By Topic

Optimal Scheduling of a Single-Supplier Single-Manufacturer Supply Chain With Common due Windows

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wing-Kwan Yeung ; Inst. of Textiles & Clothing, Hong Kong Polytech. Univ., Kowloon, China ; Tsan-Ming Choi ; Cheng, T.C.E.

We study a supply chain scheduling control problem involving a single supplier, a single manufacturer and multiple retailers, where the manufacturer with limited production capacity can only take some of the orders of the retailers. The manufacturer aims to maximize its profit, which is a function of the storage time, storage quantity, order sequence dependent weighted storage costs, and idle time of the orders to be accepted. We formulate the problem as a two-machine common due windows proportionate flow shop scheduling problem. We show that the problem is NP-hard. We provide the first pseudo-polynomial algorithm to optimally solve the problem. We show that for an accepted set of orders the shortest processing time earliest permutation schedule yields an optimal schedule. We prove that we can reduce the latest due dates of the due windows, which are given parameters, for minimizing the computational effort. We establish a tight upper bound on the enumeration process to manage the order idle time. We eliminate the need for generating all the optimal partial schedules to obtain an optimal solution, thus reducing the running time of our algorithm for solving the problem. We computationally tested the algorithm and the results show that the algorithm can solve large-sized problems very efficiently.

Published in:

Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 12 )