Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Flexible resources allocation techniques: characteristics and modelling

At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multi–skilled actor). On the one hand, multi–skilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Applicability of Heuristic Approach in Planning and Scheduling Project

This paper focuses on the application of heuristic approach in solving scheduling problems of ongoing project. This study is primarily aimed at providing a suitable heuristic for finding an optimum resource level, which ought to be kept over the project execution period, and a sensitivity analysis of which resource type ought to be increased in order to reduce the project completion time towards the ultimate. The use of resource utilization and a “constraining index” in the search for optimal solutions to this problem of meeting delivery date requirements, and optimize utilization of multiple resources in project and minimize other resources. Keywords: Heuristic; resource constraints; constraining index.

Comparing Critical Chain Project Managemenet with Critical Path Method: A Case Study

Scheduling is a major task in project management. The current scheduling technique, Critical Path Method (CPM), has been widely applied for several decades, but a large number of projects fail to be completed on time and schedule delays occur in many projects. This raises question about the validity of the current project scheduling system. Critical Chain Project Management (CCPM), derived from Theory of Constraints, is a relatively new alternative approach toward scheduling projects. This study compared CCPM and CPM to determine which scheduling method delivers a shorter project duration and has a better usage of resources. A scheduling software called ProChain was used to reschedule a CPM based construction project using CCPM. The study concluded that the CCPM has the possibility to deliver shorter project duration and better resource usage in comparison to CPM. It was revealed that ProChain has limitation in the process of transforming a CPM schedule to a CCPM schedule. For example, ProChain treats any tasks without any predecessor as a project terminating task and puts a project buffer after it

Optimal crew routing for linear repetitive projects using graph theory and entropy maximization metric

Construction projects that contain several identical or similar units are usually known as repetitive or linear projects. Repetitive projects are regarded as a wide umbrella that includes various categories of construction projects and represents a considerable portion of the construction industry, and contain uniform repetition of work. CPM has been proved to be inefficient in scheduling linear projects because CPM does not address two key aspects, which are maintaining crew work continuity, and achieving a constant rate of progress to meet a given deadline. Line-of-balance (LOB) is a linear scheduling methodology that produces a work schedule in which resource allocation is automatically performed to provide a continuous and uninterrupted use of resource. The fundamental principles of LOB have several shortfalls that raise many concerns about LOB, which need to be attuned and improved in order to suit the nature of construction projects. Hence, this thesis proposes a hybrid approach for scheduling linear projects that stresses on the limitation of LOB scheduling technique. To meet the physical limitation of resources in linear projects, this study presents a flexible optimization model for resolving resource constraint dilemma in linear scheduling problems .The proposed model utilizes a MATLAB code as the searching algorithm to automate the model formulation. The novelty of this model is supplementing a new optimization engine and a decision supporting system that formulate the optimal crews routing between different activities in different units and guarantee the optimal crew distribution for cost efficiency. This model investigates the mechanics of allocating a multi- task skilled workforce between different activities in different units that can lead to increased productivity, flexibility, and work continuity; besides, this model has the capability of accurately identifying the critical path in linear projects. Furthermore, to avoid day-to-day fluctuation in resource demands, this study encompasses a simulation model for handling the resource leveling in linear construction projects. The proposed model was implemented using crystal ball ribbon based on an entropy maximization metric. The entropy-maximization method accounts for such possibility of allowing activity duration to be stretched or crunched relying on activity type without affecting total completion date of a project and provides more optimized resource allocation solutions. A case study for a 4-km sewage pipeline is used to demonstrate the capability of the proposed models, which illustrates the implementation of the proposed models in construction projects

Determination of Optimal Crew Size in Project Segmentation to Minimize Cost

Stakeholders and contracting board have begun to utilize innovative contracting methods that provide new incentives for reducing construction duration. These methods have placed increasing pressure on decision makers in the construction industry to search for an optimal construction plan that minimizes construction time and cost while preserving quality. This paper considers the problem of finding the optimal number of segment(s) that minimizes total project cost of a non-homogenous road construction project located in a city in South West, Nigeria. A mathematical programming model approach was adopted to obtain the optimal number of segments as opposed to when the activities of the project are scheduled sequentially. Also, Binary interaction matrix (BIM) was used to define stated relationships between fixed and variable quantities of the cost and duration respectively. Given stipulated due date of 8 months with a penalty / bonus of N 63091791/month. The analysis shows that the optimal number of segments into which the project can be divided into is three and that by working in parallel the project will be completed in 5 months at a cost of N1,409,609,413. This is opposed to the original contract lump sum of  N 1, 577, 294, 775 with a normal duration of 12 months. A saving of 1.55% was realized. This work demonstrated the possibility of dividing a continuous repetitive project into a numbers of segments of equal work content, working in parallel, using optimum crew size such that the duration and the total cost of the project can be minimized. Key words: Project Management, Scheduling, Construction planning, Natural Rhythm, Project Segmentatio

A Semantic Agent Framework for Cyber-Physical Systems

The development of accurate models for cyber-physical systems (CPSs) is hampered by the complexity of these systems, fundamental differences in the operation of cyber and physical components, and significant interdependencies among these components. Agent-based modeling shows promise in overcoming these challenges, due to the flexibility of software agents as autonomous and intelligent decision-making components. Semantic agent systems are even more capable, as the structure they provide facilitates the extraction of meaningful content from the data provided to the software agents. In this book chapter, we present a multi-agent model for a CPS, where the semantic capabilities are underpinned by sensor networks that provide information about the physical operation to the cyber infrastructure. As a specific example of the semantic interpretation of raw sensor data streams, we present a failure detection ontology for an intelligent water distribution network as a model CPS. The ontology represents physical entities in the CPS, as well as the information extraction, analysis and processing that takes place in relation to these entities. The chapter concludes with introduction of a semantic agent framework for CPS, and presentation of a sample implementation of the framework using C++

A Computer Modeling Approach Using Critical Resource Diagramming Network Analysis in Project Scheduling

The problem of resource constrained project scheduling (RCPSP) continues to be an important topic in project management. Different scheduling processes have been introduced to solve cases of RCPSP. Most of the developed methods are based on a network analysis approach. The two main technique of project network analysis used for planning, scheduling, and control are PERT and CPM. These approaches assume unlimited resource availability in project network analysis. In realistic projects, both the time and resource requirements of activities should be considered in developing network schedules. Another particularity of the methods created, so far, is the focus on activities during the scheduling process. Therefore, from a resource point of view, the current procedures do not allow the project manager to incorporate information concerning each resource unit under supervision in the scheduling process of a project. There is a need for simple tools for resource planning, scheduling, tracking, and control. Critical resource diagramming (CRD) is a relatively new resource management tool. CRD is a simple extension to the CPM technique developed for resource management purposes. Unlike activity networks, CRD uses nodes to represent each resource unit. Also, contrasting with activities, a resource unit may appear more than once in a CRD network, specifying all different tasks to which a particular unit is assigned. Similar to CPM, the same backward and forward computations may be performed to CRD. The CRD method can also be used in solving RCPSP problems. This present study explores that advantage of CRD. The purposes are to develop a methodology, based on CRD, which allows its use in specific case of RCPSP, to implement the developed technique using a computer model, to conduct test to validate the CRD computer model, and then to investigate the advantages and disadvantages of the introduced method. The CRD computer model will be implemented using the Visual Basic 6.0 language

Applying Bayesian networks to model uncertainty in project scheduling

PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge. An important component of Project Risk Management (PRM) is risk analysis, which attempts to measure risk and its impact on different project parameters such as time, cost and quality. By highlighting the trade-off between project parameters, the thesis concentrates on project time management under uncertainty. The earliest research incorporating uncertainty/risk in projects started in the late 1950’s. Since then, several techniques and tools have been introduced, and many of them are widely used and applied throughout different industries. However, they often fail to capture uncertainty properly and produce inaccurate, inconsistent and unreliable results. This is evident from consistent problems of cost and schedule overrun. The thesis will argue that the simulation-based techniques, as the dominant and state-of-the-art approach for modelling uncertainty in projects, suffers from serious shortcomings. More advanced techniques are required. Bayesian Networks (BNs), are a powerful technique for decision support under uncertainty that have attracted a lot of attention in different fields. However, applying BNs in project risk management is novel. The thesis aims to show that BN modelling can improve project risk assessment. A literature review explores the important limitations of the current practice of project scheduling under uncertainty. A new model is proposed which applies BNs for performing the famous Critical Path Method (CPM) calculation. The model subsumes the benefits of CPM while adding BN capability to properly capture different aspects of uncertainty in project scheduling