TY - JOUR
T1 - Production and transportation integration for commit-to-delivery mode with general shipping costs
AU - Li, Feng
AU - Xu, Zhou
AU - Chen, Zhi Long
N1 - Funding Information:
History: Accepted by Antonio Frangioni, Area Editor for Design and Analysis of Algorithms— Continuous. Funding: This research was supported by the Hong Kong Polytechnic University [Grant SB84, Project ID: P0006371] and the National Natural Science Foundation of China [Grants 71801103, 71821001, 71831008, 71931005]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ijoc.2019.0935.
Publisher Copyright:
Copyright: © 2020 INFORMS
PY - 2020/9
Y1 - 2020/9
N2 - We study an integrated production and transportation problem for a make-to-order manufacturing company that operates under the commit-to-delivery mode and uses third-party logistics service providers to deliver products to customers on or before certain committed delivery dates. Such third-party logistics service providers often provide various shipping modes with quantity discounts and different guaranteed shipping times. As a result, the company’s shipping costs need to be represented by general shipping cost functions that are typically nondecreasing, subadditive, and piecewise linear with shipping quantities, and nonincreasing with guaranteed shipping times. To the best of our knowledge, this paper is the first attempt to solve such an integrated production and transportation problem for the commit-to-delivery mode with general shipping costs. We prove that with general shipping costs, the problem is strongly 13-hard when the planning horizon consists of an arbitrary number of days. For the two-day problem, we show that it is ordinarily 13-hard, but is unlikely to have a fully polynomial time approximation scheme (FPTAS) unless 13 = 3. Interestingly, we find that when the unit inventory holding cost is relatively small, which is often true in practice, there exists an FPTAS for the two-day problem, the development of which hinges on a newly discovered property for minimizing the sum of two general piecewise linear functions. For the multiday problem, we develop a heuristic algorithm based on column generation, which novelly uses a dynamic program for a variant of the problem with a single customer. Results from computational experiments demonstrate that the heuristic algorithm can find near-optimal solutions with optimality gaps less than 1% in a short running time.
AB - We study an integrated production and transportation problem for a make-to-order manufacturing company that operates under the commit-to-delivery mode and uses third-party logistics service providers to deliver products to customers on or before certain committed delivery dates. Such third-party logistics service providers often provide various shipping modes with quantity discounts and different guaranteed shipping times. As a result, the company’s shipping costs need to be represented by general shipping cost functions that are typically nondecreasing, subadditive, and piecewise linear with shipping quantities, and nonincreasing with guaranteed shipping times. To the best of our knowledge, this paper is the first attempt to solve such an integrated production and transportation problem for the commit-to-delivery mode with general shipping costs. We prove that with general shipping costs, the problem is strongly 13-hard when the planning horizon consists of an arbitrary number of days. For the two-day problem, we show that it is ordinarily 13-hard, but is unlikely to have a fully polynomial time approximation scheme (FPTAS) unless 13 = 3. Interestingly, we find that when the unit inventory holding cost is relatively small, which is often true in practice, there exists an FPTAS for the two-day problem, the development of which hinges on a newly discovered property for minimizing the sum of two general piecewise linear functions. For the multiday problem, we develop a heuristic algorithm based on column generation, which novelly uses a dynamic program for a variant of the problem with a single customer. Results from computational experiments demonstrate that the heuristic algorithm can find near-optimal solutions with optimality gaps less than 1% in a short running time.
KW - Algorithm analysis
KW - Column generation
KW - Commit-to-delivery mode
KW - Computational complexity
KW - General shipping cost functions
KW - Integer programming
KW - Integrated production and transportation
UR - http://www.scopus.com/inward/record.url?scp=85096822003&partnerID=8YFLogxK
U2 - 10.1287/ijoc.2019.0935
DO - 10.1287/ijoc.2019.0935
M3 - Journal article
AN - SCOPUS:85096822003
SN - 1091-9856
VL - 32
SP - 1012
EP - 1029
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
IS - 4
ER -