Abstract: Problem statement: The CapacitatedVehicleRoutingProblem (CVRP) is a well-known combinatorial optimizationproblem which is concerned with the distribution of goods between the depot and customers. It is of economic importance to businesses as approximately 10-20% of the final cost of the goods is contributed by the transportation process. Approach: This problem was tackled using an AntColonyOptimization (ACO) combined with heuristic approaches that act as the route improvement strategies. The proposed ACO utilized a pheromone evaporation procedure of standard ant algorithm in order to introduce an evaporation rate that depends on the solutions found by the artificial ants. Results: Computational experiments were conducted on benchmark data set and the results obtained from the proposed algorithms shown that the application of combination of two different heuristics in the ACO had the capability to improve the ants’ solutions better than ACO embedded with only one heuristic. Conclusion: ACO with swap and 3-opt heuristic has the capability to tackle the CVRP with satisfactory solution quality and run time. It is a viable alternative for solving the CVRP.
Crainic et al. (2008) propuseram as heurísticas Split- large-route, Add and Exchange para o problema 2E-CVRP, baseadas nas abordagens de clustering e multi depósitos. Crainic et al. (2010) resolveram o 2E-CVRP com o uso de uma família de heurísticas Multi-Start, de maneira que cada nível foi tratado com um subproblema e resolvido separa- damente e sequencialmente, num processo iterativo. Um al- goritmo híbrido AntColonyOptimization foi desenvolvido por Meihua et al. (2011) a partir da combinação de três meta-heurísticas para a resolução do 2E-CVRP. O método resolve o problema dividindo-o em subproblemas CVRPs e as metaheurísticas AntColonyOptimization e Multiple Neighborhood Descent são combinadas para a resolução desses CVRP. Em seguida, a meta-heurística Threshold- Based Local Search é utilizada para melhorar a solução vi- ável encontrada anteriormente. Hemmelmayr et al. (2012) resolveram o 2E-CVRP no contexto da Logística Urbana com o desenvolvimento de uma meta-heurística Adaptive Large Neighborhood Search (ALNS).
This work presented the vehicleroutingoptimization system developed to be integrated with an existing ERP. The optimization procedure takes into consideration the need for a near real-time routing solution under dynamic orders and interactions with the system administrator. In this sight, this work described the interactions and dependencies between the system’s four main components, namely: i3FR-Opt (where the computation of the routes is done), the i3FR-Hub (implementing a channel to all the communications inside the system and to the exterior), the i3FR-DB (provider of local storage to the information relevant to the optimization procedure), and i3FR-Maps (a cartography subsystem of routing informations). With this structure it is possible to deal with late orders and diﬀerent states for the routes, which allows to do a phased picking and loading of the vehicles. As mere examples, some results for the Algarve’s region were presented showing diﬀerent solution depending on the time windows restrictions.
Surveys of existing methods for multi-objective problems were presented in Jozefowiez et al. (2008) and Zhou et al. (2011). In Jozefowiez et al. (2008), the authors examined multiobjective versions of several variants of the VehicleRoutingProblem (VRP) in terms of their objectives, their characteristics and the types of proposed algorithms to solve them. A survey of the state of the art of the multi-objective evolutionary algorithms was proposed by Zhou et al. (2011). This papers covers algorithms frameworks for multiobjective combinatorial problems during the last eight years. However, in the literature reviewed, there are few works considering the multi-objective version of the MDVRPB. Multiobjective metaheuristic approaches for combinatorial problems were presented in Doerner et al. (2004), Liu et al. (2006) and Lau et al. (2009). A multiobjective methodology by Pareto AntColonyOptimization for solving a portfolio problem was introduced by Doerner et al. (2004). A multi-objective mixed zero-one integer-programming model for the vehicleroutingproblem with balanced workload and delivery time was introduced by Liu et al. (2006). In this work, a heuristic-based solution method was developed. A fuzzy multi-objective evolutionary algorithm for the problem of optimization of vehiclerouting problems with multiple depots, multiple customers, and multiple products was proposed by Lau et al. (2009). In this work, two objectives were considered: minimization of the traveling distance and also the traveling time.
Location-Routing problems involve locating a number of facilities among can- didate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamilto- nian p-Median problem (HpMP). This research applies the metaheuristic method of antcolonyoptimization (ACO) to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicleroutingproblem (VRP) in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.
The problem of total investment-cost minimization, subject to reliability constraints, is well known as the redundancy optimizationproblem (ROP). The ROP is studied in many different forms as summarized in , and more recently in . The ROP for the multi-state reliability was introduced in . In  and , genetic algorithms were used to find the optimal or nearly optimal transformation system structure. This work uses an antcolonyoptimization approach to solve the ROP for multi- state petroleum transpot system. The idea of employing a colony of cooperating agents to solve combinatorial optimization problems was recently proposed in . The antcolony approach has been successfully applied to the classical travelling salesman problem in , and to the quadratic assignment problem in . Antcolony shows very good results in each applied area. It has been recently adapted for the reliability design of binary state systems in . The antcolony has also been adapted with success to other combinatorial optimization problems such as the vehicleroutingproblem in . The antcolony method has not yet been used for the redundancy optimization of multi-state systems. 1.2- Approach and Aoutlines
A survey of the VRPTW is given by Desrosiers et al. (1995); Cordeau et al. (2002) and Bräysy and Gendreau (2005). Significant improvements in Solomon's benchmark problem instances were established by Rochat and Taillard (1995) using a tabu search metaheuristic method. Some studies on tabu searches for VRPTW can be found in Taillard et al. (1997); Chiang and Russell (1997) and Cordeau et al. (2001). Gambardella et al. (1999) proposed an antcolony optimisation while, Liu and Shen (1999) applied a route-neighborhood-based metaheuristic to solve VRPTW. Rousseau et al. (1999) used constraint-based operators with variable neighborhood search to solve the problem.
In this work, we cross three combinatorial optimization problems – the Traveling Salesperson Problem (TSP), the CapacitatedVehicleRoutingProblem (CVRP) and the Quadratic Assignment Problem (QAP) – against four different metaheuristics: the Greedy Randomized Adaptive Search Procedure (GRASP), the Iterated Local Search (ILS), the Tabu Search (TS) and the Variable Neighborhood Search (VNS). To do that, we took instance collections of each problem from public libraries and made ten independent runs with each one, using an execution time limit of 600 seconds. We looked for solutions with Optimal or Better-Known Values (OBKV), according to the more recent information available on the Internet.
This paper proposes the advances in edge detection techniques, which is used for the mammogram images for cancer diagnosis. It compares the evaluation of edge detection with the proposed method antcolonyoptimization. The study shows that the edge detection technique is applied on the mammogram images because it will clearly identify the masses in mammogram images. This will help to identify the type of cancer at the early stage. ACO edge detector is best in detecting the edges when compared to the other edge detectors. The quality of various edge detectors is calculated based on the parameters such as Peak signal to noise ratio (PSNR) and Mean square error (MSE).
Second, rule Rt constructed by Ant is pruned in order to remove irrelevant terms may have been included in the rule due to stochastic variations in the term selection procedure and/or due to the use of a short- sighted, local heuristic function – which considers only one-attribute-at-a-time, ignoring attribute interactions.
The AntColonyOptimization (ACO) firstly described by Dorigo (Colorni et al., 1991) is a local search optimization algorithm that mimics the behaviour of ants as a sociable species. In Dorigo’s adaptation, an ant is a conceptual unit performing a random construction of a solution. This solution is the set of nodes visited by the ant; in nature, these are geographic points in the field on which they are looking for food. The convergence to optimization occurs because ants communicate with each other through stigmergy, i.e., they give feedback about a specific solution through the so-called pheromones. Therefore, when an ant is on the verge of choosing the next node, the ones with the highest pheromone levels are more likely to be chosen.
Bellman-Ford successive approximation algorithm (Lawler, 1976). These algorithms have major shortcomings such as they search only for the shortest route and they exhibit high computational complexity for real-time communications. Artificial Neural Networks (ANN) has been examined to solve the shortest path problem relying on their parallel architecture to provide a fast solution (Araujo et al., 2001). However, the ANN approach has several limitations. These include the complexity of the hardware which increases considerably with increasing number of network nodes; at the same time, the reliability of the solution decreases. Secondly, they are less adaptable to topological changes in the network graph. Evolutionary algorithms such as Genetic Algorithm (GA) (Ahn and Ramakrishna, 2002) and Particle Swarm Optimization (PSO) (Mohemmed et al., 2008) have been used. However, the approaches are meant to find single-objective optimization of either cost or delay, mostly cost only. It is apparent that there is a need for more efficient algorithm which gives multi-objective trade-off solutions involving cost, delay and bandwidth.
Most of the ideas behind ACO come from the foraging behavior observed in real ant colonies (Hölldobler and Wil- son, 1990). When an ant moves through the environment and discovers a food source, it deposits a chemical substance on the ground, named trail pheromone. The pheromone leads other ants from the nest to the food source. Other ants follow the first ant’s pheromone trail and reinforce it by deposition of additional pheromone. If there are several pheromone trails leading to the same food source, ants will choose probabilistically the path to follow, based on the pheromone concentrations on the existing paths. Ants that traverse the shortest path between a nest and a food source return to the nest sooner than ants that choose longer paths. Thus, after multiple traverses involving nest and food source, the short- est path will have a stronger pheromone concentration than longer paths. Consequently, ants will concentrate on this path determining cooperatively the optimal path to the food source. Ants operate under stigmergy (Grassé, 1959), a mechanism of indirect communication that coordinates the work of independent entities which have access only to local information about the environment.
This proposed CBMIR is implemented with the image database of 1000 images which are gray level images and basically related images includes some parts of human body like lung, liver, kidney, brain. A vast number of features are extracted from the image and the sample lung feature extraction is represented in Table 5. This would upturn the complexity of the system. So FPSO-ACO would extract the features that are appropriate to retrieve images from the database. This study streamlines the system, rises the accuracy and reduces the complexity of the system. Table 6 shows the ten optimization results of FPSO-FRVM and FPSO-ACO-FRVM in respect to Relevance vectors and Particle Fitness Value.
The problem discussed above when we use threshold alone for classifying the pixel to which group it belongs to, that can lead to wrong assumptions, where overlapping of the regions occur. It is very general that the pixels in different regions will be having same intensity some times, and causes for ambiguity for segmentation. This paper uses the threshold with fuzzy rules to classify the pixels to the region that it exactly belongs. With the help of the fuzzy inference rules the decision will be easy to classify the pixel based on the grey level intensity. This is unsupervised solution making use \of both threshold value and fuzzy inference rules.
A typical cement plant receives hundreds of trucks every day. Each one of them has one or more locations to visit, in order to load or unload materials, depending on each truck. This process is, in this sense, unpredictable, due to the fact that it is not possible to know the locations each truck must visit before arriving at the plant. Besides this, the truck driver usually does not know the plants ’ map, due to their big dimensions. Even if the driver already knows the facility, the choice of the route will be made only by what he knows of it. Either way, the driver will much probably follow a disadvantageous route, forcing him to stay more time inside the plant, causing delays to him and to other truck drivers that already are inside, or who will still enter the plant. Additionally, the driver may load or unload the materials in wrong locations, causing delays, additional costs to the company, etc. One other big problem caused by the trucks is the congestion in the roads of the plant. Each truck driver chooses its own route, and this ‘irreflective’ choice will overload some roads in the plant.
ABSTRACT: An equipment replacement decision takes into account economic engineering models based on discounted cash flow (DCF) such as the Annual Equivalent Cost (AEC). Despite a large number of researches on industrial assets replacement, there is a lack of studies applied to farm goods. This study aimed at assessing an alternative model for economic decision analysis on farm machinery replacement, with no restrictions on the number of replacements and assessed goods during a defined timeline. The results of the hybrid model based on the combination of the vehicleroutingproblem and the equipment replacement problem (RVPSE) applied to three different farm tractors showed the model reliability, providing a wider range of decisions for management support. KEYWORDS: economic engineering, annual equivalent cost, integer linear programming.
Resumo: Empresas que operam com logística urbana direcionam seus esforços para soluções que buscam reduções de custo, desconsiderando questões ambientais. Isso ocorre em função da crença que soluções ambientalmente corretas são mais caras. No entanto, com as crescentes preocupações ambientais, as empresas têm levado em conta os fatores ambientais buscando a responsabilidade social. Assim, este artigo apresenta dois modelos matemáticos, ambos baseados no Time Dependent VehicleRoutingProblem (TDVRP), sendo um com objetivo de avaliar a redução do tempo das rotas e o outro com objetivo avaliar a redução da emissão de poluentes. Para testar o modelo, foi realizada uma aplicação real de uma empresa de distribuição de alimentos na região metropolitana de Vitória, ES. Usou-se o CPLEX 12.6 para rodar os modelos propostos com cenários baseados em dados reais da empresa. Os resultados mostraram que a solução com viés ambiental pode ser financeiramente vantajosa para a empresa.
The main source of inspiration behind ACO (AntColonyOptimization)  and ACO routing is a behavior that is displayed by certain species of ants in nature during foraging. It has been observed that ants are able to find the shortest path between their nest and a food source . This is remarkable because each individual ant is a rather simple creature, with very limited vision and computing power, and finding the shortest among several available paths is certainly beyond its capabilities. The only way that this diﬃcult task can be realized is through the cooperation between the individuals in the colony. This algorithm is implemented in different sectors like manufacturing , healthcare etc.