In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems / genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.

In the last decade, market pressures for greater variety of products forced a gradual shift from continuous manufacturing to batch manufacturing in various industries. Consequently batch scheduling problems have attracted the attention of researchers in production and operations management. This thesis addresses the scheduling of parallel non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. This problem abstracts the scheduling of heat-treatment furnace operations of castings in a steel foundry. The problem is of considerable interest in this sector as a large proportion of the total production time is spent in heat treatment processing. This problem is also encountered in other industrial settings such as burn-in operation in the final testing stage of semiconductor manufacturing, and manufacturing of steel, ceramics, aircraft parts, footwear, etc. A detailed literature review and personal communications with experts revealed that this class of batch scheduling problems have not been addressed hitherto. A major concern in the management of foundries is to maximize throughput and reduce flow time and work-in-process inventories. Therefore we have chosen the primary scheduling objective to be the utilization of batch processors and as secondary objectives the minimization of overall flow time and weighted average waiting time per job. This formulation can be considered as an extension of problems studied by DOBSON AND NAMBINADOM (1992), UZSOY (1995), ZEE et a/. (1997) and MEHTA AND UZSOY (1998). Our effort to carefully catalogue the large number of variants of deterministic batch scheduling problems led us to the development of a taxonomy and notation. Not surprisingly, we are able to show that our problem is NP-hard and is therefore in the company of many scheduling problems that are difficult to solve. Initially two heuristic algorithms, one a mathematical programming based heuristic algorithm (MPHA) and the other a greedy heuristic algorithm were developed. Due to the computational overheads in the implementation of MPHA when compared with the greedy heuristic, we chose to focus on the latter as the primary scheduling methodology. Preliminary experimentation led us to the observation that the performance of greedy heuristics depends critically on the selection of job-families. So eight variants of the greedy heuristic that differ mainly in the decision on "job-family selection" were proposed. These eight heuristics are basically two sets {Al, A2, A3, A4} and the modified (MAI, MA2, MA3, MA4}, which differ only on how the "job-family" index, weighted shortest processing time, is computed. For evaluating the performance of the eight heuristics, computational experiments were carried out. The analysis of the experimental data is presented in two perspectives. The goal of the first perspective was to evaluate the absolute quality of the solutions obtained by the proposed heuristic algorithms when compared with estimated optimal solutions. The second perspective was to compare the relative performance of the proposed heuristics. The test problems generated were designed to reflect real-world scheduling problems that we have observed in the steel-casting industry. Three important problem parameters for the test set generation are the number of jobs [n], job-priority [P], and job-family [F]. We considered 5 different levels for n, 2 different levels for P and 2 different levels for F. The test set reflects (i) the size of the jobs vary uniformly (ii) there are two batch processors and (iii) five incompatible job-families with different processing times. 15 problem instances were generated for each level of (n, P, and F). Out of many procedures available in the literature for estimating optimal value for combinatorial optimization problems, we used the procedure based on Weibull distribution as discussed in Rardin and Uzsoy (2001). For each problem instance of the randomly generated 300 problem instances, 15 feasible solutions (i.e., the average utilization of batch processors (AUBP)) were obtained using "random decision rule for first two stages and using a "best-fit heuristic' for the last stage of the scheduling problem. These 15 feasible solutions were used to estimate the optimal value. The generated 15 feasible solutions are expected to provide the estimated optimal value of the problem instance with a very high probability. Both average performance and worst-case performance of the heuristics indicated that, the heuristic algorithms A3 and A4, on the average yielded better utilization than the estimated optimal value. This indicates that the Weibull-based technique may have yielded conservative estimates of the optimal value. Further, the other heuristic algorithms found inferior solutions when compared with the estimated optimal value. But the deviations were very small. From this, we may infer that all the proposed heuristic algorithms are acceptable. The relative evaluation of heuristics was in terms of both computational effort and the quality of the solution. For the heuristics, it was clear that the computational burden is low enough on the average to run all the proposed heuristics on each problem instance and select the best solution. Also, it is observed that any algorithm from the first set of {Al, A2, A3, and A4} takes more computational time than any one from the second set {MAI, MA2, MA3, and MA4}. Regarding solution quality, the following inferences were made: ٭ In general the heuristic algorithms are sensitive to the choice of problem factors with respect to all the scheduling objectives. ٭ The three algorithms A3, MA4 and MAI are observed to be superior with respect to the scheduling objectives: maximizing average utilization of batch processors (AUBP), minimization of overall flow time (OFT) and minimizing weighted average waiting time (WAWT) respectively. Further, the heuristic algorithm MAI turns out to be the best choice if we trade-off all three objectives AUBP, OFT and WAWT. Finally we carried out simple sensitivity analyses experiments in order to understand the influence of some parameters of the scheduling on the performance of the heuristic algorithms. These were related to one at a time changes in (1) job-size distribution, (2) capacities of batch processors and (3) processing time of job-families. From the analyses it appears that there is an influence of changes in these input parameters. The results of the sensitivity analyses can be used to guide the selection of a heuristic for a particular combination of input parameters. For example, if we have to pick a single heuristic algorithm, then MAI is the best choice when considering the performance and the robustness indicated by the sensitivity analysis. In summary, this thesis examined a problem arising in the scheduling of heat-treatment operations in the steel-casting industry. This problem was abstracted to a class of deterministic batch scheduling problems. We analyzed the computational complexity of this problem and showed that it is NP-hard and therefore unlikely to admit a scalable exact method. Eight variants of a fast greedy heuristic were designed to solve the scheduling problem of interest. Extensive computational experiments were carried out to compare the performance of the heuristics with estimated optimal values (using the Weibull technique) and also for relative effectiveness and this showed that the heuristics are capable of consistently obtaining near-estimated) optimal solutions with very low computational burden for the solution of large scale problems. Finally, a comprehensive sensitivity analysis was carried out to study the influence of a few parameters, by changing them one at a time, on the performance of the heuristic algorithms. This type of analysis gives users some confidence in the robustness of the proposed heuristics.

<p>Many companies have traveling salesmen that market and sell their products.This results in much traveling by car due to the daily customer visits. Thiscauses costs for the company, in form of travel expenses compensation, and environmentaleffects, in form of carbon dioxide pollution. As many companies arecertified according to environmental management systems, such as ISO 14001,the environmental work becomes more and more important as the environmentalconsciousness increases every day for companies, authorities and public.The main task of this thesis is to compute reasonable limits on the mileage ofthe salesmen; these limits are based on specific conditions for each salesman’sdistrict. The objective is to implement a heuristic algorithm that optimizes thecustomer tours for an arbitrary chosen month, which will represent a “standard”month. The output of the algorithm, the computed distances, will constitute amileage limit for the salesman.The algorithm consists of a constructive heuristic that builds an initial solution,which is modified if infeasible. This solution is then improved by a local searchalgorithm preceding a genetic algorithm, which task is to improve the toursseparately.This method for computing mileage limits for traveling salesmen generates goodsolutions in form of realistic tours. The mileage limits could be improved if theinput data were more accurate and adjusted to each district, but the suggestedmethod does what it is supposed to do.</p>

Η Ορθογώνια Πολυπλεξία Διαίρεσης Συχνότητας (OFDM) έχει προταθεί ως τεχνική διαμόρφωσης σε οπτικά δίκτυα, λόγω της καλής φασματικής απόδοσής της, της ευελιξίας και της ανοχής της σε βλάβες. Η διαμόρφωση OFDM επιτρέπει την ελαστική ανάθεση φάσματος, χρησιμοποιώντας μεταβλητό πλήθος υποφερουσών, καθώς και την επιλογή του κατάλληλου επιπέδου διαμόρφωσης με βάση την απόσταση της μετάδοσης. Το «Πρόβλημα Δρομολόγης και Ανάθεσης Φάσματος» (RSA) έχει αποδειχθεί ότι είναι ένα NP-πλήρες πρόβλημα, γεγονός που υποδηλώνει τη χρήση γραμμικού προγραμματισμού για τη λύση του. Στόχος της διπλωματικής εργασίας είναι η βελτίωση της απόδοσης του υπάρχοντος αλγορίθμου ακέραιου γραμμικού προγραμματισμού, με χρήση μεταευριστικών, έτσι ώστε στο ίδιο χρονικό διάστημα να υπολογίζεται αποδοτικότερη χρησιμοποίηση του συνολικού απαιτούμενου φάσματος, για το σύνολο των μεταδόσεων στο δίκτυο. / Orthogonal Frequency Division Multiplexing (OFDM) has been proposed as a modulation technique for optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments. OFDM modulation allows elastic spectrum allocation, using a variable number of subcarriers and choosing an appropriate modulation level, taking into account the transmission distance. The “Routing and Spectrum Allocation” (RSA) problem has been proved to be a NP-complete problem, which suggests the usage of linear programming in order to be solved. This diploma thesis aims to improve the efficiency of the existing integer linear programming algorithm, by using metaheuristics, so that at the same time period a more efficient utilization of the required spectrum is computed, for all network transmissions.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-12-19T12:53:20Z No. of bitstreams: 1 raphaelpaulobragapoubel.pdf: 1692665 bytes, checksum: e8fb69a9681a9af78d618fd006ce0ebd (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-12-19T13:07:51Z (GMT) No. of bitstreams: 1 raphaelpaulobragapoubel.pdf: 1692665 bytes, checksum: e8fb69a9681a9af78d618fd006ce0ebd (MD5) / Made available in DSpace on 2016-12-19T13:07:51Z (GMT). No. of bitstreams: 1 raphaelpaulobragapoubel.pdf: 1692665 bytes, checksum: e8fb69a9681a9af78d618fd006ce0ebd (MD5) Previous issue date: 2012-02-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho apresenta um modelo baseado em métodos heurísticos construtivos para solução do planejamento dinâmico de linhas de transmissão. O acoplamento temporal entre as decisões é representado através de uma modificação nas equações do modelo de fluxo de carga CC onde as perdas nas linhas são incluídas. O problema resultante desta formulação é um problema de otimização inteira com variáveis acopladas no período de planejamento representadas por um parâmetro de expansão. O algoritmo proposto resolve o problema de forma contínua e acoplada para decidir sobre o planejamento de cada ano de forma a evitar a explosão combinatória da programação inteira. Para tanto são utilizadas, para as decisões de expansão, as informações dos coeficientes de Lagrange e do parâmetro de expansão. Testes com o sistema da região Sul e Sudeste do Brasil apontam para uma metodologia eficaz e promissora. / This work presents a model to solve the dynamic planning of transmission lines based on heuristics technique. The temporal coupling among decisions is represented by a modification on the equations of the DC load flow model, in which losses in transmission lines are included. This formulation generates an integer optimization problem with coupled variables in the planning period, represented by an expansion parameter. The proposed algorithm solves the problem in a continuous and coupled way, in order to decide the planning of each year, as well as to avoid combinatorial explosion of the integer technique. Information obtained from the Lagrange multiplier and the expansion parameter are used to take decisions. Tests with Brazilian southern and southeastern systems indicate an effective and promising methodology.

Submitted by Miriam Lucas (miriam.lucas@unioeste.br) on 2018-02-22T16:42:27Z No. of bitstreams: 2 Miguel_Angel_Sanchez_Cuno_2016.pdf: 1159111 bytes, checksum: 5e8f5e6fcd310a19270e2164cb09c3e3 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-02-22T16:42:27Z (GMT). No. of bitstreams: 2 Miguel_Angel_Sanchez_Cuno_2016.pdf: 1159111 bytes, checksum: 5e8f5e6fcd310a19270e2164cb09c3e3 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-08-16 / Fundação Parque Tecnológico de Itaipu / This work presents the development of a Hybrid Algorithm to solve the problem of Planning the Expansion of Medium Voltage Overhead Networks. The Hybrid Algorithm uses two strategies to solve the problem. First uses a Constructive Heuristic Algorithm that tries to work with parameters instead of working with variables, with the objective of reducing the convergence time to the research process trying not to impair the quality of the solution. The second strategy is based in a Branch and Bound Algorithm, that uses the solution of the problem obtained as a starting point while the first strategy is running. Thus, this solution is used like incumbent in the second process. In this context the hybrid algorithm developed and implemented in this work, takes advantage of reducing the convergence time of the Constructive Heuristic Algorithm and the advantage of guarantee that the solution has the best quality, which are the solutions produced by algorithms type Branch and Bound. The Algorithm has been tested in three test systems, being established a plan to expand overhead medium voltage networks for each system. / Neste trabalho é apresentado um Algoritmo Híbrido para resolver o problema de Planejamento da Expansão de Redes Aéreas de Média Tensão. O Algoritmo Híbrido utiliza duas estratégias para resolver o problema. A primeira utiliza um Algoritmo Heurístico Construtivo que procura trabalhar com parâmetros ao invés de trabalhar com variáveis, com o objetivo de reduzir o tempo de convergência do processo de busca procurando não prejudicar a qualidade da solução. A segunda estratégia é baseada em um Algoritmo do tipo Branch and Bound, que utiliza a solução do problema obtida durante a execução da primeira estratégia como um ponto de partida. Assim, esta solução é usada como incumbente neste segundo processo. Neste contexto, o Algoritmo Híbrido desenvolvido e implementado neste trabalho, aproveita a vantagem de reduzir o tempo de convergência do Algoritmo Heurístico Construtivo e a vantagem de garantir que a solução seja a de melhor qualidade, que são as soluções produzidas por algoritmos do tipo Branch and Bound. O Algoritmo foi testado em três sistemas testes, sendo estabelecido um plano para a expansão de redes aéreas de média tensão para cada sistema

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-09-12T12:17:41Z No. of bitstreams: 1 gustavojosesantiagorosseti.pdf: 1406328 bytes, checksum: e250ced1df20ff9c96f2e06c95c77543 (MD5) / Approved for entry into archive by Diamantino Mayra (mayra.diamantino@ufjf.edu.br) on 2016-09-13T13:22:34Z (GMT) No. of bitstreams: 1 gustavojosesantiagorosseti.pdf: 1406328 bytes, checksum: e250ced1df20ff9c96f2e06c95c77543 (MD5) / Made available in DSpace on 2016-09-13T13:22:34Z (GMT). No. of bitstreams: 1 gustavojosesantiagorosseti.pdf: 1406328 bytes, checksum: e250ced1df20ff9c96f2e06c95c77543 (MD5) Previous issue date: 2011-09-15 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Este trabalho apresenta uma metodologia para reconfiguração e alocação ótima de geração distribuída em redes elétricas de distribuição com o objetivo de minimizar as perdas técnicas de energia elétrica. A metodologia proposta é composta de dois algoritmos heurísticos passo a passo baseados em índices de sensibilidade, sendo um para a reconfiguração e o outro para a alocação de geração distribuída. O índice proposto para reconfiguração é baseado nos parâmetros operativos e o índice para alocação de geração distribuída baseia-se em uma estimativa dos multiplicadores de Lagrange, obtida a partir da solução do problema de fluxo de potência. O modelo proposto considera a variação da demanda do sistema através das curvas de carga e a opção de construir um novo circuito para conexão de um gerador distribuído a uma barra do sistema. Os algoritmos propostos são aplicados em sistemas da literatura, incluindo um sistema real de médio porte. / This work presents a methodology for optimal reconfiguration and optimal distributed generation allocation aiming to minimize technical energy losses in electric power distribution systems. The proposed methodology includes two step by step heuristic algorithms, based on sensitivity indexes, one for the reconfiguration and the other for the distributed generation allocation. The index proposed for reconfiguration is based on operating parameters and the index for distributed generation allocation uses the Lagrange multipliers obtained from the power flow solution. The proposed model considers the demand variation from the system load curves and the options to build a new branch for connecting a distributed generator to a bus of the network. The proposed algorithms are applied in systems of the literature, including a medium scale practical system.

This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.

Le système de fabrication des plaquettes de silicium (wafer) est la partie la plus complexe et la plus coûteuse du processus de fabrication des semi-conducteurs et son ordonnancement pour la production a un impact significatif sur la rentabilité économique. Le système d’outils Multi-cluster pour la fabrication de plaquettes est un système de type multi-boucles, largement utilisé dans la fabrication de plaquettes de 300 mm et 450 mm. Le problème d’ordonnancement dans ce système de production présente des caractéristiques pour les modèles de flux de plaquettes compliqué, des contraintes résidentielles strictes et des conflits de ressources à gérer, ce qui rend le problème très complexe. Dans cette thèse, l'outil multi-cluster est étudié et les recherches se concentrent principalement sur les caractéristiques des contraintes sur le temps de séjour, les contraintes sur les ressources utilisés et les flux plaquettes de silicium. Plus particulièrement, cette thèse traite trois problèmes d'ordonnancement: le problème d'ordonnancement cyclique unitaire pour un flux unique de plaquettes, le problème d'ordonnancement cyclique multi-unitaires dans un modèle de flux unique de plaquettes et le problème d'ordonnancement non-cyclique. Pour résoudre ces problèmes, des modèles robustes sont développés ainsi que certains algorithmes heuristiques efficaces sont construits pour atteindre les objectifs. L'objectif principal étant d'améliorer la performance des outils multi-cluster et d'augmenter le rendement des flux des plaquettes de silicium. Des tests de simulation et des analyses sont effectuées afin d’évaluer la performance des algorithmes proposés. Les résultats montrent la stabilité et l'efficacité de ces algorithmes. / Multi-cluster tool is a highly automated and costly wafer fabrication system with multi-loop coupling structure, and scheduling of such equipment directly affects the overall efficiency of semiconductor manufacturing enterprises. Multi-cluster tools scheduling problem has the features of large scale, complex wafer flow patterns, strict residency time constraints and intense resource conflict, which are significantly different from any other manufacturing system. Since the existing literatures have proved that most of the wafer fabrication systems scheduling problems are NP-hard, it’s difficult to obtain the optimal solution by using exact algorithms. Thus, how to develop an efficient heuristic algorithm to solve the multi-cluster tools scheduling problem attracts considerable attention both in academia and in industry. After reviewing the literatures, it is found that the research on the cyclic scheduling problem of multi-cluster tools rarely takes into account the characteristics of residency constraints. The scale of the object is limited to three single cluster tools, and the proposed scheduling methods are mostly mathematical programming and simple scheduling rules. Therefore, in this thesis, the multi-cluster tool is studied and our research mainly focuses on the characteristics of residency constraints, resource constraints and wafer flow patterns. Based on the descriptions of research domains, some solid models are developed for different scheduling problems and some efficient heuristic algorithms are constructed to realize the objectives. To deal with the problem, different approaches are proposed: A non-linear mixed-integer programming model, a two-stage = approximate-optimal scheduling algorithm, and a chaos-based particle swarm optimization-tabu search hybrid heuristic algorithm. Simulation experiments and analysis demonstrate the effectiveness of these algorithms. Results show the stability and efficiency of proposed algorithms.

Les réseaux électriques actuels connaîtront un profond changement dans les années à venir. La nouvelle génération est le Smart Grid (SG) ou le réseau électrique intelligent qui se caractérise par une couche d'information et de communication qui permet aux différents composants du réseau de communiquer. Il doit considérer tous les aspects du réseau électrique, le rendant plus intelligent et flexible. Cette notion est présentée comme une réponse à l'évolution du marché de l'électricité, visant à gérer l’augmentation de la demande tout en assurant une meilleure qualité de service et plus de sécurité.Premièrement, nous présentons une formulation de programmation linéaire mixte en entier pour optimiser les systèmes de production et de consommation d'énergie dans une maison intelligente avec un déploiement efficace de plusieurs ressources énergétiques distribuées. Ensuite, à travers la conception d'expériences avec la méthode de Taguchi, divers scénarios sont introduits en faisant varier des facteurs significatifs. Par la suite, une technique heuristique est proposée pour résoudre le problème de la gestion de l'énergie résidentielle en trouvant la solution optimale globale pendant plusieurs jours consécutifs avec une réduction significative du temps d'exécution.Deuxièmement, un modèle de gestion de l'énergie est assuré grâce à des modèles mathématiques pour optimiser l’utilisation du réseau, des ressources énergétiques renouvelables, des véhicules électriques et de la batterie, ainsi que pour différents types d'appareils thermiques et électriques. Une méthode de solution exacte est mise en œuvre pour réduire le coût de l'électricité dans une maison intelligente et pour trouver des modes de fonctionnement de différentes charges. Ensuite, un algorithme d'optimisation math-heuristique est proposé pour résoudre le problème avec un temps de simulation étendu.Enfin, nous étudions le problème de gestion de l'énergie dans un microréseau constitué de plusieurs maisons intelligentes. Chacune d'elles dépose de ressources énergétiques renouvelables, d’un véhicule électrique et d’appareils intelligents. Les ressources d'énergie renouvelable injectent l’excès de l'énergie dans un système de stockage d'énergie partagé. Un modèle mathématique linéaire mixte en entier pour la gestion d'énergie est proposé pour réduire le coût total de fonctionnement du microréseau. Des comparaisons avec des scénarios conventionnels où chaque maison intelligente possède son propre système de stockage d'énergie sont effectuées pour démontrer l’efficacité de la démarche proposée. / The current electricity grids will experience a profound change in the coming years. The new generation is the Smart Grid (SG) which is characterized by information and communication layer enabling the communication between the different components of the grid. It needs to consider all sides of power grid, making it more intelligent and flexible. This notion is presented as an answer to changes in the electricity market, aiming to manage the increased demand while ensuring a better quality of service and more safety.First, we present a mixed integer linear programming formulation to optimize the energy production and consumption systems in a smart home with an effective deployment of several distributed energy resources. Then through the design of experiments with the Taguchi method, diverse scenarios are introduced by varying significant factors. Afterward, a heuristic technique is proposed to solve the problem of residential energy management by finding the global optimum solution for many consecutive days with significant reduction of execution time.Second, an energy management model is proposed thanks to mathematical models to optimize the grid, renewable energy resources, battery and electric vehicles are presented as well as for different type of thermal and electrical appliances. An exact solution method is implemented to reduce the electricity cost in a smart home and find out operation modes of different loads. Then a math-heuristic optimization algorithm is proposed to solve the problem with extended simulation time horizon.Finally, we study a microgrid energy management problem which comprises multiple smart homes. Each of them owns renewable energy resources, one electric vehicle and smart appliances. The renewable energy resources inject the excess energy in the shared energy storage system. An optimized energy management model using mixed integer linear programming is proposed to reduce the total electricity cost in the microgrid. Comparisons with conventional scenarios where each smart home has its individual small energy storage system without sharing energy with their neighbors are done to ensure that the proposed formulation is well efficient.