International Journal of Production Research

Management Theories and Methods for Safe Operations of Hazardous Chemicals in Production and Logistics Systems

Safe operation of hazardous chemicals is a complex decision problem at the heart of modern production and logistics systems. Chemical manufacturing, pharmaceuticals, electronics, batteries, and energy depend on materials whose handling couples production scheduling, storage and inventory control, facility location, transport routing, and end-of-life recovery into a single risk-bearing system (Erkut, Tjandra and Verter, 2007). IJPR is the natural home for this agenda: the journal’s mission is decision aid for the design, measurement, management and control of production and logistics systems, and hazardous-chemical safety is precisely where that mission meets hard, irreversible operational constraints. The defining feature of this problem is that safety is not an add-on objective but a binding, system-wide coupling. The field’s foundations were built around isolated functions: risk-based route choice between an origin and destination (Erkut and Verter, 1998) and road-network design for shipments (Kara and Verter, 2004). More recent work extends single functions under uncertainty, such as reliable multi-modal transport network design (Mohammadi, Jula and Tavakkoli-Moghaddam, 2017) and hazardous-waste location-routing for circular treatment and recycling systems (Yu and Solvang, 2016). Yet a schedule optimised for throughput lengthens inventory dwell time and raises storage exposure; routing decided without regard to facility siting yields network-level risk; storage policies set without reference to batch sizes or transport windows create bottlenecks that amplify exposure. Integrated formulations that span scheduling, storage and transport remain comparatively rare (Mohammadi, 2020); recent comprehensive reviews map this still-fragmented landscape and call for integration (Mohri et al., 2022). This leaves a persistent gap between siloed academic models and the regulation-bound reality of hazardous-chemical operations, where cross-functional coordination is the precondition for safe, efficient and sustainable performance.

This gap distinguishes the special issue from IJPR’s current portfolio. Where recent issues address AI-driven decision-making, agentic supply chains, and net-zero circular manufacturing in general settings, the hazardous-materials context imposes qualitatively different requirements: hard regulatory constraints, irreversible failure consequences, and risk objectives that cannot be traded off linearly against cost. These features demand new decision models rather than the transfer of existing ones, which is why a dedicated issue is warranted now, as supply chains electrify and chemical-intensive sectors expand. We invite original research that develops and rigorously tests decision models for safe hazardous-chemical operations across production and logistics, integrating optimisation, simulation, and data-driven methods with domain knowledge of hazardous-materials management. Priority themes include integrated production planning under safety constraints; robust storage and inventory control under uncertainty; joint optimisation of facility location, fleet deployment, and routing; dynamic risk-aware transport routing; safe disposal and resource recovery in circular systems; multi-objective analytics reconciling efficiency, safety and sustainability; data-driven risk prediction and real-time coordination (Brintrup et al., 2024; Wyrembek, Baryannis and Brintrup, 2025); disruption recovery and resilience; incentive and game-theoretic mechanisms for multi-stakeholder operations (Mohri et al., 2020); and policy-regulated decision models. Empirical studies with strong industrial grounding and clear managerial insight are encouraged.

By bridging operations research, production engineering, logistics management, and safety science, the issue aims to advance both the theoretical foundations and the practical decision-support capabilities needed for safer, more efficient, and more sustainable hazardous-chemical operations.

References

Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G. and MacCarthy, B.L. (2024) ‘Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks’, International Journal of Production Research, 62(13), pp. 4674–4695. https://doi.org/10.1080/00207543.2023.2270719

Erkut, E. and Verter, V. (1998) ‘Modeling of transport risk for hazardous materials’, Operations Research, 46(5), pp. 625–642. https://doi.org/10.1287/opre.46.5.625

Erkut, E., Tjandra, S.A. and Verter, V. (2007) ‘Chapter 9 Hazardous materials transportation’, in Barnhart, C. and Laporte, G. (eds.) Handbooks in Operations Research and Management Science, Vol. 14. Amsterdam: Elsevier, pp. 539–621. https://doi.org/10.1016/S0927-0507(06)14009-8

Kara, B.Y. and Verter, V. (2004) ‘Designing a road network for hazardous materials transportation’, Transportation Science, 38(2), pp. 188–196. https://doi.org/10.1287/trsc.1030.0065

Mohammadi, M. (2020) ‘Designing an integrated reliable model for stochastic lot-sizing and scheduling problem in hazardous materials supply chain under disruption and demand uncertainty’, Journal of Cleaner Production, 274, 122621. https://doi.org/10.1016/j.jclepro.2020.122621

Mohammadi, M., Jula, P. and Tavakkoli-Moghaddam, R. (2017) ‘Design of a reliable multi-modal multi-commodity model for hazardous materials transportation under uncertainty’, European Journal of Operational Research, 257(3), pp. 792–809. https://doi.org/10.1016/j.ejor.2016.07.054

Mohri, S.S., Asgari, N., Zanjirani Farahani, R., Bourlakis, M. and Laker, B. (2020) ‘Fairness in hazmat routing-scheduling: a bi-objective Stackelberg game’, Transportation Research Part E: Logistics and Transportation Review, 140, 102006. https://doi.org/10.1016/j.tre.2020.102006

Mohri, S.S., Mohammadi, M., Gendreau, M., Pirayesh, A., Ghasemaghaei, A. and Salehi, V. (2022) ‘Hazardous material transportation problems: a comprehensive overview of models and solution approaches’, European Journal of Operational Research, 302(1), pp. 1–38. https://doi.org/10.1016/j.ejor.2021.11.045

Wyrembek, M., Baryannis, G. and Brintrup, A. (2025) ‘Causal machine learning for supply chain risk prediction and intervention planning’, International Journal of Production Research, 63(15), pp. 5629–5648. https://doi.org/10.1080/00207543.2025.2458121

Topics

• Integrated Production Planning and Hazardous Chemical Management under Safety Constraints
• Robust Storage and Inventory Control for Hazardous Chemicals under Uncertainty
• Intelligent Optimization of Hazardous Chemical Logistics Networks and Transportation Scheduling
• Road Transport Routing and Distribution Optimization for Hazardous Materials with Dynamic Risk
• Safe Disposal and Resource Utilization of Hazardous Chemical Waste in Circular Systems
• Multi-Objective Decision Analytics Balancing Efficiency, Safety, and Sustainability
• Data-Driven Risk Prediction and Real-Time Coordination in Hazardous Chemical Supply Chains
• Emergency Response and Disruption Recovery in Hazardous Chemicals Management Systems
• Game-Theoretic and Incentive Mechanisms for Multi-Stakeholder Hazardous Material Operations
• Policy-Regulated Decision Models for Safe and Circular Hazardous Chemical Management

Keywords
Hazardous Chemical Operations; Production and Storage Management; Logistics Network Optimization; Transportation Routing and Distribution; Safe Disposal and Resource Utilization; Circular Economy; Risk and Safety Analytics; Data-Driven Decision Models; Emergency Response and Resilience; Multi-Objective Optimization