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

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

Optimization in finance : approaches for modeling and solving the multi-period loss offset problem in German income tax system

The major objective of this thesis is to study optimization techniques applied in financial planning. As financial optimization is a diverse field, we restrict our work onto tax planning. Our effort is directed towards studying the Loss Offset Problem which arises in German income tax system. The Loss Offset Problem deals with a situation where individuals or companies confront a loss in some financial years and profits in the years before and after the “loss years”. When such a situation occurs, it is allowed to divide a loss amount into two parts: the loss carry-backward and loss carry-forward. This will reduce the taxable income in other years, therefore reduces tax payments. The problem is of significant importance for a number of reasons. First, potentials for optimization procedures exist as there is a trade-off between the amount of loss to be carried back and forward. Second, from international perspective over the last several years, German loss offset regulations are still rather generous as many other countries do not allow a tax loss carry-backward at all. Besides, we consider two possible choices of taxation options in each period. The focus of this study is the multi-period scenario. As we will see, this hides many interesting dynamics in the interactive behavior of decisions. We formulate the mathematical model so as to optimize an objective function subject to appropriate constraints. The objective function itself is a discontinuous, non-linear, non-convex function with recursive characteristics, which makes the problem difficult to solve. In order to achieve this goal, we first study the complexity of the problem in two cases: a 3-period-model and a multiple-period-model and then apply optimization algorithms from Operations Research to search for solutions. We discuss several algorithms and their corresponding commonly mentioned application. We differentiate between exact and heuristic algorithms. An exact algorithm attempts to obtain the global optimal solution, no matter how long it takes for computational time. However, such approaches do not always work. For many practical problems in business, it is unlikely to acquire a global optimal solution in an acceptable amount of time. To the contrary, a heuristic algorithm may discover a very good feasible solution in a given number of iterations, but not necessarily the optimal solution for the specific problem being considered. To refine our analysis, both types of algorithms need to be adapted, applied, and analyzed under different scenarios of data setting.Gegenstand dieser Arbeit ist die Analyse der Optimierungstechniken in der Finanzplanung. Als Schwerpunkt gilt die betriebswirtschaftliche Steuerplanung, wobei die genaue Untersuchung auf das Verlustzuweisungsproblem eingeschränkt wird. Das Verlustzuweisungsproblem wird im deutschen Einkommensteuersystem formuliert und beschreibt die Situation, in der ein Steuerzahler einerseits finanzielle Gewinne in bestimmten Wirtschaftsperioden und andererseits Verluste in anderen Perioden erwirtschaftet. In so einer Konstellation erlaubt das deutsche Steuerrecht dem Steuerzahler, die Verluste in zwei Komponente aufzuteilen: den Verlustrücktrag und den Verlustvortrag. Diese Wahlmöglichkeiten führen dazu, dass das zu versteuernde Einkommen in den Gewinnperioden verringert wird und somit auch insgesamt die Steuerverpflichtungen. Die Formulierung des Problems sowie die Erarbeitung von passenden Lösungsverfahren sind aufgrund einer Vielzahl von Gründen nicht trivial. Erstens entstehen signifikante Optimierungspotenziale durch die Wahlmöglichkeiten bei der Zuweisung des Verlustes in den vorherigen und nachstehenden Perioden. Zweitens besteht der Vorteil, dass der Gesetzgeber in Deutschland – im Gegensatz zu den meisten anderen Ländern auf internationaler Ebene - einen Verlustrücktrag erlaubt. Darüber hinaus werden zwei unterschiedliche Besteuerungsalternativen, die in jeder einzelnen Periode ausgewählt werden müssen, berücksichtigt. Da das Problem einen multiperiodischen Charakter besitzt, verbergen sich interessante Wechselwirkungen in der Vielzahl der Entscheidungsmöglichkeiten dahinter. Für das Problem wird zunächst ein mathematisches Modell mit geeigneten Nebenbedingungen und einer Zielfunktion formuliert. Die analytische Komplexität entsteht durch eine nichtlineare, nichtstetige und nichtkonvexe Zielfunktion, die selbst ein rekursives Verhalten darstellt. In zwei Szenarien wird das Problem systematisch untersucht: ein 3 periodisches Modell und ein multiperiodisches Modell. Nach der Komplexitätsanalyse werden Algorithmen des Operations Research ausgewählt, auf das Problem angepasst und Lösungsverfahren erarbeitet. Dabei werden zwischen exakten und heuristischen Optimierungsverfahren unterschieden. Ein exaktes Suchverfahren findet das globale Optimum des Problems, jedoch kann der rechnerische Aufwand so hoch sein, dass keine Lösung in realistischer Zeit gefunden werden kann. Für viele Optimierungsprobleme in der Praxis ist es außerdem nicht notwendig, das absolute Optimum unbedingt zu erreichen. Ein heuristischer Algorithmus kann in solchen Situationen eine zulässige, zugleich sehr gute Lösung bei einer akzeptabler Laufzeit und geringem Aufwand berechnen. Bei der Analyse und Anwendung dieser beiden Gruppen von Algorithmen für das beschriebene Problem kamen verschiedenen Datenkonstellationen in Betracht

Optimization in finance : approaches for modeling and solving the multi-period loss offset problem in German income tax system

The major objective of this thesis is to study optimization techniques applied in financial planning. As financial optimization is a diverse field, we restrict our work onto tax planning. Our effort is directed towards studying the Loss Offset Problem which arises in German income tax system. The Loss Offset Problem deals with a situation where individuals or companies confront a loss in some financial years and profits in the years before and after the “loss years”. When such a situation occurs, it is allowed to divide a loss amount into two parts: the loss carry-backward and loss carry-forward. This will reduce the taxable income in other years, therefore reduces tax payments. The problem is of significant importance for a number of reasons. First, potentials for optimization procedures exist as there is a trade-off between the amount of loss to be carried back and forward. Second, from international perspective over the last several years, German loss offset regulations are still rather generous as many other countries do not allow a tax loss carry-backward at all. Besides, we consider two possible choices of taxation options in each period. The focus of this study is the multi-period scenario. As we will see, this hides many interesting dynamics in the interactive behavior of decisions. We formulate the mathematical model so as to optimize an objective function subject to appropriate constraints. The objective function itself is a discontinuous, non-linear, non-convex function with recursive characteristics, which makes the problem difficult to solve. In order to achieve this goal, we first study the complexity of the problem in two cases: a 3-period-model and a multiple-period-model and then apply optimization algorithms from Operations Research to search for solutions. We discuss several algorithms and their corresponding commonly mentioned application. We differentiate between exact and heuristic algorithms. An exact algorithm attempts to obtain the global optimal solution, no matter how long it takes for computational time. However, such approaches do not always work. For many practical problems in business, it is unlikely to acquire a global optimal solution in an acceptable amount of time. To the contrary, a heuristic algorithm may discover a very good feasible solution in a given number of iterations, but not necessarily the optimal solution for the specific problem being considered. To refine our analysis, both types of algorithms need to be adapted, applied, and analyzed under different scenarios of data setting.Gegenstand dieser Arbeit ist die Analyse der Optimierungstechniken in der Finanzplanung. Als Schwerpunkt gilt die betriebswirtschaftliche Steuerplanung, wobei die genaue Untersuchung auf das Verlustzuweisungsproblem eingeschränkt wird. Das Verlustzuweisungsproblem wird im deutschen Einkommensteuersystem formuliert und beschreibt die Situation, in der ein Steuerzahler einerseits finanzielle Gewinne in bestimmten Wirtschaftsperioden und andererseits Verluste in anderen Perioden erwirtschaftet. In so einer Konstellation erlaubt das deutsche Steuerrecht dem Steuerzahler, die Verluste in zwei Komponente aufzuteilen: den Verlustrücktrag und den Verlustvortrag. Diese Wahlmöglichkeiten führen dazu, dass das zu versteuernde Einkommen in den Gewinnperioden verringert wird und somit auch insgesamt die Steuerverpflichtungen. Die Formulierung des Problems sowie die Erarbeitung von passenden Lösungsverfahren sind aufgrund einer Vielzahl von Gründen nicht trivial. Erstens entstehen signifikante Optimierungspotenziale durch die Wahlmöglichkeiten bei der Zuweisung des Verlustes in den vorherigen und nachstehenden Perioden. Zweitens besteht der Vorteil, dass der Gesetzgeber in Deutschland – im Gegensatz zu den meisten anderen Ländern auf internationaler Ebene - einen Verlustrücktrag erlaubt. Darüber hinaus werden zwei unterschiedliche Besteuerungsalternativen, die in jeder einzelnen Periode ausgewählt werden müssen, berücksichtigt. Da das Problem einen multiperiodischen Charakter besitzt, verbergen sich interessante Wechselwirkungen in der Vielzahl der Entscheidungsmöglichkeiten dahinter. Für das Problem wird zunächst ein mathematisches Modell mit geeigneten Nebenbedingungen und einer Zielfunktion formuliert. Die analytische Komplexität entsteht durch eine nichtlineare, nichtstetige und nichtkonvexe Zielfunktion, die selbst ein rekursives Verhalten darstellt. In zwei Szenarien wird das Problem systematisch untersucht: ein 3 periodisches Modell und ein multiperiodisches Modell. Nach der Komplexitätsanalyse werden Algorithmen des Operations Research ausgewählt, auf das Problem angepasst und Lösungsverfahren erarbeitet. Dabei werden zwischen exakten und heuristischen Optimierungsverfahren unterschieden. Ein exaktes Suchverfahren findet das globale Optimum des Problems, jedoch kann der rechnerische Aufwand so hoch sein, dass keine Lösung in realistischer Zeit gefunden werden kann. Für viele Optimierungsprobleme in der Praxis ist es außerdem nicht notwendig, das absolute Optimum unbedingt zu erreichen. Ein heuristischer Algorithmus kann in solchen Situationen eine zulässige, zugleich sehr gute Lösung bei einer akzeptabler Laufzeit und geringem Aufwand berechnen. Bei der Analyse und Anwendung dieser beiden Gruppen von Algorithmen für das beschriebene Problem kamen verschiedenen Datenkonstellationen in Betracht

Management: A bibliography for NASA managers

This bibliography lists 731 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1990. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Analysis of construction and stakeholder risks for Public Private Partnership projects in developing countries : a comparative analysis using Artificial Neural Networks to determine the effect of poor stakeholder management and construction risks on the project’s schedule (PPP vs. traditional projects)

There has been a continuously increasing demand for public services and infrastructure all over the world especially in developing countries in order to respond to the rapidly growing population and the targeted economic growth in these countries. Accordingly resorting to the PPP scheme is a way for the governmental authorities to achieve the objectives of better services to the end user in energy, educational, water and wastewater, and transportation projects with the help and expertise of the private sector. While PPP was proven to be successful in several instances, there are also several failure stories where the PPP scheme was used. In order to avoid such problems and due to the complex nature of PPP projects and their extended life span, an adequate risk management technique should be performed for PPP projects to ensure their success. One of the crucial steps linked to risk management is stakeholder management. This research primarily aims to develop a mathematical model that analyzes the expected total effect of risks associated with poor stakeholder management during the construction phase on PPP projects’ schedule based on historical details of previous PPP projects in a comparative study with traditional construction projects using Artificial Neural Networks. In order to develop “the risks checklist” that will be inserted in the model, an extensive literature review of 30 sources was thoroughly studied in order to develop the list of the risks affecting PPP projects. To properly develop a comprehensive list of risks, the journal papers, research and publications that were studied covered the time span between 1998 until 2018. Furthermore, the literature review performed for the sake of developing the risk factors was covering different countries such as: the United Kingdom, Hong Kong, Scotland, China, Australia, India, Indonesia, Singapore, Iran, Malaysia, Thailand, Portugal and South Africa. These countries were chosen to encompass different levels of PPP experience. Accordingly, a comprehensive list of 118 risks was developed. In addition to the ranking and classification of risks into various risks categories, each one of the identified risks was mapped to its corresponding country. The purpose of this step is to determine the critical risks that the literature identified for each country in order to establish a cross-country comparison. From this mapping, it is found that most of the risks affecting PPP projects around the world are political, legal, stakeholder and construction risks. The inadequate PPP experience, lack of support from government, force majeure and permits delays are affecting PPP projects in all the countries included in this research. It is also noticed that risks affecting developed countries such as Hong Kong, China and UK are of similar nature to the risks affecting developing countries. The model was developed using Neural Designer ® Software. This software was used in particular as it is a powerful user-friendly interface able to make complex operations and build predictive models in an intuitive way with a graphical user interface.To build the model, the input variables were the 44 risk factors related to consttruction and stakehoders while the schedule Growth (or total project delay) was used as the target variable.The dataset contains 12 instances (or 12 projects) and was divided into three sets:a.Training comprising 66.7% of the projects (8 traditional projects)b.Selection (testing) comprising 16.7% of the projects (2 traditional projects)c.Validation comprising 16.7 of the projects (2 PPP projects)Once all the dataset information has been set, some analytics were performed in order to check the quality of the data. Model performance was detected using Mean Squared Error (MSE) and Normalized Squared Error (NSE) over the training, testing and validation datasets.Ten trials of the ANN model were performed using different training and testing strategies in order to be able choose the optimum model that has the best learning capabilities and delivering the least possible errors during the training and testing.Based on the different trials output, it is concluded that Model 4 delivers the smallest range of error (MSE and NSE) for training and testing. The architecture of this particular model is: 18 input nodes, three hidden neurons on two layers and one output. It was trained using a logistic function. It is noticed that having the hidden perceptron on two layers improved the model’s performance significantly and decreased errors for both training and testing. After performing training and testing of all models, and in all trials, it was noticed that the error decreased considerably by decreasing the number of input nodes.In order to validate the model’s performance, sensitivity analysis was performed to determine the cause and effect relationship between inputs and outputs of the ANN model. The most significant risk factor is “the lack of coordination” as it is the most important contributor to the model´s ability to predict total project´s delay. On the other hand, the least significant risk factor in this case is the “constructability” and the “protection of geological and historical objects”. Comparing the results of this sensitivity analysis to the risk mapping to different countries, it is noticed that the lack of coordination risk is not present in other countries such as Australia, Hong Kong and the UK. Based on the model´s outcomes, correlations between all input and target variables ranked in descending order based on the best model out of the ten models were calculated. The maximum correlation (0.803336) is yield between the input variable “Delay in resolving contractual dispute” and the target variable “Schedule growth”. 37 risk factors out of the 44 have a high correlation factor (more than 0.1) with the total project´s delay. Furthermore, a comparison was established between this new ranking and the ranking previously obtained from the literature review based on content analysis and on the ranking obtained from the sensitivity analysis. The following observations were drawn:•Based on the literature review, the material availability risk occupies the first position in terms of the most critical risks. This ranking is similar to a great extent to its ranking based on the correlation calculations according to which this risk occupies the third position. •The “Delay in resolving contractual dispute” occupies the highest rank in terms of correlation with the total project delay based on the ANN model’s outputs. This ranking is also similar to the results of the sensitivity analysis where it occupies the second position in terms of the risks having the highest contribution to the total project’s delay. On the other hand, the same risk is ranked 31st based on the results of the analysis of the literature review. Since the ANN model was based on real case projects, it makes more sense that this particular risk can be of detrimental effect to the project’s completion time. The same goes for the risk “Inadequate negotiation period prior to initiation”. This risk, based on the model’s deliverables, is ranking 11th and 17th in sensitivity analysis and correlation to the total project’s delay while, based on the literature review, is ranking 42nd out of 44. •The “Public opposition” risk is one of the most severe risks facing PPP projects based on the literature review as it occupies the second position based on the various sources taken into account. Nevertheless, based on the sensitivity analysis and on the correlation analysis, this risk occupies the 35th and 44th positions respectively. This difference in ranking can be caused by the relatively small sample size of PPP projects studied in this research. The dataset studied was not encompassing such risk as it was not faced in the projects that were analyzed. However, this does not mean that this risk is not significant especially for PPP projects. •For other risks such as “Constructability”, “staff crisis” and “subjective evaluation”, the literature review and the model deliverables produced very close results. •Based on the literature review, the material availability risk occupies the first position in terms of the most critical risks. This ranking is similar to a great extent to its ranking based on the correlation calculations according to which this risk occupies the third position. On the other hand, the ranking of this same risk is 31 based on the sensitivity analysis in terms of its effect and contribution to the total project delay. The “Delay in resolving contractual dispute” occupies the highest rank in terms of correlation with the total project delay based on the ANN model’s outputs. This ranking is also similar to the results of the sensitivity analysis where it occupies the second position in terms of the risks having the highest contribution to the total project’s delay. The “Public opposition” risk is one of the most severe risks facing PPP projects based on the literature review as it occupies the second position based on the various sources taken into account. Nevertheless, based on the sensitivity analysis and on the correlation analysis, this risk occupies the 35th and 44th positions respectively. A future destination for this study is to provide, in addition to the ANN model determining the contribution of the risks to the overall project delay, a tool assisting the public sector to choose and determine whether the PPP scheme in a particular project is the optimum scheme to use or not