The Optimising Known Warehouses Framework for Supply Chain Optimisation
Résumé
In this research, a novel generic hybrid multi-echelon, multi-product supply chain optimisation framework, known as the Optimising Known Warehouses (OKW) Framework, was developed. The framework is designed to optimise inventory and routing within manufacturing supply chains, while also enabling comparisons among different supply chain configurations. The methodology involved the development of the OKW inventory and routing optimisation models using linear programming techniques, while the OKW simulation model was developed as a discrete-event simulation model. The interrelationship among the components of the OKW framework, as well as its interdisciplinary applications, were also examined. The results obtained from applying the framework to five manufacturing supply chains demonstrated its effectiveness in optimising diverse supply chain systems. For example, in the water bottling supply chain, the optimal inventory and routing policies generated by the framework reduced backorders of 50cl, 75cl, and 150cl bottled water products by 8.7%, 18.7%, and 34.7%, respectively. Similarly, in the biscuit manufacturing supply chain, the optimal inventory and routing policies reduced backorders of cookies, strawberry wafers, vanilla wafers, and chocolate wafers by 5.2%, 22.0%, 17.1%, and 22.3%, respectively. It is recommended that the contemporary OKW Framework be mainstreamed within optimisation theory and utilised alongside conventional methods so that industries and the general public can benefit from the numerous advantages offered by the framework.
Téléchargements
Références
Aastrup, J., & Kotzab, H. (2010). Forty years of out-of-stock research—and shelves are still empty. The International Review of Retail, Distribution and Consumer Research, 20(1), 147–164.
Acar, Y., Kadipasaoglu, S., & Schipperijn, P. (2010). A decision support framework for global supply chain modelling: An assessment of the impact of demand, supply and lead-time uncertainties on performance. International Journal of Production Research, 48(11), 3245–3268.
Aliev, R. A., Fazlollahi, B., Guirimov, B., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177(20), 4241–4255.
Almeder, C., Preusser, M., & Hartl, R. F. (2009). Simulation and optimization of supply chains: Alternative or complementary approaches? OR Spectrum, 31, 95–119.
Attajer, A., & Mecheri, B. (2024). Multi-agent simulation approach for modular integrated construction supply chain. Applied Sciences, 14(12), 1–25.
Balali, A., & Yunusa-Kaltungo, A. (2025). Selection of passive energy consumption optimisation strategies for buildings. Renewable and Sustainable Energy Reviews, 210, 1–21.
Bertolini, M., Ferretti, G., Vignali, G., & Volpi, A. (2013). Reducing out of stock, shrinkage and overstock through RFID in the fresh food supply chain: Evidence from an Italian retail pilot. International Journal of RF Technologies, 4(2), 107–125.
Bose, S., & Pekny, J. (2000). A model predictive framework for planning and scheduling problems: A case study of consumer goods supply chain. Computers & Chemical Engineering, 24(2–7), 329–335.
Brown, S. T., Schreiber, B., Cakouros, B. E., Wateska, A. R., Dicko, H. M., Connor, D. L., Jaillard, P., Mvundura, M., Norman, B. A., & Levin, C. (2014). The benefits of redesigning Benin's vaccine supply chain. Vaccine, 32(32), 4097–4103.
Budi, R., & Agustia, D. (2024). Supplier selection optimisation to enhance procurement strategies in power generation operation and maintenance service. IEEE Engineering Management Review, 99, 1–17.
Bunt, S., Berdanier, C. G., Menold, J., & Brown, N. C. (2025). Comparing optimisation strategies of building design students and expert practitioners across subtasks. Journal of Engineering Design, 1–27.
Byrne, P., & Heavey, C. (2006). The impact of information sharing and forecasting in capacitated industrial supply chains: A case study. International Journal of Production Economics, 103(1), 420–437.
Dabbene, F., Gay, P., & Sacco, N. (2008). Optimisation of fresh-food supply chains in uncertain environments, Part I: Background and methodology. Biosystems Engineering, 99(3), 348–359.
Dai, Z., & Zheng, X. (2015). Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model. Computers & Industrial Engineering, 88, 444–457.
Das, A. K., & Chilukuri, B. R. (2025). Bi-objective optimization for last-mile routing of garbage trucks: Aesthetics and public exposure. Waste Management & Research, 43(3), 369–377.
Disney, S. M., & Towill, D. R. (2002). A procedure for the optimization of the dynamic response of a vendor managed inventory system. Computers & Industrial Engineering, 43(1–2), 27–58.
Fox, M. S., Barbuceanu, M., & Teigen, R. (2001). Agent-oriented supply-chain management. Information-Based Manufacturing: Technology, Strategy and Industrial Applications, 81–104.
Garnett, R. (2023). Bayesian Optimization. Cambridge University Press.
Górtowski, S. (2025). A data-driven framework for incremental supply chain optimization. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, 221, 169–184.
Guerrero, B., Mula, J., & Poler, R. (2025). Sustainable optimisation approaches for production planning and control to evolve towards Industry 5.0. International Journal of Production Research, 1–33.
Gupta, A., & Maranas, C. D. (2000). A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty. Industrial & Engineering Chemistry Research, 39(10), 3799–3813.
Haseltalab, S., & Karimi, H. (2025). Enhancing efficiency in delivering petroleum products: Optimizing the deteriorating inventory-routing problem with heterogeneous multi-compartment vehicles. International Journal of Management Science and Engineering Management, 20(2), 159–182.
Hess, C., Dragomir, A. G., Doerner, K. F., & Vigo, D. (2024). Waste collection routing: A survey on problems and methods. Central European Journal of Operations Research, 32(2), 399–434.
Huzzey, J. M., von Keyserlingk, M. A., & Overton, T. R. (2012). The behavioral and physiological consequences of overstocking dairy cattle. American Association of Bovine Practitioners Conference Proceedings.
Ivanov, D., Dolgui, A., & Sokolov, B. (2025). Ripple effect in the supply chain: Definitions, frameworks and future research perspectives. In Handbook of Ripple Effects in the Supply Chain (pp. 1–34). Springer.
Khallaf, N., Abd-El Rouf, O., Algarni, A. D., Hadhoud, M., & Kafafy, A. (2025). Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms. Frontiers in Artificial Intelligence, 8, 1–14.
Kiesmüller, G., De Kok, A., & Fransoo, J. (2005). Transportation mode selection with positive manufacturing lead time. Transportation Research Part E: Logistics and Transportation Review, 41(6), 511–530.
Ko, H. J., Ko, C. S., & Kim, T. (2006). A hybrid optimization/simulation approach for a distribution network design of 3PLs. Computers & Industrial Engineering, 50(4), 440–449.
Lee, Y. H., & Kim, S. H. (2000). Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach. 2000 Winter Simulation Conference Proceedings (Cat. No. 00CH37165).
Lu, F., Zhou, G., Zhang, C., Liu, Y., & Taisch, M. (2025). Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing. Robotics and Computer-Integrated Manufacturing, 91, 1–19.
Manzini, R., Ferrari, E., Gamberi, M., Persona, A., & Regattieri, A. (2005). Simulation performance in the optimisation of the supply chain. Journal of Manufacturing Technology Management, 16(2), 127–144.
McCall, J. (2005). Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184(1), 205–222.
Memari, A., Galankashi, M. R., Anjomshoae, A., & Rahim, A. R. B. A. (2012). Scenario-based simulation in production-distribution network under demand uncertainty using ARENA. 2012 7th International Conference on Computing and Convergence Technology (ICCCT).
Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8(5), 321–339.
Moyaux, T., Chaib-Draa, B., & D'Amours, S. (2003). Multi-agent coordination based on tokens: Reduction of the bullwhip effect in a forest supply chain. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems.
Mweshi, G. K. (2022). Effects of overstocking and stockouts on the manufacturing sector. International Journal of Advances in Engineering and Management, 4(9), 1054–1064.
Nie, Y., Hicks, B., Ferreira, L., Halog, A., & Valero, M. (2024). Sustainability-driven food supply chain design and optimisation through digital twinning. Procedia CIRP, 130, 671–676.
Nogueira, F. (2014). Bayesian Optimization: Open Source Constrained Global Optimization Tool for Python. GitHub. BayesianOptimization GitHub Repository
Ogundipe, O. B., Okwandu, A. C., & Abdulwaheed, S. A. (2024). Optimizing construction supply chains through AI: Streamlining material procurement and logistics for project success. GSC Advanced Research and Reviews, 20(1), 147–158.
Ostovari, A., Benyoucef, L., Haddou Benderbal, H., & Delorme, X. (2025). Enhancing scalable reconfigurable manufacturing systems through robust optimisation: Energy efficiency and cost minimisation under uncertainty. International Journal of Production Research, 63(8), 3064–3089.
Preusser, M., Almeder, C., Hartl, R. F., & Klug, M. (2005). LP modelling and simulation of supply chain networks. Supply Chain Management und Logistik: Optimierung, Simulation, Decision Support.
Quinton, F. A., Myhr, P. A. S., Barani, M., Crespo del Granado, P., & Zhang, H. (2025). Quantum annealing applications, challenges and limitations for optimisation problems compared to classical solvers. Scientific Reports, 15(1), 1–15.
Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396–419.
Senyigit, E., & Soylemez, I. (2012). The analysis of heuristics for lot sizing with supplier selection problem. Procedia - Social and Behavioral Sciences, 62, 672–676.
Shambayati, H., Nikabadi, M. S., & Rahmanimanesh, M. (2025). Customer satisfaction optimisation in a dynamic closed-loop supply chain under uncertainty. International Journal of Industrial and Systems Engineering, 49(1), 18–56.
Sharma, M. J., Moon, I., & Bae, H. (2008). Analytic hierarchy process to assess and optimize distribution network. Applied Mathematics and Computation, 202(1), 256–265.
Shukla, S. K., Tiwari, M., Wan, H.-D., & Shankar, R. (2010). Optimization of the supply chain network: Simulation, Taguchi, and Psychoclonal algorithm embedded approach. Computers & Industrial Engineering, 58(1), 29–39.
Silva, V. A., Wang, A., Ferreira Filho, V. J. M., & Gounaris, C. E. (2024). Routing and scheduling of platform supply vessels in offshore oil and gas logistics. Computers & Operations Research, 164, 106556.
Sitek, P., & Wikarek, J. (2015). A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management. International Journal of Production Research, 53(21), 6611–6628.
Smew, W., Young, P., & Geraghty, J. (2013). Supply chain analysis using simulation, Gaussian process modelling and optimisation. International Journal of Simulation Modelling, 12(3), 178–189.
Taktak, S., Hachicha, W., & Masmoudi, F. (2012). A computer-assisted performance analysis and optimization (CPAO) of manufacturing systems based on ARENA® software. International Journal of Advanced Science and Technology, 39, 93–106.
Truong, & Azadivar. (2003). Simulation-based optimization for supply chain configuration design. Proceedings of the 2003 Winter Simulation Conference.
Ugoji, K. U., Isaac, O. E., Nkoi, B., & Wofuru-Nyenke, O. (2022). Improving the operational output of marine vessel main engine system through cost reduction using reliability. International Journal of Engineering and Modern Technology (IJEMT), 8(2), 36–52.
Van der Vorst, J. G., Beulens, A. J., & van Beek, P. (2000). Modelling and simulating multi-echelon food systems. European Journal of Operational Research, 122(2), 354–366.
Wofuru-Nyenke, O. (2024a). Routing and facility location optimization in a dairy products supply chain. Future Technology, 3(2), 44–49.
Wofuru-Nyenke, O., & Briggs, T. (2022). Predicting demand in a bottled water supply chain using classical time series forecasting models. Journal of Future Sustainability, 2(2), 65–80.
Wofuru-Nyenke, O. K. (2022). Forecasting model accuracy assessment in a bottled water supply chain. International Journal of Engineering and Modern Technology, 8(5), 101–108.
Wofuru-Nyenke, O. K. (2023). Mechanized cover crop farming: Modern methods, equipment and technologies. Circular Agricultural Systems, 3(1).*
Wofuru-Nyenke, O. K. (2024b). Critical path method utilization for optimal scheduling of production activities. Future Sustainability, 2(3), 1–5.
Wofuru-Nyenke, O. K. (2024c). Multi-Attribute Utility Theory modelling for product design evaluation. International Journal of Technology & Engineering Studies, 10(1), 26–33.
Wofuru-Nyenke, O. K., & Aikhuele, D. O. (2025). Optimal order quantity estimation in a manufacturing supply chain. International Journal of Production and Maintenance Engineering, 1(1), 1–12.
Wofuru-Nyenke, O. K., Briggs, T. A., & Aikhuele, D. O. (2023). Advancements in sustainable manufacturing supply chain modelling: A review. Process Integration and Optimization for Sustainability, 7(1), 3–27.
Xu, X., Li, F., Wu, T., Huang, X., Guan, X., Zheng, T., & Shen, L. (2025). Location-routing optimization problem of pharmaceutical cold chain logistics with oil-electric mixed fleets under uncertainties. Computers & Industrial Engineering, 201, 110932.
Zainali, S., Lu, S. M., Fernández-Solas, Á., Cruz-Escabias, A., Fernández, E. F., Zidane, T. E. K., Honningdalsnes, E. H., Nygård, M. M., Leloux, J., & Berwind, M. (2025). Modelling, simulation, and optimisation of agrivoltaic systems: A comprehensive review. Applied Energy, 386, 1–29.
Zou, H., & Jiang, J. (2025). Pharmaceutical logistics network planning considering low-carbon policy and demand uncertainty. Applied Mathematical Modelling, 141, 115933.
