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A novel multi-neighborhood simulated annealing algorithm efficiently solves complex multi-activity multi-day shift scheduling problems, outperforming previous solvers by finding new best solutions for all benchmark instances, including 118 previously unsolved ones. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

August 29, 2026Journal of SchedulingOpen Access

Multi-neighborhood simulated annealing for the multi-activity multi-day shift scheduling problem

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Authors

LTLászló Kálmán Trautsch BKBence Kővári

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Overview

Computational benchmark study demonstrates optimal workforce assignments across 225 test instances, suggesting multi-neighborhood simulated annealing efficiently resolves complex scheduling.

Key Points

Develop an efficient multi-neighborhood simulated annealing algorithm to solve the multi-activity multi-day shift scheduling problem while satisfying operational constraints and minimizing quadratic overstaffing costs.
Designed a simulated annealing framework utilizing nine distinct neighborhood relations adapted to short time intervals and homogeneous workforces.
Structured the search space and transformation operators to optimize computational speed across large, heavily constrained problem instances.
Tested performance on a benchmark dataset consisting of 225 scheduling problem instances of varying complexity.
Generated feasible schedules for all 225 benchmark instances, resolving 118 instances that were previously unsolved.
Surpassed the prior best-performing solver to establish new best-known solutions across all 225 evaluated instances.
Produced high-quality, constraint-compliant schedules within execution times of a few seconds.

Cite This Study

Trautsch et al. (2026) studied this question.

synapsesocial.com/papers/6a92991a8e5d7d1fc0c10e8b https://doi.org/10.1007/s10951-026-00886-z

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August 29, 2026Journal of SchedulingOpen Access

Multi-neighborhood simulated annealing for the multi-activity multi-day shift scheduling problem

View Full Paper

Ask AI

Bookmark

Authors

LTLászló Kálmán Trautsch BKBence Kővári

Discussion

Member takes

Overview

Computational benchmark study demonstrates optimal workforce assignments across 225 test instances, suggesting multi-neighborhood simulated annealing efficiently resolves complex scheduling.

Key Points

Develop an efficient multi-neighborhood simulated annealing algorithm to solve the multi-activity multi-day shift scheduling problem while satisfying operational constraints and minimizing quadratic overstaffing costs.
Designed a simulated annealing framework utilizing nine distinct neighborhood relations adapted to short time intervals and homogeneous workforces.
Structured the search space and transformation operators to optimize computational speed across large, heavily constrained problem instances.
Tested performance on a benchmark dataset consisting of 225 scheduling problem instances of varying complexity.
Generated feasible schedules for all 225 benchmark instances, resolving 118 instances that were previously unsolved.
Surpassed the prior best-performing solver to establish new best-known solutions across all 225 evaluated instances.
Produced high-quality, constraint-compliant schedules within execution times of a few seconds.

Cite This Study

Trautsch et al. (2026) studied this question.

synapsesocial.com/papers/6a92991a8e5d7d1fc0c10e8b https://doi.org/10.1007/s10951-026-00886-z

View Full Paper

Ask AI

Bookmark