An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.     

A variable neighborhood search heuristic algorithm for the double vehicle routing problem with multiple stacks

This paper addresses the double vehicle routing problem with multiple stacks (DVRPMS) in which a fleet of vehicles must collect items in a pickup region and then travel to a delivery region where all items are delivered. The load compartment of all vehicles is divided into rows (horizontal stacks) of fixed profundity (horizontal heights), and on each row, the unloading process must respect the last‐in‐first‐out policy. The objective of the DVRPMS is to find optimal routes visiting all pickup and delivery points while ensuring the feasibility of the vehicle loading plans. We propose a new integer linear programming formulation, which was useful to find inconsistencies in the results of exact algorithms proposed in the literature, and a variable neighborhood search based algorithm that was able to find solutions with same or higher quality in shorter computational time for most instances when compared to the methods already present in the literature.     

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.     

Top-k-size keyword search on tree structured data

Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its algorithms as the number of keywords and the size and complexity of the data increase. To face this challenge most of the known approaches restrict their ranking to a subset of the LCAs (e.g., SLCAs, ELCAs), missing relevant results.In this work, we design novel top-k-size stack-based algorithms on tree-structured data. Our algorithms implement ranking semantics for keyword queries which is based on the concept of LCA size. Similar to metric selection in information retrieval, LCA size reflects the proximity of keyword matches in the data tree. This semantics does not rank a predefined subset of LCAs and through a layered presentation of results, it demonstrates improved effectiveness compared to previous relevant approaches. To address performance challenges our algorithms exploit a lattice of the partitions of the keyword set, which empowers a linear time performance. This result is obtained without the support of auxiliary precomputed data structures. An extensive experimental study on various and large datasets confirms the theoretical analysis. The results show that, in contrast to other approaches, our algorithms scale smoothly when the size of the dataset and the number of keywords increase.     

Constructing General Orthogonal Fractional Factorial Split-Plot Designs

While the orthogonal design of split-plot fractional factorial experiments has received much attention already, there are still major voids in the literature. First, designs with one or more factors acting at more than two levels have not yet been considered. Second, published work on nonregular fractional factorial split-plot designs was either based only on Plackett–Burman designs, or on small nonregular designs with limited numbers of factors. In this article, we present a novel approach to designing general orthogonal fractional factorial split-plot designs. One key feature of our approach is that it can be used to construct two-level designs as well as designs involving one or more factors with more than two levels. Moreover, the approach can be used to create two-level designs that match or outperform alternative designs in the literature, and to create two-level designs that cannot be constructed using existing methodology. Our new approach involves the use of integer linear programming and mixed integer linear programming, and, for large design problems, it combines integer linear programming with variable neighborhood search. We demonstrate the usefulness of our approach by constructing two-level split-plot designs of 16–96 runs, an 81-run three-level split-plot design, and a 48-run mixed-level split-plot design. Supplementary materials for this article are available online.     

A comparison of pivot selection techniques for permutation-based indexing

Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots (or reference objects). Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query, following the assumption that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection techniques do not reveal better performance. However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivot selection techniques on three permutation-based similarity access methods. Among those, we propose a novel technique specifically designed for permutations. Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested techniques. Second, there is no technique that is universally the best for all permutation-based access methods; rather different techniques are optimal for different methods. This indicates that the pivot selection technique should be considered as an integrating and relevant part of any permutation-based access method.     

Group search optimization for economic fuel scheduling

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.     

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.     

Testing local search move operators on the vehicle routing problem with split deliveries and time windows

The vehicle routing problem (VRP) is an important transportation problem. The literature addresses several extensions of this problem, including variants having delivery time windows associated with customers and variants allowing split deliveries to customers. The problem extension including both of these variations has received less attention in the literature. This research effort sheds further light on this problem. Specifically, this paper analyzes the effects of combinations of local search (LS) move operators commonly used on the VRP and its variants. We find when paired with a MAX-MIN Ant System constructive heuristic, Or-opt or 2-opt⁎ appear to be the ideal LS operators to employ on the VRP with split deliveries and time windows with Or-opt finding higher quality solutions and 2-opt⁎ requiring less run time.     

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L₂ norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕ_p), stopband error (ϕ_s), transition band error (ϕ_t), peak reconstruction error (PRE), stopband attenuation (A_s) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique.