Adaptive Sparrow Search Algorithm with Dynamic Penalty and Repair Operators for Constrained Disposal Scheduling

Solid waste disposal scheduling coordinates waste generation, facility capacities, fleet resources, treatment compatibility, disposal priorities, and environmental costs over multiple periods. The deterministic model considered in this study is a continuous linear program with a convex polyhedral feasible region. To provide a domain-specific population-search framework for this constraint-intensive setting, this paper proposes an Adaptive Sparrow Search Algorithm with Dynamic Penalty and Repair Operators (ADP-SSA). Its distinctive feature is a closed-loop design: population diversity, feasibility ratio, violation type, and stagnation jointly regulate sparrow roles, penalty pressure, and schedule repair. The repair stage performs compatibility correction, capacity redistribution, fleet-resource adjustment, priority-preserving backlog control, terminal clearance, and feasible local refinement. Experiments on six benchmark instances constructed from public New York City and Philadelphia municipal-waste records compare ADP-SSA with SSA, PSO, GA, GWO, and DE under a common evaluation budget. The reported aggregate results indicate lower mean feasible cost and stronger feasibility for ADP-SSA. Friedman and repeated-measures ANOVA tests computed from the six matched instance means identify a global algorithm effect, while exact pairwise Wilcoxon tests are reported with multiplicity correction. The present formulation and evidence do not establish superiority over commercial linear-programming solvers or unexecuted recent optimizers; these comparisons are identified as limitations and priorities for future work.