Knowledge based-system for alternative design,cost estimating and scheduling

The traditional design, cost estimating and scheduling procedures focus on assessing costs and schedule after design decisions are made and conveyed to construction drawings and design that is, they do not have a mechanism that allow the designer to perform a rapid ‘what if’ analysis on design alternatives and cost analysis of different types of building materials and alternative schedule analysis at early stage of project without accompanying of detailed design and drawing. Moreover, the available scheduling tools are not integrated to design and cost estimating system and hence require manual input of activities lists, activities sequencing, resources, costs and durations to generate project schedule and they are unable to consider the effect of productivity factors and the resulting uncertainty inherent in each activity. This paper presents an integrated knowledge based system for construction cost estimating and scheduling. The knowledge based system supports an automated alternative design analysis with on line schematic drawing, material selection, crew selection and productivity analysis for generating activities sequencing, normal durations, productivity adjusted durations, cost estimate, list of materials selected and their respective quantities and list of assigned crew. An analysis of alternative design, cost estimating and scheduling for a residential building using the integrated system is presented.     

A knowledge-based evolutionary assistant to software development project scheduling

The scheduling of software development projects is a central, non-trivial and costly task for software companies. This task is not exempt of erroneous decisions caused by human limitations inherent to project managers. In this paper, we propose a knowledge-based evolutionary approach with the aim of assisting to project managers at the early stage of scheduling software projects. Given a software project to be scheduled, the approach automatically designs feasible schedules for the project, and evaluates each designed schedule according to an optimization objective that is priority for managers at the mentioned stage. Our objective is to assign the most effective set of employees to each project activity. For this reason, the evaluation of designed schedules in our approach is developed based on available knowledge about the competence of the employees involved in each schedule. This knowledge arises from historical information about the participation of the employees in already executed projects. In order to evaluate the performance of our evolutionary approach, we present computational experiments developed over eight different sets of problem instances. The obtained results are promising since this approach has reached an optimal level of effectivity on seven of the eight mentioned sets, and a high level of effectivity on the remaining set.     

Knowledge integration using problem spaces: A study in resource-constrained project scheduling

The need to develop schedules for projects with resource constraints and cash flows arises in organizational settings ranging from construction planning to research and development. Given the intractable nature of the problem, a variety of knowledge sources relevant to the project scheduling task have been identified in the Operations Management literature. These include a large number of heuristic procedures that can be used to generate feasible project schedules as well as recent neural network-based approaches that can select appropriate heuristic procedures to apply to a specific instance of the project scheduling problem. While integrated application of these knowledge sources is required to effectively support scheduling, previous work has focussed on developing and implementing them in isolation. The problem space computational model presented in this paper addresses this shortcoming by integrating these various knowledge sources, thus enabling the development of decision support systems for resource constrained project scheduling. More generally, the modeling approach used in this paper can be applied to create systems to assist knowledge intensive tasks that arise in many organizational settings.     

A graph-based approach for unpacking construction sequence analysis to evaluate schedules

Construction schedules can mitigate delay risks and are essential to project success. Yet, creating a quality construction schedule is often the outcome of experienced schedulers, and what makes it harder is the fact that historic information including decision reasoning was not documented and disseminated for future use. This study proposes a graph-based method to find the most time-efficient construction sequence from historic projects to improve scheduling productivity and accuracy. The proposed method captured the textual, numerical, and graphical features of construction schedules, and was validated on 353 construction schedules obtained from a Tier-1 contractor in the UK. The results indicate that earthwork sequences can be finished in 4.0% of the project time on average, but earthwork sequences are the least time-efficient ones in a construction project (29% delayed), particularly in road construction (88% delayed). This study compared the time efficiency of sequences learned from previous projects with case study sequences. Results indicated that frequent sequences learned from past projects are 26.7% closer to the actual schedule than the planned ones. Results of this study could assist inexperienced schedulers to create more quality construction schedules and project managers to benchmark project performances.     

Construction scheduling using Genetic Algorithm based on Building Information Model

The construction project schedule is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry that makes them able to track and manage the time, cost, and quality (a.k.a. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners’ knowledge of work packages, on-the-job-experience, planning capability and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a 3-dimensional representation of a project (a.k.a. Building Information Model or BIM) has recently drawn attention to the construction industry.In this paper, the authors demonstrate a novel approach of retrieving enough information from the BIM of a project and then develop construction sequencing for the installation of the project elements. For this reason a computer application is developed that can automatically derive a structurally (statically) stable construction sequence, using the concept of the Genetic Algorithm (GA). The term “structurally stable sequencing” in this article refers to the sequencing order of erection in which the structure remains statically stable locally and globally during the entire installation process. To validate the proposed methodology, the authors designed 21 different experiments and used the proposed method for generating stable construction schedules, which all were successfully accomplished. Therefore, this methodology proposes a novel approach of construction project application of the GA, as an Expert System tool.     

A genetic algorithm-based method for look-ahead scheduling in the finishing phase of construction projects

Genetic algorithms (GAs) are widely used in finding solutions for resource constrained multi-project scheduling problems (RCMPSP) in construction projects. In the finishing phase of a complex construction project, each room forms a confined space for crews to conduct a series of activities and can thus be considered as an individual sub-project. Generating the look-ahead schedule (LAS) which takes into account the limited resources available at the job site falls in the domain of RCMPSP. Therefore GAs can be used to address this scheduling problem and help construction managers to guide the daily work on site. However, current GAs do not consider three key practical aspects that the project planers and construction managers deal with frequently at the job sites: the engineering priorities of each individual sub-project, the zone constraint and the blocking constraint. By addressing these aspects, this paper proposes a GA-based method that takes them into account in the search process for optimum project duration and/or cost. Two examples are used for the discussion of the effectiveness of this method and to showcase its capability in project scheduling when the scale of a project increases.     

A GA optimization model for workgroup-based repetitive scheduling (WoRSM)

Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently Huang and Sun [Huang RY, Sun KS. Non-unit based planning and scheduling of repetitive construction project. J Constr Eng Manage ASCE 2006;132(6):585–97] developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. A sample case study is used for model validation and demonstration. Results and findings are reported.     

Computer-aided activity planning (CAAP) in large-scale projects with an application in the yachting industry

The present paper provides the schema for an innovative and modular computer-based approach to the planning of activities in large-scale projects. Such projects are characterized by tens of thousands of tasks, which are consequently burdensome and difficult to plan manually. This is true to the point that in many shipyards only a low level of detail is used and poor planning is generally performed. The proposed approach is called computer-aided activity planning (CAAP), and an application in the yachting industry is shown to demonstrate its effectiveness. In particular, the so-called outfitting planning problem is faced. The CAAP system, taking into account the available shipyard resources and the knowledge on the building rules is able to automatically define, sequence, and schedule the activities of the whole outfitting process acting as a “planning configurator”. Moreover, it allows the industry-specific knowledge to be stored, maintained and shared within the (extended) organization. Owing to these “building blocks”, plans can be defined accurately and in a shorter time starting from pre-defined templates, with particular impact on lead times whenever variations to complex projects are needed. Finally, to verify the actual capabilities of the approach, the CAAP was implemented within a prototypical software called NautiCAAP.     

An Optimization-Based Approach for Design Project Scheduling

Concurrent engineering has been widely used in managing design projects to speed up the design process by concurrently performing multiple tasks. Since the progress of a design task often depends on the knowledge about other tasks and requires effective communication, tasks and communication activities need to be properly coordinated to avoid delays caused by waiting for information or the need for rework. This paper presents a novel formulation for design project scheduling with explicit modeling of task dependencies and the associated communication activities. General dependencies are modeled as combinations of three basic types representing sequential, concurrent, and independent processes. Communication activities are also modeled as tasks, and their interactions with design tasks are described by sets of intertask constraints. The objective is to achieve timely project completion with limited resources. To improve algorithm convergence and schedule quality, penalties on the violation of constraints coupling design tasks are added to the objective function. A solution methodology that combines Lagrangian relaxation, dynamic programming, and heuristic is developed to schedule design and communication tasks, and a surrogate optimization framework is used to overcome the “inseperability” caused by nonadditive penalties. A heuristic procedure is then developed to obtain scheduling policies from optimization results and to dynamically construct schedules. Numerical results show that the approach is effective to handle various task dependencies and the associated communication activities to provide high-quality schedules.      

Scheduling of linear projects with single loop structures

In the last decade, a number of linear scheduling methods based on the theories of linear and dynamic programming were developed for the scheduling of linear projects. However, these methods are only suitable for the scheduling of serial linear projects (i.e. sequential activities). In this paper, a nonserial dynamic programming formulation for the scheduling and control of linear projects that involve single loop structures is presented. The objective is to provide for the selection of crew formations, interruptions, and lags for the production activities that result in the minimum project duration. The advantage of this nonserial dynamic programming approach is its ability to handle both serial and nonserial linear projects with variable crew formations and work interruptions. In addition, the system presented produces a graphical representation of the project schedule of minimum duration and updates the original schedule based on current information supplied by the user. A numerical example is included to illustrate the input and output of the system presented.     

Recognition of process patterns for BIM-based construction schedules

Construction scheduling is a very demanding and time intensive process. Building information modeling (BIM) is becoming increasingly important for planning and scheduling, as it provides significant support for this difficult assignment. Further improvements can be achieved by applying predefined process templates for BIM-based schedules. It can reduce the planning time and thus increase the productivity. However, a manual definition of proper and application-specific process templates is very challenging. The automatic detection of recurring similar configurations of construction processes, called process patterns, would greatly support this complex task. Identified process patterns can be subsequently generalized, supporting the design of process templates. This contribution presents an overall concept for process pattern recognition in BIM-based construction schedules by applying graph-based methods. Due to the fact that graph matching algorithms are in general very time- and resource-consuming, an indexing technique based on features is used to solve this problem more efficiently. The paper focuses on the estimation of similarity in construction schedules, describing feature-based methods and similarity measure definitions in detail. Another emphasis is the preparation of schedules for the recognition of process patterns, including decomposition of schedules into smaller parts, referred to as subschedules, and normalization of features. The potential of this concept is demonstrated by two different case studies. The proper results of the evaluation show that the proposed method and similarity metrics are sufficient for the recognition of process patterns in construction schedules.     

A formal mapping-based approach for distributed schedule coordination on projects

Construction projects involve a large number of participants with overlapping scope of work. Coordination of their activities is usually an iterative manual task undertaken by a general contractor that is often unaware of the detailed constraints of other participants. Project schedules play a key role in this coordination and form the backbone of almost all current approaches to process coordination. However, no single schedule represents the perspective of all participants involved in a project. Rather, each participant keeps in some manner a schedule for its own activities, resulting in multiple schedules that need to be coordinated. The current literature does not support simultaneous reasoning across multiple distributed, overlapping schedules. This paper introduces constructs to formalize the integration of participants’ overlapping schedules that represent the same project tasks, but use a different set breakdown structures and level of detail. Implementation of these constructs allows linking of the master schedule to the other participants’ schedules thereby representing the perspectives of all project participants. This integrated perspective facilitates initial schedule coordination and allows rapid identification of schedule conflicts in response to any schedule changes.     

New concepts for activity float in resource-constrained project management

The concepts of float and critical path are central to analyzing activity networks in project management. In resource-constrained projects, schedule multiplicity makes it difficult to calculate float and identify critical activities accurately. In this work, new concepts of float and critical activity are developed to ascertain critical activities more precisely without reference to activity start and end times in specific schedules. The notions of float, group float, float set, negative float and zero critical activity are introduced, which the project manager can use to deal more effectively with critical activities, duration uncertainty, activity buffering, and resource allocation than the currently available tools in literature. For practical implementation, algorithms are provided and tested to calculate the new measures on the PSPLIB benchmark instances, specifically the J30, J60 and J120 test sets, for the resource constrained project management, illustrating the effectiveness of the proposed concepts in helping to identify flexibility in scheduling activities.     

Resource‐constrained project scheduling through the goal programming model: integration of the manager's preferences

The aim of this paper is to present a multi‐objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorporated to generate this baseline schedule that represents the best compromise among a set of conflicting project objectives. An efficient computerized procedure based on the tabu search algorithm is proposed and enables the handling of large planning and scheduling projects.     

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs.     

The development of a university sports complex: A project management application

The development of a university sports complex requires an integrated planning, scheduling and management approach, a requirement which is underscored by the desired to overlap design and construction activities in order to complete the facility on an accelerated schedule. The analysis of the project utilizes project management techniques: development of network models, identification of critical paths, and scheduling, cost control and resource allocation considerations. The use of computerized project planning and control tools provides flexibility and enables easy modification of the plans.     

A classification of predictive-reactive project scheduling procedures

The vast majority of the project scheduling research efforts over the past several years have concentrated on the development of workable predictive baseline schedules, assuming complete information and a static and deterministic environment. During execution, however, a project may be subject to numerous schedule disruptions. Proactive-reactive project scheduling procedures try to cope with these disruptions through the combination of a proactive scheduling procedure for generating predictive baseline schedules that are hopefully robust in that they incorporate safety time to absorb anticipated disruptions with a reactive procedure that is invoked when a schedule breakage occurs during project execution. In this paper we discuss the results obtained by a large experimental design set up to evaluate several predictive-reactive resource-constrained project scheduling procedures under the composite objective of maximizing both the schedule stability and the timely project completion probability.     

An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.     

A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling

Traditionally, the resource-constrained project scheduling problem (RCPSP) is modeled as a static and deterministic problem and is solved with the objective of makespan minimization. However, many uncertainties, such as unpredictable increases in processing times caused by rework or supplier delays, random transportation and/or setup, may render the proposed solution obsolete. In this paper, we present a two-stage algorithm for robust resource-constrained project scheduling. The first stage of the algorithm solves the RCPSP for minimizing the makespan only using a priority-rule-based heuristic, namely an enhanced multi-pass random-biased serial schedule generation scheme. The problem is then similarly solved for maximizing the schedule robustness while considering the makespan obtained in the first stage as an acceptance threshold. Selection of the best schedule in this phase is based on one out of 12 alternative robustness predictive indicators formulated for the maximization purpose. Extensive simulation testing of the generated schedules provides strong evidence of the benefits of considering robustness of the schedules in addition to their makespans. For illustration purposes, for 10 problems from the well-known standard set J30, both robust and non-robust schedules are executed with a 10% duration increase that is applied to the same randomly picked 20% of the project activities. Over 1000 iterations per instance problem, the robust schedules display a shorter makespan in 55% of the times while the non-robust schedules are shown to be the best performing ones in only 6% of the times.