A learning-based two-stage optimization method for customer order scheduling

2021 ◽  
pp. 105488
Author(s):  
Zhongshun Shi ◽  
Hang Ma ◽  
Meiheng Ren ◽  
Tao Wu ◽  
Andrew J. Yu
Keyword(s):  
Optimization Method ◽  
Order Scheduling ◽  
Two Stage ◽  
Customer Order ◽  
Customer Order Scheduling

A robust customer order scheduling problem along with scenario-dependent component processing times and due dates

2021 ◽  
Vol 58 ◽  
pp. 291-305
Author(s):  
Chin-Chia Wu ◽  
Danyu Bai ◽  
Xingong Zhang ◽  
Shuenn-Ren Cheng ◽  
Jia-Cheng Lin ◽  
...  
Keyword(s):  
Scheduling Problem ◽  
Due Dates ◽  
Order Scheduling ◽  
Processing Times ◽  
Customer Order ◽  
Dependent Component ◽  
Customer Order Scheduling

scholarly journals Metaheuristics for Order Scheduling Problem with Unequal Ready Times

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jan-Yee Kung ◽  
Jiahui Duan ◽  
Jianyou Xu ◽  
I-Hong Chung ◽  
Shuenn-Ren Cheng ◽  
...  
Keyword(s):  
Real Life ◽  
Experimental Tests ◽  
Service Systems ◽  
Scheduling Problem ◽  
Due Dates ◽  
Order Scheduling ◽  
Customer Order ◽  
Scheduling Model ◽  
Unequal Ready Times ◽  
Customer Order Scheduling

In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.

scholarly journals Customer order scheduling with job-based processing on a single-machine to minimize the total completion time

2021 ◽  
Vol 12 (3) ◽  
pp. 273-292 ◽  
Author(s):  
Ferda Can Çetinkaya ◽  
Pınar Yeloğlu ◽  
Hale Akkocaoğlu Çatmakaş
Keyword(s):  
Completion Time ◽  
Numerical Control ◽  
Total Completion Time ◽  
Mixed Integer ◽  
Order Scheduling ◽  
Customer Order ◽  
Processing Approach ◽  
Problem Instances ◽  
Customer Order Scheduling ◽  
Customer Orders

This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order’s completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders’ total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times.

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