Time Reduction for Completion of a Civil Engineering Construction Using Fuzzy Clustering Techniques

In the civil engineering field, there are usually unexpected troubles that can cause delays during execution. This situation involves numerous variables (resource number, execution time, costs, working area availability, etc.), mutually dependent, that complicate the definition of the problem analytical model and the related resolution. Consequently, the decision-maker may avoid rational methods to define the activities that could be conveniently modified, relying only on his personal experience or experts’ advices. In order to improve this kind of decision from an objective point of view, the authors analysed the operation correction using a data mining technique, called Fuzzy Clustering. This allows the analysts to represent complex real scenarios and classify the various activities according to their influence on the reduction of the total execution time. The proposed procedure provides positive results that are also in compliance with significant operational constraints, such as the control of costs and areas needed by the workers to perform the tasks. Finally, it is possible to increase the input variable number preserving the algorithm simplicity and avoiding lacks of accuracy in the final numerical outcomes

Review on bio-based plastic for future applications

This paper reviews the future applications of bio-based plastics. Most plastics are made through petrochemical processes. In other words, they start out as the chemical byproducts of oil refining, which are turned into a variety of plastics through chemical processes that form long molecular chains known as polymers. These polymers give plastics their structure. Bioplastics are biodegradable materials that come from renewable sources and can be used to reduce the problem of plastic waste that is suffocating the planet and contaminating the environment. The advantages of using bioplastics are bioplastics won’t leach chemicals into food, non- toxic and offer a zero waste end life options. Bioplastics can be recycled with conventional plastics to produce a great material for food packaging. It also has a socio�economic benefit that often have a positive impact on the consumers who are increasingly becoming aware of environmental issues. As conclusion, it is proven that bioplastics give promising future to cleaner and safer world

Metodološki aspekti optimizacije resursa u mrežnom dijagramu

U ovoj disertaciji biće prikazani osnovni elementi svakog projekta kao i životni ciklus projekta. Analiziraće se organizacione forme za upravljanje projektima kao i metodologija mrežnog planiranja. Prikazaće se minimizacija troškova projekta, raspored resursa u mrežnom dijagramu, kao i analiza troškova po metodi PERT/COST i analiza vremena korišćenjem modifikovane PERT metode. Ovaj rad predstavlja nadogradnju i nastavak istraživanja započetih u magistarskom radu pod nazivom ″Neke mogućnosti optimizacije resursa u mrežnom dijagramu″. Na primeru informacionog sistema Fakulteta prikazaće se optimizacija mrežnog dijagrama. Analiziraće se trajanje, broj kritičnih puteva i broj aktivnosti na kritičnom putu, posmatrana korelacija je ispitivana po linearnoj regresiji i krivolinijskoj geometrijskoj progresiji. Ispitivaće se i višestruka korelacija između pomenutih veličina. Za svaku aktivnost u projektu biće prikazani odgovarajući dekompozicioni dijagrami u programu BPWin. Za posmatrani informacioni sistem biće kreiran programski kod u programu Visual Basic, takođe će posmatrani projekat biti analiziran u programu MS Project.This dissertation will present the basic elements of any project as well as project life cycle. It will be analyzed organizational forms of project management methodology of network planning. It will be displayed minimal cost, schedule resources in the network diagram, and cost analysis according to the method PERT / cost analysis, using the modified PERT methods. This paper is an upgrade and further exploration initiated in MA thesis titled Some optimization capabilities of resources in a network diagram. On example of the Faculty information system will be displayed optimization of network diagrams. It will be analyzed duration, the number of critical paths, number of activities on the critical path, the observed correlation was tested by linear regression and geometric progression. It will be examined and multiple correlation between these quantities. For each activity in the project will be presented an appropriate decomposition diagram in BPwin program. The observed information system will be created the code in Visual in Basic, the project will also be analyzed and observed in MS Project

PERT Scheduling with Convex Cost Functions

International audienceThis paper deals with the problem of finding a minimum cost schedule for a set of dependent activities when a convex cost function is attached to the starting time of each activity. A first optimality necessary and sufficient condition bearing on the head and tail blocks of a schedule is first established. A second such condition that uses the spanning active equality trees of a schedule leads to design a generic algorithm for the general case. When the cost function is the usual earliness–tardiness linear function with assymetric and independent penalty coefficients, the problem is shown to be solved in O(n max{n, m}). Finally, the special cases when the precedence graph is an intree or a family of chains are then also shown to be solved by efficient polynomial algorithms

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de TroyesLe problème de tournées de véhicules (VRP) implique de planifier les itinéraires d'une flotte de véhicules afin de desservir un ensemble de clients à moindre coût. Ce problème d'optimisation combinatoire NP-difficile apparait dans de nombreux domaines d'application, notamment en logistique, télécommunications, robotique ou gestion de crise dans des contextes militaires et humanitaires. Ces applications amènent différents contraintes, objectifs et décisions supplémentaires ; des "attributs" qui viennent compléter les formulations classiques du problème. Les nombreux VRP Multi-Attributs (MAVRP) qui s'ensuivent sont le support d'une littérature considérable, mais qui manque de méthodes généralistes capables de traiter efficacement un éventail significatif de variantes. Par ailleurs, la résolution de problèmes "riches", combinant de nombreux attributs, pose d'importantes difficultés méthodologiques. Cette thèse contribue à relever ces défis par le biais d'analyses structurelles des problèmes, de développements de stratégies métaheuristiques, et de méthodes unifiées. Nous présentons tout d'abord une étude transversale des concepts à succès de 64 méta-heuristiques pour 15 MAVRP afin d'en cerner les "stratégies gagnantes". Puis, nous analysons les problèmes et algorithmes d'ajustement d'horaires en présence d'une séquence de tâches fixée, appelés problèmes de "timing". Ces méthodes, développées indépendamment dans différents domaines de recherche liés au transport, ordonnancement, allocation de ressource et même régression isotonique, sont unifiés dans une revue multidisciplinaire. Un algorithme génétique hybride efficace est ensuite proposé, combinant l'exploration large des méthodes évolutionnaires, les capacités d'amélioration agressive des métaheuristiques à voisinage, et une évaluation bi-critère des solutions considérant coût et contribution à la diversité de la population. Les meilleures solutions connues de la littérature sont retrouvées ou améliorées pour le VRP classique ainsi que des variantes avec multiples dépôts et périodes. La méthode est étendue aux VRP avec contraintes de fenêtres de temps, durée de route, et horaires de conducteurs. Ces applications mettent en jeu de nouvelles méthodes d'évaluation efficaces de contraintes temporelles relaxées, des phases de décomposition, et des recherches arborescentes pour l'insertion des pauses des conducteurs. Un algorithme de gestion implicite du placement des dépôts au cours de recherches locales, par programmation dynamique, est aussi proposé. Des études expérimentales approfondies démontrent la contribution notable des nouvelles stratégies au sein de plusieurs cadres méta-heuristiques. Afin de traiter la variété des attributs, un cadre de résolution heuristique modulaire est présenté ainsi qu'un algorithme génétique hybride unifié (UHGS). Les attributs sont gérés par des composants élémentaires adaptatifs. Des expérimentations sur 26 variantes du VRP et 39 groupes d'instances démontrent la performance remarquable de UHGS qui, avec une unique implémentation et paramétrage, égalise ou surpasse les nombreux algorithmes dédiés, issus de plus de 180 articles, révélant ainsi que la généralité ne s'obtient pas forcément aux dépends de l'efficacité pour cette classe de problèmes. Enfin, pour traiter les problèmes riches, UHGS est étendu au sein d'un cadre de résolution parallèle coopératif à base de décomposition, d'intégration de solutions partielles, et de recherche guidée. L'ensemble de ces travaux permet de jeter un nouveau regard sur les MAVRP et les problèmes de timing, leur résolution par des méthodes méta-heuristiques, ainsi que les méthodes généralistes pour l'optimisation combinatoire.The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Practical routing applications are usually quite distinct from the academic cases, encompassing additional sets of specific constraints, objectives and decisions which breed further new problem variants. The resulting "Multi-Attribute" Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some "rich" VRPs, i.e. those that involve several attributes, may be difficult to address because of the wide array of combined and possibly antagonistic decisions they require. This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The "winning strategies" of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a unifying review. Another analysis is targeted on "timing" problems and algorithms for adjusting the execution dates of a given sequence of tasks. Such methods, independently studied in different research domains related to routing, scheduling, resource allocation, and even isotonic regression are here surveyed in a multidisciplinary review. A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is then introduced, which combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and a bi-criteria evaluation of solutions based on cost and diversity measures. Results of remarkable quality are achieved on classic benchmark instances of the capacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions of the method to VRP variants with constraints on time windows, limited route duration, and truck drivers' statutory pauses are also proposed. New route and neighborhood evaluation procedures are introduced to manage penalized infeasible solutions w.r.t. to time-window and duration constraints. Tree-search procedures are used for drivers' rest scheduling, as well as advanced search limitation strategies, memories and decomposition phases. A dynamic programming-based neighborhood search is introduced to optimally select the depot, vehicle type, and first customer visited in the route during local searches. The notable contribution of these new methodological elements is assessed within two different metaheuristic frameworks. To further advance general-purpose MAVRP methods, we introduce a new component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS), which relies on modular self-adaptive components for addressing problem specifics. Computational experiments demonstrate the groundbreaking performance of UHGS. With a single implementation, unique parameter setting and termination criterion, this algorithm matches or outperforms all current problem-tailored methods from more than 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To address rich problems, UHGS was included in a new parallel cooperative solution framework called "Integrative Cooperative Search (ICS)", based on problem decompositions, partial solutions integration, and global search guidance. This compendium of results provides a novel view on a wide range of MAVRP and timing problems, on efficient heuristic searches, and on general-purpose solution methods for combinatorial optimization problems