Heuristic algorithms for payment models in project scheduling

Imagine that the city council of Ghent has approved the construction of a new bridge across the Leie. The bridge will serve as a means to reduce traffic congestion in the city center, and the city council imposes a deadline to ensure the bridge is completed in time. Based on the specifications, a contractor subsequently determines the required resources (e.g. manpower, machines) and constructs a project schedule. This schedule holds the start and finish times of each activity (e.g. pouring concrete for the bridge foundations), and respects the imposed resource restrictions and the order in which the activities have to be executed (e.g. excavate the river banks before pouring concrete for the foundations). Whereas the objective of the client (i.e. the city council) is clear, they want the bridge to be constructed within the specified deadline, the objective for the contractor is less obvious. Is the goal to minimize the project duration, minimize total costs, maximize net present value (NPV), etc.? Assume that the contractor can construct two schedules. The first schedule minimizes the project duration, obtains a duration of 6 weeks less than the deadline and has a NPV of € 1 mio. The second schedule, on the contrary, maximizes the project NPV, which results in a duration equal to the deadline and a NPV of € 1.2 mio. The latter schedule is obtained by delaying certain activities within the imposed restrictions, starting from the first schedule. If we assume that sufficient margins are included in the proposed schedules to compensate for any delays, the contractor would obviously prefer the second schedule, since the financial return is larger. The crucial question here is, however, how the second schedule can be obtained in an effective and efficient manner starting from the first schedule. This dissertation aims to develop algorithms, which optimize the project NPV under different restrictions, by means of five studies. The first paper chapter focuses on NPV optimization subject to precedence and resource restrictions. It is furthermore assumed that both cash inflows (payments received from the client) and cash outflows (payments to subcontractors) occur at the end of each activity. This way, the size of payments is set in advance by the client and corresponds with each activity’s cash flows, whereas the timing depends on the project schedule by means of the selected activity finish times, and is controlled by the contractor. The second and third studies consider other payment models, in which the client determines the payment times in advance, rather than the size of payments. As an example, the client may stipulate that the contractor is paid every month, whereas the size of the payments depends on the work performed by the contractor in each month. Both studies furthermore include several alternatives or modes for each activity. These modes constitute different duration-resource combinations for an activity, out of which one has to be selected by the contractor, and allow for a greater degree of flexibility. The fourth paper chapter introduces capital management on the side of the contractor, by imposing that the total funds available should not become negative during the project. The total funds or cash balance consider the initial capital available and respectively add or subtract cash in- and outflows. A general model is constructed which affects the capital availability throughout the project. The fifth and final study integrates the resource availability in the scheduling process, and as such optimizes the NPV of the project including the resource usage cost, rather than decide on the amount of a resource made available first and schedule the activities second

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

A Two-Phase Algorithm for a Resource Constrained Project Scheduling Problem with Discounted Cash Flows

This paper presents a Resource-Constrained Project Scheduling Problem (RCPSP) settled by contractual milestones. The criterion analysed here is the maximisation of aggregate discounted cash flows from the contractor’s perspective, known as an RCPSP problem with Discounted Cash Flows (RCPSPDCF). The cash flows analysed here cover the contractor’s cash outflows (negative cash flows), related to the commencement of individual activities, and cash inflows (positive cash flows) after the fulfilment of individual milestones. The authors propose a two-phase algorithm for solving the problem defined. In the first phase, the simulated annealing metaheuristics is used, designed to identify a forward schedule with as high total DCF as possible. In the second phase, the best first-phase schedule is improved by right shifts of activities. To this end, the procedure which iteratively shifts tasks by one unit is applied, with a view to maximising the objective function. Activity shifts take into consideration precedence and resource constraints, and they are performed for a specified resource allocation to activities. This paper also includes an analysis of the problem for a sample project. The results of computational experiments are then analysed. The experiments were run with the use of standard test problems from the Project Scheduling Problem LIBrary (PSPLIB), with additionally defined cash flows and contractual milestones

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

TECHNIQUES OF GENERATING SCHEDULES FOR THE PROBLEM OF FINANCIAL OPTIMIZATION OF MULTI-STAGE PROJECT

The article presents the problem of scheduling a resource-constrained project with discounted cash flow maximization from the perspective of a contractor. The contractor's expenses (cash outflows for the contractor) are associated with the execution of activities. The client's payments (cash inflows for the contractor) are performed after fulfilling the agreed project stages. The following techniques are suggested for solving the problem: the activity right-shift procedure, the backward scheduling with the opti-mization of completion dates for the agreed project stages and the modified triple justification technique. The effect of these techniques of generating schedules is illustrated for an exemplary project. Finally, an experimental analysis of the proposed procedures is presented

Application of Fuzzy Modelling to Predict Construction Projects Cash Flow

Construction project managers are always looking for methods for forecasting future projects and preventing of potential delays in the project. One of the most crucial requirements of construction project managers and financial planners is awareness of project cash flow and financial status. On the other hand, the unique properties of construction projects with uncertainties such as activity duration, the variability of resources, material costs and also ambiguity in the employer’s payments are factors that have an effect on the correct prediction of project cash flow. Hence, the project team should examine project cash flow under uncertainty environment. There are many approaches for considering uncertainty such as fuzzy sets, interval theory, rough and grey system. But the most well-known approach is fuzzy sets which has wide applications in engineering and management. Hence in this paper, we proposed a new method for forecasting project cash flow under fuzzy environment. Finally, the proposed method was applied on an “Engineering, Procurement and Construction” (EPC) project and it is demonstrated that the proposed model has a high performance in the prediction of project cash flow

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing