Joseph Y.-T. Leung
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Joseph Y.-T. Leung has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Michael L. Pinedo
New York University
7 shared publications - Kangbok Lee
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea
5 shared publications - Byung-Cheon Choi
School of Business, ChungNam National University, Daejeon, Korea
2 shared publications - Cheng He
School of Science, Henan University of Technology, Zhengzhou, China
1 shared publication - Dirk Briskorn
Bergische Universität Wuppertal, Professur für BWL, insbesondere Produktion und Logistik, Wuppertal, Germany
1 shared publication - Haibing Li
Lehman Brothers Inc
1 shared publication
Latest Publications
- 1 October 2016
- Published by EDP Sciences in RAIRO - Operations Research
- Vol. 50 (4-5) , 995-1001
An improved binary search algorithm for the Multiple-Choice Knapsack Problem
- 1 October 2016
- Published by EDP Sciences in RAIRO - Operations Research
- Vol. 50 (4-5) , 995-1001
An improved binary search algorithm for the Multiple-Choice Knapsack Problem
- 9 January 2013
- Published by Springer Nature in Annals of Operations Research
- Vol. 204 (1) , 189-222
Makespan minimization in online scheduling with machine eligibility
- 9 January 2013
- Published by Springer Nature in Annals of Operations Research
- Vol. 204 (1) , 189-222
Makespan minimization in online scheduling with machine eligibility
- 1 January 2012
- Published by SAGE Publications in Production and Operations Management
- Vol. 21 (1) , 115-128
Container Scheduling: Complexity and Algorithms
We consider the transport of containers through a fleet of ships. Each ship has a capacity constraint limiting the total number of containers it can carry and each ship visits a given set of ports following a predetermined route. Each container has a release date at its origination port, and a due date at its destination port. A container has a size 1 or size 2; size 1 represents a 1 TEU (20‐foot equivalent unit) and size 2 represents 2 TEUs. The delivery time of a container is defined as the time when the ship that carries the container arrives at its destination port. We consider the problem of minimizing the maximum tardiness over all containers. We consider three scenarios with regard to the routes of the ships, namely, the ships having (i) identical, (ii) nested, and (iii) arbitrary routes. For each scenario, we consider different settings for origination ports, release dates, sizes of containers, and number of ports; we determine the computational complexity of various cases. We also provide a simple heuristic for some cases, with its worst case analysis. Finally, we discuss the relationship of our problems with other scheduling problems that are known to be open.
- 1 January 2012
- Published by SAGE Publications in Production and Operations Management
- Vol. 21 (1) , 115-128
Container Scheduling: Complexity and Algorithms
We consider the transport of containers through a fleet of ships. Each ship has a capacity constraint limiting the total number of containers it can carry and each ship visits a given set of ports following a predetermined route. Each container has a release date at its origination port, and a due date at its destination port. A container has a size 1 or size 2; size 1 represents a 1 TEU (20‐foot equivalent unit) and size 2 represents 2 TEUs. The delivery time of a container is defined as the time when the ship that carries the container arrives at its destination port. We consider the problem of minimizing the maximum tardiness over all containers. We consider three scenarios with regard to the routes of the ships, namely, the ships having (i) identical, (ii) nested, and (iii) arbitrary routes. For each scenario, we consider different settings for origination ports, release dates, sizes of containers, and number of ports; we determine the computational complexity of various cases. We also provide a simple heuristic for some cases, with its worst case analysis. Finally, we discuss the relationship of our problems with other scheduling problems that are known to be open.
- 23 November 2010
- Published by Springer Nature in 4OR
- Vol. 8 (4) , 331-364
Makespan minimization in online scheduling with machine eligibility
- 23 November 2010
- Published by Springer Nature in 4OR
- Vol. 8 (4) , 331-364
Makespan minimization in online scheduling with machine eligibility
- 31 July 2010
- Published by Springer Nature in Journal of Scheduling
- Vol. 14 (1) , 27-38
Scheduling jobs with equal processing times subject to machine eligibility constraints
- 31 July 2010
- Published by Springer Nature in Journal of Scheduling
- Vol. 14 (1) , 27-38
Scheduling jobs with equal processing times subject to machine eligibility constraints
- 27 July 2010
- Published by Springer Nature in Journal of Combinatorial Optimization
- Vol. 22 (4) , 797-818
Minimizing makespan in an ordered flow shop with machine-dependent processing times
- 27 July 2010
- Published by Springer Nature in Journal of Combinatorial Optimization
- Vol. 22 (4) , 797-818
Minimizing makespan in an ordered flow shop with machine-dependent processing times
- 1 December 2007
- Published by Springer Nature in Annals of Operations Research
- Vol. 159 (1) , 107-123
Scheduling orders on either dedicated or flexible machines in parallel to minimize total weighted completion time
- 1 December 2007
- Published by Springer Nature in Annals of Operations Research
- Vol. 159 (1) , 107-123
Scheduling orders on either dedicated or flexible machines in parallel to minimize total weighted completion time