With the increasing demand for customized logistics services in the manufacturing industry, the key factor in realizing the competitiveness of a logistics service supply chain (LSSC) is whether it can meet specific requirements with the cost of mass service. In this case, in-depth research on the time-scheduling of LSSC is required. Setting the total cost, completion time, and the satisfaction of functional logistics service providers (FLSPs) as optimal targets, this paper establishes a time scheduling model of LSSC, which is constrained by the service order time requirement. Numerical analysis is conducted by using Matlab 7.0 software. The effects of the relationship cost coefficient and the time delay coefficient on the comprehensive performance of LSSC are discussed. The results demonstrate that with the time scheduling model in mass-customized logistics services (MCLSs) environment, the logistics service integrator (LSI) can complete the order earlier or later than scheduled. With the increase of the relationship cost coefficient and the time delay coefficient, the comprehensive performance of LSSC also increases and tends towards stability. In addition, the time delay coefficient has a better effect in increasing the LSSC’s comprehensive performance than the relationship cost coefficient does. 1. Introduction In the face of growing demand for customized logistics services, numerous logistics enterprises are not only providing customers with mass services, but are also beginning to meet the demand for customized services and are considering a change in their logistics service modes. Specifically, they try to provide mass customized logistics services (MCLS) instead of mass logistics services . MCLS represents a significant development in logistics services, and a logistics company’s capability to offer MCLS is crucial to enhance its market competitiveness. In the MCLS environment, to meet customized service demand and offer large-scale services, several logistics enterprises form a logistics service supply chain (LSSC) via union and integration [2, 3]. LSSC is a new supply chain of which logistics service integrator (LSI) is the core enterprise. The basic structure of LSSC is functional logistics service provider (FLSP) → LSI → customer. FLSP is integrated by LSI when LSI builds the integrated logistics to customer. The main purpose of LSSC is to provide the flexible logistics service for manufacturing supply chain . As the core enterprise of a LSSC, the LSI integrates the advantages of the FLSPs, such as various logistics processes and
C. Chandra and J. Grabis, “Managing logistics for mass customization: the new production frontier,” in Proceedings of the 6th Biannual World Automation Congress—Image Processing, Biomedicine, Multimedia, Financial Engineering and Manufacturing—International Forum on Multimedia Image Processing, IFMIP (WAC '04), vol. 18, pp. 335–340, July 2004.
K. L. Choy, C. L. Li, S. C. K. So, H. Lau, S. K. Kwok, and D. W. K. Leung, “Managing uncertainty in logistics service supply chain,” International Journal of Risk Assessment and Management, vol. 7, no. 1, pp. 19–22, 2007.
W. H. Liu, X. C. Xu, Z. X. Ren, and Y. Peng, “An emergency order allocation model based on multi-provider in two-echelon logistics service supply chain,” Supply Chain Management, vol. 16, no. 6, pp. 390–400, 2011.
P. R. Philipoom, “The choice of dispatching rules in a shop using internally set due-dates with quoted leadtime and tardiness costs,” International Journal of Production Research, vol. 38, no. 7, pp. 1641–1655, 2000.
X. G. Xu, “Position of customer order decoupling point in mass customization,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), pp. 302–307, August 2007.
N. Mishra, A. K. Choudhary, and M. K. Tiwari, “Modeling the planning and scheduling across the outsourcing supply chain: a chaos-based fast Tabu-SA approach,” International Journal of Production Research, vol. 46, no. 13, pp. 3683–3715, 2008.
J. C. P. Su, Y. L. Chang, M. Ferguson, and J. C. Ho, “The impact of delayed differentiation in make-to-order environments,” International Journal of Production Research, vol. 48, no. 19, pp. 5809–5829, 2010.
E. Selvarajah and G. Steiner, “Approximation algorithms for the supplier's supply chain scheduling problem to minimize delivery and inventory holding costs,” Operations Research, vol. 57, no. 2, pp. 426–438, 2009.
R. Bhatnagar, P. Mehta, and C. Chong Teo, “Coordination of planning and scheduling decisions in global supply chains with dual supply modes,” International Journal of Production Economics, vol. 131, no. 2, pp. 473–482, 2011.
A. A. Taleizadeh, S. T. A. Niaki, N. Shafii, R. G. Meibodi, and A. Jabbarzadeh, “A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain,” International Journal of Advanced Manufacturing Technology, vol. 51, no. 9–12, pp. 1209–1223, 2010.
D. Tang and J. Chen, “Identification of postponement point in service delivery process: A description model,” in Proceedings of the 6th International Conference on Service Systems and Service Management (ICSSSM '09), pp. 335–339, June 2009.
C. Jue and T. Daijian, “Modeling the influential factors on determination of CODP in service process,” in Proceedings of the International Conference on Management and Service Science (MASS '09), September 2009.
B. Manderick, M. K. de Weger , and P. Spiessens, “The Genetic Algorithm and the structure of the fitness landscape,” in Proceedings of the 4th International Conference on Genetic Algorithms, pp. 143–150, Morgan Kaufman, San Diego, Calif, USA, 1991.