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The purpose of this paper is to present a study of the effect of different types of annealing schedules for a ribonucleic acid (RNA) secondary structure prediction algorithm based on simulated annealing (SA).

An RNA folding algorithm was implemented that assembles the final structure from potential substructures (helixes). Structures are encoded as a permutation of helixes. An SA searches this space of permutations. Parameters and annealing schedules were studied and fine-tuned to optimize algorithm performance.

In comparing with mfold, the SA algorithm shows comparable results (in terms of F-measure) even with a less sophisticated thermodynamic model. In terms of average specificity, the SA algorithm has provided surpassing results.

Most of the underlying thermodynamic models are too simplistic and incomplete to accurately model the free energy for larger structures. This is the largest limitation of free energy-based RNA folding algorithms in general.

The algorithm offers a different approach that can be used in practice to fold RNA sequences quickly.

The algorithm is one of only two SA-based RNA folding algorithms. The authors use a very different encoding, based on permutation of candidate helixes. The in depth study of annealing schedules and other parameters makes the algorithm a strong contender. Another benefit is that new thermodynamic models can be incorporated with relative ease (which is not the case for algorithms based on dynamic programming).

Considers the daily production‐scheduling problem in the “make‐to‐order” apparel‐manufacturing industry and presents a solution procedure for the problem based on the simulated annealing technique. The development is aimed at the quick generation of a feasible solution and the improvement on the solution.

In order to achieve excellence in manufacturing, goals like lean, economic and quality production with enhanced productivity play a crucial role in this competitive environment. It also necessitates major improvements in generally three primary technical areas: variation reduction, equipment reliability, and production scheduling. Complexity of the real world scheduling problems also increases with interactive multiple decision‐making criteria. This paper aims to deal with multi‐objective flow shop scheduling problems, including sequence dependent set up time (SDST). The paper also aims to consider the objective of minimizing the weighted sum of total weighted tardiness, total weighted earliness and makespan simultaneously. It proposes a new heuristic‐based hybrid simulated annealing (HSA) for near optimal solutions in a reasonable time.

Six modified NEH's based HSA algorithms are proposed for efficient scheduling of jobs in a multi‐objective SDST flow shop. Problems of up to 200 jobs and 20 machines are tested by the proposed HSA and a defined relative percentage improvement index is used for analysis and comparison of different MNEH's based hybrid simulated annealing algorithms.

From the results, it has been derived that performance of SA_EWDD (NEH) up to ten machines' problems, and SA_EPWDD (NEH) up to 20 machines' problems, were better over others especially for large sized SDST flow shop scheduling problems for the considered multi‐objective fitness function.

HSA and multi‐objective decision making proposed in the present work is a modified approach in the area of SDST flow shop scheduling.

The purpose of this paper is to minimize the mean flow time in open shop scheduling problem (OSSP). The scheduling problems consist of n jobs and m machines, in which each job has O operations. The processing time for 50 OSSP was generated using a linear congruential random number.

Different evolutionary algorithms are used to minimize the mean flow time of OSSP. This research study used simulated annealing (SA), Discrete Firefly Algorithm and a Hybrid Firefly Algorithm with SA. These methods are referred as A1, A2 and A3, respectively.

A comparison of the results obtained from the three methods shows that the Hybrid Firefly Algorithm with SA (A3) gives the best mean flow time for 76 percent instances. Also, it has been observed that as the number of jobs increases, the chances of getting better results also increased. Among the first 25 problems (i.e. job ranging from 3 to 7), A3 gave the best results for 13 instances, i.e., for 52 percent of the first 25 instances. While for the last 25 problems (i.e. Job ranging from 8 to 12), A3 gave the best results for all 25 instances, i.e. for 100 percent of the problems.

From the literature it has been observed that no researchers have attempted to solve OOSPs using Firefly Algorithm (FA). In this research work an attempt has been made to apply the FA and its hybridization to solve OSSP. Also the research work carried out in this paper can also be applied for a real-time Industrial problem.

The purpose of this paper is to present a new particle swarm optimization (PSO) algorithm called HelixPSO for finding ribonucleic acid (RNA) secondary structures that have a low energy and are similar to the native structure.

Two variants of HelixPSO are described and compared to the recent algorithms Rna‐Predict, SARNA‐Predict, SetPSO and RNAfold. Furthermore, a parallel version of the HelixPSO is proposed.

For a set of standard RNA test sequences it is shown experimentally that HelixPSO obtains a better average sensitivity than SARNA‐Predict and SetPSO and is as good as RNA‐Predict and RNAfold. When best values for different measures (e.g. number of correctly predicted base pairs, false positives and sensitivity) over several runs are compared, HelixPSO performs better than RNAfold, similar to RNA‐Predict, and is outperformed by SARNA‐Predict. It is shown that HelixPSO complements RNA‐Predict and SARNA‐Predict well since the algorithms show often very different behavior on the same sequence. For the parallel version of HelixPSO it is shown that good speedup values can be obtained for small to medium size PC clusters.

The new PSO algorithm HelixPSO for finding RNA secondary structures uses different algorithmic ideas than the other existing PSO algorithm SetPSO. HelixPSO uses thermodynamic information as well as the centroid as a reference structure and is based on a multiple swarm approach.

International business research has long acknowledged the importance of regional factors for foreign direct investment (FDI) by multinational corporations (MNCs). However, significant differences when defining these regions obscure the analysis about how and why regions matter. In response, we develop and empirically document support for a framework to evaluate alternative regional grouping schemes. We demonstrate application of this evaluative framework using data on the global location decisions by US-based MNCs from 1980 to 2000 and two alternative regional grouping schemes. We conclude with discussion of implications for future academic research related to understanding the impact of country groupings on MNC FDI decisions.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Examines an alternative approach to labour utilisation, based on the concept of simulated annealing, implemented on a microcomputer. Demonstrates the use of the new approach in a study of the potential labour utilisation effect of two types of scheduling flexibility: shift length flexibility and meal‐break placement flexibility. Finally, offers implications of the new approach for management.

Locating hub facilities is important in different types of transportation and communication networks. The p‐Hub Median Problem (p‐HMP) addresses a class of hub location problems in which all hubs are interconnected and each non‐hub node is assigned to a single hub. The hubs are uncapacitated, and their number p is initially determined. Introduces an Artificial Intelligence (AI) heuristic called simulated annealing to solve the p‐HMP. The results are compared against another AI heuristic, namely Tabu Search, and against two other non‐AI heuristics. A real world data set of airline passenger flow in the USA, and randomly generated data sets are used for computational testing. The results confirm that AI heuristic approaches to the p‐HMP outperform non‐AI heuristic approaches on solution quality.