Optimization of large-scale water transfer networks: Conic integer programming model and distributed parallel algorithms

AIChE Journal ◽  
2016 ◽  
Vol 63 (5) ◽  
pp. 1566-1581 ◽  
Author(s):  
Li-Juan Li ◽  
Rui-Jie Zhou
Keyword(s):  
Integer Programming ◽  
Parallel Algorithms ◽  
Large Scale ◽  
Programming Model ◽  
Water Transfer ◽  
Integer Programming Model

scholarly journals A Fuzzy Integer Programming Model to Locate Temporary Medical Facilities as Part of Pre-Disaster Management

2019 ◽  
Vol 10 (1) ◽  
pp. 21-40
Author(s):  
Berk Ayvaz ◽  
Ali Osman Kuşakcı
Keyword(s):  
Integer Programming ◽  
Disaster Response ◽  
Large Scale ◽  
Planning Process ◽  
Programming Model ◽  
First Aid ◽  
Optimum Number ◽  
Integer Programming Model ◽  
Earthquake Scenario ◽  
Response Planning

The number and the scale of natural disasters have drastically increased over the last decades. One of the most vital stages of disaster preparedness is disaster response planning, and it plays an important role in limiting material and immaterial consequences, such as those caused by large scale earthquakes. In order to minimize human suffering and death, the aim of establishing a well-designed humanitarian relief chain must be to provide medicine, water, shelter, emergency food and supplies to the affected areas. From a holistic perspective, providing timely first aid and rapid transfer of injured victims to a medical facility is one of the most essential component of such chain. Thus, the location of first aid hospitals must be determined following a careful thought and planning process. This study presents a fuzzy integer programming model to determine the best location of the temporary hospitals which are expected to support extant state hospitals after a major earthquake. This study applies the proposed fuzzy model to the Üsküdar province of Istanbul and identifies optimum number and locations of field hospitals for a severe earthquake scenario.

Mixed integer programming improves spray operation planning

1987 ◽  
Vol 17 (12) ◽  
pp. 1602-1608
Author(s):  
Emanuel Melachrinoudis ◽  
David Rumpf ◽  
Ramon Venegas
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Programming Model ◽  
Minimum Cost ◽  
Forest Service ◽  
Mixed Integer ◽  
Integer Programming Model ◽  
Mixed Integer Programming Model ◽  
Northern United States

The spruce budworm is the most destructive pest of spruce and fir forests in the northern United States and Canada. To counter the insect, the Maine Forest Service has, for the last decade (1975–1985), conducted a large-scale spray operation in Maine's northern and eastern forests. The operation involves the use of several types of aircraft flying from several airfields to spray hundreds of target areas. A minimum cost mixed integer programming model was developed to determine the airfield locations, the number of types of aircraft, and the flight plans to best conduct the spray operation. The model extends and complements a microcomputer linear programming model previously developed for and used by the Maine Forest Service. The savings obtained by the mixed integer programming model can be seen by the included comparison of their results.

scholarly journals Reshuffle minimisation to improve storage yard operations efficiency

2021 ◽  
Vol 15 ◽  
pp. 174830262199401
Author(s):  
Hammed Bisira ◽  
Abdellah Salhi
Keyword(s):  
Integer Programming ◽  
Programming Model ◽  
Heuristic Approach ◽  
Container Terminals ◽  
Exact Method ◽  
Computational Results ◽  
Binary Integer Programming ◽  
Integer Programming Model ◽  
Compatibility Test ◽  
Storage Area

There are many ways to measure the efficiency of the storage area management in container terminals. These include minimising the need for container reshuffle especially at the yard level. In this paper, we consider the container reshuffle problem for stacking and retrieving containers. The problem was represented as a binary integer programming model and solved exactly. However, the exact method was not able to return results for large instances. We therefore considered a heuristic approach. A number of heuristics were implemented and compared on static and dynamic reshuffle problems including four new heuristics introduced here. Since heuristics are known to be instance dependent, we proposed a compatibility test to evaluate how well they work when combined to solve a reshuffle problem. Computational results of our methods on realistic instances are reported to be competitive and satisfactory.

scholarly journals Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 219
Author(s):  
Dhananjay Thiruvady ◽  
Kerri Morgan ◽  
Susan Bedingfield ◽  
Asef Nazari
Keyword(s):  
Integer Programming ◽  
Gender Equity ◽  
Programming Model ◽  
Original Data ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Integer Programming Model ◽  
Problem Instances ◽  
Work Integrated Learning ◽  
Time Required

The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.

Research on the Freight Station Layout

2013 ◽  
Vol 380-384 ◽  
pp. 4506-4510
Author(s):  
Miao Du ◽  
Yong Qin ◽  
Zi Yang Wang ◽  
Zhong Xin Zhao ◽  
Hong Fei Yu ◽  
...  
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Rapid Expansion ◽  
Mixed Integer ◽  
Railway Stations ◽  
Integer Programming Model ◽  
Rail Freight ◽  
Mixed Integer Programming Model ◽  
Train Operation

At present, there are many problems existing in railway stations, such as excessive numbers and over-crowded layout, which seriously affect the scale benefit generation and rapid expansion of rail freight capacity. Aimed at these problems, a Mixed Integer programming model is proposed. Taking Lanzhou train operation depot for example, applying lingo software, the layout of freight station is obtained.

scholarly journals Optimization of Classification Track Assignment Considering Block Sequence at Train Marshaling Yard

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Yingqun Zhang ◽  
Rui Song ◽  
Shiwei He ◽  
Haodong Li ◽  
Xiaole Guo
Keyword(s):  
Integer Programming ◽  
Numerical Experiment ◽  
Real World ◽  
Programming Model ◽  
Block Sequence ◽  
Operational Process ◽  
Integer Programming Model ◽  
The Real ◽  
Reasonable Amount ◽  
Number Of Factors

An operational process at train marshaling yard is considered in this study. The inbound trains are decoupled and disassembled into individual railcars, which are then moved to a series of classification tracks, forming outbound trains after being assembled and coupled. We focus on the allocation plan of the classification tracks. Given are the disassembling and assembling sequence, the railcars connection plan, and a number of classification tracks. Output is the assignment of the railcars to the classification tracks. An integer programming model is proposed, aimed at reducing the number of coupling operations, as well as the number of dirty tracks which is related to the rehumping operation, and the order of the railcars on the outbound train must satisfy the block sequence. Tabu algorithm is designed to solve the problem, and the model is also tested by CPLEX in comparison. A numerical experiment based on a real-world case is analyzed, and the result can be reached within a reasonable amount of time. We also discussed a number of factors that may affect the track assignment and gave suggestions for the real-world case.

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