Determination of optimal tool path in drilling operation using Modified Shuffled Frog Leaping Algorithm

Applications like boilerplates, food-industry processing separator, printed circuit boards, drum and trammel screens, etc. consists of a matrix of a large number of holes. The primary issue involved in hole-making operations is a tool travel time. It is often necessary to find the optimal sequence of operations so that the total processing cost of hole-making operations can be minimized. In this work, therefore an attempt is made to reduce the total tool travel of hole-making operations by applying a relatively new optimization algorithm known as modified shuffled frog leaping for determining the optimal sequence of operations. Modification is made in the existing shuffled frog-leaping algorithm by introducing three parameters with their positive values to widen the search capability of existing algorithms. A case study of the printed circuit board is considered in this work to demonstrate the proposed approach. Obtained results of optimization using modified shuffled frog leaping algorithm are compared with those obtained using particle swarm optimization, firefly algorithm and shortest path search algorithm

Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model

This article introduces a sustainable integrated bi-objective location-routing model, its two-phase solution approach and an analysis procedure for the distribution side of three-echelon logistics networks. The mixed-integer programming model captures several real-world factors by introducing an additional objective function and a set of new constraints in the model that outbound logistics channels find difficult to reconcile. The sustainable model minimises CO2 emissions from transportation and total costs incurred in facilities and the transportation channels. Design of Experiment (DoE) is integrated to the meta-heuristic based optimiser to solve the model in two phases. The DoE-guided solution approach enables the optimiser to offer the best stable solution space by taking out solutions with poor design features from the space and refining the feasible solutions using a convergence algorithm thereby selecting the realistic results. Several alternative solution scenarios are obtained by prioritising and ranking the realistic solution sets through a multi-attribute decision analysis tool, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The robust model provides the decision maker the ability to take decisions on sustainable open alternative optimal routes. The outcomes of this research provide theoretical and methodological contributions, in terms of integrated bi-objective location-routing model and its two-phase DoE-guided meta-heuristic solution approach, for the distribution side of three-echelon logistics networks

A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

the Bees Algorithm: a novel optimisation tool

This work introduces the Bees Algorithm, a new optimisation algorithm inspired by the foraging behaviour of honey-bees. In its basic version, the Bees Algorithm performs a kind of neighbourhood search combined with global random search and can be used for both continuous and discrete optimisation problems. An improved version of the Bees Algorithm including replacing global random search with interpolation and extrapolation, shrinking neighbourhood size, and abandoning sites with no new information was developed. The improved version could solve benchmark function optimisation problems with less sampling of the search space. The Bees Algorithm has been applied to mechanical design optimisation. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the Bees Algorithm against other optimisation techniques. Computer-aided preliminary design can be regarded as a special case of optimisation, where the goal is to generate as many solutions as possible above a predefined performance threshold. The higher the number of solutions satisfying the preliminary selection criteria, the greater is the chance to produce a good final solution. An adapted version of the Bees Algorithm for discrete function optimisation was developed and tested on a simple machine design task, preliminary gearbox design. The test consists of finding alternative gearbox configurations that approximately produce the required output speeds using one of the available input speeds. Experimental results show that the Bees Algorithm outperforms random search and a genetic optimisation algorithm. A modified version of the Bees Algorithm was used to search for multiple Pareto optimal solutions in a multi-objective optimisation design problem. Compared to two non-dominated genetic algorithms, the Bees Algorithm was able to find more trade-off solutions. Finally, the Bees Algorithm was employed to train Radial Basis Function (RBF) neural networks for two different problems. Despite the high dimensionality of the problems - each bee represented 2345 parameters in the control chart pattern recognition case and 1581 parameters in the wood defect classification case - the algorithm successfully trained very accurate classifiers. Although the accuracies achieved were marginally lower than those obtained with conventional RBF training methods, the total output errors were less than those for conventionally RBF-trained networks with same number of hidden neurons

An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases

An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints