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Use agent-based modeling (ABM) when the key question is how different actors make decisions and influence one another; use discrete-event simulation (DES) when it is how materials and products move through timed processes such as collection, repair, queues, and inventory. Choose a hybrid when both kinds of mechanisms can materially change the result.
What each method represents
Agent-based modeling focuses on actors
In ABM, a model represents actors—such as manufacturers, hospitals, suppliers, households, collectors, or reprocessors—and the rules governing their decisions and interactions. Actors can differ in their goals, information, incentives, or behavior. Depending on the model, they may also adapt or learn.
This makes ABM useful for questions about adoption and diffusion of reuse, remanufacturing, or recycling; producer and consumer choices; industrial symbiosis; and waste behavior. Its central question is often how local choices and interactions produce a larger system outcome.
Discrete-event simulation focuses on processes
DES represents a system as entities or materials moving through activities and changing state at particular events. A circular supply-chain model might track a returned product as it is collected, inspected, repaired or reprocessed, and returned to stock. Processing times, queues, capacity, inventory, and lead times can be modeled explicitly.
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DES is a strong fit when the decision concerns operational performance over time: for example, whether a reprocessing facility has enough capacity, how turnaround affects safety stock, or where returned items wait in a reverse-flow network.
Hybrid models represent both
A hybrid can use agents for supply-chain members and their internal policies, with DES representing timed activities and flows. For example, hospitals might choose among product options while a DES component tracks use, collection, decontamination, repair, and replenishment. This is useful only when the interaction between choices and operations matters to the question; otherwise, the extra mechanisms can make the model harder to build, integrate, and validate.
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How to choose for a circular supply-chain question
| Decision axis | ABM is a stronger fit when… | DES is a stronger fit when… | A hybrid is a stronger fit when… |
|---|---|---|---|
| Main question | Different firms, households, or other actors make choices that affect one another. | Operational flows, processing, lead times, queues, inventory, or capacity are the focus. | Actor policies and detailed process behavior both affect the outcome. |
| Representation | Actors have meaningful differences in attributes, information, incentives, or decision rules. | Entities or materials pass through defined activities and event-driven state changes. | Agents represent members and policies; DES represents their timed operations and flows. |
| Typical circular mechanisms | Adoption of reuse, remanufacturing, or recycling; producer or consumer choices; industrial symbiosis. | Collection, inspection, repair, reprocessing, distribution, replenishment, and reverse-flow turnaround. | Adoption or policy choices change bottlenecks, capacity, inventory, costs, or environmental performance. |
| Main trade-off | Behavioral detail can raise data, calibration, and validation demands. | Process logic can be clear and efficient for operations questions, but actor heterogeneity may need explicit representation. | More mechanisms can be represented, at the cost of added complexity, coupling assumptions, and validation work. |
Start by naming the decision the model will inform and the outcome used to compare alternatives. If the outcome mainly depends on how many actors adopt a policy and how they influence one another, ABM is a natural starting point. If it mainly depends on processing time, resource limits, or the flow of returns through operations, start with DES. If both are causal to the decision, define a hybrid’s boundary before adding detail.
Why circular supply chains need explicit reverse flows
A circular system is not a linear supply chain with a recycling label added. Its return routes may include collection, cleaning, decontamination, inspection, repair, remanufacturing, or recovery of reusable components. Those routes have their own actors, timings, yields, and capacity constraints, and they can feed products or components back into inventory.
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For example, a used item may be sent for reprocessing, while a modular product may require a reusable component to be cleaned, inspected, and repaired before it is returned to stock. Treating both routes as a single disposal or return step can hide the operational differences that determine turnaround, inventory needs, and costs.
What a recent healthcare case demonstrates
A case study by Mohd Shoaib, Antuela Tako, and Shahin Rahimifard, posted as an arXiv preprint on 17 August 2026, models circular supply chains for laparoscopic scissors using a hybrid DES and ABM approach. It considers manufacturers, distribution centers, hospitals, and reprocessors, combining member-level policies with product flows and recovery operations. Scenarios include a single-use baseline, remanufactured single-use devices, modular scissors with a reusable handle and disposable blade, and fully reusable devices. Recovery processes vary by scenario and include reprocessing, cleaning, decontamination, inspection, and repair.
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The authors’ model estimated the following scenario-specific results:
- In a controlled-adoption scenario, extending remanufactured single-use scissors from one to two use cycles reduced modeled emissions from 130.2 to 88.0 tonnes of CO₂-equivalent, approximately 33%.
- For fully reusable scissors, the model reached environmental breakeven at approximately three cycles and reported a 79% modeled emissions reduction relative to its single-use baseline by 20–25 use cycles.
- The modeled modular hybrid scissors scenario did not reach financial breakeven against the single-use baseline, even at 70 use cycles; the paper attributes this case result primarily to recurring disposable-blade costs.
- In a high-variability outsourced-decontamination scenario, the model estimated an initial requirement of about 43 scissors, declining to around 25 after 40 use cycles. This illustrates how uncertain turnaround can affect safety-stock requirements.
These are model outputs under the paper’s scenario design, input distributions, costs, emissions assumptions, and expert-informed parameters—not measured hospital-wide effects or evidence that every reusable or modular strategy will perform similarly. The paper is a preprint; its publication status should not be assumed to be peer reviewed.
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What the literature says—and does not establish
Rizzati and Landoni’s 2024 systematic review describes circular-economy ABM work as clustered around producers and industrial systems, consumers and waste disposal, and innovation diffusion. It characterizes the evidence base as fragmented and argues for a more comprehensive framework. The review also includes an earlier example combining ABM with discrete-event supply-chain design to study producer choices among reuse, remanufacturing, and recycling.
A 2026 repository record for a structured supply-chain simulation review by Romagnoli, Moroni, Mercogliano, and Zammori reports coverage of 259 peer-reviewed contributions, identifies DES as the most common approach in that review, and flags limited coverage of circular configurations, reverse flows, and multi-tier structures. Those statements are from the repository record; the full paper was not available in the cited material. 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 the review’s corpus counts, not a census of all disciplines or proof of field-wide prevalence.
These reviews help describe the literature, but they do not establish that one simulation method is generally more accurate or performs better. The available sources do not provide a common controlled benchmark comparing standalone ABM and standalone DES on the same circular-supply-chain question.
What to specify so the model can be interpreted
- Question and decision: Identify whose decision the model informs and which outcomes matter for comparing strategies.
- System boundary: State the product life stages, actors, geography, time horizon, and included forward and reverse flows. Reuse can shift impacts into transport, cleaning, repair, energy, or material-quality constraints.
- Behavior and rules: For ABM, document actor differences, information, incentives, adaptation, and policy logic.
- Process detail: For DES, state event logic, queues, resource constraints, inventory rules, lead times, and return or repair-time distributions.
- Data and uncertainty: Identify input sources and probability distributions, and explain assumptions, sensitivity analyses, and scenario design.
- Verification and validation: Check that the implementation behaves as intended and compare it with appropriate data or expert knowledge. A model running successfully or producing plausible numbers does not, by itself, establish validation.
- Hybrid coupling: Specify which component owns each mechanism, how state and timing pass between components, and whether feedback can change actor behavior or operations.
Boundary choices also matter beyond operational accounting. Walzberg and colleagues’ 2023 analysis of circular-economy modeling discusses thermodynamics, system boundaries, physical scale, path dependence and lock-in, governance and management, and social and cultural limits. These considerations can change what counts as a feasible or beneficial circular strategy.
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