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Sexual harassment are situations where the unwelcome sexual conduct of co‐workers or supervisors interferes with an individual’s ability to work or creates an intimidating or offensive atmosphere. It involves situations where a workplace superior or co‐worker demands some degree of sexual favour and threatens to or actually does retaliate in a way that has a tangible effect on the working conditions of the harassment victim if he or she refuses to acquiesce.

In this study, the performance of ordinal GA-based trading strategies is evaluated under six classes of time series model, namely, the linear ARMA model, the bilinear model, the ARCH model, the GARCH model, the threshold model and the chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. Asymptotic test statistics for these criteria are derived. The hypothesis as to the superiority of GA over a benchmark, say, buy-and-hold, can then be tested using Monte Carlo simulation. From this rigorously-established evaluation process, we find that simple genetic algorithms can work very well in linear stochastic environments, and that they also work very well in nonlinear deterministic (chaotic) environments. However, they may perform much worse in pure nonlinear stochastic cases. These results shed light on the superior performance of GA when it is applied to the two tick-by-tick time series of foreign exchange rates: EUR/USD and USD/JPY.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge‐based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non‐linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector.

This paper aims to assess the fiscal sustainability in Egypt during the period 1990–2018 using deficit accounts (DA) approach. It also tries to investigate the possibility of applying generational accounts (GA) in Egypt as a new approach to assess fiscal sustainability.

This paper tries to assess fiscal sustainability in Egypt during 1990–2018 using DA and GA approaches. DA approach includes primary deficit indicator, tax gap indicator, augmented Dickey-Fuller stationarity test for debt/GDP ratio and Johansen co-integration test between government revenues and expenditures. However, concerning the possibility of applying GA in Egypt, field study form was designed including specific questions to academic and executive economic experts to investigate if it is possible to apply GA in Egypt.

The empirical findings of the field study indicate that Egypt witnessed fiscal sustainability during the period 1990–2018 using DA. On the other hand, there are various obstacles, including administrative, technical, legal and political obstacles which hinder Egypt from applying GA to assess fiscal sustainability.

To the best of the authors' knowledge, this paper assesses fiscal sustainability in Egypt using DA for a longer and updated time series within 1990–2018. In addition, it is the first paper to examine the possibility of assessing fiscal sustainability using GA approach in Egypt.

The purpose of this paper is to present a model of democratic therapeutic community (DTC) for people with a diagnosis of schizophrenia and psychotic disorder, namely the Group-Apartment (GA). The authors will describe it in more detail, discussing the ideas which lie behind it, considering the relative cost of treating people in larger residential DTCs and in GAs, outlining findings from the first data gathered on a GA and looking at the usefulness of this model in post-modern societies, with particular reference to Sicily.

In brief a GA is a flat, located in an urban apartment building, inhabited by a small group of people. In this paper the authors consider an apartment inhabited by a group of three or four patients with the presence of clinical social workers who work in shifts for several hours a day on all or most days of the week (Barone et al., 2009, 2010). GA is also inspired by the pioneering work of Pullen (1999, 2003), in the UK tradition of the apartment post TC for psychosis.

GAs in Italy have become one of the main methods of support housing in recovery-oriented treatment, because it allows the empowerment of the users and fights against the stigma of mental illness (Barone et al., 2014; Bruschetta et al., 2014). The main therapeutic activities provided in the GA depend on the type of recovery route being supported, on the level of autonomy being developed and on the level of participation in the democratic life of the local community.

GAs appear better, cheaper and a more appropriate treatment for mental problems in the current financial and social climate than larger institutions. Where they have been tried out, they have been found to be effective, by users and by stakeholders. They exemplify the advantages of the DTC for encouraging recovery, but cost less to run. In accordance with DTC principles, the social democratic process is used not only to evaluate the clinical effectiveness of GAs, but also to build a network to support the development of innovative mental health services and new enabling environments (Haigh et al., 2012).

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson‐Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the ga can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the ga weights. Since the ga has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

The assembly sequence in the product assembly process has effect on the final product quality. To solve the assembly sequence optimization problem, such as rotor blade assembly sequence optimization, this paper proposes a small world networks-based genetic algorithm (SWN_GA) to solve the assembly sequence optimization problem. The proposed approach SWN_GA consists of a combination between the standard Genetic Algorithm and the NW Small World Networks.

The selection operation and the crossover operation are improved in this paper. The selection operation remains the elite individuals that have greater fitness than average fitness and reselects the individuals that have smaller fitness than average fitness. The crossover operation combines the NW Small World Networks to select the crossover individuals and calculate the crossover probability.

In this paper, SWN_GA is used to optimize the assembly sequence of steam turbine rotor blades, and the SWN_GA was compared with standard GA and PSO algorithm in a simulation experiment. The simulation results show that SWN_GA cannot only find a better assembly sequence which have lower rotor imbalance, but also has a faster convergence rate.

Finally, an experiment about the assembly of a steam turbine rotor is conducted, and SWN_GA is applied to optimize the blades assembly sequence. The feasibility and effectiveness of SWN_GA are verified through the experimental result.

This study aims to explore how firms can design their government affairs (GAs) units in ways that improve their ability to monitor and influence legislative developments in their firms’ corporate political environments.

This conceptual work is informed by existing research into organizational design, brought to life with illustrative examples of firms’ political actions derived from interviews conducted with practitioners in the field.

In line with organizational design thinking, the authors find that high-performing GA units need to be designed and built using a blend of mutually reinforcing organizational mechanisms. GA units should be staffed by autonomous managers with mixed skills-sets. Moreover, they should not be constrained by formal rules, but instead given autonomy and support to create lateral relations with other business units.

This study provides a “recipe” that managers can follow to create opportunities for the exchange of political information within their firms and enable and motivate GAs practitioners to monitor and influence political developments more effectively.

This research exposes important, organizational antecedents of firms’ political strategies, which have not been systematically explored in the existing literature.