A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches

Multi-skill resource-constrained project scheduling problems : models and algorithms

Tese de doutoramento, Estatística e Investigação Operacional (Otimização), Universidade de Lisboa, Faculdade de Ciências, 2018In this dissertation, project scheduling problems with multi-skill resources are investigated. These problems are commonly found in companies making use of human resources or multi-purpose machinery equipment. The general problem consists of a single project comprising a set of activities. There are precedence relations between the activities. Each activity requires one or several skills for being processed and for each of these skills, more than one resource may be needed. The resources have a unitary capacity per time unit and may master more than one skill. The resources can contribute with at most one skill to at most one activity that requires it, in each time unit. It is usually assumed that the resources are homogeneous, i.e., the proficiency at which each skill is performed is the same across all resources that master that skill. Preemption is not allowed, which implies that once an activity starts being processed it cannot be interrupted. When a resource is assigned to perform a skill for an activity, it remains in that status for the whole processing time of the activity. The objective of the problem is to schedule all the activities, satisfying all constraints such that the makespan of the project is minimized. After introducing a framework to the realm of project scheduling problems with multi-skill resources and highlighting the main objectives and contributes of this thesis, a state-of the-art review on the topic is presented. The particular problem investigated in this document is then described in detail and its specific features are discussed. To that end, a continuous-time mathematical formulation from the literature is revisited, an example of the problem is presented and some aspects related to the computation of feasible solutions are discussed. This last topic is of major relevance when dealing with problems that combine personnel staffing with project scheduling. In order to properly assess the quality of solutions obtained by the methodological developments proposed in this thesis, it became necessary to develop an instance generator to build a set of instances larger than those existing in the literature. After formally proposing such generator, we detail the characteristics of the two sets of instances considered for the computational experiments to be performed. In the next sections of the document, the solution methodologies developed within the scope of this thesis are presented and thoroughly discussed. A wide range of mathematical formulations is studied, two of which are first proposed in this document. From the assessment of their ability both to compute feasible and possibly optimal solutions and to derive good lower bounds (stemming from their linear programming relaxations) to the problem, it will become clear that the so-called discrete-time formulations yield the strongest lower bounds whereas a continuous-time formulation from the literature proved to be the most suitable for solving instances of the problem to optimality. This trend is observed for both sets of instances considered. Two constructive lower bound mechanisms proposed for the resource-constrained project scheduling problem are extended to account for the existence of multi-skill resources and multi skill requirements of the activities. The results reveal that such methods improve the lower bounds achieved by the studied mathematical formulations for some instances. Real-world project scheduling problems usually involve a large number of activities, resources and skills. Hence, the use of exact methods such as the standard techniques for tackling the aforementioned mathematical models, is often impractical. When faced with this kind of situations, a project manager may consider preferable to have a good feasible solution, not necessarily an optimal one, within an admissible time, by means of an approximate method. A close look into the problem being investigated in this thesis reveals that it has some features that are not present in some well-studied particular cases of it, namely the notion of skill—multi skill resources and skill requirements of the activities. Hence, with the objective of developing approximate solution methodologies that better exploit the specific characteristics of the problem at hand, two new concepts are introduced: activity grouping and resource weight. The well-known parallel and serial scheduling schemes, proposed originally for the class of resource-constrained project scheduling problems, are extended to our problem setting and the two above-mentioned concepts are incorporated into these two new frameworks. Such frameworks use well-known activity priority rules for defining the order by which the activities are selected to be scheduled and resource weight rules to determine a set of resources that meets the requirements of all the activities to be scheduled at each time with the least total cost (weight). Thereafter, two heuristic procedures making use of those schedule generation schemes are proposed, namely a multi-pass heuristic built upon the parallel scheduling scheme and a biased random-key genetic algorithm. The idea of computing a feasible solution using the so-called backward planning is also considered in both methods. The multi-pass heuristic retrieves the solution with the minimum makespan after performing a specific number of passes, each associated with a unique combination of the considered activity priority rules, the developed resource weight rules and the two precedence networks: forward and backward. The biased random-key genetic algorithm is a metaheuristic whose developed chromosome structure encodes information related to: (i) the priority values of the activities; (ii) the weights of the resources; (iii) how a chromosome is decoded, i.e., the scheduling scheme and precedence network scheme to be used for computing the associated makespan. By embedding all this information into the chromosomes, it becomes possible to take advantage of the evolutionary framework of the biased random-key genetic algorithm, which tends to allow the evolution of such data (change in their values) over time, towards better makespan valued solutions. Three variants of the biased random-key genetic algorithm are considered with regard to the type of scheduling generation scheme to be used for decoding its chromosomes: (i) all chromosomes are decoded with the parallel scheduling scheme; (ii) all chromosomes are decoded with the serial scheduling scheme; (iii) the scheduling scheme to be used for decoding each chromosome depends on the value of the associated parameter which is embedded in the chromosome. The computational results revealed that the proposed multi-pass heuristic is an efficient algorithm for computing feasible solutions of acceptable quality within a small computational time whereas the biased random-key genetic algorithm is a robust algorithm and a more competitive approximate approach for computing feasible solutions of higher quality, especially for harder instances such as those of medium and large dimensions. We conclude this thesis with an overview of the work done and with some directions for further research in terms of methodological developments and of some potentially interesting extensions of the addressed problem

A multi-objective GA-based optimisation for holistic Manufacturing, transportation and Assembly of precast construction

Resource scheduling of construction proposals allows project managers to assess resource requirements, provide costs and analyse potential delays. The Manufacturing, transportation and Assembly (MtA) sectors of precast construction projects are strongly linked, but considered separately during the scheduling phase. However, it is important to evaluate the cost and time impacts of consequential decisions from manufacturing up to assembly. In this paper, a multi-objective Genetic Algorithm-based (GA-based) searching technique is proposed to solve unified MtA resource scheduling problems (which are equivalent to extended Flexible Job Shop Scheduling Problems). To the best of the authors' knowledge, this is the first time that a GA-based optimisation approach is applied to a holistic MtA problem with the aim of minimising time and cost while maximising safety. The model is evaluated and compared to other exact and non-exact models using instances from the literature and scenarios inspired from real precast constructions

Mining Explicit and Implicit Relationships in Data Using Symbolic Regression

Identification of implicit and explicit relations within observed data is a generic problem commonly encountered in several domains including science, engineering, finance, and more. It forms the core component of data analytics, a process of discovering useful information from data sets that are potentially huge and otherwise incomprehensible. In industries, such information is often instrumental for profitable decision making, whereas in science and engineering it is used to build empirical models, propose new or verify existing theories and explain natural phenomena. In recent times, digital and internet based technologies have proliferated, making it viable to generate and collect large amount of data at low cost. This inturn has resulted in an ever growing need for methods to analyse and draw interpretations from such data quickly and reliably. With this overarching goal, this thesis attempts to make contributions towards developing accurate and efficient methods for discovering such relations through evolutionary search, a method commonly referred to as Symbolic Regression (SR). A data set of input variables x and a corresponding observed response y is given. The aim is to find an explicit function y = f (x) or an implicit function f (x, y) = 0, which represents the data set. While seemingly simple, the problem is challenging for several reasons. Some of the conventional regression methods try to “guess” a functional form such as linear/quadratic/polynomial, and attempt to do a curve-fitting of the data to the equation, which may limit the possibility of discovering more complex relations, if they exist. On the other hand, there are meta-modelling techniques such as response surface method, Kriging, etc., that model the given data accurately, but provide a “black-box” predictor instead of an expression. Such approximations convey little or no insights about how the variables and responses are dependent on each other, or their relative contribution to the output. SR attempts to alleviate the above two extremes by providing a structure which evolves mathematical expressions instead of assuming them. Thus, it is flexible enough to represent the data, but at the same time provides useful insights instead of a black-box predictor. SR can be categorized as part of Explainable Artificial Intelligence and can contribute to Trustworthy Artificial Intelligence. The works proposed in this thesis aims to integrate the concept of “semantics” deeper into Genetic Programming (GP) and Evolutionary Feature Synthesis, which are the two algorithms usually employed for conducting SR. The semantics will be integrated into well-known components of the algorithms such as compactness, diversity, recombination, constant optimization, etc. The main contribution of this thesis is the proposal of two novel operators to generate expressions based on Linear Programming and Mixed Integer Programming with the aim of controlling the length of the discovered expressions without compromising on the accuracy. In the experiments, these operators are proven to be able to discover expressions with better accuracy and interpretability on many explicit and implicit benchmarks. Moreover, some applications of SR on real-world data sets are shown to demonstrate the practicality of the proposed approaches. Besides, in related to practical problems, how GP can be applied to effectively solve the Resource Constrained Scheduling Problems is also presented

Resource assignment in short life technology intensive (SLTI) new product development (NPD)

Enterprises managing multiple concurrent New Product Development (NPD) projects face significant challenges assigning staff to projects in order to achieve launch schedules that maximize financial returns. The challenge is increased with the class of Short Life Technology Intensive (SLTI) products characterized by technical complexity, short development cycles and short revenue life cycles. Technical complexity drives the need to assign staffing resources of various technical disciplines and skill levels. SLTI products are rapidly developed and launched into stationary market windows where the revenue life cycle is short and decreasing with any time-to-market delay. The SLTI-NPD project management decision is to assign staff of varying technical discipline and skill level to minimize the revenue loss due to product launch delays across multiple projects. This dissertation considers an NPD organization responsible for multiple concurrent SLTI projects each characterized by a set of tasks having technical discipline requirements, task duration estimates and logical precedence relationships. Each project has a known potential launch date and potential revenue life cycle. The organization has a group of technical professionals characterized by a range of skill levels in a known set of technical disciplines. The SLTI-NPD resource assignment problem is solved using a multi-step process referred to as the Resource Assignment and Multi-Project Scheduling (RAMPS) decision support tool. Robust scheduling techniques are integrated to develop schedules that consider variation in task and project duration estimates. A valuation function provides a time-value linkage between schedules and the product revenue life cycle for each product. Productivity metrics are developed as the basis for prioritizing projects for resources assignment. The RAMPS tool implements assignment and scheduling algorithms in two phases; (i) a constructive approach that employs priority rule heuristics to derive feasible assignments and schedules and (ii) an improvement heuristic that considers productivity gains that can be achieved by interchanging resources of differing skill levels and corresponding work rates. An experimental analysis is conducted using the RAMPS tool and simulated project and resource data sets. Results show significant productivity and efficiency gains that can be achieved through effective project and resource prioritization and by including consideration of skill level in the assignment of technical resources

Operating room planning and scheduling: A literature review.

This paper provides a review of recent research on operating room planning and scheduling. We evaluate the literature on multiple fields that are related to either the problem setting (e.g. performance measures or patient classes) or the technical features (e.g. solution technique or uncertainty incorporation). Since papers are pooled and evaluated in various ways, a diversified and detailed overview is obtained that facilitates the identification of manuscripts related to the reader's specific interests. Throughout the literature review, we summarize the significant trends in research on operating room planning and scheduling and we identify areas that need to be addressed in the future.Health care; Operating room; Scheduling; Planning; Literature review;

Longterm schedule optimization of an underground mine under geotechnical and ventilation constraints using SOT

Long-term mine scheduling is complex as well time and labour intensive. Yet in the mainstream of the mining industry, there is no computing program for schedule optimization and, in consequence, schedules are still created manually. The objective of this study was to compare a base case schedule generated with the Enhanced Production Scheduler (EPS®) and an optimized schedule generated with the Schedule Optimization Tool (SOT). The intent of having an optimized schedule is to improve the project value for underground mines. This study shows that SOT generates mine schedules that improve the Net Present Value (NPV) associated with orebody extraction. It does so by means of systematically and automatically exploring the options to vary the sequence and timing of mine activities, subject to constraints. First, a conventional scheduling method (EPS®) was adopted to identify a schedule of mining activities that satisfied basic sets of constraints, including physical adjacencies of mining activities and operational resource capacity. Additional constraint scenarios explored were geotechnical and ventilation, which negatively effect development rates. Next, the automated SOT procedure was applied to determine whether the schedules could be improved upon. It was demonstrated that SOT permitted the rapid re-assessment of project value when new constraint scenarios were applied. This study showed that the automated schedule optimization added value to the project every time it was applied. In addition, the reoptimizing and re-evaluating was quickly achieved. Therefore, the tool used in this research produced more optimized schedules than those produced using conventional scheduling methods.Master of Applied Science (MASc) in Natural Resources Engineerin

Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival

The resource constrained multi-project scheduling problem (RCMPSP) is a general and classic problem, which is usually considered and solved in a deterministic environment. However, in real project management, there are always some unforeseen factors such as one or more new project arrivals that give rise to intermittent changes in the activity duration (or stochastic duration) of the current project in execution by inserting the new project. This study takes two practical factors in terms of stochastic duration of project activities and new project arrivals waiting for insertion into account of the problem space to form a stochastic resource constrained multi-project scheduling problem with new project arrivals (SRCMPSP-NPA). Based on the benchmark of the PSPLIB (Project Scheduling Problem Library), a new data set is built and 20 priority rules (PRs) are applied to solve the problem and their performances are analyzed. In addition, a heuristic hybrid method is designed for solving the problem timely by dividing the entire scheduling process into multi-state scheduling problems solved by the corresponding rules separately. This approach has been verified by experiments and its performance is better than that of a single rule in most situations