Large‐scale heat exchanger networks synthesis using simulated annealing and the novel rocket fireworks optimization

Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large‐scale HEN. In this work, a hybrid meta‐heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named rocket fireworks optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C++, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1582–1601, 2017     

Variable neighborhood search to solve the generalized orienteering problem

This paper presents a variable neighborhood search (VNS) algorithm to solve the extension of the orienteering problem known as the generalized orienteering problem (GOP). Our algorithm aims to use a reduced number of neighborhoods without compromising the quality of the results. This reduced number of neighborhoods, together with the precalculation of scores associated with points of interest, allows us, in most cases, to outperform all previous metaheuristics proposed for this problem. This is the first time a VNS is being applied to the GOP, and it provides promising computational results. In particular, in the case studies considered in the paper, we were able to find 35 new best solutions, all of which were found using a shorter computational time. Furthermore, the information regarding other best‐known solutions provided in the literature has also been improved, with corrections to some previously published errors regarding scores and distances. In addition, the benchmark has been extended with the incorporation of new case studies based on real data from three of the most popular tourist cities in Spain.     

A discrete electromagnetism-like mechanism for parallel machine scheduling under a grade of service provision

We consider the problem of minimising total weighted tardiness on identical parallel machines with grade of service eligibility. Due to the essential complexity of the problem, we apply an electromagnetism-like mechanism (EM), which is a novel metaheuristic, to solve the problem. In the proposed EM, the particle is redesigned to represent a valid assignment of jobs to machines. A distance measure between particles, called ‘1A2B’ distance, is proposed by the concept of a number guessing game. Then, the new attraction and repulsion operators are developed to move a particle to the new particle. To verify the proposed EM, computational experiments are conducted to make a comparison with a recent genetic algorithm (GA). The results show that the proposed EM has a good performance and outperforms the GA for the considered problem.     

Automatic clustering using nature-inspired metaheuristics: A survey

In cluster analysis, a fundamental problem is to determine the best estimate of the number of clusters; this is known as the automatic clustering problem. Because of lack of prior domain knowledge, it is difficult to choose an appropriate number of clusters, especially when the data have many dimensions, when clusters differ widely in shape, size, and density, and when overlapping exists among groups. In the late 1990s, the automatic clustering problem gave rise to a new era in cluster analysis with the application of nature-inspired metaheuristics. Since then, researchers have developed several new algorithms in this field. This paper presents an up-to-date review of all major nature-inspired metaheuristic algorithms used thus far for automatic clustering. Also, the main components involved during the formulation of metaheuristics for automatic clustering are presented, such as encoding schemes, validity indices, and proximity measures. A total of 65 automatic clustering approaches are reviewed, which are based on single-solution, single-objective, and multiobjective metaheuristics, whose usage percentages are 3%, 69%, and 28%, respectively. Single-objective clustering algorithms are adequate to efficiently group linearly separable clusters. However, a strong tendency in using multiobjective algorithms is found nowadays to address non-linearly separable problems. Finally, a discussion and some emerging research directions are presented.     

Minimizing weighted earliness–tardiness on parallel machines using hybrid metaheuristics

We consider the problem of scheduling a set of jobs on a set of identical parallel machines where the objective is to minimize the total weighted earliness and tardiness penalties with respect to a common due date. We propose a hybrid heuristic algorithm for constructing good solutions, combining priority rules for assigning jobs to machines and a local search with exact procedures for solving the one-machine subproblems. These solutions are then used in two metaheuristic frameworks, Path Relinking and Scatter Search, to obtain high quality solutions for the problem.The algorithms are tested on a large number of test instances to assess the efficiency of the proposed strategies.The results show that our algorithms consistently outperform the best reported results for this problem.     

A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music

A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighborhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist's hand in order to generate a fingering that is user‐specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.     

Simulated annealing for improving the solution of the response time variability problem

The response time variability problem (RTVP) is a scheduling problem that has a wide range of real-world applications in mixed-model assembly lines, multi-threaded computer systems, machine maintenance, waste collection and others. The RTVP arises whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the points at which they receive the necessary resources is minimised. The RTVP has been demonstrated to be NP-hard and heuristic and metaheuristic techniques are needed to solve large, real-life instances. The best results, on average, in the literature for the RTVP were obtained with variable neighbourhood search hybidised with multi-start. Several algorithms based on simulated annealing are proposed to solve the RTVP. An extensive computational experiment is carried out and it is shown that the solution of the RTVP is clearly improved.     

Material handling considerations in the FMS loading problem with full routing flexibility

This study exploits machining and routing flexibility to effectively deal with the material handling requirements resulting from a frequently changing demand mix in a manufacturing system where material handling is a bottleneck. For this purpose, the objective function of the operation and tool loading problem is selected as the minimisation of the total distance traveled by parts during their production. Versatile machines and the flexible process plans offer full routing flexibility that enable the same workpiece to be processed using alternative sequences of operations on alternative machines. Three mathematical programming (MP) models and a genetic algorithm (GA) are proposed to solve this problem. The proposed MP formulations include a mixed-integer nonlinear programming (MINLP) model and two mixed-integer programming (MIP) models, which offer different representations for the flexible process plans. The GA is integrated with linear programming for fitness evaluation and incorporates several adaptive strategies for diversification. The performances of these solution methods are tested through extensive numerical experiments. The MP models are evaluated on the basis of the exact solutions they yield as well as how they lend themselves for GA fitness evaluation. The GA–LP integration works successfully for this hard-to-solve problem.     

A collaborative ant colony metaheuristic for distributed multi-level uncapacitated lot-sizing

The paper presents an ant colony optimization metaheuristic for collaborative planning. Collaborative planning is used to coordinate individual plans of self-interested decision-makers with private information in order to increase the overall benefit of the coalition. The method consists of a new search graph based on encoded solutions. Distributed and private information are integrated via voting mechanisms and via a simple but effective collaborative local search procedure. The approach is applied to a distributed variant of the multi-level lot-sizing problem and evaluated by means of 352 benchmark instances from the literature. The proposed approach clearly outperforms existing approaches on the sets of medium- and large-sized instances. While the best method in the literature so far achieves an average deviation from the best-known non-distributed solutions of 75% for the set of the largest instances, for example, the presented approach reduces the average deviation to 7%.     

Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness

Global optimisation for manufacturing problems is mandatory for obtaining versatile benefits to facilitate modern industry. This paper deals with an original approach of globally optimising tool paths to CNC-machine sculptured surfaces. The approach entails the development of a fully automated manufacturing software interface integrated by a non-conventional genetic/evolutionary algorithm to enable intelligent machining. These attributes have been built using already existing practical machining modelling tools such as CAM systems so as to deliver a truly viable computer-aided manufacturing solution. Since global optimisation is heavily based on the formulation of the problem, emphasis has been given to the definition of optimisation criteria as crucial elements for representing performance. The criteria involve the machining error as a combined effect of chord error and scallop height, the tool path smoothness and productivity. Experiments have been designed considering several benchmark sculptured surfaces as well as tool path parameters to validate the aforementioned criteria. The new approach was implemented to another sculptured surface which has been extensively tested by previous research works. Results were compared to those available in the literature and it was found that the proposed approach can indeed constitute a promising and trustworthy technique for the global optimisation of sculptured surface CNC tool paths.