An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Dodaro, Carmine; Galatà, Giuseppe; Grioni, Andrea; Maratea, Marco; Mochi, Marco; Porro, Ivan

doi:10.1017/s1471068421000363

Cited by 29 publications

(15 citation statements)

References 21 publications

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“…For the healthcare problems we have already published some works. In particular, one of the main work that we published is [8], that enriched a previous work [9]. The paper was presented at ICLP 2021 and was awarded as the best application paper.…”

Section: Healthcare Domain Problemsmentioning

confidence: 95%

“…Other than satisfy all the constraints of the problem, to obtain an optimal solution the scheduler has to minimize the number of patients assigned to the wrong resources and the number of concurrent patients starting phase 2. In our paper [8], differently from other works, we defined the requirements according to the indications of the S. Martino Hospital in Genova, Italy. Moreover, we used real data for the empirical analysis.…”

Section: Introduction and Problem Descriptionmentioning

confidence: 99%

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Planning and Scheduling in Digital Health with Answer Set Programming

Mochi¹

2022

Electron. Proc. Theor. Comput. Sci.

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In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered. The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account. Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios. We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available. Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g. those based on Answer Set programming will be limited. Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.

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Section: Healthcare Domain Problemsmentioning

confidence: 95%

Section: Introduction and Problem Descriptionmentioning

confidence: 99%

Planning and Scheduling in Digital Health with Answer Set Programming

Mochi¹

2022

Electron. Proc. Theor. Comput. Sci.

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“…While decision versions of scheduling problems can be successfully modeled and solved by an extension of ASP with Difference Logic (DL) constraints (Gebser et al 2016), implemented by clingo [DL] on top of the (multi-shot) ASP system clingo (Gebser et al 2019), the optimization capacities of clingo [DL] come to their limits on moderately sized yet highly combinatorial JSP instances (El-Kholany and Gebser 2020), for some of which optimal solutions are so far unknown (Shylo and Shams 2018). Successful applications in areas beyond JSP include industrial printing (Balduccini 2011), team-building (Ricca et al 2012), shift design (Abseher et al 2016), course timetabling (Banbara et al 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

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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: Introductionmentioning

confidence: 99%

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

El-Kholany¹,

Gebser²,

Schekotihin³

2022

Preprint

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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 goodquality 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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An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Cited by 29 publications

References 21 publications

Planning and Scheduling in Digital Health with Answer Set Programming

Planning and Scheduling in Digital Health with Answer Set Programming

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

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

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