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Text normalization is the task of transforming lexically variant words to their canonical forms. The importance of text normalization becomes apparent while developing natural language processing applications. This paper proposes a novel technique called Transliteration based Encoding for Roman Hindi/Urdu text Normalization (TERUN). TERUN utilizes the linguistic aspects of Roman Hindi/Urdu to transform lexically variant words to their canonical forms. It consists of three interlinked modules: transliteration based encoder, filter module and hash code ranker. The encoder generates all possible hash-codes for a single Roman Hindi/Urdu word. The next component filters the irrelevant codes, while the third module ranks the filtered hash-codes based on their relevance. The aim of this study is not only to normalize the text but to also examine its impact on text classification. Hence, baseline classification accuracies were computed on a dataset of 11,000 non-standardized Roman Hindi/Urdu sentiment analysis reviews using different machine learning algorithms. The dataset was then standardized using TERUN and other established phonetic algorithms, and the classification accuracies were recomputed. The cross-scheme comparison showed that TERUN outperformed all the phonetic algorithms and significantly reduced the error rate from the baseline. TERUN was then enhanced from a corpus specific to a corpus independent text normalization technique. To this end, a parallel corpus of 50,000 Urdu and Roman Hindi/Urdu words was manually tagged using a set of comprehensive annotation guidelines. Also, different phonetic algorithms and TERUN were intrinsically evaluated using a dataset of 20,000 lexically variant words. The results clearly showed the superiority of TERUN over well-known phonetic algorithms.

The Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP) is a challenging NP-hard optimization problem, that schedules activities under a set of resource constraints. Although, over the last few decades, different solution approaches have been proposed, no single algorithm has consistently been the best for a wide range of MMRCPSPs. In this paper, we have proposed an effective hybrid algorithm, in which two multi-operator evolutionary algorithms perform sequentially under two sub-populations, with their sizes dynamically adapted based on their performance during the evolutionary process. In addition, two heuristics are proposed, the first one is based on a linear programming approach with an aim to obtain feasible modes, while the second one is based on a modified forward and backward justification approach with an aim of obtaining feasible schedules. Also, a classification technique is used to determine the complexity of a given problem, based on its resource’s availability. The proposed approach is tested by solving a wide-range of multi-mode resource-constrained project scheduling problems, including available larger test problems, with the results revealing that the proposed method outperforms well-known algorithms.

Differential evolution is one of the most powerful and popular evolutionary algorithms, which is primarily used to solve continuous-based optimization problems. Although differential evolution has been considered unsuitable for solving many permutation-based real-world combinatorial problems, several efforts for designing an efficient discrete version of the differential evolution algorithm have occurred in recent years. This paper introduces a novel discrete differential evolution algorithm for improving the performance of the standard differential evolution algorithm when it is used to solve complex discrete traveling salesman problems. In this approach, we propose to use a combination of, 1) an improved mapping mechanism to map continuous variables to discrete ones and vice versa, 2) a k-means clustering-based repairing method for enhancing the solutions in the initial population, 3) an ensemble of mutation strategies for increasing the exploration capability of the algorithm. Finally, for improving the local capability of the proposed algorithm when solving discrete problems, two well-known local searches have also been adapted. To judge its performance, the proposed algorithm is compared with those of 27 state-of-the-art algorithms, for solving 45 instances of traveling salesman problems, with different numbers of cities. The experimental results demonstrated that our technique significantly outperforms most of the comparative methods, in terms of the average errors from the best-known solutions, and achieved very competitive results with better computational time than others.

Sentiment analysis is a computational process to identify positive or negative sentiments expressed in a piece of text. In this paper, we present a sentiment analysis system for Roman Urdu. For this task, we gathered Roman Urdu data of 779 reviews for five different domains, i.e., Drama, Movie/Telefilm, Mobile Reviews, Politics, and Miscellaneous (Misc). We selected unigram, bigram and uni-bigram (unigram + bigram) features for this task and used five different classifiers to compute accuracies before and after feature reduction. In total, thirty-six (36) experiments were performed, and they established that Naive Bayes (NB) and Logistic Regression (LR) performed better than the rest of the classifiers on this task. It was also observed that the overall results were improved after feature reduction.

© 2016, Springer Science+Business Media New York. In this paper, we develop a quantitative reactive mitigation approach for managing supply disruption for a supply chain. We consider a three-tier supply chain system with multiple raw material suppliers, a single manufacturer and multiple retailers, where the system may face sudden disruption in its raw material supply. First, we develop a mathematical model that generates a recovery plan after the occurrence of a single disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We develop an efficient heuristic to solve the model for a single disruption. Second, we also consider multiple disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions. We also develop a new dynamic mathematical and heuristic approach that is capable of dealing with multiple disruptions, after the occurrence of each disruption as a series, on a real-time basis. We compare the heuristic solutions with those obtained by a standard search algorithm for a set of randomly generated disruption test problems, which shows the consistent performance of our heuristic. Finally, a simulation model is developed to analyze the effect of randomly generated disruption events that are not known in advance. The numerical results and many random experiments are presented to explain the usefulness of the developed models and methodologies.

In Simulation Optimization, the computational cost, due to the huge number of simulation replications along with the optimization process cost, is considered as a key challenge. This encourages the investigation of the effect of the number of simulation replications on the performance of the optimization algorithm, in terms of the solution fidelity versus the computational cost. Existing simulation replication strategies with optimization algorithms have been proposed utilizing a single probability distribution to fit their parameter settings for solving stochastic simulation problems. In this paper, simulation replication strategies are developed by a proposed Strategy Design Technique using three distributions: Bimodal Gaussian, Exponential, and Poisson for involving discrete and continuous stochastic parameters. A Differential Evolution algorithm is hybridized with Monte-Carlo simulation to solve a set of stochastic, continuous and constrained problems, using the designed strategies. Several experiments are conducted, using modified IEEE-CEC'2006 test problems. Optimization results' fidelity is empirically assessed in terms of a proposed probability of correct final selection of the solution. It reflects the ability of the proposed simulation strategies to direct the Differential Evolutionary algorithm in the search space, even under different stochastic settings and low simulation budget. Additional experiments have been conducted to study the compromising between the simulation and the optimization budgets under different settings using the same total computational load. The results indicate that the proposed strategies obtain robust results with a remarkable reduction in the simulation budget, under different stochastic settings.

The traditional differential evolution algorithm was originally, and still is mainly, used to solve continuous optimization problems. As a result, it has not commonly been considered as applicable for several real-world problems in the permutation-based domain. In this paper, a novel differential evolution algorithm, which incorporates several effective components, is introduced. These components increase search effectiveness by providing a good balance between exploration (discovering new solutions) and exploitation (further exploring current solutions) processes. Moreover, a dual representation of solutions, which has the capability to allow normal continuous handling of variables by differential evolution operators, and at same time provide binary variables for fitness measurement, is employed. To judge the performance of the proposed algorithm, 14 instances of 0–1 knapsack problems have been solved and the results have been compared with those obtained from 11 state-of-the-art algorithms. Results show that the proposed algorithm was able to outperform other algorithms in solving small and medium sized knapsack problems and is competitive in large-sized problems.

Over the last two decades, many different differential evolution (DE) variants have been developed for solving constrained optimization problems. However, none of them performs consistently when solving different types of problems. To deal with this drawback, multiple search operators are used under a single DE algorithm structure where a higher selection pressure is placed on the best performing operator during the evolutionary process. In this paper, we propose to use the landscape information of the problem in the design of the selection mechanism. The performance of this algorithm with the proposed selection mechanism is analysed by solving 10 real-world constrained optimization problems. The experimental results revealed that the proposed algorithm is capable of producing high quality solutions compared to those of state-of-the-art algorithms.

Although many evolutionary algorithms (EAs) have successfully solved different optimization problems, no single EA has consistently been the best for all these problems. During the last decade, to alleviate this limitation, many proposals which utilize multiple EAs in a single algorithmic framework, called multi-methods or multi-operators, have been introduced. However, there is still room to enhance their performance. In this paper, an improved variant of a united multi-operator algorithm is introduced with few improvements that are capable of providing a balance between diversification and intensification properties during the optimization. The proposed algorithm is tested on the CEC2017 unconstrained benchmark problems, with the results revealing that the proposed algorithm is capable of producing high quality solutions compared with those of state-of-the-art algorithms.

Over the last few years, handling big optimization problems has become a key challenge for many domains, including, but not limited to, cyber security and healthcare systems, which generate a high volume of data, from different sources and requiring real-time processing. In the healthcare domain, dealing with captured Electroencephalographic (EEG) brain signals is vital. Although evolutionary algorithms have been successful in solving many complex optimization problems, limited work on using them for big optimization has been carried out. In addition, they are still computationally insufficient. In this paper, a fast differential evolution algorithm is designed to remove artifacts from EEG signals captured from non-brain sources, by using the parallel computing power of a graphics processing unit. The algorithm is tested on problems with 1024 decision variables, with and without noise, with the results showing the capability of the proposed algorithm of achieving high-quality solutions and reducing the computational time by 81%, compared with state-of-the-art algorithms.

Considered an aggregate production planning problem with imprecise parameters.Developed a mathematical model for representing the problem.A variant of PSO algorithm is proposed to solve the model.A case study is conducted to demonstrate the usefulness of the proposed approach.Our approach provides better solutions than the known approaches compared. Development of an Aggregate Production Plan (APP), the top most level in a hierarchical production planning system, is a difficult task, especially when input and other production planning parameters are uncertain because of their inherent impreciseness. This therefore makes generation of a master production schedule highly complex. Regarding this point, in this paper, we present a scheme of a multi-period and multi-product APP which is formulated as an integer linear programming model. The proposed approach uses a triangular possibility distribution for handling all the imprecise operating costs, demands, and also for the capacity data. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs and minimizing the risk of obtaining higher total costs. A modified variant of a possibilistic environment based particle swarm optimization (PE-PSO) approach is used to solve the APP model. A numerical model for demonstrating the feasibility of the proposed model is also carried out. In the computational study, the considered case study data were experimented with and analyzed to evaluate the performance of the PE-PSO over both a standard genetic algorithm (GA) and a fuzzy based genetic algorithm (FBGA). The experimental results demonstrate that the PE-PSO variant provides better qualities in the aspects of its accuracy when compared to the other two algorithms.

Cooperative Coevolutionary algorithms (CC) have been successful in solving large scale optimization problems. The performance of CC can be improved by decreasing the number of interdependent variables among decomposed subproblems. This is achieved by first identifying dependent variables, and by then grouping them in common subproblems. This approach has potential because so far no grouping technique has been mainly developed for constrained problems. In this paper, a new variable interaction identification technique to identify the dependent variables in large scale constrained problems is proposed. The proposed technique is tested on both a new test suite of constrained problems with medium and high dimensions, which include overlapping subproblems and different levels of complexity and nonseparability and also the established DED problem. The experimental results have shown that the proposed technique contributes to the decomposition approach over a range of high dimensions, in comparison with other state-of-the art grouping techniques. It achieves better performance with higher feasibility ratios and less computational time.

This paper presents a literature review on risk and disruption management in production-inventory and supply chain systems. The review is conducted on the basis of comparing various works published in this research domain, specifically the papers, which considered real-life risk factors, such as imperfect production processes, risk and disruption in production, supply, demand, and transportation, while developing models for production-inventory and supply chain systems. Emphasis is given on the assumptions and the types of problems considered in the published research. We also focus on reviewing the mathematical models and the solution approaches used in solving the models using both hypothetical and real-world problem scenarios. Finally, the literature review is summarized and future research directions are discussed.

The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.

A new real-time disruption recovery plan is developed.An imperfect production-inventory system is considered.A new mathematical and dynamic solution approach is developed for managing disruptions.We solved the problem for the both single and multiple disruptions on a real-time basis.We compared the results for a good number of randomly generated disruption test problems. In this paper, a disruption recovery model is developed for an imperfect single-stage production-inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production-inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption.

Measure the performance of SAMO-GA on a diverse set of constrained problems.Extend the idea of training and testing with EAs for solving COPs.The mean square error measure is used to quantify the results.The results provide interesting insights and a new way of choosing parameters. Over the last two decades, many different evolutionary algorithms (EAs) have been introduced for solving constrained optimization problems (COPs). Due to the variability of the characteristics in different COPs, no single algorithm performs consistently over a range of practical problems. To design and refine an algorithm, numerous trial-and-error runs are often performed in order to choose a suitable search operator and the parameters. However, even by trial-and-error, one may not find an appropriate search operator and parameters. In this paper, we have applied the concept of training and testing with a self-adaptive multi-operator based evolutionary algorithm to find suitable parameters. The training and testing sets are decided based on the mathematical properties of 60 problems from two well-known specialized benchmark test sets. The experimental results provide interesting insights and a new way of choosing parameters.

A simulation study for a three stage supply chain system with multiple suppliers subject to unexpected disruptions is presented in this paper. Two types of disruptions were considered, namely supply and transportation disruption. The objective of this study is to examine the effects of disruption on the systems’ total recovery costs and other performance measures. Various disruption scenarios were predefined by combining different disruption types and locations. Overall, transportation disruption with a damaged lot has more damaging effects to the whole supply chain compared to supply disruption, due to the higher lost sales quantity. In addition, back order quantity and recovery duration have a strong positive relationship to total recovery costs. The combined benefits of simulation and optimization have enabled an increased understanding of the impacts of random disruption events on the total system behaviour.

We have considered a supplier–retailer system, that operates under an agreed coordinated policy, with an imperfect production process and a possibility of having demand fluctuation. In this paper, a dynamic planning process is proposed to deal with short-term demand fluctuations. To do this, a mathematical model was first developed for a single fluctuation, either for increasing or decreasing demand rate. The model generates a revised plan, after the occurrence of the fluctuation event. We also propose a new and efficient heuristic to solve the developed model. Secondly, multiple fluctuations have been considered, for which a new occurrence may or may not affect the revised plan of earlier occurrences and we extend the heuristic so that is capable of dealing with multiple demand fluctuations on a real time basis. We have generated a good number of random test problems and also solved the model using a genetic algorithm, in order to compare the solutions with our heuristic. The comparison confirmed the consistent performance of our developed heuristic, and also its lower computational time. Numerical examples and sensitivity analysis have been presented to explain the usefulness of the developed model.

In this paper, we consider a class of dynamic optimization problems in which the number of active variables and their boundaries vary as time passes (DOPUAVBs). We assume that such changes in different time periods are not known to decision makers due to certain internal and external factors. Here, we propose three variants of genetic algorithm to deal with a dynamic problem class. These proposed algorithms are compared with one another, as well as with a standard genetic algorithm based on the best of feasible generations and feasibility percentage. Experimental results and statistical tests clearly show the superiority of our proposed algorithms. Moreover, the proposed algorithm, which simultaneous addresses two sub-problems of such dynamic problems, shows superiority to other algorithms in most cases.

Simulation Optimization is computationally expensive, especially in large-scale stochastic problem solving, where the computational budget is considered as an important factor. A higher computational budget attempts to generate highly accurate solutions while a lower budget might result in biased or unrealistic solutions. In this paper, the effect of computational budget on the quality of the solution, in the context of Simulation Optimization, has been studied. The study is conducted by combining a Multi-operator Differential Evolutionary Algorithm with Monte-Carlo simulation. For experimentation, the stochastic test problems were generated based on the IEEE-CEC'2006 constrained optimization competition test problems. The experimental results provide interesting insights about the behavior of simulation optimization that would allow to reduce the computational time not compromising the quality of solutions.

Mining is a complex, expensive, yet lucrative business. Today’s open-pit mines are huge projects in Australia. To keep the projects profitable, planners and schedulers are under constant pressure to make mine plans that are as accurate as possible and optimize production at all stages, from mine to market. In general, two different systems are available for extracting material in the mining industries: the traditional truck and shovel (T&S) and the modern in-pit crushing and conveying (IPCC) systems. While T&S has been extensively studied by operations research (OR) community, there are, however, almost no studies for optimizing the operations in IPCC systems. Despite great advantages of IPCC systems, mining companies are often reluctant to use it, due to the lack of an optimum strategic plan that makes it difficult, if not impossible, to estimate the costs of IPCC systems. In most cases, industry is still relying on the judgement or best estimate of experienced personnel in strategic decision making. This is without any guarantee of optimality and will be refined manually through multiple time-consuming iterations. This chapter introduces IPCC to the OR community and points out the need for OR research. Subsequently, we will develop a research agenda that provides an apt ground to study this system.

Over the last few decades, the resource-constrained project scheduling problem (RCPSP) has been considered a challenging research topic in operations research and computer science. In this paper, we have primarily proposed two different integer linear programming (ILP) models for RCPSPs. As the computational effort required for solving such models depends on the number of variables and constraints, our proposed mathematical models were carried out while attempting to reduce the required number of variables and constraints. For better demonstration, four other ILP models for RCPSPs were also considered so that they could be compared with our proposed models. That comparative study was conducted by solving standard benchmark instances while using a common objective function. The study provides interesting insights about the problem characteristics, model sizes, solution quality and computational efforts required for solving those ILP models.

Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.

There are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems. However, none of them was considered as being a well-accepted approach for solving a broad range of problems with different mathematical properties. Therefore, in this paper, for a better coverage of the problem characteristics, a self-adaptive differential evolution algorithm is introduced. To do that, it uses multiple search operators in conjunction with multiple constraint handling techniques. The need for such an approach is justified by experimental analysis on a well-known set of problems. The results show that the proposed algorithm is superior to other state-of-the-art algorithms.

Supply chains are becoming increasingly competitive in order to meet customer demands. The task of optimizing highly evolved supply chains is not easy, especially when it is particularly sensitive to unexpected disruption. This paper presents a newly developed real-time recovery mechanism for a two stage serial supply chain system, consisting of one manufacturer and one retailer, where the production is disrupted for a given period of time during the production up time. The model is capable of determining the recovery schedule for the manufacturer and the retailer, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule by the end of the recovery time window. The model was solved using an efficient heuristic developed in this paper, which performed well in giving quality solutions within reasonable time. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as on the extent of the disruption. The presented model is useful to assist decision makers to take a pro-active approach for maintaining business continuity in the event of a disruption in the supply chain system.

Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.

A dynamic Optimisation Problem with Unknown Active Variables (DOPUAV) is a dynamic problem in which the activity of the variables changes as time passes, to simulate the dynamicity in the problem’s variables. In this paper, several variations of genetic algorithms are proposed to solve DOPUAV. They are called Detectable techniques. These techniques try to detect where the problem changes, before detecting the active variables. These variations are tested, then the best variation is compared with the best previously used algorithms namely Hyper Mutation (HyperM), Random Immigration GA (RIGA), as well as simple GA (SGA). The results and statistical analysis show the superiority of our proposed algorithm.

In the literature, many different evolutionary algorithms (EAs) with different search operators have been reported for solving optimization problems. However, no single algorithm is consistently able to solve all types of problems. To overcome this problem, the recent trend is to use a mix of operators within a single algorithm. There are also cases where multiple methodologies, each with a single search operator, have been used under one approach. These approaches outperformed the single operator based single algorithm approaches. In this paper, we propose a new algorithm framework that uses multiple methodologies, where each methodology uses multiple search operators. We introduce it as the EA with Adaptive Configuration, where the first level is to decide the methodologies and the second level is to decide the search operators. In this approach, all operators and population sizes are updated adaptively. Although the framework may sound complex, one can gain significant benefits from it in solving optimization problems. The proposed framework has been tested by solving two sets of specialized benchmark problems. The results showed a competitive, if not better, performance when it was compared to the state-of-the-art algorithms. Moreover, the proposed algorithm significantly reduces the computational time in comparison to both single and multi-operator based algorithms.

This paper addresses a problem of an imperfect production system under fuzzy demand and inventory holding cost. Production process reliability is considered because of the imperfect production process. In this problem, reliability of the system in regards to producing defective and non-defective items is considered as a decision variable. The objective is to maximize the graded mean integration value (GMIV) of the expected average profit while considering revenues as well as any other relevant costs. The developed model belongs to the class of a geometric programming. We have developed a simple mathematical methodology to solve the model. Genetic algorithm and simulated annealing algorithms are also applied to solve and validate the results. A numerical example has been presented to interpret the solutions.

The job scheduling problem (JSP) belongs to the well-known combinatorial optimization domain. After scheduling, if a machine maintenance issue affects the scheduled processing of jobs, the delivery of jobs must be delayed. In this paper, we have first proposed a Hybrid Evolutionary Algorithm (HyEA) for solving JSPs. We have then analyzed the effect of machine maintenance, whether preventive or breakdown, on the job scheduling. For the breakdown maintenance case, it is required to revise the algorithm to incorporate a rescheduling option after the breakdown occurs. The algorithm has been tested by solving a number of benchmark problems and thence comparing them with the existing algorithms. The experimental results provide a better understanding of job scheduling and the necessary rescheduling operations under process interruption.

Many real-world optimization problems are difficult to solve as they do not possess the nice mathematical properties required by the exact algorithms. Evolutionary algorithms are proven to be appropriate for such problems. In this paper, we propose an improved differential evolution algorithm that uses a mix of different mutation operators. In addition, the algorithm is empowered by a covariance adaptation matrix evolution strategy algorithm as a local search. To judge the performance of the algorithm, we have solved well-known benchmark as well as a variety of real-world optimization problems. The real-life problems were taken from different sources and disciplines. According to the results obtained, the algorithm shows a superior performance in comparison with other algorithms that also solved these problems.

Supply chains are becoming increasingly competitive and complex in order to effectively meet customer demands. These characteristics make supply chains vulnerable to various risks, including disruptions. In this study, a recovery model is explored for a two-stage production and inventory system with the possibility of transportation disruption. This model is capable of determining the optimal ordering and production quantities during the recovery window, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule. An efficient heuristic was developed to solve the model. The results showed that the optimal recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters. In addition, the heuristic was able to give quality solutions for the model, with very small deviations of the heuristic solutions from the optimal value. Such tools are useful in assisting managers towards effective decision making, particularly in determining the optimal recovery strategy for the longevity and sustainability of their firms undergoing disruptions.

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.

In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

This paper considers a static resource constrained multi-project scheduling problem (RCMPSP) with two lateness objectives: project lateness and portfolio lateness. To solve the RCMPSP, we have proposed an evolutionary local search heuristic that uses a variable neighborhood (ELSH-VN) approach. The heuristic is further analyzed by incorporating different priority-rules. To judge the performance of these priority rule based heuristics, an extensive simulation-based analysis has been conducted with different scenario-based schedules. For the experimental study, we have considered a standard set of 77 generated RCMPSP test instances of 20 activities. The experimental analysis indicates that the proposed heuristic is able to solve multiple projects with reasonable computational burden. The influence of the variation of resource distribution and resource contention on the algorithm’s performance for different priority rules is also analyzed and discussed.

Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.

Over the last few years, constraint consensus methods have been used for the movement of infeasible solutions towards feasible space, when solving constrained optimization problem. In this paper, a novel approach is proposed that is based on the concept of constraint consensus to improve feasible individuals, rather than infeasible ones, in which a feasible individual is considered as an infeasible one, if its fitness value is worse than a dynamic reference point. The obtained new solutions are then passed to differential evolution to be evolved. The proposed algorithm has been tested on the CEC2010 benchmark constrained problems. The results demonstrate better performance of the proposed algorithm, in terms of quality of solutions and computational time, in comparison with a standard differential evolution algorithm, as well as a set of state-of-the-art algorithms.

Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.

This paper presents a newly developed disruption recovery model for a single stage production and inventory system, where the production is disrupted for a given period of time during the production up time. The model is categorized as a constrained non-linear optimization program which we have solved using an efficient heuristic developed in this paper. The model was also solved using an evolutionary algorithm and a comparison of the results from both methods was performed. The heuristic was able to accurately solve the model with significantly less time compared to the evolutionary algorithm. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as the extent of the disruption. The proposed model offers a potentially useful tool to help manufacturers decide on the optimal recovery plan in real time whenever the production system experiences a sudden disruption.

Constrained optimization is an important research topic that assists in quality planning and decision making. To solve such problems, one of the important aspects is to improve upon any constraint violation, and thus bring infeasible individuals to the feasible region. To achieve this goal, different constraint consensus methods have been introduced, but no single method performs well for all types of problems. Hence, in this research, for solving constrained optimization problems, we introduce different variants of the Differential Evolution algorithm, with multiple constraint consensus methods. The proposed algorithms are tested and analyzed by solving a set of well-known bench mark problems. For further improvements, a local search is applied to the best variant. We have compared our algorithms among themselves, as well as with other state of the art algorithms. Those comparisons show similar, if not better performance, while also using significantly lower computational time.

In this paper, we have proposed a progressive alignment method using a genetic algorithm for multiple sequence alignment, named GAPAM. We have introduced two new mechanisms to generate an initial population: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with GAPAM. To test the performance of our algorithm, we have compared it with existing well-known methods, such as PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on genetic algorithms (GA), such as SAGA, MSA-GA, and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. To make a fairer comparison with the GA based algorithms such as MSA-GA and RBT-GA, we have performed further experiments covering all the datasets reported by those two algorithms. The experimental results showed that GAPAM achieved better solutions than the others for most of the cases, and also revealed that the overall performance of the proposed method outperformed the other methods mentioned above.

Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms.

A considerable number of differential evolution variants have been proposed in the last few decades. However, no variant was able to consistently perform over a wide range of test problems. In this paper, propose two novel differential evolution based algorithms are proposed for solving constrained optimization problems. Both algorithms utilize the strengths of multiple mutation and crossover operators. The appropriate mix of the mutation and crossover operators, for any given problem, is determined through an adaptive learning process. In addition, to further accelerate the convergence of the algorithm, a local search technique is applied to a few selected individuals in each generation. The resulting algorithms are named as Self-Adaptive Differential Evolution Incorporating a Heuristic Mixing of Operators. The algorithms have been tested by solving 60 constrained optimization test instances. The results showed that the proposed algorithms have a competitive, if not better, performance in comparison to the-state-of-the-art algorithms.

Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.

This paper introduces the Localized Optimization Framework (LOF). This framework is an iterative procedure between two phases, Optimization and De-optimization. Optimization is done on the problem parts rather than the problem as a whole, while de-optimization is done on the whole problem. To test our hypothesis, we have chosen a genetic algorithm as an optimization methodology and Vehicle Routing Problem with Time Windows (VRPTW) as a domain space. We call this new scheme the Localized Genetic Algorithm (LGA). We demonstrate that the LGA is, on average, able to produce better solutions than most of the other heuristics on small scale problems of VRPTW. Furthermore the LGA has attained several new best solutions on popular datasets.

The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.

We investigate interactions between evolution, development and lifelong layered learning in a combination we call evolutionary developmental evaluation (EDE), using a specific implementation, developmental tree-adjoining grammar guided genetic programming (GP). The approach is consistent with the process of biological evolution and development in higher animals and plants, and is justifiable from the perspective of learning theory. In experiments, the combination is synergistic, outperforming algorithms using only some of these mechanisms. It is able to solve GP problems that lie well beyond the scaling capabilities of standard GP. The solutions it finds are simple, succinct, and highly structured. We conclude this paper with a number of proposals for further extension of EDE systems.

The primary objective of this research is to solve the job-shop scheduling problems (JSSPs), by minimizing the makespan, with and without process interruptions. In this paper, we first developed a genetic algorithm for solving JSSPs, and then improved the algorithm by integrating it with two simple priority rules and a hybrid rule. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. In addition, we have studied the job-shop scheduling under process interruptions such as machine unavailability and breakdown. For convenience of implementation, we have developed a decision support system (DSS). In the DSS, we built a graphical user interface for user friendly data inputs, model choices, and output generation. An overview of the DSS and an analysis of the experimental results are provided. The incorporation of priority rules, and a hybrid rule, not only improves the solutions but also reduces the overall computational time. The experimental results show that when the machine unavailability information is known in advance, the effect on the schedule is very little compared to the sudden machine breakdown scenario.