The Impact of the Learning and Forgetting Effect on the Cost of a Multi-Unit Construction Project with the Use of the Simulated Annealing Algorithm

Abstract

Construction projects are often delayed. Their budgets are being exceeded. This is partly because construction project models rarely take human factors into account. The article focuses on the repetitive multi-unit projects that are often found in construction practices. They are an excellent example of construction works for which the optimization of certain processes can bring measurable financial benefits. In particular, the focus was on improving the key investment success indicator, the total cost of a project. This article presents the use of learning and forgetting phenomenon to improve construction schedules. The authors proposed an innovative model of the construction phase and optimized the schedule using a proprietary calculation algorithm using the simulated annealing phenomenon. The method was verified by a series of experiments and showcased by a case study. The results show that the presented methods can improve the construction planning process and better reflect the current construction conditions. The findings may impact residential, infrastructure, industrial, and commercial construction branches.

Optimization of Ecological and Economic Aspects of the Construction Schedule with the Use of Metaheuristic Algorithms and Artificial Intelligence

“…The results obtained after applying the ANN form a Pareto front, and the location of individual solutions on the graph (Figure 12) corresponds to the values of weights 𝑜 assumed in the objective function (the same cannot be said about the original solutions). This is typical for common trade-off problems [18]. As shown in Figure 11, the results obtained after using artificial neural networks (AMTANN) showed less variation and generally had better performance (greater average distance from the center of the coordinate system).…”

“…The results obtained after applying the ANN form a Pareto front, and the location of individual solutions on the graph (Figure 12) corresponds to the values of weights o i assumed in the objective function (the same cannot be said about the original solutions). This is typical for common trade-off problems [18].…”

“…However, in order to use such methods, it is necessary to build a correct model that represents real-life projects in the best possible way. The most common scheduling problems include RCPSP (Resource-Constrained Project Scheduling Problem) and its variations [14,[16][17][18][19]. However, there are not enough solutions that take into account aspects such as value, functionality, or other requirements expected by clients.…”

Robust Scheduling of Multi-Skilled Workforce Allocation: Job Rotation Approach

“…In fact, employees can hone their skills during software development, i.e., an employee may have a low level of a skill, but if they work long enough on a task that requires that skill, they will increase the level of that skill through the experience they have gained. On the other hand, when they rarely use a particular skill, they experience a forgetting effect, and the skill level decreases [48], which affects the efficiency and working time of programmers and thus the timely completion of projects [49].…”