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CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments

Mingjie Bi

Abstract

Industrial disruption replanning demands multi-agent coordination under strict latency and communication budgets, where disruptions propagate through tightly coupled physical dependencies and rapidly invalidate baseline schedules and commitments. Existing coordination schemes often treat communication as either effectively free (broadcast-style escalation) or fixed in advance (hand-tuned neighborhoods), both of which are brittle once the disruption footprint extends beyond a local region. We present \CASCADE, a budgeted replanning mechanism that makes communication scope explicit and auditable rather than fixed or implicit. Each agent maintains an explicit knowledge base, solves role-conditioned local decision problems to revise commitments, and coordinates through lightweight contract primitives whose footprint expands only when local validation indicates that the current scope is insufficient. This design separates a unified agent substrate (Knowledge Base / Decision Manager / Communication Manager) from a scoped interaction layer that controls who is contacted, how far coordination propagates, and when escalation is triggered under explicit budgets. We evaluate \CASCADE on disrupted manufacturing and supply-chain settings using unified diagnostics intended to test a mechanism-design claim -- whether explicit scope control yields useful quality-latency-communication trade-offs and improved robustness under uncertainty -- rather than to provide a complete algorithmic ranking.

CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments

Abstract

Industrial disruption replanning demands multi-agent coordination under strict latency and communication budgets, where disruptions propagate through tightly coupled physical dependencies and rapidly invalidate baseline schedules and commitments. Existing coordination schemes often treat communication as either effectively free (broadcast-style escalation) or fixed in advance (hand-tuned neighborhoods), both of which are brittle once the disruption footprint extends beyond a local region. We present \CASCADE, a budgeted replanning mechanism that makes communication scope explicit and auditable rather than fixed or implicit. Each agent maintains an explicit knowledge base, solves role-conditioned local decision problems to revise commitments, and coordinates through lightweight contract primitives whose footprint expands only when local validation indicates that the current scope is insufficient. This design separates a unified agent substrate (Knowledge Base / Decision Manager / Communication Manager) from a scoped interaction layer that controls who is contacted, how far coordination propagates, and when escalation is triggered under explicit budgets. We evaluate \CASCADE on disrupted manufacturing and supply-chain settings using unified diagnostics intended to test a mechanism-design claim -- whether explicit scope control yields useful quality-latency-communication trade-offs and improved robustness under uncertainty -- rather than to provide a complete algorithmic ranking.

Paper Structure

This paper contains 57 sections, 36 equations, 6 figures, 2 tables, 1 algorithm.

Figures (6)

  • Figure 1: A unified agent substrate (KB/DM/CM) supports heterogeneous local modeling, while gate-triggered scoped communication escalates from local to broader coordination only when needed.
  • Figure 2: Unified agent architecture (KB/DM/CM). KB maintains local beliefs, priorities, intentions, and uncertainty; DM diagnoses disruptions and computes local commitments; CM routes physical I/O and peer messages.
  • Figure 3: Gate-triggered scoped communication propagation. Unmet needs propagate along $G_p$ while coordination is executed on $G_a$ through contract primitives; feasibility and stability gates trigger targeted scope expansion and role transitions without global flooding.
  • Figure 4: R1: Quality--latency--communication frontier. The axes are domain-specific (cycle-time-based recovery proxy in manufacturing; overage cost with feasibility labels in supply chain), but the shared question is how much recovery quality is achieved per unit communication and latency.
  • Figure 5: R2: Propagation footprint vs. network structure. Footprint proxies ($N_c$, $N_a$), communication $M$, and quality impact $O$ are stratified by network-attribute groups (LL/LH/HL/HH). The grouped analysis is restricted to the 73 supplier-loss scenarios in which both centralized and distributed recovery are feasible; group sizes are LL$=53$, LH$=16$, HL$=2$, and HH$=13$.
  • ...and 1 more figures