66 research outputs found

    Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

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    Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM) and an improved artificial immune network algorithm (aiNet) are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony optimization (ACO)

    An Accelerating Two-Layer Anchor Search With Application to the Resource-Constrained Project Scheduling Problem

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    A Dynamic Intelligent Decision Approach to Dependency Modeling of Project Tasks in Complex Engineering System Optimization

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    Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decision-making problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach

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

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    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

    Considering the flexibility of human resources in planning and scheduling industrial activities

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    The growing need of responsiveness for manufacturing companies facing the market volatility raises a strong demand for flexibility in their organization. This flexibility can be used to enhance the robustness of a baseline schedule for a given programme of activities. Since the company personnel are increasingly seen as the core of the organizational structures, they provide the decision-makers with a source of renewable and viable flexibility. First, this work was implemented to model the problem of multi-period workforce allocation on industrial activities with two degrees of flexibility: the annualizing of the working time, which offers opportunities of changing the schedules, individually as well as collectively. The second degree of flexibility is the versatility of operators, which induces a dynamic view of their skills and the need to predict changes in individual performances as a result of successive assignments. The dynamic nature of workforce’s experience was modelled in function of learning-by-doing and of oblivion phenomenon during the work interruption periods. We firmly set ourselves in a context where the expected durations of activities are no longer deterministic, but result from the number and levels of experience of the workers assigned to perform them. After that, the research was oriented to answer the question “What kind of problem is raises the project we are facing to schedule?”: therefore the different dimensions of the project are inventoried and analysed to be measured. For each of these dimensions, the related sensitive assessment methods have been proposed. Relying on the produced correlated measures, the research proposes to aggregate them through a factor analysis in order to produce the main principal components of an instance. Consequently, the complexity or the easiness of solving or realising a given scheduling problem can be evaluated. In that view, we developed a platform software to solve the problem and construct the project baseline schedule with the associated resources allocation. This platform relies on a genetic algorithm. The model has been validated, moreover, its parameters has been tuned to give the best performance, relying on an experimental design procedure. The robustness of its performance was also investigated, by a comprehensive solving of four hundred instances of projects, ranked according to the number of their tasks. Due to the dynamic aspect of the workforce’s experience, this research work investigates a set of different parameters affecting the development of their versatility. The results recommend that the firms seeking for flexibility should accept an amount of extra cost to develop the operators’ multi functionality. In order to control these over-costs, the number of operators who attend a skill development program should be optimised, as well as the similarity of the new developed skills relative to the principal ones, or the number of the additional skills an operator may be trained to, or finally the way the operators’ working hours should be distributed along the period of skill acquisition: this is the field of investigations of the present work which will, in the end, open the door for considering human factors and workforce’s flexibility in generating a work baseline program

    Prise en compte de la flexibilitĂ© des ressources humaines dans la planification et l’ordonnancement des activitĂ©s industrielles

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    Le besoin croissant de rĂ©activitĂ© dans les diffĂ©rents secteurs industriels face Ă  la volatilitĂ© des marchĂ©s soulĂšve une forte demande de la flexibilitĂ© dans leur organisation. Cette flexibilitĂ© peut ĂȘtre utilisĂ©e pour amĂ©liorer la robustesse du planning de rĂ©fĂ©rence d’un programme d’activitĂ©s donnĂ©. Les ressources humaines de l’entreprise Ă©tant de plus en plus considĂ©rĂ©es comme le coeur des structures organisationnelles, elles reprĂ©sentent une source de flexibilitĂ© renouvelable et viable. Tout d’abord, ce travail a Ă©tĂ© mis en oeuvre pour modĂ©liser le problĂšme d’affectation multi-pĂ©riodes des effectifs sur les activitĂ©s industrielles en considĂ©rant deux dimensions de la flexibilitĂ©: L’annualisation du temps de travail, qui concerne les politiques de modulation d’horaires, individuels ou collectifs, et la polyvalence des opĂ©rateurs, qui induit une vision dynamique de leurs compĂ©tences et la nĂ©cessitĂ© de prĂ©voir les Ă©volutions des performances individuelles en fonction des affectations successives. La nature dynamique de l’efficacitĂ© des effectifs a Ă©tĂ© modĂ©lisĂ©e en fonction de l’apprentissage par la pratique et de la perte de compĂ©tence pendant les pĂ©riodes d’interruption du travail. En consĂ©quence, nous sommes rĂ©solument placĂ©s dans un contexte oĂč la durĂ©e prĂ©vue des activitĂ©s n’est plus dĂ©terministe, mais rĂ©sulte du nombre des acteurs choisis pour les exĂ©cuter, en plus des niveaux de leur expĂ©rience. Ensuite, la recherche a Ă©tĂ© orientĂ©e pour rĂ©pondre Ă  la question : « quelle genre, ou quelle taille, de problĂšme pose le projet que nous devons planifier? ». Par consĂ©quent, les diffĂ©rentes dimensions du problĂšme posĂ© sont classĂ©es et analysĂ©s pour ĂȘtre Ă©valuĂ©es et mesurĂ©es. Pour chaque dimension, la mĂ©thode d’évaluation la plus pertinente a Ă©tĂ© proposĂ©e : le travail a ensuite consistĂ© Ă  rĂ©duire les paramĂštres rĂ©sultants en composantes principales en procĂ©dant Ă  une analyse factorielle. En rĂ©sultat, la complexitĂ© (ou la simplicitĂ©) de la recherche de solution (c’est-Ă -dire de l’élaboration d’un planning satisfaisant pour un problĂšme donnĂ©) peut ĂȘtre Ă©valuĂ©e. Pour ce faire, nous avons dĂ©veloppĂ© une plate-forme logicielle destinĂ©e Ă  rĂ©soudre le problĂšme et construire le planning de rĂ©fĂ©rence du projet avec l’affectation des ressources associĂ©es, plate-forme basĂ©e sur les algorithmes gĂ©nĂ©tiques. Le modĂšle a Ă©tĂ© validĂ©, et ses paramĂštres ont Ă©tĂ© affinĂ©s via des plans d’expĂ©riences pour garantir la meilleure performance. De plus, la robustesse de ces performances a Ă©tĂ© Ă©tudiĂ©e sur la rĂ©solution complĂšte d’un Ă©chantillon de quatre cents projets, classĂ©s selon le nombre de leurs tĂąches. En raison de l’aspect dynamique de l’efficacitĂ© des opĂ©rateurs, le prĂ©sent travail examine un ensemble de facteurs qui influencent le dĂ©veloppement de leur polyvalence. Les rĂ©sultats concluent logiquement qu’une entreprise en quĂȘte de flexibilitĂ© doit accepter des coĂ»ts supplĂ©mentaires pour dĂ©velopper la polyvalence de ses opĂ©rateurs. Afin de maĂźtriser ces surcoĂ»ts, le nombre des opĂ©rateurs qui suivent un programme de dĂ©veloppement des compĂ©tences doit ĂȘtre optimisĂ©, ainsi que, pour chacun d’eux, le degrĂ© de ressemblance entre les nouvelles compĂ©tences dĂ©veloppĂ©es et les compĂ©tences initiales, ou le nombre de ces compĂ©tences complĂ©mentaires (toujours pour chacun d’eux), ainsi enfin que la façon dont les heures de travail des opĂ©rateurs doivent ĂȘtre rĂ©parties sur la pĂ©riode d’acquisition des compĂ©tences. Enfin, ce travail ouvre la porte pour la prise en compte future des facteurs humains et de la flexibilitĂ© des effectifs pendant l’élaboration d’un planning de rĂ©fĂ©rence. ABSTRACT : The growing need of responsiveness for manufacturing companies facing the market volatility raises a strong demand for flexibility in their organization. This flexibility can be used to enhance the robustness of a baseline schedule for a given programme of activities. Since the company personnel are increasingly seen as the core of the organizational structures, they provide the decision-makers with a source of renewable and viable flexibility. First, this work was implemented to model the problem of multi-period workforce allocation on industrial activities with two degrees of flexibility: the annualizing of the working time, which offers opportunities of changing the schedules, individually as well as collectively. The second degree of flexibility is the versatility of operators, which induces a dynamic view of their skills and the need to predict changes in individual performances as a result of successive assignments. The dynamic nature of workforce’s experience was modelled in function of learning-by-doing and of oblivion phenomenon during the work interruption periods. We firmly set ourselves in a context where the expected durations of activities are no longer deterministic, but result from the number and levels of experience of the workers assigned to perform them. After that, the research was oriented to answer the question “What kind of problem is raises the project we are facing to schedule?”: therefore the different dimensions of the project are inventoried and analysed to be measured. For each of these dimensions, the related sensitive assessment methods have been proposed. Relying on the produced correlated measures, the research proposes to aggregate them through a factor analysis in order to produce the main principal components of an instance. Consequently, the complexity or the easiness of solving or realising a given scheduling problem can be evaluated. In that view, we developed a platform software to solve the problem and construct the project baseline schedule with the associated resources allocation. This platform relies on a genetic algorithm. The model has been validated, moreover, its parameters has been tuned to give the best performance, relying on an experimental design procedure. The robustness of its performance was also investigated, by a comprehensive solving of four hundred instances of projects, ranked according to the number of their tasks. Due to the dynamic aspect of the workforce’s experience, this research work investigates a set of different parameters affecting the development of their versatility. The results recommend that the firms seeking for flexibility should accept an amount of extra cost to develop the operators’ multi functionality. In order to control these over-costs, the number of operators who attend a skill development program should be optimised, as well as the similarity of the new developed skills relative to the principal ones, or the number of the additional skills an operator may be trained to, or finally the way the operators’ working hours should be distributed along the period of skill acquisition: this is the field of investigations of the present work which will, in the end, open the door for considering human factors and workforce’s flexibility in generating a work baseline program

    Foundations of Human-Aware Planning -- A Tale of Three Models

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    abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

    Advances and Novel Approaches in Discrete Optimization

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    Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Applications

    Get PDF
    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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