The impact of solution representations on heuristic net present value optimization in discrete time/cost trade-off project scheduling with multiple cash flow and payment models. (March 2019)

• Record Type: Journal Article
• Authors: Leyman, Pieter; Van Driessche, Niels; Vanhoucke, Mario; De Causmaecker, Patrick
• Is Part Of: Computers & operations research. Volume 103(2019)
• Page Start: 184
• Page End: 197
• Publication Date: 2019-03
• Subjects: Project scheduling -- Net present value -- Discrete time/cost trade-off -- Heuristics -- Solution representations; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.11.011
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Project net present value is optimized with several cash flow and payment models. A general model for the contractor's cash flow management is proposed. We investigate the impact of different solution representations. Our results show a clear impact of representations on metaheuristic performance. Abstract: The goal of this paper is to investigate the impact of different solution representations, as part of a metaheuristic approach, on net present value optimization in project scheduling. We specifically consider the discrete time/cost trade-off problem with net present value optimization and apply three payment models from literature. Each of these models determines the timing and size of cash flows from the contractor's viewpoint. The contribution of this paper to literature is twofold. First, we include cash flow distribution variants in the payment models, to also distinguish between different manners in which value is created and costs are incurred, as part of a general model for the contractor's cash flow management. This general model is developed in order to explicitly include the progress of activities in the determination of the timing and size of payments to the contractor, which is currently lacking in literature. Second, we employ an iterated local search framework to compare different solution representations and their corresponding local search and repair heuristics. The goal is to unambiguously show that the choice of a solution representation deserves a fair amount of attention, alongside the selection of appropriate diversification and intensification operators, even though this is not always the case in literature. Each part of the proposed algorithm is validated on a large dataset of test instances, generated to allow for a broad comparison of the solution representations. Our results clearly quantify the statistically significant differences between three types of representations for the project scheduling problem under study.