An Integer Programming Model of Reinvestment Strategy Based Project Portfolio Selection and Scheduling with Constrained Resource

2018 ◽  
pp. 551-559
Author(s):  
Baolong Wang
Keyword(s):  
Integer Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Project Portfolio ◽  
Integer Programming Model ◽  
Project Portfolio Selection

Optimal project portfolio selection with reinvestment strategy considering sustainability in an uncertain environment: a multi-objective optimization approach

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadali Zarjou ◽  
Mohammad Khalilzadeh
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Sustainability Indicators ◽  
Selection Problem ◽  
Project Portfolio ◽  
Mathematical Programming Model ◽  
Content Type ◽  
Multi Objective ◽  
Project Portfolio Selection

PurposeThis study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.Design/methodology/approachA multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).FindingsThe criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.Originality/valueThis study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

scholarly journals A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kyle Robert Harrison ◽  
Saber Elsayed ◽  
Ivan L. Garanovich ◽  
Terence Weir ◽  
Michael Galister ◽  
...  
Keyword(s):  
Portfolio Selection ◽  
Project Portfolio ◽  
Scheduling Problem ◽  
Population Approach ◽  
Project Portfolio Selection

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.

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