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This paper presents an improved watermarking scheme for soft Intellectual Property (IP) -Cores using Genetic Algorithms (GAs). For this purpose, a watermark signature and an IP-Core behavioral description are translated into Finite State Machines (FSMs). Both FSMs are merged in a single one containing a watermarked IP-Core without disrupting its original functionality. Therefore, we tackle NP-completeness of the subgraph isomorphism problem found during FSMs merging process via a tailored GA. However, not deeply embedded states are a key problem that may ease watermark’s removal. To overcome this issue, a FSM reduction algorithm is also considered in the proposed watermarking scheme. Moreover, the proposed scheme also targets transitions regrouping which impact the amount of hardware resources usage and a mechanism for selecting the best watermarked FSM. A thorough empirical assessment shows a significant improvement in terms of reduction and watermark embedding strength.
Jorge Echavarria; Alicia Morales-Reyes; René Cumplido; Miguel A. Salido; Claudia Feregrino-Uribe. IP-cores watermarking scheme at behavioral level using genetic algorithms. Engineering Applications of Artificial Intelligence 2021, 104, 104386 .
AMA StyleJorge Echavarria, Alicia Morales-Reyes, René Cumplido, Miguel A. Salido, Claudia Feregrino-Uribe. IP-cores watermarking scheme at behavioral level using genetic algorithms. Engineering Applications of Artificial Intelligence. 2021; 104 ():104386.
Chicago/Turabian StyleJorge Echavarria; Alicia Morales-Reyes; René Cumplido; Miguel A. Salido; Claudia Feregrino-Uribe. 2021. "IP-cores watermarking scheme at behavioral level using genetic algorithms." Engineering Applications of Artificial Intelligence 104, no. : 104386.
In many transport companies, one of the main objectives is to optimize the travel cost of their fleet. Other objectives are related to delivery time, fuel savings, etc. However warehouse stock management is not properly considered. Warehouse stock control is based on the correct allocation of resources to each order. In this paper, we combine the warehouse stock management problem and the routing problem to be applied in a real company that allows negative stock in their warehouses. The proposed multi-objective problem is modeled and solved by the greedy randomized adaptive search (GRASP) algorithm. The results shows that the proposed algorithm outperforms the current search technique used by the company mainly in stock balancing, improving the negative average stock by up to 82%.
Christian Perez; Miguel A. Salido; David Gurrea. A Metaheuristic Search Technique for Solving the Warehouse Stock Management Problem and the Routing Problem in a Real Company. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 187 -201.
AMA StyleChristian Perez, Miguel A. Salido, David Gurrea. A Metaheuristic Search Technique for Solving the Warehouse Stock Management Problem and the Routing Problem in a Real Company. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():187-201.
Chicago/Turabian StyleChristian Perez; Miguel A. Salido; David Gurrea. 2020. "A Metaheuristic Search Technique for Solving the Warehouse Stock Management Problem and the Routing Problem in a Real Company." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 187-201.
A new optimization method namely the Search and Rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The ε-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible minima in some complex constrained optimization problems. SAR is applied to solve 18 benchmark constraint functions presented in CEC 2010, 13 benchmark constraint functions, and 7 constrained engineering design problems reported in the specialized literature. The performance of SAR is compared with some state-of-the-art optimization algorithms. According to the statistical comparison results, the performance of SAR is better or highly competitive against the compared algorithms on most of the studied problems.
Amir Shabani; Behrouz Asgarian; Miguel Salido; Saeed Asil Gharebaghi. Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications 2020, 161, 113698 .
AMA StyleAmir Shabani, Behrouz Asgarian, Miguel Salido, Saeed Asil Gharebaghi. Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications. 2020; 161 ():113698.
Chicago/Turabian StyleAmir Shabani; Behrouz Asgarian; Miguel Salido; Saeed Asil Gharebaghi. 2020. "Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems." Expert Systems with Applications 161, no. : 113698.
In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.
Amir Shabani; Behrouz Asgarian; Saeed Asil Gharebaghi; Miguel A. Salido; Adriana Giret. A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering 2019, 2019, 1 -23.
AMA StyleAmir Shabani, Behrouz Asgarian, Saeed Asil Gharebaghi, Miguel A. Salido, Adriana Giret. A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering. 2019; 2019 ():1-23.
Chicago/Turabian StyleAmir Shabani; Behrouz Asgarian; Saeed Asil Gharebaghi; Miguel A. Salido; Adriana Giret. 2019. "A New Optimization Algorithm Based on Search and Rescue Operations." Mathematical Problems in Engineering 2019, no. : 1-23.
Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.
Min Dai; Ziwei Zhang; Adriana Giret; Miguel A. Salido. An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability 2019, 11, 3085 .
AMA StyleMin Dai, Ziwei Zhang, Adriana Giret, Miguel A. Salido. An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability. 2019; 11 (11):3085.
Chicago/Turabian StyleMin Dai; Ziwei Zhang; Adriana Giret; Miguel A. Salido. 2019. "An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints." Sustainability 11, no. 11: 3085.
Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.
Min Dai; Dunbing Tang; Adriana Giret; Miguel A. Salido. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing 2019, 59, 143 -157.
AMA StyleMin Dai, Dunbing Tang, Adriana Giret, Miguel A. Salido. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing. 2019; 59 ():143-157.
Chicago/Turabian StyleMin Dai; Dunbing Tang; Adriana Giret; Miguel A. Salido. 2019. "Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints." Robotics and Computer-Integrated Manufacturing 59, no. : 143-157.
With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach.
Qi Wang; Dunbing Tang; Shipei Li; Jun Yang; Miguel A. Salido; Adriana Giret; Haihua Zhu. An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability 2019, 11, 460 .
AMA StyleQi Wang, Dunbing Tang, Shipei Li, Jun Yang, Miguel A. Salido, Adriana Giret, Haihua Zhu. An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability. 2019; 11 (2):460.
Chicago/Turabian StyleQi Wang; Dunbing Tang; Shipei Li; Jun Yang; Miguel A. Salido; Adriana Giret; Haihua Zhu. 2019. "An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products." Sustainability 11, no. 2: 460.
Within the emerging industrial sustainability domain, production efficiency interventions are gaining practical interest since manufacturing plants are facing increasing pressure to reduce their carbon footprint, driven by concerns related to energy costs and climate changes. This work focuses on the challenging issue of energy aware production scheduling and rescheduling systems (EAPSRS). The proposed multi-agent architecture (MA-EAPSRS) is hybrid, combining the predictive and the reactive phase while taking into account sustainability in both parts. It is composed of two cooperating multi-agent systems: the first one represents the smart manufacturing plant and the second one is the smart energy supply plant. It is based on interactions and negotiations between factory schedulers and energy providers. Uncertainties in term of machine’s disruptions and variation of processing time and in term of energy availability are also considered. In order to assess the proposed approach, an illustrative case study addressing the problem is presented and discussed.
Maroua Nouiri; Damien Trentesaux; Abdelghani Bekrar; Adriana Giret; Miguel A. Salido. Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective. Artificial Intelligence: Foundations, Theory, and Algorithms 2018, 197 -210.
AMA StyleMaroua Nouiri, Damien Trentesaux, Abdelghani Bekrar, Adriana Giret, Miguel A. Salido. Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective. Artificial Intelligence: Foundations, Theory, and Algorithms. 2018; ():197-210.
Chicago/Turabian StyleMaroua Nouiri; Damien Trentesaux; Abdelghani Bekrar; Adriana Giret; Miguel A. Salido. 2018. "Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 197-210.
New stricter environmental regulations and consumer rising issues are making greenhouse gases (GHG) emission an increasing and urgent concern for manufacturing companies. Companies and researchers are seeking appropriate methods to reduce GHG emission of the manufactured products. Previous studies on low-carbon product design mainly concern on a single product. Currently, it is common to design a product family instead of a single product for increasing varieties to satisfy customers’ requirements. Owing to the difference in design methods, the low-carbon design method for a single product cannot handle a product family. In addition, nowadays, the sourcing strategy is widely adopted by companies. A key problem of the procurement is supplier selection. The supplier selection affects not only profit but also GHG emission. However, it has not been simultaneously considered in low-carbon product design. In this article, an optimization model for coordinating low-carbon design of product family and supplier selection is proposed. In the model, the profit and the GHG emission of a product family are taken into consideration at the same time. Moreover, a genetic algorithm is developed to solve the established model. Finally, a case study is performed to verify the validity of the proposed approach.
Qi Wang; Dunbing Tang; Leilei Yin; Inayat Ullah; Miguel A. Salido; Adriana Giret. An Optimization Method for Coordinating Supplier Selection and Low-Carbon Design of Product Family. International Journal of Precision Engineering and Manufacturing 2018, 19, 1715 -1726.
AMA StyleQi Wang, Dunbing Tang, Leilei Yin, Inayat Ullah, Miguel A. Salido, Adriana Giret. An Optimization Method for Coordinating Supplier Selection and Low-Carbon Design of Product Family. International Journal of Precision Engineering and Manufacturing. 2018; 19 (11):1715-1726.
Chicago/Turabian StyleQi Wang; Dunbing Tang; Leilei Yin; Inayat Ullah; Miguel A. Salido; Adriana Giret. 2018. "An Optimization Method for Coordinating Supplier Selection and Low-Carbon Design of Product Family." International Journal of Precision Engineering and Manufacturing 19, no. 11: 1715-1726.
Advance in applied scheduling is a source of innovation in the manufacturing field, where new results help industrial practitioners in production management. The literature of rescheduling problems for single-objective optimization is well study, while there is a lack of extensive studies for the case of rescheduling for multi-objective optimization, especially for energy aware scheduling. This paper extends a previous work over an energy aware scheduling problem, modelled from a real industrial case study, extending its manufacturing environment to a dynamic one. To this end, two rescheduling techniques are developed to tackle machines disruptions (greedy-heuristic and meta-heuristic). They are compared to existing approach thought manufacturing environment simulations. The results give insight to improve the production management in terms of rescheduling quality and computational time.
Sergio Ferrer; Giancarlo Nicolò; Miguel A. Salido; Adriana Giret; Federico Barber. Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems. Security Education and Critical Infrastructures 2018, 232 -240.
AMA StyleSergio Ferrer, Giancarlo Nicolò, Miguel A. Salido, Adriana Giret, Federico Barber. Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems. Security Education and Critical Infrastructures. 2018; ():232-240.
Chicago/Turabian StyleSergio Ferrer; Giancarlo Nicolò; Miguel A. Salido; Adriana Giret; Federico Barber. 2018. "Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems." Security Education and Critical Infrastructures , no. : 232-240.
The sustainability of urban logistics is an important issue for rapidly growing cities worldwide. Although many cities and research works have developed strategies to move people more efficiently and safely within the urban environment, much less attention has been paid to the importance of optimizing the delivery of goods to people at work and home taking into account sustainable goals. In this work we propose a framework that aids to register and measure a set of sustainable Key Performance Indicators (KPIs) for delivery routes and plans in urban zones. The approach is general and based on a set of well defined KPIs from the specialized research field.
Adriana Giret; Vicente Julián; Juan Manuel Corchado; Alberto Fernández; Miguel A. Salido; Dunbing Tang. How to Choose the Greenest Delivery Plan: A Framework to Measure Key Performance Indicators for Sustainable Urban Logistics. Security Education and Critical Infrastructures 2018, 181 -189.
AMA StyleAdriana Giret, Vicente Julián, Juan Manuel Corchado, Alberto Fernández, Miguel A. Salido, Dunbing Tang. How to Choose the Greenest Delivery Plan: A Framework to Measure Key Performance Indicators for Sustainable Urban Logistics. Security Education and Critical Infrastructures. 2018; ():181-189.
Chicago/Turabian StyleAdriana Giret; Vicente Julián; Juan Manuel Corchado; Alberto Fernández; Miguel A. Salido; Dunbing Tang. 2018. "How to Choose the Greenest Delivery Plan: A Framework to Measure Key Performance Indicators for Sustainable Urban Logistics." Security Education and Critical Infrastructures , no. : 181-189.
Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine, and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives: energy efficiency, robustness, and makespan, and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exist a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.
M. A. Salido; J. Escamilla; Federico Barber; A. Giret; D. B. Tang; M. Dai. Energy Efficiency, Robustness, and Makespan Optimality in Job-Shop Scheduling Problems. Sustainable Manufacturing and Remanufacturing Management 2018, 213 -233.
AMA StyleM. A. Salido, J. Escamilla, Federico Barber, A. Giret, D. B. Tang, M. Dai. Energy Efficiency, Robustness, and Makespan Optimality in Job-Shop Scheduling Problems. Sustainable Manufacturing and Remanufacturing Management. 2018; ():213-233.
Chicago/Turabian StyleM. A. Salido; J. Escamilla; Federico Barber; A. Giret; D. B. Tang; M. Dai. 2018. "Energy Efficiency, Robustness, and Makespan Optimality in Job-Shop Scheduling Problems." Sustainable Manufacturing and Remanufacturing Management , no. : 213-233.
Resource-Constrained Project Scheduling Problems (RCPSP) are some of the most important scheduling problems due to their applicability to real problems and their combinatorial complexity (NP-hard). In the literature, it has been shown that metaheuristic algorithms are the main option to deal with real-size problems. Among them, population-based algorithms, especially genetic algorithms, stand out for being able to achieve the best near-optimal solutions in reasonable computational time. One of the main components of metaheuristic algorithms is the solution representation (codification) since all search strategies are implemented based on it. However, most codings are affected by generating redundant solutions, which obstruct incorporating new information. In this paper, we focus on the study of the mutation operator (responsible for diversity in the population), in order to determine how to implement this operator to reduce the obtaining of redundant solutions. The computational assessment was done on the well-known PSPLIB library and shows that the proposed algorithm reaches competitive solutions compared with the best-proposed algorithms in the literature.
Daniel Morillo; Federico Barber; Miguel A. Salido. Chromosome Mutation vs. Gene Mutation in Evolutive Approaches for Solving the Resource-Constrained Project Scheduling Problem (RCPSP). Privacy Enhancing Technologies 2018, 601 -612.
AMA StyleDaniel Morillo, Federico Barber, Miguel A. Salido. Chromosome Mutation vs. Gene Mutation in Evolutive Approaches for Solving the Resource-Constrained Project Scheduling Problem (RCPSP). Privacy Enhancing Technologies. 2018; ():601-612.
Chicago/Turabian StyleDaniel Morillo; Federico Barber; Miguel A. Salido. 2018. "Chromosome Mutation vs. Gene Mutation in Evolutive Approaches for Solving the Resource-Constrained Project Scheduling Problem (RCPSP)." Privacy Enhancing Technologies , no. : 601-612.
Adriana Giret; Damien Trentesaux; Miguel A. Salido; Emilia Garcia; Emmanuel Adam. A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. Journal of Cleaner Production 2017, 167, 1370 -1386.
AMA StyleAdriana Giret, Damien Trentesaux, Miguel A. Salido, Emilia Garcia, Emmanuel Adam. A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. Journal of Cleaner Production. 2017; 167 ():1370-1386.
Chicago/Turabian StyleAdriana Giret; Damien Trentesaux; Miguel A. Salido; Emilia Garcia; Emmanuel Adam. 2017. "A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems." Journal of Cleaner Production 167, no. : 1370-1386.
This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.
Daniel Morillo; Federico Barber; Miguel A. Salido. Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering 2017, 2017, 1 -15.
AMA StyleDaniel Morillo, Federico Barber, Miguel A. Salido. Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering. 2017; 2017 ():1-15.
Chicago/Turabian StyleDaniel Morillo; Federico Barber; Miguel A. Salido. 2017. "Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem." Mathematical Problems in Engineering 2017, no. : 1-15.
This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating [Formula: see text] and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/ .
Daniel Morillo Torres; Federico Barber; Miguel A Salido. A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2017, 233, 293 -305.
AMA StyleDaniel Morillo Torres, Federico Barber, Miguel A Salido. A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2017; 233 (1):293-305.
Chicago/Turabian StyleDaniel Morillo Torres; Federico Barber; Miguel A Salido. 2017. "A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 1: 293-305.
The areas of Artificial Intelligence planning and scheduling have seen important advances thanks to the application of constraint satisfaction models and techniques. Especially, solutions to many real-world problems need to integrate plan synthesis capabilities with resource allocation, which can be efficiently managed by using constraint satisfaction techniques. Constraint satisfaction plays an important role in solving such real life problems, and integrated techniques that manage planning and scheduling with constraint satisfaction are particularly useful.
Miguel A. Salido; Roman Barták. Introduction to the special issue on constraint satisfaction for planning and scheduling. The Knowledge Engineering Review 2016, 31, 415 -416.
AMA StyleMiguel A. Salido, Roman Barták. Introduction to the special issue on constraint satisfaction for planning and scheduling. The Knowledge Engineering Review. 2016; 31 (5):415-416.
Chicago/Turabian StyleMiguel A. Salido; Roman Barták. 2016. "Introduction to the special issue on constraint satisfaction for planning and scheduling." The Knowledge Engineering Review 31, no. 5: 415-416.
Many real life problems can be modeled as a scheduling problem. The main objective of these problems is to obtain optimal solutions in terms of processing time, cost and quality. Nowadays, energy-efficiency is also taken into consideration. However, these problems are NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the classical job-shop scheduling problem. In the extended version, each operation has to be executed by one machine and this machine can work at different speed rates. The machines consume different amounts of energy to process tasks at different rates. The evaluation section shows that a powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.
Joan Escamilla; Miguel A. Salido; Adriana Giret; Federico Barber. A metaheuristic technique for energy-efficiency in job-shop scheduling. The Knowledge Engineering Review 2016, 31, 475 -485.
AMA StyleJoan Escamilla, Miguel A. Salido, Adriana Giret, Federico Barber. A metaheuristic technique for energy-efficiency in job-shop scheduling. The Knowledge Engineering Review. 2016; 31 (5):475-485.
Chicago/Turabian StyleJoan Escamilla; Miguel A. Salido; Adriana Giret; Federico Barber. 2016. "A metaheuristic technique for energy-efficiency in job-shop scheduling." The Knowledge Engineering Review 31, no. 5: 475-485.
Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a low-carbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support low-carbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study.
Qi Wang; Dunbing Tang; Leilei Yin; Miguel A. Salido; Adriana Giret; Yuchun Xu. Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing 2016, 41, 53 -65.
AMA StyleQi Wang, Dunbing Tang, Leilei Yin, Miguel A. Salido, Adriana Giret, Yuchun Xu. Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing. 2016; 41 ():53-65.
Chicago/Turabian StyleQi Wang; Dunbing Tang; Leilei Yin; Miguel A. Salido; Adriana Giret; Yuchun Xu. 2016. "Bi-objective optimization for low-carbon product family design." Robotics and Computer-Integrated Manufacturing 41, no. : 53-65.
Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.
Dunbing Tang; Min Dai; Miguel A. Salido; Adriana Giret. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry 2016, 81, 82 -95.
AMA StyleDunbing Tang, Min Dai, Miguel A. Salido, Adriana Giret. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry. 2016; 81 ():82-95.
Chicago/Turabian StyleDunbing Tang; Min Dai; Miguel A. Salido; Adriana Giret. 2016. "Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization." Computers in Industry 81, no. : 82-95.