Production rates for various activities and overall construction project duration are significantly influenced by crew formation. Crews are composed of available renewable resources. Construction companies tend to reduce the number of permanent employees, which reduces fixed costs, but at the same time limits production capacity. Therefore, construction project planning must be carried out by means of scheduling methods which allow for resource constrains. Authors create a mathematical model for optimized scheduling of linear construction projects with consideration of resources and work continuity constraints. Proposed approach enables user to select optimal crew formation under limited resource supply. This minimizes project duration and improves renewable resource utilization in construction linear projects. This paper presents mixed integer linear programming to model this problem and uses a case study to illustrate it.
The article presents the problem of scheduling a multi-stage project with limited availability
of resources with the discounted cash flow maximization criterion from the perspective of
a contractor. The contractor’s cash outflows are associated with the execution of activities.
The client’s payments (cash inflows for the contractor) are performed after completing the
agreed project’s stages. The proposed solution for this problem is the use of insertion algorithms.
Schedules are generated using forward and backward schedule generation schemes
and modified justification techniques. The effectiveness of the proposed procedures is the
subject of the examination with the use of standard test instances with additionally defined
financial settlements of a project.
Redundancy based methods are proactive scheduling methods for solving the Project
Scheduling Problem (PSP) with non-deterministic activities duration. The fundamental
strategy of these methods is to estimate the activities duration by adding extra time to the
original duration. The extra time allows to consider the risks that may affect the activities
durations and to reduce the number of adjustments to the baseline generated for the project.
In this article, four methods based on redundancies were proposed and compared from two
robustness indicators. These indicators were calculated after running a simulation process.
On the other hand, linear programming was applied as the solution technique to generate
the baselines of 480 projects analyzed. Finally, the results obtained allowed to identify the
most adequate method to solve the PSP with probabilistic activity duration and generate
robust baselines.
A project scheduling problem investigates a set of activities that have to be scheduled
due to precedence priority and resource constraints in order to optimize project-related
objective functions. This paper focuses on the multi-mode project scheduling problem concerning
resource constraints (MRCPSP). Resource allocation and leveling, renewable and
non-renewable resources, and time-cost trade-off are some essential characteristics which are
considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid
algorithm is proposed based on non-dominated sorting ant colony optimization and genetic
algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to
improve the efficiency of the ant colony algorithm. The test problems are generated based on
the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed
algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result
verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II
algorithm.
Most scheduling methods used in the construction industry to plan repetitive projects assume that process durations are deterministic. This assumption is acceptable if actions are taken to reduce the impact of random phenomena or if the impact is low. However, construction projects at large are notorious for their susceptibility to the naturally volatile conditions of their implementation. It is unwise to ignore this fact while preparing construction schedules. Repetitive scheduling methods developed so far do respond to many constructionspecific needs, e.g. of smooth resource flow (continuity of work of construction crews) and the continuity of works. The main focus of schedule optimization is minimizing the total time to complete. This means reducing idle time, but idle time may serve as a buffer in case of disruptions. Disruptions just happen and make optimized schedules expire. As process durations are random, the project may be delayed and the crews’ workflow may be severely affected to the detriment of the project budget and profits. For this reason, the authors put forward a novel approach to scheduling repetitive processes. It aims to reduce the probability of missing the deadline and, at the same time, to reduce resource idle time. Discrete simulation is applied to evaluate feasible solutions (sequence of units) in terms of schedule robustness.
Most construction projects involve subcontracting some work packages. A subcontractor is employed on the basis of their bid as well as according to their availability. A viable schedule must account for resource availability constraints. These resources (e.g. crews, subcontractors) engage in many projects, so they become at the disposal for a new project only in certain periods. One of the key tasks of a planner is thus synchronizing the work of resources between concurrent projects. The paper presents a mathematical model of the problem of selecting subcontractors or general contractor’s crews for a time-constrained project that accounts for the availability of contractors, as well as for the cost of subcontracting works. The proposed mixed integer-binary linear programming model enables the user to perform the time/cost trade-off analysis.