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Vehicle routing problems (VRP) are a kind of typical combinational optimization problem, particularly in the logistics industry. This paper proposes a constrained evolutionary optimization algorithm, called CEOA, for solving many-objective VRP ...

• This paper designs an adapting weight value vector to indicate infeasible solutions, which cannot compare together.
• We use a constrained satisfaction strategy to select feasible solutions and select infeasible solutions, respectively.

Relaxations for the constraint satisfaction problem (CSP) include bounded width, linear program (LP), semidefinite program (SDP), affine integer program (AIP), and the combined LP+AIP of Brakensiek, Guruswami, Wrochna, and Živný (SICOMP 2020). Tightening ...

Constraint satisfaction problems (CSPs) for first-order reducts of finitely bounded homogeneous structures form a large class of computational problems that might exhibit a complexity dichotomy, P versus NP-complete. A powerful method to obtain polynomial-...

Solving constraint satisfaction problems often involves a large amount of redundant exploration stemming from the existence of subproblems whose information can be reused for other subproblems. Subproblem dominance is a general notion of ...

Multiplicative cut sparsifiers, introduced by Benczúr and Karger [STOC’96], have proved extremely influential and found various applications. Precise characterisations were established for sparsifiability of graphs with other 2-variable predicates on ...

Given a sequence of observations over a plan execution, plan and goal recognition are considered as interchangeable tasks in AI planning. However, strictly speaking, the former tries to identify a plan, and the latter a set of goals, that explain ...

Constraint Optimization Problems (COPs) ask for an assignment of values to variables in order to optimize an objective subject to constraints that restrict the value combinations in the assignment. They are usually solved by the ...

We study polynomial-time approximation schemes (PTASes) for constraint satisfaction problems (CSPs) such as Maximum Independent Set or Minimum Vertex Cover on sparse graph classes.

Baker’s approach gives a PTAS on planar graphs, excluded-minor classes, ...

Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing ...

The Sudoku puzzle solving, a Constraint Satisfaction Problem (CSP), is challenging due to various complexity levels. Although, deterministic as well as meta-heuristics techniques are present to solve the Sudoku puzzle. Still, such techniques are ...${}_{\mathrm{}}$${}_{\mathrm{}}\mathrm{}$

Parallel test task scheduling is an efficient way to shorten the final makespan of several huge test projects. Put simply, a set of test tasks should be processed on several unrelated resources, and several test tasks must satisfy the ...

We study the fine-grained complexity of NP-complete, infinite-domain constraint satisfaction problems (CSPs) parameterised by a set of first-order definable relations (with equality). Such CSPs are of central importance since they form a subclass ...$\mathrm{}$${}^{}$${\mathrm{}}^{}$$\mathrm{}$${}^{}$${}^{}$

Concrete domains have been introduced in the area of Description Logic to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. Unfortunately, in ...$\mathcal{}$$$$$$$$$$$$\mathcal{}$$$$\mathcal{}$

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP ...

Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more ...

Constraints are ubiquitous in artificial intelligence and operations research. They appear in logical problems like propositional satisfiability, in discrete problems like constraint satisfaction, and in full-fledged mathematical optimization ...

Learning, as a discovery task from past observations, is interesting in engineering contexts for identifying structures and improving accuracy. Learning in planning scenarios aims at recognizing past behavior to predict action models ...

Constraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales ...

Restoration of the 3D structure of a protein from the sequence of its amino acids (“folding”) is one of the most important and challenging problems in computational biology. The most accurate methods require enormous computational resources due to ...

The complexity and approximability of the constraint satisfaction problem (CSP) has been actively studied over the past 20 years. A new version of the CSP, the promise CSP (PCSP), has recently been proposed, motivated by open questions about the ...