Carlos A. Silva

Rescheduling and optimization of logistic processes using GA and ACO

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.

Distributed supply chain management using ant colony optimization

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.

Soft computing optimization methods applied to logistic processes

This paper discusses the methodologies that can be used to optimize a logistic process of a supply chain described as a scheduling problem. First, a model of the system based on a real-world example is presented. Then, a new objective function called Global Expected Lateness is proposed, in order to describe multiple optimization criteria. Finally, three different optimization methodologies are proposed: a classical dispatching rule, and two soft computing techniques, Genetic Algorithms (GA) and Ant Colony Optimization (ACO). These methodologies are compared to the dispatching policy in the real-world example. The results show that dispatching heuristics are outperformed by the GA and ACO meta-heuristics. Further, it is shown that GA and ACO provide statistically identical scheduling solutions and from the optimization performance point of view, it is equivalent to use any of the meta-heuristics.

High voltage multilevel converter with regeneration capability

This paper presents a multilevel converter with regeneration capability. The converter is based on the series connection of several power cells, each working with reduced voltage and with an active front end at the line side. This paper presents the control method of each cell, the use of phase-shifting techniques to reduce the current distortion and criteria to select the connection of the cells. The converter generates practically sinusoidal currents at the load and at the input and works with very high power factor.

Distributed optimisation of a logistic system and its suppliers using ant colonies

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.

Concrete Delivery using a combination of GA and ACO

The timely production and distribution of rapidly perishable goods such as concrete is a complex combinatorial optimization problem in the context of supply chain management. The problem involves several tightly interrelated scheduling and routing problems that have to be solved considering a trade-off of production and delivery costs. A hybrid meta-heuristic method combining genetic algorithms with constructive heuristics has been previously presented. This paper introduces a novel approach, by replacing the constructive heuristic with another meta-heuristic, the ant colony optimization approach. The simulation examples show that the concrete supply chain improves the performance with the novel GA-ACO algorithm.

Optimization of logistic systems using fuzzy weighted aggregation

Logistic scheduling problems are often multi-criteria optimization problems, with many contradictory objectives and constraints, which cannot be properly described by conventional cost functions. The use of fuzzy decision making may improve the performance of this type of systems, since it allows an easier and suitable description of the confluence of the different criteria of the scheduling process. This paper introduces the application of fuzzy weighted aggregation to formulate the logistic system optimization problem. Further, this paper also extends the application of this framework to different types of optimization methodologies: dispatching rules, if it is used as a performance index; or meta-heuristics, such as genetic algorithms (GA) or ant colony optimization (ACO), if it is used as an objective function. Simulation results show that the fuzzy combination of criteria improves the scheduling results whatever optimization methodology is used.

Fuzzy active noise modeling and control

Abstract The design of active noise control (ANC) has been developed in the last two decades based on linear identification and control tools. However, acoustic processes present nonlinearities coming both from the characteristics of the actuator and from the nature of the process. Recent research has emphasized the importance of nonlinear model-based controllers, which increase the performance of several types of systems. From the different nonlinear techniques, fuzzy modeling is one of the most utilized. Direct and inverse multivariable fuzzy models can be identified directly from data using fuzzy clustering. Inverse models can then be applied directly as controllers, which can be included in an active noise control scheme. This paper proposes the use of fuzzy techniques in ANC. The performance of the proposed control schemes is compared to classical finite impulse response ANC in an experimental setup. The proposed fuzzy control scheme outperforms classical active noise controllers.

Optimization of Logistic Processes in Supply-Chains Using Meta-heuristics

This paper addresses the optimization of logistic processes in supply-chains using meta-heuristics: genetic algorithms and ant colony optimization. The dynamic assignment of components to orders and choosing the solution that is able to deliver more orders at the correct date, is a scheduling problem that classical scheduling methods can not cope with. However, the implementation of meta-heuristics is done only after a positive assessment of the performance’s expectation provided by the fitness-distance correlation analysis. Both meta-heuristics are then applied to a simulation example that describes a general logistic process. The performance is similar for both methods, but the ant colony optimization method provides more information at the expenses of computational costs.

Polygeneration Energy Container: Designing and Testing Energy Services for Remote Developing Communities

Nearly one and a half billion people in the world do not have access to electricity, and even when energy resources are available, millions of people are unable to pay for them. As so, the access to modern affordable and renewable-based energy services in developing countries is essential to achieve a sustainable development by reducing poverty and improving the living conditions (health, security, etc.). In this paper, we analyze how rural electrification efforts can benefit from the implementation of microgrid systems. Following a systematic methodology, we estimate a demand that can evolve through time and design a system that copes with the demand increase. Then, we test the operation of such a facility using a pilot installation, the polygeneration energy container (PEC), an experimental setup that mimics the deployment of a hybrid microgrid system. The development of the experiment has allowed us to test design issues and solve operation and maintenance challenges associated with autonomous electricity production systems. The results demonstrated that it is possible to design systems that are robust and flexible to operate under different conditions.