# Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario
## 2 Literature Review
### Knowledge Gaps  
A summary of related literature is described in Table 1. It could be seen that although this body of research has added considerable knowledge, existing studies on scheduling optimization based on LSM still have shortcomings in three key areas.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Model Characteristics**} & \multicolumn{1}{c}{**Flexible**} & \multicolumn{1}{c}{**Flexible**} & \multicolumn{1}{c}{**Solute**} \\
**Authors** & **Methodology** & **Multi-Mode** & **Multi-Crew** & **Multi-Section** & **Flexible Section** & **Activity-** & **Reverse** & **Solution** \\
**(Year)** & **Methodology** & **Multi-Mode** & **Multi-Crew** & **Multi-Section** & **Selection-Crew** & **Section-Crew** & **Construction** & **Approach** \\ \hline \hline [PERSON], [PERSON] (2020)[35] & CFM & & & & & & & CP \\ [PERSON] et al. (2019)[36] & CFM & & & & & & & CG \\ [PERSON] & LSM & \(\surd\) & & & \(\surd\) & & & Gurobi \\ [PERSON] & & LSM & \(\surd\) & \(\surd\) & \(\surd\) & & & Gurobi \\ et al. (2019)[37] & LSM & \(\surd\) & \(\surd\) & \(\surd\) & & & Gurobi \\ [PERSON] & LSM & & & & \(\surd\) & & Heuristic \\ et al. (2021)[24] & CFM & & & & & & EA \\ [PERSON], [PERSON] (2021)[9] & CFM & & & & & & GA \\ [PERSON] et al. (2020)[40] & LSM & \(\surd\) & \(\surd\) & \(\surd\) & & & GPSO \\ [PERSON] et al. (2019)[41] & CFM & & & & & & SA \\ [PERSON], [PERSON] (2017)[42] & CFM & & & & & & Heuristic \\ [PERSON], & CFM & & & & & & GA+LP \\ [PERSON] (2019)[44] & LSM & \(\surd\) & & & \(\surd\) & & GA \\ [PERSON], [PERSON] (2019)[45] & LSM & \(\surd\) & & & \(\surd\) & & GA \\ [PERSON] et al. (2020)[46] & CFM & & & & & & SOS \\ [PERSON], [PERSON] (2021)[47] & CFM & & & & & & NSGA-III \\ [PERSON] et al. (2023)[48] & CFM & & & & & & SO \\ [PERSON] (2021)[49] & LSM & \(\surd\) & & \(\surd\) & & & GA \\ [PERSON] et al. (2021)[50] & LSM & \(\surd\) & \(\surd\) & \(\surd\) & & & GA \\ \hline \hline \end{tabular} Note: CP: constant programming: CG: column generation; EA: evolutionary algorithm; CA: genetic algorithm; LP: linear programming. QS: column particle swarm optimization; SA: simulated annealing; SOS: symbiotic organisms search; SO: streamlined optimization; GWO: grey wolf optimization.  
\end{table}
Table 1: Summary of related literature.  
First, linear project naturally has a bi-directional construction scenario. On the one hand, some special construction activities, such as tunnel engineering, require bi-directional construction to overcome the insufficient workspace to accelerate construction progress. On the other hand, the introduction of bi-directional construction brings more spatiotemporal flexibility to linear project-scheduling optimization, which may further lead to better construction schedules that are potentially unknown. However, no description system has been proposed for the spatiotemporal logical relationship between activities based on LSM in a reverse construction scenario. This not only limits the model building based on reverse construction scenarios, but also means research on the effect of incorporating reverse construction activities in linear project-scheduling optimization remains insufficient.  
Second, the linear characteristics and the typically large scale of linear projects necessitate multisection and multicrew scenarios. However, most existing studies employ a unified perspective in dividing sections, essentially overlooking the unique requirements of activities, which may reduce the practicality of the models. The consideration of personalized section division will increase the complexity of spatiotemporal constraints between activities, which brings necessity for a spatiotemporal logical constraint system considering actual scenarios.  
Further, the application of a more flexible model for activity-section-crew assignment demands additional exploration.