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Villarreal, B.; Garza-Reyes, J. A.; Kumar, V. (2016)
Publisher: Emerald
Languages: English
Types: Article
Subjects:
Purpose: The improvement of routing operations performance has been of great concern for organisations. This has led to the development of alternative lean-based methods, however the literature research on the applications of lean thinking in the transportation sector is still considered rather limited. Therefore, this paper presents a lean thinking and simulation-based approach to improve the efficiency of warehousing and routing operations. \ud \ud Design/Methodology/Approach: The paper reviews the existing literature in the area of lean transportation and then presents and applies a novel approach to improve the vehicle routing operations of a Mexican firm. The proposed approach suggests the classification of wastes into those relevant to transport operations, their identification through a Transportation Value Stream Mapping (TVSM) study, and the use of the Transportation Overall Vehicle Effectiveness (TOVE) index for the measure of the overall performance of the transport operations. \ud \ud Findings: The results obtained from the case study indicate that the proposed approach is an effective alternative for the improvement of vehicle routing operations as the number of routes decreased from 30 to 22 and the distance travelled by 32%. Similarly, the average number of clients served by each route increased by 23% as well as the TOVE index increased from 6.9% to 19.3%. The TOVE component measures of vehicle performance and operating availability efficiencies also increased significantly while quality issues, in the form of number of customers not served per route, were reduced from 6 to 0. \ud \ud Originality/value: The improvement of routing operations performance has been traditionally addressed through operations research and mathematical modelling approaches. This paper presents an alternative and novel lean thinking and simulation-based approach to improve the efficiency of routing operations.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Ak, A., Erera, A.L. (2007), “A Paired-Vehicle Recursive Strategy for the Vehicle Routing Problem with Stochastic Demands”, Transportation Science, Vol. 41, No. 2, pp. 222- 237.
    • Boudia, M., Ould Louly, M.A., Prins, C. (2008), “Fast heuristics for a combined production planning and vehicle routing problem”, Production Planning and Control: The Management of Operations, Vol. 19, No. 2, pp. 85-96.
    • Cabral, I., Grilo, A., Cruz-Machado, V. (2012), “A decision-making model for lean, agile, resilient and green supply chain management”, International Journal of Production Research, Vol. 50, No. 17, pp. 4830-4845.
    • Cameron, S., Price, D. (2009), Business research methods: a practical approach, Chartered Institute of Personal and Development, London.
    • Chase, R.B., Apte, U.M. (2007), “A history of research in service operations: What's the big idea?”, Journal of Operations Management, Vol. 25, No. 2, pp. 375-386.
    • Chan, F. T. S., Kumar, V., & Tiwari, M. K. (2009). “The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model”, International Journal of Production Research, Vol. 47, No. 1, pp.
    • Chiu, H.N., Lee, Y.S., Chang, J.H. (2006), “Two approaches to solving multi-depot vehicle routing problem with time windows in a time-based logistic environment”, Production Planning and Control: The Management of Operations Vol. 17, No. 5, pp.
    • Chung, S. H., Tse, Y. K., Choi, T. M. (2015), “Managing disruption risk in express logistics via proactive planning”, Industrial Management & Data Systems, Vol. 115, No.
    • 8, pp. 1481-1509.
    • Cigolini, R., Pero, M, Rossi, T. and Sianesi, A. (2015), “Using simulation to manage project supply chain in the offshore oil and gas industry”, Production Planning and Control: The Management of Operations, Vol. 23, No. 3, pp. 167-177.
    • Dantzig, G.B., Ramser, J.H. (1959), “The Truck Dispatching Problem”, Management Science, Vol. 6, No.1, pp. 80-91.
    • Dekker, R., Bloemhof, J., Mallidis, I., (2012), “Operations Research for green logistics - An overview of aspects, issues, contributions and challenges”, European Journal of Operational Research, Vol. 219. No. 3, pp. 671-679.
    • Demir, E., Bektas, T., Laporte, G. (2014), “A review of recent research on green road freigth transportation”, European Journal of Operational Research. Vol. 237 No. 3, pp.
    • Dennis, P. (2002), Lean Production Simplified: A Plain Language Guide to the World's Most Powerful Production System, Productivity Press, New York, NY.
    • Guan, T.S., Mason, K., Disney, S. (2003), “MOVE: Modified Overall Vehicle Effectiveness”, 8th International Symposium on Logistics, Seville, Spain, July 2003.
    • Harrell, C., Ghosh, B.K., Bowden, R. (2011), Simulation using PROMODEL, 3rd Ed., McGraw-Hill Education, NY.
    • Hasle, G., Kloster, O. (2007), “Índustrial Vehicle Routing”, In Geir Hasle, Knut-Andreas LieAND Ewald Quak, editors, Geometric Modelling, Numerical Simulation and Optimization, Springer, Berlin Heidelberg, pp. 397-435.
    • Hines, P., Taylor, D. (2000), Going Lean, Lean Enterprise Research Centre, Cardiff Business School.
    • Instituto Mexicano para la Competitividad (2004), Elementos para mejorar la competitividad del transporte de carga, Octubre.
    • Irnich, S. (2008), “Solution of Real-World Postman Problems”, European Journal of Operational Research, Vol. 190, No. 1, pp. 52-67.
    • Jemai, Z., Rekik, Y., Kalaï, R. (2013), “Inventory routing problems in a context of vendor-managed inventory system with consignment stock and transshipment”, Production, Planning and Control: The Management of Operations, Vol. 24, Nos. 8-9, pp. 671-683.
    • Jovanoski, B., Nove Minovski, R., Lichtenegger, G., Voessner, S. (2013), “Managing strategy and production through hybrid simulation”, Industrial Management & Data Systems, Vol. 113, No. 8, pp. 1110-1132.
    • Kleijnen, J.P.C. (1995), “Verification and Validation of Simulation Models”, European Journal of Operational Research, Vol. 82, No. 1, pp. 145-162.
    • Law, A. (2006), Simulation Modeling and Analysis, 4th Edition, McGraw-Hill Higher Education, Columbus, OH.
    • Lu, M., Wong, L.C. (2007), “Comparison of two simulation methodologies in modeling construction systems: Manufacturing-oriented PROMODEL vs. construction-oriented SDESA”, Automation in Construction, Vol. 16, No. 1, pp. 86-95.
    • Lyons, A.C., Vidamour, K., Jain, R., Sutherland, M. (2013), “Developing an understanding of lean thinking in process industries”, Production, Planning and Control: The Management of Operations, Vol. 24, No.6, pp. 475-494.
    • MapInfo Corporation (2015), available at: http://www.mapinfo.com (accessed on 17 May 2015).
    • Marvel, J.H., Standridge, C.R. (2009), “A Simulation-enhanced Lean Design Process”, Journal of Industrial Engineering and Management, Vol. 2, No. 1, pp. 90-113.
    • Masson-Jones, R., Naylor, B., Towill, D. (2000), “Engineering the Leagile Supply Chain”, International Journal of Agile Management Systems, Vol. 2, No. 1, pp. 54-61.
    • McDonald, T., Van Aken, M.E., Rentes, A.F., (2002), “Utilizing Simulation to Enhance Value Stream Mapping: A Manufacturing Case Application”, International Journal of Logistics Research and Applications, Vol. 5, No. 2, pp. 213-232.
    • McKinnon, A., Campbell J., Leuchars, D. (1999), Benchmarking vehicle utilisation and energy consumption measurement of key performance indicators, Energy Consumption Guide 76 (DETR).
    • McKinnon, A.C., Ge, Y. & Leuchars, D., (2003), Key Performance Indicators for the Food Supply Chain”, Transport Energy Benchmarking Guide 78, London, Department for Transport.
    • Monden, Y. (1998), Toyota Production System: an integrated approach to just-in-time, 2nd ed., Chapman & Hall, London.
    • Mostafa, T. S., Talaat, H. (2015). “Intelligent Geographical Information System for Commercial Vehicle Routing (IGIS-VR): A Simulation Base Evaluation Model”, Journal of Traffic and Logistics Engineering Vol, 3, No. 2.
    • Nakajima, S. (1988), An introduction to TPM, Productivity Press, Portland, OR.
    • Ohno, T. (1988), Toyota Production System: beyond large-scale production, Productivity Press, Portland, OR.
    • Oppen, J., Lokketangen, A., Desrosiers, J. (2010), “Solving a rich vehicle routing and inventory problem using column generation”, Computers & Operations Research, Vol.
    • 37, No. 7, pp. 1308-1317.
    • Parthanadee, (2014), “Production efficiency improvement in batch production system using value stream mapping and simulation: a case study of the roasted and ground coffee industry”, Production Planning and Control: The Management of Operations, Vol.
    • 25, No. 5, pp. 425-446.
    • Ropke, S., Pisinger, D. (2006), “A unified heuristic for a large class of Vehicle Routing Problems with Backhauls”, European Journal of Operations Research, Vol. 171, No. 3, pp. 750-775.
    • Roessler, M. P., Metternich, J., Abele, E. (2014). “Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability”, Business and Management Research, Vol. 3, No. 2, pp. 93.
    • Samaddar, S., Heiko, L. (1993). “Waste elimination: The common denominator for improving operations”. Industrial Management & Data Systems, Vol 93, No. (8), pp.13- 19.
    • Simmons, D., Mason, R., Gardner, B. (2004), “Overall Vehicle Effectiveness”, International Journal of Logistics: Research and Applications, Vol. 7, No. 2, pp. 119-34.
    • Sternberg, H., Stefansson, G., Westernberg, E., Boije af Gennas, R., Allenstrom, E., Nauska, M.L. (2013), “Applying a Lean Approach to Identify Waste in Motor Carrier Operations”, International Journal of Productivity and Performance Management, Vol.
    • 62 No. 1, 2013, pp. 47-65.
    • Subsecretaría de Transporte (2013), Estadística básica del autotransporte federal 2013, Dirección General de Autotransporte Federal.
    • Sutherland, J.L., Bennett, B. (2007), “The seven deadly wastes of logistics: applying Toyota Production System principles to create logistics value”, White Paper No. 701, Center for Value Chain Research, Lehigh University, August.
    • Villarreal, B., Sañudo, M., Duran, B., Avila, L. (2009b), “A lean approach to vehicle routing”, 2008 IERC Proceedings, Miami, Florida.
    • Villarreal, B., Sañudo, M., Vega, A., Macias, S., Garza, E. (2012), “A lean scheme for improving vehicle routing operations”, Proceedings of the 2012 International Conference on Industrial and Operations Management, Istanbul, Turkey, July 3-6.
    • Villarreal, B. (2012), “The Transportation Value Stream Map (TVSM)”, European Journal of Industrial Engineering, Vol. 6, No. 2, pp. 216-233.
    • Wang, Z. and Lin, L., (2013),” A simulation-based algorithm for the capacitated vehicle routing problem with stochastic travel times”, Journal of Applied Mathematics, Vol.
    • Wang, T. K., Yang, T., Yang, C. Y., & Chan, F. T.S. (2015), “Lean principles and simulation optimization for emergency department layout design”, Industrial Management & Data Systems, Vol. 115, No. 4, pp. 678-699.
    • Wainer, G.A., (2009), Discrete-event Modeling and Simulation: A Practicioner´s Approach, CRC Press.
    • Zammori, F., Braglia, M., Frosolini, M. (2011), “Stochastic Overall Equipment Effectiveness”, International Journal of Production Research, Vol. 49, No. 21, pp. 6469- 6490.
    • Zhao, Q.H., Wang, S.Y., Xia, G.P., Lai, K.K. (2003), “Designing optimal routing strategies for a manufacturer: A case study”, Production Planning and Control: The Management of Operations, Vol. 14, No. 1, pp. 33-41.
    • Zhong, H., Hall, R.W., Dessouky, M. (2007), “Territory planning and vehicle dispatching with driver learning”, Transportation Science, Vol. 41, No. 1, pp. 74-89.
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