Choose agent-based modeling (ABM) when different supply-chain actors’ decisions and interactions drive the question; choose discrete-event simulation (DES) when the question is about operational flows, timing, queues, inventory, or capacity. Use a hybrid when both actor behavior and process performance can change the outcome. There is no universally superior method: the model should match the decision being studied and the evidence available to validate it.
How do ABM, DES, and hybrid models differ?
The key difference is what the model treats as the main source of change. ABM represents actors and their behavior; DES represents entities moving through processes as events occur over time. A hybrid assigns each kind of mechanism to the modeling approach best suited to it.
| Decision axis | Agent-based modeling | Discrete-event simulation | Hybrid |
|---|---|---|---|
| Main question | How do different firms, households, suppliers, collectors, or other actors make decisions and affect one another? | How do operational flows perform over time, including processing, lead times, queues, inventory, and resource capacity? | How do actor-level choices and detailed operational behavior jointly affect results? |
| What the model represents | Actors with potentially different attributes, information, incentives, and rules; some may adapt or learn. | Entities or materials passing through defined activities and event-driven state changes. | Supply-chain members as agents, with timed operations and flows represented as processes. |
| Common circular-supply-chain mechanisms | Adoption of reuse, remanufacturing, or recycling; producer and consumer choices; industrial symbiosis; waste behavior. | Collection, inspection, repair, reprocessing, distribution, stock replenishment, and reverse-flow turnaround. | Adoption and policy choices that affect operational bottlenecks, capacity, inventory, costs, or environmental performance. |
| Primary trade-off | Explicit behavioral differences can require more data, calibration, and validation. | Process logic can be clear and efficient for operational questions, but actor differences may need separate representation. | Can capture more interacting mechanisms, but adds integration assumptions and validation work. |
Which method fits the question you need to answer?
Start with the decision the model is meant to inform, then identify the mechanism that could change that decision. Avoid choosing a method because it is familiar or because one approach sounds more sophisticated.
- Name the decision-maker and decision. Specify whose choice the model informs—for example, a producer’s recovery policy or a hospital’s inventory plan—and what alternatives are being compared.
- Identify the mechanism that drives the outcome. If different actors’ incentives, information, or choices shape the result, ABM is a natural candidate. If the result depends mainly on processing times, queues, resource limits, or material movements, DES is a natural candidate.
- Test whether either mechanism can be left out. If a change in actor behavior could alter operational performance, or operational constraints could alter actor decisions, consider a hybrid. Do not add a second modeling approach unless its mechanism matters to the question.
- Match detail to evidence. Include only the behavioral and operational detail that can be supported with data, defensible assumptions, or appropriate expert knowledge and then checked through validation.
- Compare strategies within a stated boundary. Define the product life stages, actors, geography, time horizon, and forward and reverse flows included in each scenario.
Why does circularity make the model boundary important?
A circular strategy changes more than the forward shipment of a product. Returns and recovery create additional activities and dependencies: collection, cleaning, decontamination, inspection, repair, reprocessing, and return to inventory may all affect cost, timing, or environmental performance. A linear disposal route does not represent those loops.
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For example, a used instrument sent for reprocessing follows a different operational path from a reusable component that is cleaned, inspected, repaired if needed, and returned to stock. If a model counts avoided production but omits transport, energy, repair, material quality, or recovery capacity, its comparison may shift burdens outside the modeled boundary rather than show a complete system effect.
Walzberg and colleagues’ 2023 article on circular-economy ABM identifies six kinds of limits to consider when interpreting models: thermodynamics; system boundaries; physical scale; path dependence and lock-in; governance and management; and social and cultural definitions. These are useful prompts for deciding what a model does—and does not—represent.
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What does a hybrid circular-supply-chain case show?
A healthcare case by Mohd Shoaib, Antuela Tako, and Shahin Rahimifard combines DES and ABM to model circular supply chains for laparoscopic scissors. The arXiv preprint, posted 17 August 2026, considers a linear single-use baseline alongside scenarios for remanufactured single-use devices, modular scissors with a reusable handle and disposable blade, and fully reusable devices. It represents manufacturers, distribution centers, hospitals, and reprocessors, with operational logic, inventory behavior, and scenario-dependent recovery processes.
The modeled recovery processes include reprocessing, cleaning, decontamination, inspection, and repair, depending on the scenario. The authors explain the rationale for the actor view this way: “Conceptualising the supply and recovery chain in this way offers an effective approach to represent the interactions between individual supply chain members and to depict their individual behaviour based on internal policies.”
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The paper reports the following as outputs of its scenarios, not as measured outcomes from hospitals:
| Modeled case | Reported output | How to interpret it |
|---|---|---|
| Remanufactured single-use scissors extended from one to two use cycles | Estimated emissions fell from 130.2 to 88.0 tonnes of CO₂-equivalent, approximately 33%, in the controlled-adoption scenario. | A result for this case, scenario, and its assumptions; not a universal reduction from remanufacturing. |
| Fully reusable scissors | The model estimated environmental breakeven at approximately three cycles and a 79% emissions reduction relative to its single-use baseline by 20–25 use cycles. | The cycle counts and reduction apply to the modeled scenario and baseline. |
| Modular hybrid scissors with a reusable handle and disposable blade | The model did not reach financial breakeven against its single-use baseline even at 70 use cycles, primarily because of recurring disposable blade costs. | A case-specific modeled result, not a general verdict on modular products. |
| High-variability outsourced decontamination | The model estimated a need for about 43 scissors initially, declining to around 25 after 40 use cycles. | This scenario illustrates how turnaround uncertainty can affect safety-stock requirements; the values are not general inventory recommendations. |
The preprint’s cost, emissions, and inventory results depend on its scenario design, input distributions, cost and emissions assumptions, expert-informed parameters, and modeled processes. They show how a hybrid model can connect actor policies with operational consequences; they do not establish that every circular strategy will produce the same savings or performance.
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What should a credible model document and validate?
A model’s output is only as interpretable as its assumptions and implementation. Document the mechanisms that matter to the decision and make the points below explicit:
- Question and outcome: identify the decision the model informs and the measure used to compare strategies.
- System boundary: state the product life stages, actors, geography, time horizon, and included forward and reverse flows.
- Behavioral rules for ABM: describe differences between agents, what information they have, their incentives, adaptation, and policy logic.
- Process logic for DES: specify events, queues, resource constraints, inventory rules, lead times, and return or repair-time distributions.
- Data and uncertainty: disclose input sources, probability distributions, assumptions, scenario design, and sensitivity analyses.
- Verification and validation: check that the implementation behaves as intended and compare results with suitable data or expert knowledge. A model running successfully or producing plausible numbers is not, by itself, evidence of validation.
- Hybrid coupling: say which component owns each mechanism, how state and timing pass between components, and whether feedback can change behavior or operations.
What does the literature establish—and what remains uncertain?
Rizzati and Landoni’s 2024 systematic review describes circular-economy ABM research as fragmented, with work clustering around producers and industrial systems, consumers and waste disposal, and innovation diffusion. It argues that these strands could be brought together in a broader framework. The review also discusses an earlier example combining ABM with DES for supply-chain design, in which producers choose among reuse, remanufacturing, and recycling.
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A 2026 repository record for a structured supply-chain simulation review by Romagnoli, Moroni, Mercogliano, and Zammori says the review covers 259 peer-reviewed contributions, identifies DES as the most common approach, and flags limited coverage of circular configurations and underrepresented reverse flows and multi-tier structures. Those findings are from the repository record; they should not be treated as a complete assessment of the full paper here.
A 2026 bibliographic record for a critical review by Hague and Craig reports 45 articles employing ABM and 13,797 circular-economy articles not employing ABM in its corpus. Those are counts from that review’s corpus, not an estimate of the total literature or proof that ABM is rare across all disciplines.
These reviews help describe the literature, but they do not establish a general accuracy or performance ranking between ABM and DES. The available sources provide no common, controlled benchmark comparing stand-alone ABM with stand-alone DES on the same circular-supply-chain question. A method choice should therefore be justified by the question, mechanisms, boundary, and validation—not by a claim that one method is always better.
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