”

Section: Discussionmentioning

confidence: 99%

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Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

El-Kholany

Gebser²,

Schekotihin³

2022

Theory and Practice of Logic Programming

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Scheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, 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. Given that already moderately sized JSP instances can be highly combinatorial, and neither optimal schedules nor the runtime to termination of complete optimization methods is known, efficient approaches to approximate good-quality schedules are of interest. In this paper, we propose 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 so that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. Regarding the feasibility and quality of solutions, problem decomposition must respect the precedence of operations within their jobs and partial schedules optimized by time windows should yield better global solutions than obtainable in similar runtime on the entire instance. 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 window-wise optimization limitations restricted to partial schedules. Our experiments on JSP benchmark sets of several sizes show that successive optimization by multi-shot ASP solving leads to substantially better schedules within the runtime limit than global optimization on the full problem, where the gap increases with the number of operations to schedule. While the obtained solution quality still remains behind a state-of-the-art Constraint Programming system, our multi-shot solving approach comes closer the larger the instance size, demonstrating good scalability by problem decomposition.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

El-Kholany

Gebser²,

Schekotihin³

2022

Theory and Practice of Logic Programming

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“…This work depends on collecting information from the problem itself and the solutions obtained by heuristics like FIFO/MTWR to define the similar operations and assign them to the same cluster where a cluster is a time window and apply multi-shot ASP for the optimization. This work has been published in PADL 2022 [5]. Secondly, We have developed other decomposition strategies that depend on ranking the operations.…”

Section: Accomplished Workmentioning

confidence: 99%

Decomposition Strategies for Solving Scheduling Problems in Industrial Applications

El-Kholany

2022

Electron. Proc. Theor. Comput. Sci.

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This article presents an overview of a research study of a crucial optimization problem in the Computer Science/Operations research field: The Job-shop Scheduling Problem (JSP). The JSP is a challenging task in which a set of operations must be processed using a set of scarce machines to optimize a particular objective. The main purpose of the JSP is to determine the execution order of the processes assigned to each machine to optimize an objective. Our main interest in this study is to investigate developing decomposition strategies using logic programming to solve the JSP. We split our goal into two main phases. The first phase is to apply the decomposition approach and evaluate the proposed model by solving a set of known benchmark instances. The second phase is to apply the successful decomposition methods obtained from the first phase to solve a scheduling problem in the real-life application. In the current state, we finished the first phase and started the second one aiming to have a model that can provide a schedule of a factory for a short-time period.

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Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling

Cappanera¹,

Gavanelli²,

Nonato³

et al. 2023

Theory and Practice of Logic Programming

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In answer set programming (ASP), the user can define declaratively a problem and solve it with efficient solvers; practical applications of ASP are countless and several constraint problems have been successfully solved with ASP. On the other hand, solution time usually grows in a superlinear way (often, exponential) with respect to the size of the instance, which is impractical for large instances. A widely used approach is to split the optimization problem into subproblems (SPs) that are solved in sequence, some committing to the values assigned by others, and reconstructing a valid assignment for the whole problem by juxtaposing the solutions of the single SPs. On the one hand, this approach is much faster due to the superlinear behavior; on the other hand, it does not provide any guarantee of optimality: committing to the assignment of one SP can rule out the optimal solution from the search space. In other research areas, logic-Based Benders decomposition (LBBD) proved effective; in LBBD, the problem is decomposed into a master problem (MP) and one or several SPs. The solution of the MP is passed to the SPs that can possibly fail. In case of failure, a no-good is returned to the MP that is solved again with the addition of the new constraint. The solution process is iterated until a valid solution is obtained for all the SPs or the MP is proven infeasible. The obtained solution is provably optimal under very mild conditions. In this paper, we apply for the first time LBBD to ASP, exploiting an application in health care as case study. Experimental results show the effectiveness of the approach. We believe that the availability of LBBD can further increase the practical applicability of ASP technologies.

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Decomposition-Based Job-Shop Scheduling with Constrained Clustering

Cited by 4 publications

References 35 publications

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

Decomposition Strategies for Solving Scheduling Problems in Industrial Applications

Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling

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