Abstract
In sustainable project management, time and cost are two critical factors affecting the success of a project. Time/cost trade-offs in projects accelerate the execution of some activities by increasing the amount of non-renewable resources committed to them and therefore shorten the project duration. The discrete time/cost trade-off problem (DTCTP) has been extensively studied during the past 20 years. However, due to its complexity, the DTCTP—especially the DTCTP curve problem (DTCTP-C)—has only been solved for relatively small instances. To the best of our knowledge, there is no computational performance analysis for solving the DTCTP-C on large project instances with up to 500 activities. This paper aims to fill this gap. We present two bi-objective heuristic algorithms for the DTCTP-C where both project duration and cost are minimized. The objective is to obtain a good appropriate efficient set for the large-scale instances. The first algorithm is based on the non-dominated sorting genetic algorithm II (NSGA-II) and uses a specially designed critical path-based crossover operator. The second algorithm is a steepest descent heuristic which generates efficient solutions by iteratively solving the DTCTP with different deadlines. Computational experiments are conducted to validate the proposed algorithms on a large set of randomly generated problem instances.
Highlights
The importance of time/cost trade-offs in projects have been recognized since the development of the critical path method (CPM) in the late 1950s [1]
The contributions of this paper are three-fold: (1) We propose a bi-objective hybrid genetic algorithm (BHGA) for the discrete time/cost trade-off problem (DTCTP)-C by introducing a critical path based crossover operator in the non-dominated sorting genetic algorithm II (NSGA-II) [20]
We presented two bi-objective heuristic algorithms for solving large-scale DTCTP curve problem (DTCTP-C) with the aim of obtaining a good appropriate efficient solution set
Summary
The importance of time/cost trade-offs in projects have been recognized since the development of the critical path method (CPM) in the late 1950s [1]. Sustainable project management requires the resources to be used in an economical and sustainable way [2,3,4]. The project duration can usually be shortened by accelerating the execution of activities. The duration of project activities can be treated as discrete non-increasing functions of the cost. This results in the discrete time/cost trade-off problem (DTCTP) [1]. Harvey and Patterson [5] and Hindelang and Muth [6] first proposed the DTCTP, which is a special case of the multi-mode resource-constrained project scheduling problem [7]
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