Solving a Multi-resource Partial-Ordering Flexible Variant of the Job-Shop Scheduling Problem with Hybrid ASP

Francescutto, Giulia; Schekotihin, Konstantin; El-Kholany, Mohammed M. S.

doi:10.1007/978-3-030-75775-5_21

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…2021) and multi-shot solving (Francescutto et al . 2021) by clingo [DL] to tackle real-world scheduling problems evaluate threshold values on DL variables and perform optimization by means of common statements of clingo rather than minimizing the value of a DL variable like in Listing 4. Applying a similar approach can be advantageous in our future work as it may potentially improve computational efficiency as well as solution quality further and is even a necessity to switch from the makespan as optimization objective to tardiness w.r.t.…”

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%

“…2019), lab resource allocation (Francescutto et al . 2021), and medical treatment planning (Dodaro et al . 2021), pointing out the general attractiveness of ASP for modeling and solving scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

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

El-Kholany

Gebser²,

Schekotihin³

2022

Theory and Practice of Logic Programming

Self Cite

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“…The Workspace for the project can be divided into 6 categories according to the contents and purposes of the occupied objects: component space, manpower space, equipment space, hazard space, protection space and temporary space [24] . Workspace interference refers to the situation where different workspaces occupy each other in actual scenes.…”

Section: (A) the Definition Of Disassembling Workpacementioning

confidence: 99%

“…The tests conducted in Francescutto et al [24] Multi-resource Li et al [25] Reinforcement Feng et al [26] deep reinforcement learning (RL) method…”

Section: Introductionmentioning

confidence: 99%

Optimization of Asynchronous Parallel Tasks Scheduling with Multi-resource Constraints

Xinyu,

JinJian,

Guanwei

et al. 2024

IJCAI

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Complex equipment disassembly tasks often require a group of people to complete, and the same time satisfying various resource constraints. This paper proposes an improved elite genetic algorithm (IGA) for the asynchronous parallel disassembly (APD) problem with the task priority, disassembly workspace interference and human resource constraints. The feasibility and effectiveness of the algorithm are verified through a hydraulic turbine hoisting and disassembling task instance. Results show that compared with classical algorithms (ACO&AGA) applied in APD, the proposed method is feasible and effective, and also has practical guiding significance for planning complex disassembling projects.Povzetek: Članek predstavlja izboljšan elitni genetski algoritem za razstavljanje kompleksne opreme z asinhronimi nalogami, ki upošteva prioritete, medsebojne motnje in človeške vire.

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“…A number of recent ASP applications feature difference constraints for problems that involve timing constraints (Eiter et al 2021;El-Kholany and Gebser 2020;Francescutto, Schekotihin, and El-Kholany 2021;Abels et al 2019). For unrestricted integer constraints, clingcon (Banbara et al 2017) or other constraint ASP systems (Balduccini and Lierler 2017;Lierler 2014) can be used.…”

Section: Introductionmentioning

confidence: 99%

Large-Neighbourhood Search for Optimisation in Answer-Set Solving

Eiter

Geibinger

Ruiz

et al. 2022

AAAI

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While Answer-Set Programming (ASP) is a prominent approach to declarative problem solving, optimisation problems can still be a challenge for it. Large-Neighbourhood Search (LNS) is a metaheuristic for optimisation where parts of a solution are alternately destroyed and reconstructed that has high but untapped potential for ASP solving. We present a framework for LNS optimisation in answer-set solving, in which neighbourhoods can be specified either declaratively as part of the ASP encoding, or automatically generated by code. To effectively explore different neighbourhoods, we focus on multi-shot solving as it allows to avoid program regrounding. We illustrate the framework on different optimisation problems, some of which are notoriously difficult, including shift planning and a parallel machine scheduling problem from semi-conductor production which demonstrate the effectiveness of the LNS approach.

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Solving a Multi-resource Partial-Ordering Flexible Variant of the Job-Shop Scheduling Problem with Hybrid ASP

Cited by 9 publications

References 14 publications

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

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

Optimization of Asynchronous Parallel Tasks Scheduling with Multi-resource Constraints

Large-Neighbourhood Search for Optimisation in Answer-Set Solving

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