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Table · dataset · 2026

A multi-objective constraint programming–satisfiability model for non-unit repetitive construction scheduling

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<p>Non-unit repetitive construction projects, in which activity groups are repeatedly executed over work-group-specific units rather than over a common set of identical production units, are difficult to schedule because activity sequences, crew movements, and execution logic vary across crew options.

Description

This study develops a multi-objective Constraint Programming–Satisfiability (CP-SAT) model for non-unit repetitive construction scheduling.

The model integrates crew–option selection, interval-based scheduling, route-based crew logic, transfer-dependent travel times, optional interruption, and sequence control within a unified framework. Both soft and hard sequencing requirements are represented: soft sequences emerge from optimized crew routes, while hard sequences can enforce prescribed order, same-crew execution, and immediate-successor relationships when required.

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An exact ε-constraint procedure is used for the bi-objective time-cost case, while sampled frontier generation supports higher-dimensional trade-off analysis. From a practical perspective, the framework helps project managers compare feasible scheduling alternatives and balance competing priorities related to project deadlines, budget, construction quality, and crew continuity. The proposed model therefore provides a rigorous and practical decision-support framework for complex non-unit repetitive construction projects.</p> <p></p><p>CP-SAT model for non-unit repetitive scheduling.</p><p>Soft and hard sequencing are modeled explicitly.</p><p>Crew routes, travel, and interruption are jointly captured.</p><p>Duration, cost, quality, and interruption are optimized.</p><p>Exact and sampled Pareto strategies are distinguished.</p><p></p> <p>CP-SAT model for non-unit repetitive scheduling.</p> <p>Soft and hard sequencing are modeled explicitly.</p> <p>Crew routes, travel, and interruption are jointly captured.</p> <p>Duration, cost, quality, and interruption are optimized.</p> <p>Exact and sampled Pareto strategies are distinguished.</p>

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Sequencing 75%
Provenance · 1 source records, 20 field assertions
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figshareoai:figshare.com:article/336601959 d agoJSON v1
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