Development of a Lot-Sizing Model to Prevent Heat Stress and Work-Related Musculoskeletal Disorders

  • Sezen Korkulu Budapest University of Technology and Economics
  • Krisztián Bóna Budapest University of Technology and Economics
Keywords: ergonomics, manual material handling, metabolic cost, occupational heat stress, rest allowance


Management of heat stress and metabolic cost is vital for preventing any work-related disorders. In this paper, we integrated rest time formulations for heat strain and metabolic cost to develop a new lot sizing model for preventing heat exposure and work-related musculoskeletal disorders. The effects of heat strain and rest allowance on the total cost of the production supply process were investigated. The problem studied in this paper was the handling of the raw materials placed in boxes by manual material handling in order to supply the material requirement of a production line placed in a production area. For the realisation of the material handling transactions between the raw material warehouse and the production line, Electric Pallet Jack (EPJ) was used. The study covers the investigation of picking, storing, and carrying motions for the manual handling of these materials. The result of the analysis has shown that 8.5% savings were achieved by using the heat strain and rest time in comparison to the total cost of this part of the production line supply process with the ISO 7243 maximum metabolic work limit. Consequentially, the analysis results showed that the developed method demonstrated the viability of lot sizing model optimisation with multiple objectives and complex constraints with regards to the metabolic cost and heat strain.


Cheng Y, et al. Optimal production lot sizing when demand is proportional to stock and backorder levels. International Journal of Industrial Engineering. 2018;25(2): 137-155.

Cunha ARL, et al. Economic production quantity (EPQ) model with partial backordering and a discount for imperfect quality batches. International Journal of Production Research. 2018;56(18): 6279-6293. DOI: 10.1080/00207543.2018.1445878

Tian G, et al. A multi-objective supplier selection and order allocation through incremental discount in a fuzzy environment. Journal of Intelligent & Fuzzy Systems. 2019;37: 1435-1455. DOI: 10.3233/JIFS-182843

Tiwari S, et al. Sustainable inventory management with deteriorating and imperfect quality items considering carbon emission. Journal of Cleaner Production. 2018;192: 281-292. DOI: 10.1016/j.jclepro.2018.04.261

Wangsa DI, et al. An integrated vendor–buyer inventory model with transportation cost and stochastic demand. International Journal of Systems Science: Operations & Logistics. 2018;5(4): 295-309. DOI: 10.1080/23302674.2017.1296601

Zaho QH, et al. Model and algorithm of an ınventory problem with the consideration of transportation cost. Computers & Industrial Engineering. 2014;46(2): 389-397. DOI: 10.1016/j.cie.2003.12.019

Andriolo A, et al. A century of evolution from Harris's basic lot size model: Survey and research agenda. International Journal of Production Economics. 2014;155: 16-38. DOI: 10.1016/j.ijpe.2014.01.013

Darom NA, et al. An inventory model of supply chain disruption recovery with safety stock and carbon emission consideration. Journal of Cleaner Production. 2018;197: 1011-1021. DOI: 10.1016/J.JCLEPRO.2018.06.246

Gholamian RM, et al. Inventory control of obsolete products with price-dependent demand. Journal of Engineering Research. 2020;8(4): 169-184. DOI: 10.36909/jer.v8i4.8316

Rezaei J. Economic order quantity for growing items. International Journal of Production Economics. 2014;155: 109-113. DOI: 10.1016/j.ijpe.2013.11.026

Korkulu S, et al. Ergonomics as a social component of sustainable lot-sizing: A review. Periodica Polytechnica Social and Management Sciences. 2019;27(1): 1-8. DOI: 10.3311/PPso.12286

Al-Araidah O, et al. A Monte Carlo simulation to estimate fatigue allowance for female order pickers in high traffic manual picking systems. International Journal of Production Research. 2020;59(1): 1-12. DOI: 10.1080/00207543.2020.1770357

Andriolo A, et al. A new bi-objective approach for including ergonomic principles into EOQ model. International Journal of Production Research. 2016;54(9): 2610-2627. DOI: 10.1080/00207543.2015.1113324

Battini D, et al. New methodological framework to improve productivity and ergonomics in assembly system design. International Journal of Industrial Ergonomics. 2011;41(1): 30-42. DOI: 10.1016/j.ergon.2010.12.001

Battini D, et al. Linking human availability and ergonomics parameters in order-picking systems. IFAC-PapersOnLine. 2015;48(3): 345-350. DOI: 10.1016/j.ifacol.2015.06.105

Battini D, et al. Ergo-Lot-Sizing: Considering ergonomics in lot-sizing decisions. IFAC-PapersOnline. 2015;48(3): 326-331. DOI: 10.1016/J.IFACOL.2015.06.102

Battini D, et al. The integrated assembly line balancing and parts feeding problem with ergonomics considerations. IFAC-PapersOnLine. 2016;49(12): 191-196. DOI: 10.1016/j.ifacol.2016.07.594

Battini D, et al. Ergonomics in assembly line balancing based on energy expenditure: A multi-objective model. International Journal of Production Research. 2016;54(3): 824-845. DOI: 10.1080/00207543.2015.1074299

Battini D, et al. Ergo-lot-sizing: An approach to integrate ergonomic and economic objectives in manual materials handling. International Journal of Production Economics, 2017;185: 230-239. DOI: 10.1016/j.ijpe.2017.01.010

Botti L, et al. Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers & Industrial Engineering. 2017;111: 481-491. DOI: 10.1016/j.cie.2017.05.011

Finco S, et al. Heuristic methods to consider rest allowance into assembly balancing problem. IFAC-PapersOnLine. 2018;51(11): 669-674. DOI: 10.1016/j.ifacol.2019.11.473

Finco S, et al. Workers’ rest allowance and smoothing of the workload in assembly lines. International Journal of Production Research. 2020;58(4): 1255-1270. DOI: 10.1080/00207543.2019.1616847

Finco S, et al. A bi-objective model to include workers’ vibration exposure in assembly line design. International Journal of Production Research. 2020. p. 4017-4032. DOI: 10.1080/00207543.2020.1756512

Gholamian MR, et al. A sustainable inventory model by considering environmental ergonomics and environmental pollution, case study: Pulp and paper mills. Journal of Cleaner Production. 2018;199: 444-458. DOI: 10.1016/j.jclepro.2018.07.175

Tang Q, et al. Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach. Computers and Operations Research. 2020;118: 104905. DOI: 10.1016/j.cor.2020.104905

Bernard TE, et al. Short-term heat stress exposure limits based on wet bulb globe temperature adjusted for clothing and metabolic rate. Journal of Occupational and Environmental Hygiene. 2009;6(10): 632-638. DOI: 10.1080/15459620903133642

Chan APC, et al. Using the Thermal Work Limit as an Environmental Determinant of Heat Stress for Construction Workers. Journal of Management in Engineering, 2013;29(4): 414-423. DOI: 10.1061/(ASCE)ME.1943-5479.0000162

OSHA 2017. OSHA Technical Manual. Chapter 4: Health hazards-heat stress. Available at:

ILO 2015. Global trends on occupational accidents and diseases. Available at:

EU-OSHA 2017. An international comparison of the cost of work-related accidents and illnesses. Available at: [Accessed 8 May 2019].

Bevan S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Practice & Research Clinical Rheumatology. 2015;29(3): 356-373. DOI: 10.1016/j.berh.2015.08.002

Price AD. Calculating relaxation allowances for construction operatives — Part 1: Metabolic cost. Applied Ergonomics. 1990;21(4): 311-317. DOI: 10.1016/0003-6870(90)90202-9

Rowlinson S, et al. Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry. Annals of Occupational Hygiene. 2014;58(3): 326-339. DOI: 10.1093/annhyg/met070

Brake DJ, et al. Limiting metabolic rate (thermal work limit) as an index of thermal stress. Applied Occupational and Environmental Hygiene. 2002;17(3): 176-186. DOI: 10.1080/104732202753438261

Garg A, et al. Prediction of metabolic rates for manual materials handling jobs. The American Industrial Hygiene Association Journal. 1978;39(8): 661-674. DOI: 10.1080/0002889778507831

Digiesi S, et al. Sustainable Inventory Management. In: New Models for Sustainable Logistics. SpringerBriefs in Operations Management. Springer; 2016. DOI: 10.1007/978-3-319-19710-4

ISO 7243. Hot Environments - Estimation of the heat stress on working man, based on the WBGT-index (wet bulb globe temperature). Geneva: International Standard Organisation; 1989.

Miller VS, et al. The thermal work limit is a simple reliable heat index for the protection of workers in thermally stressful environments. Annals of Occupational Hygiene. 2007;51(6); 553-561. DOI: 10.1093/annhyg/mem035

Brake DJ, et al. A valid method for comparing rational and empirical heat stress indices. Annal of Occupational Hygiene. 2002;46(2): 165-74. DOI: 10.1093/annhyg/mef030

ISO 8996. Ergonomics of the thermal environment. Determination of metabolic rate. Geneva: International standards Organisation; 2004.

Barta Z, et al. Development of AIM method planning of inbound material handling processes. Acta Technica Jaurinensis Series Logistica. 2010;3(3): 285-299. Available at:

ISO 80000-5. Quantities and units -- Part 5: Thermodynamics. 2007.

How to Cite
Korkulu S, Bóna K. Development of a Lot-Sizing Model to Prevent Heat Stress and Work-Related Musculoskeletal Disorders. Promet [Internet]. 2021Dec.13 [cited 2024Jun.21];33(6):871-82. Available from: