Flexible supplier selection and order allocation in the big data era with various quantity discounts

This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of th...

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Bibliographic Details
Published inPloS one Vol. 18; no. 3; p. e0283585
Main Author Wang, Qing
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 27.03.2023
Public Library of Science (PLoS)
Subjects
Algorithms
Analysis
Big Data
Biology and Life Sciences
Component and supplier management
Costs
Decision making
Discounts
Dynamic programming
Ecology and Environmental Sciences
Engineering and Technology
Greedy algorithms
Integer programming
Knapsack problem
Logistics
Mathematical programming
Modelling
Optimization
Order processing
Physical Sciences
Research and Analysis Methods
Social Sciences
Suppliers
Supply chains
Vendor relations
China
Iran
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Summary:This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of the modeling and solution difficulty. All suppliers offering the same discount are far from reality, especially when the number of suppliers is large. The proposed model is a variant of the NP-hard knapsack problem. The greedy algorithm, which solves the fractional knapsack problem optimally, is applied to cope with the challenge. Three greedy algorithms are developed using a problem property and two sorted lists. Simulations show the average optimality gaps are 0.1026%, 0.0547%, and 0.0234% and the model can be solved in centiseconds, densiseconds, and seconds for supplier numbers 1000, 10000, and 100000. This allows the full use of data in the big data era.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0283585