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A Framework for Facilitating Sourcing and Allocation Decisions for Make‐to‐Order Items
Author(s) -
Murthy Nagesh N.,
Soni Samit,
Ghosh Soumen
Publication year - 2004
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2004.02408.x
Subject(s) - computer science , purchasing , order (exchange) , combinatorial auction , vendor , heuristic , block (permutation group theory) , lagrangian relaxation , operations research , build to order , variety (cybernetics) , mathematical optimization , common value auction , microeconomics , operations management , business , economics , production (economics) , mathematics , marketing , geometry , finance , artificial intelligence
This paper provides a fundamental building block to facilitate sourcing and allocation decisions for make‐to‐order items. We specifically address the buyer's vendor selection problem for make‐to‐order items where the goal is to minimize sourcing and purchasing costs in the presence of fixed costs, shared capacity constraints, and volume‐based discounts for bundles of items. The potential suppliers for make‐to‐order items provide quotes in the form of single sealed bids or participate in a dynamic auction involving open bids. A solution to our problem can be used to determine winning bids amongst the single sealed bids or winners at each stage of a dynamic auction. Due to the computational complexity of this problem, we develop a heuristic procedure based on Lagrangian relaxation technique to solve the problem. The computational results show that the procedure is effective under a variety of scenarios. The average gap across 2,250 problem instances is 4.65%.