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View corpus contextA systematic review protocol to bridge lot‑sizing theory and shop‑floor practice: the study will catalog single‑machine multiproduct scheduling models, evaluate their empirical/industrial validation, and identify gaps—notably in adoption evidence and sustainability/resilience treatment—guiding future AI and optimization work in manufacturing.
Protocol: Systematic Literature Review on Multiproduct Lotsizing and Scheduling in Single-Machine Environments: A Theoretical-Empirical Analysis
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This protocol specifies a transparent, reproducible systematic review to map multiproduct single‑machine lot‑sizing and scheduling models to industrial practice, assessing modelling assumptions, methods, levels of empirical validation, adoption drivers, and the extent to which sustainability and resilience are incorporated.
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Multiproduct lot-sizing and scheduling in single-machine or single-resource environments constitutes a classical and still relevant problem in the field of operations management. It requires coordinating lot-size decisions, production scheduling or sequencing, and the use of a constrained production resource, under setup-related conditions such as setup times, setup costs, and sequence-dependent changeovers. This problem is commonly addressed through the Economic Lot Scheduling Problem (ELSP). Although various models have been developed in the literature, their application in real industrial contexts has been less explored. In this context, the present protocol proposes the design of a systematic literature review aimed at analyzing multiproduct lot-sizing and scheduling in single-machine environments from a theoretical-empirical perspective. In particular, the review will examine the modelling assumptions and methodological approaches used in these studies, the level of empirical or industrial evidence supporting them, the factors influencing their adoption in industrial contexts, and the incorporation of sustainability and resilience criteria. The protocol also defines a structured search strategy based on conceptual search blocks, together with inclusion and exclusion criteria that distinguish between parametric and content-based criteria. This structure is intended to improve transparency, coherence, and traceability in the study selection process. The expected contribution is a systematic and reproducible review design capable of comparing formal model development with industrial applications. The review is expected to identify patterns in model formulation, levels of empirical evidence, adoption-related factors, and emerging research directions associated with sustainability, resilience, and advanced solution approaches.
Summary
Main Finding
This document is a protocol for a systematic literature review that aims to bridge theoretical model development and real-world industrial application in multiproduct lot-sizing and scheduling on a single machine/resource (the Economic Lot Scheduling Problem, ELSP, and variants). The protocol defines a transparent, reproducible search and selection approach to assess modelling assumptions, methods, empirical evidence, adoption drivers, and incorporation of sustainability and resilience into the ELSP literature.
Key Points
- Scope: Focus on multiproduct lot-sizing and scheduling in single-machine or single-resource environments under setup-related constraints (setup times, setup costs, sequence-dependent changeovers).
- Objective: Compare formal model development with industrial practice from a theoretical–empirical perspective.
- Topics to be reviewed:
- Modelling assumptions (demand, costs, inventories, setup structure, sequencing, capacity)
- Methodological approaches (analytical models, mixed-integer programming, heuristics, metaheuristics, decomposition, simulation)
- Levels of empirical/industrial validation and evidence
- Factors influencing industry adoption (practical constraints, data availability, computational tractability, organizational and economic drivers)
- Inclusion of sustainability and resilience criteria (e.g., emissions, energy, robustness to disruptions)
- Contribution: A systematic, reproducible review design that identifies patterns in model formulation, empirical support, adoption-related factors, and research directions (sustainability, resilience, advanced solution methods).
Data & Methods
- Search strategy: Structured search using conceptual search blocks (likely combining terms for lot-sizing, scheduling, single-machine/resource, setup characteristics, ELSP, empirical/industrial application, sustainability/resilience).
- Inclusion/exclusion criteria:
- Parametric criteria (e.g., time window, language, document types such as peer-reviewed articles, conference papers, theses)
- Content-based criteria distinguishing relevant model classes vs peripheral topics (e.g., must address multiproduct single-resource lot-sizing/scheduling; exclude multi-machine unrelated problems)
- Screening and selection: Transparent protocol for study selection to enhance traceability and reproducibility (title/abstract screening, full-text review, coding of modelling features and empirical evidence).
- Data extraction and synthesis: Structured coding of each selected study for modelling assumptions, methods, solution approaches, level/type of empirical validation, documented adoption factors, and treatment of sustainability/resilience; qualitative synthesis and pattern identification across studies.
- Expected outputs: Comparative tables and thematic analyses mapping theoretical approaches to levels of empirical support and industrial applicability; identification of research gaps.
Implications for AI Economics
- For AI-driven decision systems: The review will clarify which ELSP modelling assumptions and constraints are most common in practice, guiding realistic problem formulations for ML/AI methods (e.g., reinforcement learning, supervised learning for demand/lead-time prediction, hybrid optimization-ML approaches).
- For economic assessment of automation and optimization: By documenting empirical adoption factors and industrial evidence, the review can inform cost–benefit and diffusion models of scheduling/optimization technologies across sectors.
- For data-driven methods and benchmarking: Identification of gaps in empirical validation implies a need for open benchmark datasets and industrial case studies to evaluate AI/optimization methods under realistic setup and sequencing conditions.
- For sustainability and resilience modeling: The review’s focus on whether and how studies incorporate environmental and disruption-robustness objectives highlights opportunities for AI economics to evaluate trade-offs (productivity vs emissions vs robustness) and design decision-support tools that internalize externalities.
- For methodological innovation: Mapping classical analytical and heuristic solution methods to contemporary computational resources suggests fertile ground for AI-enhanced solvers (metaheuristics guided by learned policies, surrogate models to speed optimization, causal analysis of adoption determinants).
- For policy and investment: Insights on drivers and barriers to industry adoption (data readiness, computational requirements, organizational fit) can inform policy interventions or investment decisions that accelerate adoption of AI/optimization tools in manufacturing.
If you want, I can: - Draft the exact search query blocks and Boolean strings for major databases (Scopus, Web of Science, IEEE, ABI/INFORM). - Propose a data-extraction template and coding schema tailored for assessing empirical validation and AI-applicability.
Assessment
Paper Typereview_meta
Evidence Strengthn/a — This document is a protocol for a systematic literature review and does not present primary empirical or theoretical results; therefore there is no direct evidence to appraise.
Methods Rigormedium — The protocol outlines standard components of a rigorous systematic review (structured search blocks, explicit inclusion/exclusion criteria, transparent screening and coding, and planned qualitative synthesis), which supports reproducibility; however the supplied text lacks detailed operational elements (exact databases and Boolean queries, time window and language cutoffs, quality/risk-of-bias appraisal procedures, inter-rater reliability procedures, and meta-analytic plans), limiting full assessment of rigor.
SamplePlanned sample: published literature (peer‑reviewed articles, conference papers, theses and possibly gray literature) that address multiproduct lot‑sizing and scheduling on a single machine/resource under setup-related constraints (setup times/costs, sequence-dependent changeovers), with additional tagging for empirical/industrial applications and sustainability/resilience treatment; intended searches across major bibliographic databases (e.g., Scopus, Web of Science, IEEE, ABI/INFORM) though exact databases and time window are not specified in the supplied text.
Themesproductivity adoption org_design
GeneralizabilityRestricted to single‑machine/single‑resource problems; findings will not generalize to multi‑machine, flow‑shop, job‑shop, or other production environments., Excludes peripheral topics and unrelated multi‑resource formulations, so broader manufacturing optimization literature may be omitted., Potential language and publication‑type restrictions (if applied) could introduce bias against non‑English or non‑peer‑reviewed industrial reports., Heterogeneity across industries, firm sizes, and data availability may limit the ability to draw sector‑wide conclusions., As a literature review protocol, results depend on the primary studies’ quality and presence of empirical validation, which may be uneven.
Claims (10)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| The document is a protocol for a systematic literature review of multiproduct lot-sizing and scheduling on a single machine or resource, including the Economic Lot Scheduling Problem and its variants. Other | null_result | Scope and design of the planned systematic literature review |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will examine modelling assumptions concerning demand, costs, inventories, setup structures, sequencing, and capacity in the ELSP literature. Other | null_result | Prevalence and characteristics of modelling assumptions |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will compare multiple methodological approaches used in ELSP research, including analytical models, mixed-integer programming, heuristics, metaheuristics, decomposition, and simulation. Other | null_result | Distribution and use of solution methodologies |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will assess the level and type of empirical or industrial validation reported in ELSP studies. Organizational Efficiency | null_result | Level and type of empirical or industrial validation |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will investigate factors influencing industry adoption of ELSP models and methods, including practical constraints, data availability, computational tractability, and organizational and economic drivers. Adoption Rate | null_result | Drivers and barriers to industrial adoption |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will examine whether and how ELSP studies incorporate sustainability and resilience criteria, including emissions, energy, and robustness to disruptions. Other | null_result | Incorporation of environmental and disruption-resilience criteria in models |
Reading fidelity
high
Study strength
low
|
not reported
|
| The protocol uses a structured search strategy based on conceptual search blocks covering lot-sizing, scheduling, single-machine or single-resource settings, setup characteristics, ELSP, empirical or industrial application, and sustainability or resilience. Other | null_result | Search strategy transparency and reproducibility |
Reading fidelity
high
Study strength
low
|
not reported
|
| Study selection will involve title and abstract screening followed by full-text review, with coding of modelling features and empirical evidence. Other | null_result | Study screening, selection, and evidence coding |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review will synthesize selected studies qualitatively to identify patterns in model formulation, empirical support, industrial applicability, sustainability, resilience, and adoption-related factors. Organizational Efficiency | null_result | Cross-study patterns in modelling, validation, applicability, and adoption |
Reading fidelity
high
Study strength
low
|
not reported
|
| The review is intended to identify gaps in empirical validation and support the development of open benchmark datasets and industrial case studies for evaluating AI and optimization methods under realistic setup and sequencing conditions. Training Effectiveness | positive | Availability of empirical benchmarks and realistic evaluation settings |
Reading fidelity
high
Study strength
low
|
not reported
|