Abstract
Selecting an effective category of products and their distribution are a challenge in distribution centers separated in two successive stages. First, the optimal number of the products and their participation will be selected. Then, an appropriate planning for distributing and transporting the selected products is determined. Hence, this paper develops a bi-objective mathematical model to integrate truck scheduling and transportation planning in a cross-docking system in a forward/reverse logistics network. For effective products category selection, a hybrid intelligent product portfolio optimization model is proposed. To solve the bi-objective model, a hybrid of the improved version of the augmented e-constraint method (AUGMECON2) and TOPSIS is designed and utilized. Moreover, a real industrial case is provided to justify the performance and applicability of the model and the solution approach.
Similar content being viewed by others
References
Agustina, D., Lee, C., & Piplani, R. (2014). Vehicle scheduling and routing at a cross docking center for food supply chains. International Journal of Production Economics, 152, 29–41.
Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67–78.
Bodie, Z., Kane, A., & Marcus, A. J. (2009). Investments (8th ed.). Irwin, NY: McGraw Hill.
Buijs, P., Vis, I. F. A., & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational Research, 239, 593–608.
Chopra, S. (2003). Designing the distribution network in a supply chain. Transportation Research, 5, 124–140.
Cardoso, S., Barbosa-Póvoa, A., & Relvas, S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226, 436–451.
Cóccola, M., Me’ndez, C. A., & Dondo, R. G. (2015). A branch-and-price approach to evaluate the role of cross-docking operations in consolidated supply chains. Computer Chemical Engineering, 80, 15–29.
Deb, K. (2001). Multiobjective optimization using evolutionary algorithms. Chichester: Wiley.
Dondo, R., & Cerdá, J. (2015). The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system. Computers and Chemical Engineering, 76, 42–62.
Fazel Zarandi, M. H., Khorshidian, H., & Akbarpour Shirazi, M. (2014). A constraint programming model for the scheduling of JIT cross-docking systems with pre-emption. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0860-9.
Gaohao, L., & Noble, J. S. (2012). An integrated model for cross dock operations including staging. International Journal of Production Research, 50, 2451–2464.
Gokgoz, F., & Atmaca, E. M. (2012). Financial optimization in the Turkish electricity market: Markowitz’s mean-variance approach. Renewable and Sustainable Energy Reviews, 16, 357–368.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley.
Gorchels, L. (2000). The product manager’s handbook: The complete product management resource (2nd ed.). New York: McGraw-Hill.
Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333–347.
Hadavandi, E., Shavandi, H., Ghanbari, A., & Abbasian, S. (2012). Developing a hybrid artificial intelligence model for outpatient visit forecasting in hospitals. Applied Soft Computing, 12, 700–711.
Heidari, F., Zegordi, S. H., & Tavakkoli-Moghaddam, R. (2015). Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1160-3.
Hwang, C. L., Masud, A. (1979). Multiple objective decision making. In Methods and applications: a state of the art survey, Lecture notes in economics and mathematical systems (vol. 164). Berlin: Springer.
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer.
Kaul, A., & Rao, V. R. (1995). Research for product positioning and design decisions: An integrative review. International Journal of Research in Marketing, 12(4), 293–320.
Keshtzari, M., Naderi, B., & Mehdizadeh, E. (2016). An improved mathematical model and a hybrid metaheuristic for truck scheduling in cross-dock problems. Computers and Industrial Engineering, 91, 197–204.
Kilic, H. S., Cebeci, U., & Ayhan, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resource Conservation Recycling, 95, 120–132.
Kheirkhah, A. S., & Rezaei, S. (2015). Using cross-docking operations in a reverse logistics network design: a new approach. Production Engineering Research and Development. doi:10.1007/s11740-015-0646-3.
Konar, A. (2005). Computational intelligence principles, techniques. Berlin: Springer.
Konur, D., & Golias, M. (2013). Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty. Computers and Industrial Engineering, 65, 663–672.
Kuo, Y. (2013). Optimizing truck sequencing and truck dock assignment in a cross docking system. Expert Systems with Applications, 40, 5532–41.
Kwak, M., & Kim, H. (2015). Design for life-cycle profit with simultaneous consideration of initial manufacturing and end-of-life remanufacturing. Engineering Optimization, 47(1), 18–35.
Ladier, A., & Alpan, G. (2014). Crossdock truck scheduling with time windows: Earliness, tardiness and storage policies. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-014-1014-4.
Langenberg, K. U., Seifert, R. W., & Tancrez, J.-S. (2012). Aligning supply chain portfolios with product portfolios. International Journal of Production Economics, 135(1), 500–513.
Larbi, R., Alpan, G., Baptiste, P., & Penz, B. (2011). Scheduling cross docking operations under full, partial and no information on inbound arrivals. Computers and Operations Research, 38, 889–900.
Li, Y., Chu, X., Chen, D., Liu, Q., & Shen, J. (2014). An integrated module portfolio planning approach for complex products and systems. International Journal of Computer Integrated Manufacturing., 28(9), 988–998. doi:10.1080/0951192X.2014.961551.
Liao, T. W., Egbelu, P. J., & Chang, P. C. (2013). Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations. International Journal of Production Economics, 141(1), 212–229.
Liao, C.-J., Lin, Y., & Shih, S. C. (2010). Vehicle routing with crossdocking in the supply chain. Expert Systems with Applications, 37, 6868–6873.
Ma, H., Miao, Z., Lim, A., & Rodrigues, B. (2011). Cross docking distribution networks with setup cost and time window constraint. Omega, 39, 64–72.
Mahaboob Sheriff, K. M., Gunasekaran, A., & Nachiappan, S. (2012). Reverse logistics network design: A review on strategic perspective. International Journal of Logistics Systems and Management, 12(2), 171–194.
Maheut, J., Garcia-Sabater, J. P. (2012). A mixed-integer linear programming model for transportation planning in the full truck load strategy to supply products with unbalanced demand in the just in time context: a case study. Advances in Production Management Systems. International Conference, APMS 2012 Rhodes, Greece, September.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.
Mavrotas, G. (2009). Effective implementation of the \(\varepsilon \)-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213, 455–65.
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented \(\varepsilon \)-constraint method (AUGMECON2) for finding the exact Pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219, 9652–69.
Miao, Z., Lim, A., & Ma, H. (2009). Truck dock assignment problem with operational time constraint within cross-docks. European Journal of Operational Research, 199, 105–115.
Mohtashami, A. (2015). A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation. Computers and Industrial Engineering, 90, 221–240.
Mohtashami, A., Tavana, M., Santos-Arteaga, F., & Fallahian-Najafabadi, A. (2015). A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems. Applied Soft Computing, 31, 30–47.
Mokhtarinejad, M., Ahmadi, A., Karimi, B., & Rahmati, S. (2015). A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment. Applied Soft Computing, 34, 274–285.
Mousavi, S. M., & Tavakkoli-Moghaddam, R. (2013). A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain. Journal of Manufacturing Systems, 32(2), 335–347.
Musa, R., Arnaout, J.-P., & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers and Industrial Engineering, 59, 85–92.
Orfi, N., Terpenny, J., & Asli, S. S. (2011). Harnessing product complexity: Step 1—Establishing product complexity dimensions and indicators. Engineering Economics, 56(1), 59–79.
Pishvaee, M. S., Farahani, R. Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computer and Operation Research, 37(6), 1100–1112.
Pishvaee, M., & Razmi, J. (2011). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36, 3433–3446.
Rahmanzadeh Tootkaleh, S., Fatemi Ghomi, S. M. T., & Sajadieh, M. S. (2016). Cross dock scheduling with fixed outbound trucks departure times under substitution condition. Computers and Industrial Engineering, 92, 50–56.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modeling, 37, 328–344.
Ross, A., & Jayaraman, V. (2008). An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design. Computers and Industrial Engineering, 55, 64–79.
Rumelhart, D., & McClelland, J. (1986). Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1: foundations. Cambridge, MA: MIT Press.
Sadeghi, A., Alem-Tabriz, A., & Zandieh, M. (2011). Product portfolio planning: A metaheuristic-based simulated annealing algorithm. International Journal of Production Research, 49(8), 2327–2350.
Salvador, F., Forza, C., & Rungtusanatham, M. (2002). Modularity, product variety, production volume, and component sourcing: Theorizing beyond generic prescriptions. Journal of Operations Management, 20(5), 549–575.
Shahrabi, J., Hadavandi, E., & Asadi, S. (2013). Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series. Knowledge-Based Systems, 43, 112–122.
Song, Z., & Kusiak, A. (2009). Optimising product configurations with a data-mining approach. International Journal of Production Research, 47(7), 1733–1751.
Shakeri, M., Low, M., Turner, S., & Lee, E. (2012). A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock. Computers and Operations Research, 39(11), 2564–2577.
Shi, W., Liu, Z., Shang, J., & Cui, Y. (2013). Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain. European Journal of Operational Research, 229(3), 695–706.
Tang, S.-L., & Yan, H. (2010). Pre-distribution vs. post-distribution for crossdocking with transshipments. Omega, 38, 192–202.
Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the art. Omega, 40, 827–846.
Wan, X., Evers, P. T., & Dresner, M. E. (2012). Too much of a good thing: The impact of product variety on operations and sales performance. Journal of Operations Management, 30(4), 316–324.
Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavior sciences. Ph.D. Thesis: Harvard University, Cambridge, MA, USA.
Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. In IEEE transaction on system: Man, and cybernetics.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Khorshidian, H., Akbarpour Shirazi, M. & Fatemi Ghomi, S.M.T. An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock. J Intell Manuf 30, 163–184 (2019). https://doi.org/10.1007/s10845-016-1229-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-016-1229-7