Xuyin Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xuyin Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Lu Li
3 shared publicationsPeizhen Zhao
Business School, Northwest Normal University, Lanzhou 730070, China
2 shared publicationsRuifeng Zhang
Department of Postal Communication and Management, Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang 050021, China
2 shared publicationsWeiguo Liu
Business School, Northwest Normal University, Lanzhou 730070, China
2 shared publications- Weiguo Liu
School of Management, Northwest Normal University, Lanzhou, People’s Republic of China
2 shared publications Weizhe Dai
1 shared publication
Latest Publications
- 24 March 2025
- Published by Springer Nature in Bulletin of the Malaysian Mathematical Sciences Society
- Vol. 48 (3) , 1-29
Delivery Scheduling with Variable Processing Times and Due Date Assignments
- 24 March 2025
- Published by Springer Nature in Bulletin of the Malaysian Mathematical Sciences Society
- Vol. 48 (3) , 1-29
Delivery Scheduling with Variable Processing Times and Due Date Assignments
- 1 January 2024
- Published by American Institute of Mathematical Sciences (AIMS) in Journal of Industrial and Management Optimization
- Vol. 20 (1) , 313-324
Due-window assignment scheduling with job-rejection, truncated learning effects and setup times
This paper investigates the single-machine due-window assignment scheduling with truncated learning effects and past-sequence-dependent setup times simultaneously. The scheduler has the option of job-rejection, i.e., some jobs are processed and the other jobs are rejected. Under three (i.e., common, slack and different) due-window assignment methods, our goal is to minimize the sum of a general earliness-tardiness cost (i.e., the weighted sum of earliness-tardiness, number of early and tardy jobs, starting time and size of due-window) and a total rejection cost. Some optimal properties of the problem are provided, and we demonstrate that the problem is polynomially solvable.
- 1 January 2024
- Published by American Institute of Mathematical Sciences (AIMS) in Journal of Industrial and Management Optimization
- Vol. 20 (1) , 313-324
Due-window assignment scheduling with job-rejection, truncated learning effects and setup times
This paper investigates the single-machine due-window assignment scheduling with truncated learning effects and past-sequence-dependent setup times simultaneously. The scheduler has the option of job-rejection, i.e., some jobs are processed and the other jobs are rejected. Under three (i.e., common, slack and different) due-window assignment methods, our goal is to minimize the sum of a general earliness-tardiness cost (i.e., the weighted sum of earliness-tardiness, number of early and tardy jobs, starting time and size of due-window) and a total rejection cost. Some optimal properties of the problem are provided, and we demonstrate that the problem is polynomially solvable.
- Open Access
- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Mathematical Biosciences and Engineering
- Vol. 19 (6) , 5957-5967
Resource dependent scheduling with truncated learning effects
In this article, we investigate the single-machine scheduling problem with truncated learning effect and resource allocation, where the actual processing time of a job is a general function of its additional resources and position in a sequence. The goal is to determine the optimal resource allocation and optimal sequence such that a weighted sum of scheduling cost and resource consumption cost is minimized. We show that the problem can be solved in $ O(n^3) $ time by using an assignment formulation, where $ n $ is the number of jobs.- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Mathematical Biosciences and Engineering
- Vol. 19 (6) , 5957-5967
Open AccessResource dependent scheduling with truncated learning effects
In this article, we investigate the single-machine scheduling problem with truncated learning effect and resource allocation, where the actual processing time of a job is a general function of its additional resources and position in a sequence. The goal is to determine the optimal resource allocation and optimal sequence such that a weighted sum of scheduling cost and resource consumption cost is minimized. We show that the problem can be solved in $ O(n^3) $ time by using an assignment formulation, where $ n $ is the number of jobs. - Open Access
- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Mathematical Biosciences and Engineering
- Vol. 19 (5) , 5104-5119
Due date assignment scheduling with positional-dependent weights and proportional setup times
In this paper, we investigate the single-machine scheduling problem that considers due date assignment and past-sequence-dependent setup times simultaneously. Under common (slack and different) due date assignment, the objective is to find jointly the optimal sequence and optimal due dates to minimize the weighted sum of lateness, number of early and delayed jobs, and due date cost, where the weight only depends on it's position in a sequence (i.e., a position-dependent weight). Optimal properties of the problem are given and then the polynomial time algorithm is proposed to obtain the optimal solution.
- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Mathematical Biosciences and Engineering
- Vol. 19 (5) , 5104-5119
Open AccessDue date assignment scheduling with positional-dependent weights and proportional setup times
In this paper, we investigate the single-machine scheduling problem that considers due date assignment and past-sequence-dependent setup times simultaneously. Under common (slack and different) due date assignment, the objective is to find jointly the optimal sequence and optimal due dates to minimize the weighted sum of lateness, number of early and delayed jobs, and due date cost, where the weight only depends on it's position in a sequence (i.e., a position-dependent weight). Optimal properties of the problem are given and then the polynomial time algorithm is proposed to obtain the optimal solution.