The Experts below are selected from a list of 522 Experts worldwide ranked by ideXlab platform

J M C Sousa - One of the best experts on this subject based on the ideXlab platform.

  • RESCHEDULING OF LOGISTIC PROCESSES USING GA AND ACO
    IFAC Proceedings Volumes, 2020
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Abstract This paper compares the online re-optimization of a logistic scheduling problem using two different optimization techniques; genetic algorithms (GA) and ant colony optimization (ACO). This comparison is based on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the genetic algorithms are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the Pheromone Matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.

  • Distributed supply chain management using ant colony optimization
    European Journal of Operational Research, 2009
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Successful supply chain management requires a cooperative integration between all the partners in the network. At the operational level, the partners individual behavior should be optimal and therefore their activities have to be planned using sophisticated optimization tools. However, these tools should take into account the planning of the remaining partners, through the exchange of information, in order to allow some kind of cooperation between the elements of the chain. This paper introduces a new supply chain management technique, based on modeling a generic supply chain with suppliers, logistics and distributers, as a distributed optimization problem. The different operational activities are solved by the optimization meta-heuristic called ant colony optimization, which allows the exchange of information between different optimization problems by means of a Pheromone Matrix. The simulation results show that the new methodology is more efficient than a simple decentralized methodology for different instances of a supply chain.

  • Rescheduling and optimization of logistic processes using GA and ACO
    Engineering Applications of Artificial Intelligence, 2008
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler
    Abstract:

    This paper presents a comparative study of genetic algorithms (GA) and ant colony optimization (ACO) applied the online re-optimization of a logistic scheduling problem. This study starts with a literature review of the GA and ACO performance for different benchmark problems. Then, the algorithms are compared on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the GA are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the Pheromone Matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.

  • FUZZ-IEEE - Supply-Chain Management Using ACO and Beam-ACO Algorithms
    2007 IEEE International Fuzzy Systems Conference, 2007
    Co-Authors: Jorge Caldeira, C.a. Da Silva, R.c. Azevedo, J M C Sousa
    Abstract:

    The management of supply-chain can be performed using distributed optimization based on Ant Colony Optimization (ACO), which uses the Pheromone Matrix as the mean to exchange information between the several subsystems. However, ACO can be replaced by the hybrid algorithm Beam-ACO, which fuses Beam-Search and ACO algorithms. This optimization method has proven to be more powerful than ACO algorithms for scheduling problems. Since both use the Pheromone Matrix to achieve the best solution, this work proposes the implementation of Beam-ACO in supply-chain management. Beam-ACO is used in this paper to optimize the supplying and logistic agents of a supply chain. The distribution system is optimized using the standard ACO algorithm, because Beam-ACO is not suitable for this type of optimization problems. Three different instances of supply-chains have been tested. Results show that the use of Beam-ACO improves the local and global results of the supply chain, and that the distributed optimization paradigm can be applied on supply chains where different agents are optimized by different algorithms.

  • IFSA (1) - Beam-ACO Distributed Optimization Applied to Supply-Chain Management
    Lecture Notes in Computer Science, 2007
    Co-Authors: Jorge Caldeira, R. Azevedo, C.a. Da Silva, J M C Sousa
    Abstract:

    The distributed optimization paradigm based on Ant Colony Optimization (ACO) is a new management technique that uses the Pheromone Matrix to exchange information between the different subsystems to be optimized in the supply-chain. This paper proposes the use of the hybrid algorithm Beam-ACO, that fuses Beam-Search and ACO, to implement the same management concept. The Beam-ACO algorithm is used here to optimize the supplying, the distributer and the logistic agents of the supply-chain. Further, this paper implements the concept in a software platform that allows the Pheromone Matrix exchange through the different agents, using the TCP/IP protocol and data base systems. The results show that the distributed optimization paradigm can still be applied on supply chains where the different agents are optimized by different algorithms and that the use of the Beam-ACO in the supplying agent improves the local and the global results of the supply chain.

C.a. Da Silva - One of the best experts on this subject based on the ideXlab platform.

  • RESCHEDULING OF LOGISTIC PROCESSES USING GA AND ACO
    IFAC Proceedings Volumes, 2020
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Abstract This paper compares the online re-optimization of a logistic scheduling problem using two different optimization techniques; genetic algorithms (GA) and ant colony optimization (ACO). This comparison is based on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the genetic algorithms are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the Pheromone Matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.

  • Distributed supply chain management using ant colony optimization
    European Journal of Operational Research, 2009
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Successful supply chain management requires a cooperative integration between all the partners in the network. At the operational level, the partners individual behavior should be optimal and therefore their activities have to be planned using sophisticated optimization tools. However, these tools should take into account the planning of the remaining partners, through the exchange of information, in order to allow some kind of cooperation between the elements of the chain. This paper introduces a new supply chain management technique, based on modeling a generic supply chain with suppliers, logistics and distributers, as a distributed optimization problem. The different operational activities are solved by the optimization meta-heuristic called ant colony optimization, which allows the exchange of information between different optimization problems by means of a Pheromone Matrix. The simulation results show that the new methodology is more efficient than a simple decentralized methodology for different instances of a supply chain.

  • Rescheduling and optimization of logistic processes using GA and ACO
    Engineering Applications of Artificial Intelligence, 2008
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler
    Abstract:

    This paper presents a comparative study of genetic algorithms (GA) and ant colony optimization (ACO) applied the online re-optimization of a logistic scheduling problem. This study starts with a literature review of the GA and ACO performance for different benchmark problems. Then, the algorithms are compared on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the GA are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the Pheromone Matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.

  • FUZZ-IEEE - Supply-Chain Management Using ACO and Beam-ACO Algorithms
    2007 IEEE International Fuzzy Systems Conference, 2007
    Co-Authors: Jorge Caldeira, C.a. Da Silva, R.c. Azevedo, J M C Sousa
    Abstract:

    The management of supply-chain can be performed using distributed optimization based on Ant Colony Optimization (ACO), which uses the Pheromone Matrix as the mean to exchange information between the several subsystems. However, ACO can be replaced by the hybrid algorithm Beam-ACO, which fuses Beam-Search and ACO algorithms. This optimization method has proven to be more powerful than ACO algorithms for scheduling problems. Since both use the Pheromone Matrix to achieve the best solution, this work proposes the implementation of Beam-ACO in supply-chain management. Beam-ACO is used in this paper to optimize the supplying and logistic agents of a supply chain. The distribution system is optimized using the standard ACO algorithm, because Beam-ACO is not suitable for this type of optimization problems. Three different instances of supply-chains have been tested. Results show that the use of Beam-ACO improves the local and global results of the supply chain, and that the distributed optimization paradigm can be applied on supply chains where different agents are optimized by different algorithms.

  • IFSA (1) - Beam-ACO Distributed Optimization Applied to Supply-Chain Management
    Lecture Notes in Computer Science, 2007
    Co-Authors: Jorge Caldeira, R. Azevedo, C.a. Da Silva, J M C Sousa
    Abstract:

    The distributed optimization paradigm based on Ant Colony Optimization (ACO) is a new management technique that uses the Pheromone Matrix to exchange information between the different subsystems to be optimized in the supply-chain. This paper proposes the use of the hybrid algorithm Beam-ACO, that fuses Beam-Search and ACO, to implement the same management concept. The Beam-ACO algorithm is used here to optimize the supplying, the distributer and the logistic agents of the supply-chain. Further, this paper implements the concept in a software platform that allows the Pheromone Matrix exchange through the different agents, using the TCP/IP protocol and data base systems. The results show that the distributed optimization paradigm can still be applied on supply chains where the different agents are optimized by different algorithms and that the use of the Beam-ACO in the supplying agent improves the local and the global results of the supply chain.

Martin Middendorf - One of the best experts on this subject based on the ideXlab platform.

  • Solving multi-criteria optimization problems with population-based ACO
    Lecture Notes in Computer Science, 2020
    Co-Authors: Michael Guntsch, Martin Middendorf
    Abstract:

    In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all non-dominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one Pheromone Matrix for each type of optimization criterion. The matrices are derived from the chosen population of solutions, and can cope with an arbitrary number of criteria. As a test problem, Single Machine Total Tardiness with changeover costs is used.

  • EvoCOP - Quick-ACO: accelerating ant decisions and Pheromone updates in ACO
    Evolutionary Computation in Combinatorial Optimization, 2011
    Co-Authors: Wei Cheng, Bernd Scheuermann, Martin Middendorf
    Abstract:

    In Ant Colony Optimization (ACO) algorithms, solutions are constructed through a sequence of probabilistic decisions by artificial ants. These decisions are guided by information stored in a Pheromone Matrix which is repeatedly updated in two ways: Pheromone values in the Matrix are increased by the ants to mark preferable decisions (probabilistic selection of items) whereas evaporation reduces each Pheromone value by a certain percentage to weaken the relevance of former, potentially unfavorable, decisions. This paper introduces novel methods for expedited ant decisions and Pheromone update for ACO. It is proposed to speedup decisions of ants by temporarily allowing them to select any item. If this item has already been chosen before (which would result in an inadmissible solution), the ant repeats its decision until an admissible item has been chosen. This method avoids to continuously determine the probability distributions over the yet admissible items which otherwise would require frequent expensive prefix sum calculations. The procedure of Pheromone Matrix updates is accelerated by entirely abandoning evaporation while re-scaling Pheromone values and update increments. It should be empasized that both new methods do not change the optimization behavior compared to standard ACO. In experimental evaluations with a range of benchmark instances of the Traveling Salesman Problem, the new methods were able to save up to 90% computation time compared to a ACO algorithm which uses standard procedures for Pheromone update and decision making.

  • EMO - Solving multi-criteria optimization problems with population-based ACO
    Lecture Notes in Computer Science, 2003
    Co-Authors: Michael Guntsch, Martin Middendorf
    Abstract:

    In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all nondominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one Pheromone Matrix for each type of optimization criterion. The matrices are derived from the chosen population of solutions, and can cope with an arbitrary number of criteria. As a test problem, Single Machine Total Tardiness with changeover costs is used.

  • GECCO - Studies on the dynamics of Ant Colony Optimization algorithms
    2002
    Co-Authors: Daniel Merkle, Martin Middendorf
    Abstract:

    A deterministic model for Ant Colony Optimization (ACO) algorithms is proposed and used to study the dynamics of ACO. The model is based on the average expected behaviour of ants. The behaviour of ACO algorithms and the model are analysed for certain types of permutation problems. It is shown numerically that decisions of the ants are influenced in an intriguing way by the properties of the Pheromone Matrix. This explains why ACO algorithms show a complex dynamic behaviour. Simulations are done to compare the behaviour of the ACO model with the ACO algorithm. The results show that the model describes essential features of the dynamics of ACO algorithms.

J.m.g. Sá Da Costa - One of the best experts on this subject based on the ideXlab platform.

  • RESCHEDULING OF LOGISTIC PROCESSES USING GA AND ACO
    IFAC Proceedings Volumes, 2020
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Abstract This paper compares the online re-optimization of a logistic scheduling problem using two different optimization techniques; genetic algorithms (GA) and ant colony optimization (ACO). This comparison is based on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the genetic algorithms are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the Pheromone Matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.

  • Distributed supply chain management using ant colony optimization
    European Journal of Operational Research, 2009
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    Successful supply chain management requires a cooperative integration between all the partners in the network. At the operational level, the partners individual behavior should be optimal and therefore their activities have to be planned using sophisticated optimization tools. However, these tools should take into account the planning of the remaining partners, through the exchange of information, in order to allow some kind of cooperation between the elements of the chain. This paper introduces a new supply chain management technique, based on modeling a generic supply chain with suppliers, logistics and distributers, as a distributed optimization problem. The different operational activities are solved by the optimization meta-heuristic called ant colony optimization, which allows the exchange of information between different optimization problems by means of a Pheromone Matrix. The simulation results show that the new methodology is more efficient than a simple decentralized methodology for different instances of a supply chain.

  • Distributed optimisation of a logistic system and its suppliers using ant colonies
    International Journal of Systems Science, 2006
    Co-Authors: C.a. Da Silva, J M C Sousa, Thomas A. Runkler, J.m.g. Sá Da Costa
    Abstract:

    This paper introduces a new multi-agent approach for collaborative management of logistic and supply systems based on the ant colony optimisation (ACO) meta-heuristic. The logistic system and its suppliers can be modelled as partners of a supply chain. The management methodology is defined as a set of distributed scheduling problems that exchange information during the optimisation process. Each problem is solved by an ant colony agent that uses the Pheromone Matrix as the communication platform. A simulation example shows that the proposed coordination mechanism improves the supply-chain performance compared to a traditional management approach, where both problems are considered separately.

  • SMC (2) - A multi-agent approach for supply chain management using ant colony optimization
    2004 IEEE International Conference on Systems Man and Cybernetics (IEEE Cat. No.04CH37583), 2004
    Co-Authors: C.a. Suva, Inês Sousa, J.m.g. Sá Da Costa, Thomas A. Runkler
    Abstract:

    Distributed systems like supply chains can be efficiently managed by multi-agent approaches. However, the control of each sub-system in a supply chain is a complex optimization problem where optimal performance can be achieved using meta-heuristics. This paper presents a new methodology for supply chain management, a distributed optimization using ant colonies, where the concepts of agents and ant colony optimization are merged, using the Pheromone Matrix as communication platform. This paper formalizes this methodology, using as an example a supply chain with logistic, supplying and distribution sub-systems.

Zili Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Multi-objective ant colony optimization based on the Physarum-Inspired mathematical model for bi-objective traveling salesman problems
    PLOS ONE, 2016
    Co-Authors: Zili Zhang, Yuxiao Lu, Mingxin Liang
    Abstract:

    Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of Pheromone Matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the Pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

  • ICSI (2) - A New Physarum-Based Hybrid Optimization Algorithm for Solving 0/1 Knapsack Problem
    Advances in Swarm and Computational Intelligence, 2015
    Co-Authors: Shi Chen, Zili Zhang
    Abstract:

    As a typical NP-complete problem, 0/1 Knapsack Problem (KP), has been widely applied in many domains for solving practical problems. Although ant colony optimization (ACO) algorithms can obtain approximate solutions to 0/1 KP, there exist some shortcomings such as the low convergence rate, premature convergence and weak robustness. In order to get rid of the above-mentioned shortcomings, this paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved by Physarum-inspired mathematical (PM) model. By releasing additional Pheromone to items that are on the important pipelines of PM model, PM-ACO algorithms can enhance item Pheromone Matrix and realize a positive feedback process of updating item Pheromone. The experimental results in two different datasets show that PM-ACO algorithms have a stronger robustness and a higher convergence rate compared with traditional ACO algorithms.

  • ICNC - A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model
    2014 10th International Conference on Natural Computation (ICNC), 2014
    Co-Authors: Yuxiao Lu, Zili Zhang
    Abstract:

    Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both Pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized Pheromone Matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.

  • ICSI (1) - An Ant Colony System Based on the Physarum Network
    Lecture Notes in Computer Science, 2013
    Co-Authors: Tao Qian, Zili Zhang, Yuheng Wu
    Abstract:

    The Physarum Network model exhibits the feature of important pipelines being reserved with the evolution of network during the process of solving a maze problem. Drawing on this feature, an Ant Colony System (ACS), denoted as PNACS, is proposed based on the Physarum Network (PN). When updating Pheromone Matrix, we should update both Pheromone trails released by ants and the Pheromones flowing in a network. This hybrid algorithm can overcome the low convergence rate and local optimal solution of ACS when solving the Traveling Salesman Problem (TSP). Some experiments in synthetic and benchmark networks show that the efficiency of PNACS is higher than that of ACS. More important, PNACS has strong robustness that is very useful for solving a higher dimension TSP.