multi-objective programming

This study aims to develop a robust model for sequencing activities in the design and development of complex products while considering supplier capabilities. Given the intricate nature of product development and the interdependencies among various design activities, optimizing the sequence of these activities is essential for enhancing project efficiency and minimizing redundancies. This study seeks to bridge the gap between supply chain management and product development, addressing an under-researched area where supplier capabilities significantly impact project outcomes.

 This research employs a quantitative and applied methodology, utilizing mathematical modeling and multi-objective optimization techniques to tackle the problem of activity sequencing. A comprehensive literature review informs the development of a theoretical framework to identify critical parameters influencing supplier selection and activity sequencing. The study incorporates uncertainty factors, particularly time constraints, by applying an epsilon-constraint method to transform the multi-objective optimization model into a single-objective one. Data for the research is collected through structured interviews and questionnaires directed at industry experts, ensuring the model's practical applicability and relevance.

The results indicate that integrating supplier capabilities into the selection process significantly minimizes feedback loops in activity sequences, which consequently reduces the amount of rework and overall project costs. The model was applied in a case study focused on gas turbine development, demonstrating its effectiveness in optimizing project timelines and budgets. The findings highlight the importance of choosing suppliers based on their competencies and the potential for mitigating project risks linked to information feedback among interconnected activities.

 While the model offers a comprehensive framework for understanding the relationship between supplier capabilities and activity sequencing, certain limitations must be recognized. The model's applicability may differ across various industries and project types, and further research is recommended to validate the model in diverse contexts. Additionally, the study acknowledges possible biases in the expert interviews and suggests future studies incorporate a broader range of data sources for a more comprehensive analysis.

 The implications of this research for practice are significant. By implementing the proposed model, project managers can improve decision-making processes related to supplier selection and activity scheduling. This can result in shorter project durations and reduced costs while enhancing product quality and reliability. The model's framework can assist organizations in aligning supplier capabilities with project requirements, promoting more efficient collaboration within supply chains. Overall, this research contributes to improved resource management practices in complex product development scenarios.

 The societal impacts of this research are significant. By optimizing the design and development processes of complex products, businesses can promote more sustainable manufacturing practices and produce higher quality products. Enhanced collaboration among suppliers and project teams can stimulate innovation and positively influence industry standards. Furthermore, addressing supplier capabilities can strengthen the overall resilience of the supply chain, which is especially pertinent in today's dynamic market environments prone to disruptions. Additionally, by focusing on supplier capabilities and improving the overall efficiency of product development processes, this research advocates for the adoption of best practices and ethical standards within supply chains. As companies aim to collaborate with suppliers who meet specific competency benchmarks, this can lead to improved working conditions, fair labor practices, and responsible sourcing. Ultimately, such initiatives not only bolster the economic stability of communities but also promote social equity by creating opportunities for local suppliers and fostering inclusive growth within the industry.

 This article introduces a novel approach to integrating supplier capabilities into the activity sequencing of complex product development. It offers valuable insights for both practitioners and researchers, highlighting the importance of supplier selection in project success. The originality of this study lies in its comprehensive model that considers both the technical and relational aspects of supplier engagement, thus providing a fresh perspective on the intersection of supply chain management and product development.

The availability of energy is a vital aspect of a nation’s economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focused on the former, which relies on real-time pricing. Our objective was to develop a multi-objective mathematical model that considers load response programs for smart energy grids.

The study employed a scenario-based approach and classified the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices were marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario was created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model was developed that aimed to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model was run using the GAMS programming language. In addition, the study evaluated the impact of load response programs on enhancing objective functions.

The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively.

Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost-effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions.

Iran's current electricity generation systems rely on large-scale power plants situated far from consumers, utilizing fossil fuels as their primary source. However, as demand for electricity surges, greenhouse gas emissions, electricity losses, and reliability issues have become prevalent. Smart cities have emerged as a promising solution to these challenges, with smart grids being a critical component of their energy systems. Efficient energy management necessitates the optimization of resource usage, minimization of costs and environmental risks, and maximization of reliability. This study proposes a multi-objective optimization model for managing a smart grid's energy. The model aims to minimize costs, pollution, and peak consumption while simultaneously maximizing reliability. To account for uncertainties such as renewable energy output, demand, and price, a stochastic approach is utilized. The problem is formulated as a mixed integer linear programming model, and the Pareto solutions are obtained using the epsilon constraint method. In order to achieve the most optimal Pareto solutions, we have employed the use of the fuzzy satisfiability method. Results indicate that the smart energy grid can effectively reduce energy consumption, costs, and pollution while significantly increasing system reliability.

Time-cost-quality trade-off project scheduling problem is one of the most important and practical problems in project management discipline. Project Managers interest in making decisions to accomplish the project with minimum cost, minimum time, and maximum quality. Unfortunately, in real life problems, the estimation of time, cost, and quality may be mixed with uncertainty. These parameters may be far from their estimated values. So, considering uncertain parameters in time-cost-quality project scheduling is essential. In this paper, a probabilistic version of time-cost-quality trade –off problem is proposed. Some parameters of the proposed model are assumed to be probabilistic. The probabilistic nature of the proposed model is handled using stochastic chance constraint programming. The multi-objective nature of the problem is handled using goal programming. Finally, the stochastic chance constraint goal programming is proposed. The deterministic equivalence of proposed stochastic model is presented. A numerical example is analyzed using GAMS software and results are investigated.

Finding a subset of features is an issue that has been widely used in a variety of fields such as machine learning and statistical pattern recognition. Since increasing the number of features increases the computational cost of a system, it seems necessary to develop and implement systems with minimum features and acceptable efficiency.

Considering objective, it's developmental research and in terms of two Meta-heuristic algorithms, namely genetic algorithm (GA) and multi-objective non-dominated sorting genetic algorithm (NSGA II). The multi-objective method compared to the single-objective method has reduced the number of features to 50% in all instances; it doesn't make much difference in classification accuracy. The proposed method is applied on six datasets of credit data, and the results were analyzed using two common classifiers namely, support vector machine (SVM) and K-nearest neighbors (KNN). Comparing two classifiers applied on datasets, K- nearest neighbors (KNN) compared to the support vector machine (SVM) has shown relatively better performance in increasing the classification accuracy and reducing the number of attributes.

Genetic algorithm and multi objective non-dominated sorting genetic algorithm have a good performance in increasing the accuracy of classification and reducing the number of attributes in feature selection problem of multi-class data. The results also indicate an increase in classification accuracy, simultaneously with a significant decrease in the number of features in both KNN and SVM methods.

According to the results, the proposed approach has a high efficiency in features selection problem.

Knowledge Management (KM) can improve Supply Chain (SC) performance through some frameworks such as EFQM Excellence Model in order to achieve competitive advantage. In this paper first, the KM enablers in SC are classified based on EFQM enabler criteria. Then, the importance of KM enabler are evaluated by fuzzy Dematel-ANP. In addition, fuzzy AHP is applied to evaluate the importance of each KM enabler in knowledge sharing. In the research, the Multi-objective Mix-integer non-linear programming (MINLP) is used to optimize the performance of knowledge management and select KM strategy in each part of SC. Likewise, it is approved to select suitable members of SC for Research and Development unit of SC. The results represent that each part of SC should be developed by concentration on some KM enablers, and selection of a suitable strategy. In addition, they emphasis on the effect of each KM enabler on selection of suitable members.