A Taxonomy and Evidence Framework for Hybrid Metaheuristics in Smart Manufacturing and Supply Chains

Authors

https://doi.org/10.48314/ceti.v3i1.81

Abstract

Hybrid metaheuristics are often introduced by naming two algorithms, although an algorithm pair does not reveal what decisions are encoded, which component controls feasibility, when information is exchanged, or what evidence would justify deployment. This article develops a decision-contract perspective for hybrid optimization in smart manufacturing and supply chains. A hybrid is specified through five linked contracts: The decision model, component roles, information exchange, resource allocation, and evidential claim. This representation refines established high-/low-level and relay/teamwork taxonomies by making the operational interface auditable. The framework is applied to flexible job-shop scheduling, vehicle routing, supply-chain network design, maintenance, and energy-aware operations. A literature synthesis shows that recent work has expanded from handcrafted hybrids toward matheuristics, Adaptive Large-Neighborhood Search (ALNS), automated configuration, surrogate assistance, and reinforcement-learning-guided search, but comparison remains fragile when feasibility rules, tuning cost, latency, or certificate status differ. We therefore propose a matched-budget evaluation pipeline that separates feasibility, anytime quality, tail latency, robustness, and bound validity, and requires component ablation. The article is a methodological synthesis rather than a new computational benchmark: It does not rank hybrid families. Its contribution is a falsifiable language for moving from algorithm names to reproducible decision mechanisms and from isolated benchmark wins to evidence appropriate for industrial use.   

Keywords:

Hybrid metaheuristics, Matheuristics, Smart manufacturing, Scheduling, Vehicle routing, Supply-chain optimization, Algorithm benchmarking, Reproducibility

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Published

2026-02-27

Issue

Vol. 3 No. 1 (2026): Computational Engineering and Technology Innovations

Section

Articles

How to Cite

Fasihi, M., & Shemshad, A. (2026). A Taxonomy and Evidence Framework for Hybrid Metaheuristics in Smart Manufacturing and Supply Chains. Computational Engineering and Technology Innovations, 3(1), 41-51. https://doi.org/10.48314/ceti.v3i1.81

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