Dynamic Project Portfolio Optimization with Portfolio Stability and Switching Costs: A Multi Period MILP Approach
Keywords:
Project portfolio optimization; dynamic portfolio selection; mixed-integer linear programming; portfolio stability; switching costs; multi-period optimization; computational experiment.Abstract
Project portfolio optimization entails the process of choosing projects to obtain maximum portfolio value subject to budget, resource, and selection restrictions. In multi-period scenarios, there are decision interdependencies, and changes in projects' inclusion give rise to termination, resourcing, coordinating, contractual, and organizational adjustments, which should be taken into account to avoid high-value portfolios with too much project switching. In this paper, a dynamic MILP approach is proposed, where project portfolio stability is achieved via project switching costs between periods. Transition binary variables denote changes between periods, while the objective function maximizes portfolio value and accounts for project transitions, giving the possibility to investigate the trade-off between economic value and portfolio stability. The model is tested on 30 artificial instances with 250 projects in three periods, choosing 50 projects per period subject to budget and resource restrictions. Sensitivity analysis is carried out for λ = 0, 1, 2, and 3. It has been found that λ = 2 reduces project switching by 57.0% with 0.208% portfolio value loss, and λ = 3 reduces project switching by 73.1% with 0.375% portfolio value loss.
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