La résolution du problème de formation de cellules dans un contexte multicritère

Les techniques de groupement technologique sont aujourd’hui utilisées dans de nombreux ateliers de fabrication; elles consistent à décomposer les systèmes industriels en sous-systèmes ou cellules constitués de pièces et de machines. Trouver le groupement technologique le plus efficace est formulé en recherche opérationnelle comme un problème de formation de cellules. La résolution de ce problème permet de tirer plusieurs avantages tels que la réduction des stocks et la simplification de la programmation. Plusieurs critères peuvent être définis au niveau des contraintes du problème tel que le flot intercellulaire,l’équilibrage de charges intracellulaires, les coûts de sous-traitance, les coûts de duplication des machines, etc. Le problème de formation de cellules est un problème d'optimisation NP-difficile. Par conséquent les méthodes exactes ne peuvent être utilisées pour résoudre des problèmes de grande dimension dans un délai raisonnable. Par contre des méthodes heuristiques peuvent générer des solutions de qualité inférieure, mais dans un temps d’exécution raisonnable. Dans ce mémoire, nous considérons ce problème dans un contexte bi-objectif spécifié en termes d’un facteur d’autonomie et de l’équilibre de charge entre les cellules. Nous présentons trois types de méthodes métaheuristiques pour sa résolution et nous comparons numériquement ces métaheuristiques. De plus, pour des problèmes de petite dimension qui peuvent être résolus de façon exacte avec CPLEX, nous vérifions que ces métaheuristiques génèrent des solutions optimales.

Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings

The main objectives of this paper are to: firstly, identify key issues related to sustainable intelligent buildings (environmental, social, economic and technological factors); develop a conceptual model for the selection of the appropriate KPIs; secondly, test critically stakeholder's perceptions and values of selected KPIs intelligent buildings; and thirdly develop a new model for measuring the level of sustainability for sustainable intelligent buildings. This paper uses a consensus-based model (Sustainable Built Environment Tool- SuBETool), which is analysed using the analytical hierarchical process (AHP) for multi-criteria decision-making. The use of the multi-attribute model for priority setting in the sustainability assessment of intelligent buildings is introduced. The paper commences by reviewing the literature on sustainable intelligent buildings research and presents a pilot-study investigating the problems of complexity and subjectivity. This study is based upon a survey perceptions held by selected stakeholders and the value they attribute to selected KPIs. It is argued that the benefit of the new proposed model (SuBETool) is a ‘tool’ for ‘comparative’ rather than an absolute measurement. It has the potential to provide useful lessons from current sustainability assessment methods for strategic future of sustainable intelligent buildings in order to improve a building's performance and to deliver objective outcomes. Findings of this survey enrich the field of intelligent buildings in two ways. Firstly, it gives a detailed insight into the selection of sustainable building indicators, as well as their degree of importance. Secondly, it tesst critically stakeholder's perceptions and values of selected KPIs intelligent buildings. It is concluded that the priority levels for selected criteria is largely dependent on the integrated design team, which includes the client, architects, engineers and facilities managers.

Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions

Most of water distribution systems (WDS) need rehabilitation due to aging infrastructure leading to decreasing capacity, increasing leakage and consequently low performance of the WDS. However an appropriate strategy including location and time of pipeline rehabilitation in a WDS with respect to a limited budget is the main challenge which has been addressed frequently by researchers and practitioners. On the other hand, selection of appropriate rehabilitation technique and material types is another main issue which has yet to address properly. The latter can affect the environmental impacts of a rehabilitation strategy meeting the challenges of global warming mitigation and consequent climate change. This paper presents a multi-objective optimization model for rehabilitation strategy in WDS addressing the abovementioned criteria mainly focused on greenhouse gas (GHG) emissions either directly from fossil fuel and electricity or indirectly from embodied energy of materials. Thus, the objective functions are to minimise: (1) the total cost of rehabilitation including capital and operational costs; (2) the leakage amount; (3) GHG emissions. The Pareto optimal front containing optimal solutions is determined using Non-dominated Sorting Genetic Algorithm NSGA-II. Decision variables in this optimisation problem are classified into a number of groups as: (1) percentage proportion of each rehabilitation technique each year; (2) material types of new pipeline for rehabilitation each year. Rehabilitation techniques used here includes replacement, rehabilitation and lining, cleaning, pipe duplication. The developed model is demonstrated through its application to a Mahalat WDS located in central part of Iran. The rehabilitation strategy is analysed for a 40 year planning horizon. A number of conventional techniques for selecting pipes for rehabilitation are analysed in this study. The results show that the optimal rehabilitation strategy considering GHG emissions is able to successfully save the total expenses, efficiently decrease the leakage amount from the WDS whilst meeting environmental criteria.

Is what you say what you do? Analyzing and comparing the effect of buyer-supplier relationship in the Brazilian and Chinese supplier selection criteria

Choosing properly and efficiently a supplier has been challenging practitioners and academics since 1960’s. Since then, countless studies had been performed and relevant changes in the business scenario were considered such as global sourcing, quality-orientation, just-in-time practices. It is almost consensus that quality should be the selection driver, however, some polemical findings questioned this general agreement. Therefore, one of the objectives of the study was to identify the supplier selection criteria and bring this discussion back again. Moreover, Dickson (1966) suggested existing business relationship as selection criterion, then it was reviewed the importance of business relationship for the company and noted a set of potential negative effects that could rise from it. By considering these side effects of relationship, this research aimed to investigate how the relationship could influence the supplier selection and how its harmful effects could affect the selection process. The impact of this phenomenon was investigated cross-nationally. The research strategy adopted was a controlled experiment via vignette combined with discrete choice analysis. The data collections were performed in China and Brazil. By examining the results, it could be drawn five major findings. First, when purchasers were asked to declare their supplier selection priorities, quality was stated as the most important independently of country and relationship. This result was consistent with diverse studies since 60’s. However, when purchasers were exposed to a multi-criteria trade-off situation, their actual selection priorities deviate from what they had declared. In the actual decision-making without influence of buyer-supplier relationship, Brazilian purchasers focused on price and Chinese buyers prioritized delivery then price. This observation reinforced some controversial prior studies of Verma & Pullman (1998) and Hirakubo & Kublin (1998). Second, through the introduction of the buyer-supplier relationship (operationalized via relational capital) in the supplier selection process, this research extended the existing studies and found that Brazilian buyers still focused on price. The relationship became just another criterion for supplier selection such as quality and delivery. However, from the Chinese sample, the results suggested that quality was totally discarded and the decision was majorly made through price and relationship. The third finding suggested that relational capital could legitimate the quality and sustainability of the supplier and replaces these selection criteria and made the decisional task less complex. Additionally, with the relational capital, the decision-makings were associated to few biases such as availability cognition, commitment, confirmatory and perceived biases. By analyzing the purchasers’ behavior, relational capital inducted buyers of both countries to relax in their purchasing requirements (quality, delivery and sustainability) leading to potential negative effects. In the Brazilian sample, the phenomenon of willing to pay a higher price for a lower quality offer demonstrated to be a potential counterproductive and suboptimal decision. Finally, the last finding was associated to the cultural effect on the buyers’ decisions. From the outcome, it is possible to observe that if a purchaser’s cultural background is more relation-oriented, the more he will tend to use relational capital as a decision heuristic, thus, the purchaser will be more susceptible to the potential relationship’s side effects

Distributed generation impact evaluation using a multi-objective tabu search

Distribution networks paradigm is changing currently requiring improved methodologies and tools for network analysis and planning. A relevant issue is analyzing the impact of the Distributed Generation penetration in passive networks considering different operation scenarios. Studying DG optimal siting and sizing the planner can identify the network behavior in presence of DG. Many approaches for the optimal DG allocation problem successfully used multi-objective optimization techniques. So this paper contributes to the fundamental stage of multi-objective optimization of finding the Pareto optimal solutions set. It is proposed the application of a Multi-objective Tabu Search and it was verified a better performance comparing to the NSGA-II method. © 2009 IEEE.

A multi-objective evaluation of the impact of the penetration of Distributed Generation

This paper proposes a methodology to consider the effects of the integration of DG on planning. Since DG has potential to defer investments in networks, the impact of DG on grid capacity is evaluated. A multi-objective optimization tool based on the meta-heuristic MEPSO is used, supporting an alternative approach to exploiting the Pareto front features. Tests were performed in distinct conditions with two well-known distribution networks: IEEE-34 and IEEE-123. The results combined minimization and maximization in order to produce different Pareto fronts and determine the extent of the impact caused by DG. The analysis provides useful information, such as the identification of futures that should be considered in planning. A future means a set of realizations of all uncertainties. MEPSO also presented a satisfactory performance in obtaining the Pareto fronts. © 2011 IEEE.

Proposta para indicação de áreas para a implantação de aterro sanitário no município de Bauru-SP, utilizando análise multi critério de decisão e técnicas de geoprocessamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determinação de áreas prioritárias para o restabelecimento da cobertura florestal, apoiada no uso de geotecnologias

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Generalized interval vector spaces and interval optimization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Programação por metas e análise envoltória de dados na avaliação de unidades de produção global: uma aplicação em uma empresa do segmento químico

Considering the high competitivity in the market, the application of quantitative methods can assist in analyzing the efficiency of production facilities of areas of export and import processes of the chemical industry sector. In this sense, this work aims to apply the model GPDEA-BCC optimization in order to develop an analysis of the production units of this chemical industry. So, were chosen variables relevant to the process and elaborated a final comparison between the results obtained by the optimization tool and performance indexes provided by the company. These results indicated that some production units should be monitored more carefully because some of them had a low efficiency when analyzed with multi criteria

Programação por metas e análise envoltória de dados na avaliação de unidades de produção global: uma aplicação em uma empresa do segmento químico

Considering the high competitivity in the market, the application of quantitative methods can assist in analyzing the efficiency of production facilities of areas of export and import processes of the chemical industry sector. In this sense, this work aims to apply the model GPDEA-BCC optimization in order to develop an analysis of the production units of this chemical industry. So, were chosen variables relevant to the process and elaborated a final comparison between the results obtained by the optimization tool and performance indexes provided by the company. These results indicated that some production units should be monitored more carefully because some of them had a low efficiency when analyzed with multi criteria

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper's task of selecting a subset of the submitted bids which efficiently trades off total procurement cost against expected carrier performance. To solve this bi-objective winner determination problem, we propose a Pareto-based greedy randomized adaptive search procedure (GRASP). As a post-optimizer we use a path relinking procedure which is hybridized with branch-and-bound. Several variants of this algorithm are evaluated by means of artificial test instances which comply with important real-world characteristics. The two best variants prove superior to a previously published Pareto-based evolutionary algorithm.

Applying the fuzzy analytical hierarchy process in digital marketing

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today’s information overload makes digital marketing optimization, which is needed to continuously improve one’s business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities’ fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.