Scheduling of Mobile Robots with Preemptive Tasks

Nielsen, Izabela; Dang, Quang-Vinh; Nielsen, Peter Axel; Pawlewski, Paweł

doi:10.1007/978-3-319-07593-8_3

Cited by 28 publications

(13 citation statements)

References 13 publications

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“…This considerably reduces the time and e®ort of a user À À À who would have to create a model for each question in the MP environment. Further research will focus on the application of the proposed framework to the scheduling processes in manufacturing, [20][21][22] transport and distribution networks 23 and logistics. 24 Fig.…”

Section: Discussionmentioning

confidence: 99%

A Constraint-Based Declarative Programming Framework for Scheduling and Resource Allocation Problems

Wikarek

Sitek

2019

Vietnam J. Comp. Sci.

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Scheduling and resource allocation problems are widespread in many areas of today’s technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, project and human resources management, services, etc. The literature (problem classification, scheduling and resource allocation models, solutions) is vast and exhaustive. In practice, however, classical scheduling problems with fixed structures and standard constraints (precedence, disjoint, etc.) are rare. Practical scheduling problems include also logical and nonlinear constraints, and they use nonstandard criteria of schedule evaluations. Indeed, in many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as general and/or specific questions. Thus, there is a need to develop a programming framework that will facilitate the modeling and solving of a variety of diverse scheduling problems. The framework should be able to (a) model any types of constraints, (b) ask questions/criteria relating to the schedule execution mode and (c) be highly effective in finding solutions (schedule development). This paper proposes such a constraint-based declarative programming framework for modeling and solving scheduling problems which satisfies the assumptions above. It was built with the Constraint Logic Programming (CLP) environment and supported with Mathematical Programming (MP). The functionality and effectiveness of this framework are presented with the use of an illustrative example for the resource-constrained scheduling problem with additional resources.

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Section: Discussionmentioning

confidence: 99%

A Constraint-Based Declarative Programming Framework for Scheduling and Resource Allocation Problems

Wikarek

Sitek

2019

Vietnam J. Comp. Sci.

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“…[13]; Grzybowska and Gajšek [10]). The proposed method can be extended to modeling and solving soft and fuzzy constraints and multi-objective optimization.…”

Section: Discussionmentioning

confidence: 99%

A multi-level approach to ubiquitous modeling and solving constraints in combinatorial optimization problems in production and distribution

Sitek

Wikarek

2017

Appl Intell

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Constraints, although ubiquitous in production and distribution planning, scheduling and control, often lead to inconsistencies in the decision-making process. The constraint-based modeling helps circumvent many organizationimpacting issues. To address this, we developed a multi-level approach to the modeling and solving of combinatorial optimization problems. It is versatile and effective owing to the use of multi-level presolving and multiple paradigms, such as constraint programming, logic programming, mathematical programming and fuzzy logic, for their complementary strengths. The capability of this framework and its advantage over mathematical programming alone or over hybrid frameworks is shown in the illustrative example, in which combinatorial optimization is used as a benchmark to prove the effectiveness of the proposed approach. Knowledge of the problem is stored in the form of facts.

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“…It is foreseen in further research the use of a hybrid approach to (a) modeling and solving scheduling problems in production [16,17], (b) modeling and optimization of IoT processes [18], and (c) implementation of more complex models, uncertainty, fuzzy logic etc.. APPENDIX A …”

Section: Discussionmentioning

confidence: 99%