In this work, we are interested in the sources of raw materials for mineral carbonation as well as the valorization of the products resulting from this carbonation. To achieve carbonation of waste and to obtain a valuable product we need carbonatable materials such as Fe, Mg, Mn, Ca: it is necessary to seek these compounds in the waste considered. We will focus on particular sources of waste, the residues of cement industries, bottom ash (steel mills, incineration of household waste) or building deconstruction residues. The objective of this paper is to describe the functional specifications of a carbonation unit that is both flexible enough to treat a wide variety of these materials, and compact enough to allow it to be moved from one source to another. These specifications will concern two different uses of this mobile unit: that of initial feasibility demonstration, and those corresponding to its current operation.

The development of existing networks and their transformation in smart grids is a desiderate to be implemented in smart cities. The optimization of these networks, considering the rapid increase of consumption and integration of smallcapacity sources is required. Over time, more and more mathematical methods, and models for solving optimization problems have been developed. With the advent of computer processes and digitization, programs have been developed that can solve mathematical models and apply optimization methods to find an optimal solution quickly and efficiently. In this paper are presented two methods for solving the transportation problem with intermediate centers.

Responding rapidly to customer needs is one of the main targets of industrial organizations that want to survive in the current market competition. This objective can be attained through robust planning. Workforce productivity is considered one of the important entities in production planning. However, it has a dynamic nature, i.e. the productivity growths thanks to on-job training or learning phenomenon. Considering this fact in manufacturing planning enhances the robustness of the developed plans. The present paper presents a mathematical model for medium-range production planning that is used to find the optimal aggregate production plan. The model aims to optimize the total production costs while respecting most of the operational constraints and considering the process of organizational learning. The presented model is constructed relying on the real industrial practices; the outcome is a mixed-integer linear program. The model was validated and checked using real data collected from an Egyptian factory that produces electric motors for home appliances. The proposed mathematical model was optimally solved using “ILOG-CPLEX 12.6”. By comparing the results obtained versus that of the method adopted in the factory, a cost reduction of 6.3% is achieved for the presented data set. A set of managerial aspects are concluded after the model analysis. Moreover, the impact of using detailed learning rates on the production cost is discussed.

The aims of CIEM is to respond to challenges in the rapidly developing fields of Power Engineering and Environmental Engineering, and to inspire both research studies and practical applications by promoting interaction among scientists from universities, research institutions, and industry.

The ongoing development and extensions of cities, as well as the requirement for large scale deployment of renewable energy sources and electro-mobility determine the transformation of current electrical grids towards smart grids. The increasing demand and the requirement for its reliable supply is imposing new planning challenges for the development of electrical networks. The present paper describes a method of optimizing the development of urban electricity networks, by selecting from a set of available locations, the positions and the size of new power sources, using a multistage model. Results can be useful to plan the installation of new power sources in an existing network (distributed sources, renewable sources for powering electric vehicles).

In this paper, we integrate two decision problems arising in various applications such as production planning and project management: the project scheduling problem, which consists in scheduling a set of precedence-constrained tasks, where each task requires executing a set of skills to be performed, and the workforce allocation problem which includes assigning workers as scarce resources to the skills of each task. These two problems are interrelated as the tasks durations are not predefined, but depend on the number of workers assigned to that task as well as their skill levels. We here present a mixed integer linear programming model that considers important real life aspects related to the flexibility in the use of human resources, such as multi-skilled workers whose skill levels are different and measured by their efficiencies. Hence, execution times of the same workload by different workers vary according to these efficiencies. Moreover, the model considers the flexible working time of employees; i.e. the daily and weekly workload of a given worker may vary from one period to another according to the work required. Furthermore, efficient team building is incorporated in this model; i.e. assigning an expert worker and one or more apprentice worker(s) together with the purpose of skill development thanks to knowledge transfer. A numerical example is provided to check the performance of the model.

Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The proposed problem is a mixed-integer nonlinear programming problem (MINLP) and it is solved using stochastic programming. Stochastic programming is applied when uncertain environment occurs, in this case the uncertain environment refers to the production of renewable energy sources (RES) and its dependence on the short-term weather conditions. Stochastic Optimization weights all scenarios considered in this paper in order to obtain an expected total cost. The expected total cost includes the cost associated with the construction of new transmission lines, generation cost and load shedding cost.

Dans un recent article nous avons resolu le probleme de la flexibilite operationnelle d’un « flow shop » en prenant en compte la flexibilite a l’aide d’une methode exacte. Ce qui a permis d’obtenir une solution optimale, mais au prix d’un temps de resolution relativement grand, trop important pour une gestion d’atelier en temps reel. Le probleme a resoudre est de caracteriser les leviers de flexibilite lies a la variation des durees operatoires et des dates de livraison des articles d’un atelier de production a cheminement unique afin d’assurer la flexibilite operationnelle du systeme. Dans le present travail, nous utilisons cette fois les algorithmes genetiques pour approcher la solution optimale en recherchant un temps de resolution plus court. Il s’agit dans cet article d’exposer cette resolution et de presenter une comparaison avec celle de la methode exacte precedemment utilisee

Due to the fierce market competition, organizations should respond quickly to customers’ needs by reducing lead times, or/and lowering operating costs. These objectives can be reached by effectively assessing the workforce capacities. Manufacturing progress function or organizational learning is considered as one of the most important factors that affect workforce capacity. The current paper introduces an examination research that uses factory data to introduce the most appropriate organizational learning model for the manufacture of electric motors. The data used was collected for a period of 42 months for 110 manufacturing processes and 10 different styles of electric motors. By using regression analysis the significant parameters were obtained for 10 learning models. And in order to select the most reliable one, the analytical hierarchy process (AHP) was used after defining the selection criteria. Among most of monovariable learning models listed in literature the model of Wright (1936) is found to be the best one to fit the data, and then comes the model of Knecht (1974). The failure of the other models in fitting the data was also shown.

The growing need of responsiveness for manufacturing companies facing market volatility raises a strong demand for flexibility in their organisation. Since the company personnel are increasingly considered as the core of the organisational structures, a strong and forward-looking management of human resources and skills is crucial to performance in many industries. These organisations must develop strategies for the short, medium and long terms, in order to preserve and develop skills. Responding to this importance, this work presents an original model, looking at the line-up of multi-period project, considering the problem of staff allocation with two degrees of flexibility. The first results from the annualising of working time, and relies on policies of changing schedules, individually as well as collectively. The second degree of flexibility is the versatility of the operators, which induces a dynamic view of their skills and the need to predict changes in individual performance as a result of successive assignments. We are firmly in a context where the expected durations of activities are no longer predefined, but result from the performance of the operators selected for their execution. We present a mathematical model of this problem, which is solved by a genetic algorithm. An illustrative example is presented and analysed, and, the robustness of the solving approach is investigated using a sample of 400 projects with different characteristics.

Responding to the growing need of generating a robust project scheduling, in this article we present a greedy algorithm to generate the project baseline schedule. The robustness achieved by integrating two dimensions of the human resources flexibilities. The first is the operators’ polyvalence, i.e. each operator has one or more secondary skill(s) beside his principal one, his mastering level being characterized by a factor we call “efficiency”. The second refers to the working time modulation, i.e. the workers have a flexible time-table that may vary on a daily or weekly basis respecting annualized working strategy. Moreover, the activity processing time is a non-increasing function of the number of workforce allocated to create it, also of their heterogynous working efficiencies. This modelling approach has led to a nonlinear optimization model with mixed variables. We present: the problem under study, the greedy algorithm used to solve it, and then results in comparison with those of the genetic algorithms.

At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multi-skilled actor). On the one hand, multi-skilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face.

Among all strategies supporting the firms' flexibility and agility, the development of human resources versatility holds a promising place. This article presents an investigation of the factors affecting the development of this flexibility lever, related to the problem of planning and scheduling industrial activities, taking into account two dimensions of flexibility: the modulation of working time, which provides the company with fluctuating work capacities, and the versatility of operators: for all the multi-skilled workers, we adopt a dynamic vision of their competences. Therefore, this model takes into account the evolution of their skills over time, depending on how much they were put in practice in previous periods. The model was solved by using an approach relying on genetic algorithm that used an indirect encoding to build the chromosome genotype, and then a serial scheduling scheme is adopted to build the solution.

Les imperatifs croissants de reactivite des entreprises manufacturieres face a l’instabilite des marches suscite un fort besoin de flexibilite dans leur organisation. Le personnel de l’entreprise etant de plus en plus considere comme le noyau de la structure organisationnelle, une bonne gestion previsionnelle des ressources humaines et de leurs competences s’avere capitale pour les performances dans de nombreux secteurs industriels. Ces organisations se doivent d’elaborer des strategies a court, moyen et long termes concernant la preservation et le developpement des competences. Dans cet article, nous nous penchons sur la programmation de projet multi-periodes, en considerant le probleme de l’affectation des effectifs avec deux degres de flexibilite. Le premier resulte de l’annualisation du temps de travail, et concerne les politiques de modulation d’horaires, individuels ou collectifs. Le deuxieme degre de souplesse est la polyvalence des operateurs, qui induit une vision dynamique de leurs competences et la necessite de prevoir les evolutions des performances individuelles en fonction des affectations successives. Nous sommes resolument dans un contexte ou la duree prevue des activites n’est plus deterministe, mais resulte des performances des acteurs choisis pour les executer. Nous presentons ici une modelisation mathematique de ces competences, et la resolution d’un exemple de planification basee sur les algorithmes genetiques.

Nous presentons une approche de planification des activites, visant a affecter les ressources humaines, selon leurs competences, tout en optimisant les couts. Cette approche a trois dimensions. La premiere est la polyvalence des individus. La deuxieme est la modulation de leur temps de travail. La troisieme est la vision dynamique de l'evolution dans le temps des competences des acteurs – en d'autres termes, l'acquisition de l’experience par ces acteurs. Dans ce modele, la duree des tâches a effectuer n'est pas connue a l'avance, et dependra des performances des operateurs alloues pour executer la charge de travail. Dans les sections suivantes nous allons presenter brievement les caracteristiques de ce modele.

The existing distribution networks are growing with complexity more and more, due to the gradual increase of power demand and variation of loads. This paper describes several heuristic algorithms applied to the optimal configuration of loop distribution network. Configuration problems are too complicated and time consumed to be solved. These problems are basically large-scaled combinatorial optimization problems, because an urban distribution system is usually large in scale and contain numerous sectionalizing switches to be operated. It is, therefore, difficult to rapidly obtain an exact optimal solution on real system.

Dans les grands projets, il est souvent difficile de mesurer les progres accomplis en raison de la complexite, parce que la realisation est partagee entre des departements d'une entreprise voire entre des entreprises disseminees de par le monde. La litterature en gestion de projet et en recherche operationnelle est examinee pour inventorier les differentes techniques applicables. Les outils largement utilises pour le suivi et la prevision, comme la valeur acquise, graphiques de tendance, ingenierie concourante, peuvent etre employes. Cet article se base sur un probleme lie a l'industrie pharmaceutique ou l'efficacite d'un traitement medical est examinee sur des patients repartis dans un certain nombre de pays. Le nombre des variables impliquees augmente la complexite de ce probleme. L’objectif principal est d'etudier l'efficacite d'une solution pour differentes situations d’avancement dans le cadre du projet, de facon a reduire la duree du projet pour un cout acceptable. Nos resultats suggerent que la possibilite de reaffectation des patients entre pays produit de meilleurs resultats.