The paper presents a method of priority scheduling that is useful during the planning of multiple-structure construction projects. This approach is an extension of the concept of interactive scheduling. In priority scheduling, it is the planner that can determine how important each of the technological and organisational constraints are to them. A planner's preferences can be defined through developing a ranking list that defines which constraints are the most important, and those whose completion can come second. The planner will be able to model the constraints that appear at a construction site more flexibly. The article presents a general linear programming model of the planning of multiple-structure construction projects, as well as various values of each of the parameters that allow us to obtain different planning effects. The proposed model has been implemented in a computer program and its effectiveness has been presented on a calculation example.

The basic element of a project organizing construction works is a schedule. The preparation of the data necessary to specify the timings of the construction completion as indicated in the schedule involves information that is uncertain and hard to quantify. The article presents the methods of building a schedule which includes a fuzzy amount of labour, time standards and number of workers. The proposed procedure allows determining the real deadline for project completion, taking into account variable factors affecting the duration of the individual works.

This paper presents a model of scheduling of multi unit construction project based on an NP-hard permutation flow shop problem, in which the considered criterion is the sum of the costs of the works' execution of the project considering the time of the project as a constraint. It is also assumed that each job in the units constituting the project may be realized in up to three different ways with specific time and cost of execution. The optimization task relies on solving the problem with two different decision variables: the order of execution of units (permutation) and a set of ways to carry out the works in units. The task presented in the paper is performed with the use of a created algorithm which searches the space of solutions in which metaheuristic simulated annealing algorithm is used. The paper presents a calculation example showing the applicability of the model in the optimization of sub-contractors' work in the construction project.

Construction risk assessment is the final and decisive stage of risk analysis. When highly changeable conditions of works execution are predicted, risk should be evaluated in the favorable, moderate, and difficult random conditions of construction. Given the random conditions, the schedule and cost estimate of the construction are developed. Based on these values, the risk of final deadline delay and the risk of total cost increase of construction completion are calculated. Next, the charts of the risks are elaborated. Risk changes are shown in the charts and are analyzed in the range [1, 0].

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 paper present the concept of stability assessing the of solutions which are construction schedules. Rank have been obtained through the use of task scheduling rules and the application of the KASS software. The aim of the work is the choice of the equivalent solution in terms of the total time of the project. The selected solution optimization task should be characterized by the highest resistance to harmful environmental risk factors. To asses the stability of schedule simulation technique was used.

The In the paper, we investigate two single processor problems, which deal with the process of negotiation between a producer and a customer about delivery time of final products. This process is modelled by a due interval, which is a generalization of well known classical due date and describes a time interval, in which a job should be finished. In this paper we consider two diffierent mathematical models of due intervals. In both considered problems we should find such a schedule of jobs and such a determination of due intervals to each job, that the generalized cost function is minimized. The cost function is the maximum of the following three weighted parts: the maximum tardiness, the maximum earliness and the maximum due interval size. For the first problem we proved several properties of its optimal solution and next we show the mirror image property for both of considered problems, which helps us to provide an optimal solution for the second problem.

The paper presents a new elastic scheduling task model which has been used in the uniprocessor node of a control measuring system. This model allows the selection of a new set of periods for the occurrence of tasks executed in the node of a system in the case when it is necessary to perform additional aperiodic tasks or there is a need to change the time parameters of existing tasks. Selection of periods is performed by heuristic algorithms. This paper presents the results of the experimental use of an elastic scheduling model with a GRASP heuristic algorithm.

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.

In this work we consider a problem from the field of power- and energy-aware scheduling, in which a set of batteries have to be charged in a minimum time. The formulated problem is to schedule independent and nonpreemptable jobs to minimize the schedule length, where each job requires some amount of power and consumes a certain amount of energy during its processing. We assume that the power demand of each job linearly decreases with time, as it is the case when Li-ion batteries are being charged. For the assumed job model we prove that each next job should be started as soon as the required amount of power is available. Basing on the proven theorem we formulate a procedure generating a minimum-length schedule for an assumed order of jobs. We also analyze the case of identical jobs, and show some interesting properties of this case.

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.