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When solving a scheduling problem, users are often interested in finding a schedule optimizing a given objective function. However, in some settings there can be hard constraints that make the problem unfeasible. In this paper we focus on the task of repairing infeasibility in job shop scheduling problems with a hard constraint on the makespan. In this context, earlier work addressed the problem of computing the largest subset of the jobs that can be scheduled within the makespan constraint. Herein, we face a more general weighted version of the problem, consisting in computing a feasible subset of jobs maximizing their weighted sum. To this aim, we propose an efficient memetic algorithm, that combines a genetic algorithm with a local search method, also proposed in the paper. The results from an experimental study show the practical suitability of our approach. [ABSTRACT FROM AUTHOR]

Scheduling complex applications as task graphs on finite computational resources assuring task interdependencies is a well-known NP-complete optimization problem. This problem is well-addressed for microprocessor systems but for Dynamically Reconfigurable Hardware (DRHW) systems in which, in addition to tasks, the reconfiguration time and complexity also have to be scheduled; this problem is more complicated. DRHW reconfiguration overhead is considerable and can be crucial for real-world applications. To deal with this overhead, in this paper, a meta-heuristic method named Graph-Oriented Simulated Annealing (GOSA) is proposed. By introducing an innovative graph and solution structures called schedule graphs, and also some controlling functions which are inherited from the nature of the problem, the proposed method adapts itself to the characteristics of the problem. This helps the algorithm to adjust its exploration and exploitation speed and accuracy according to the requirements of the given problem and consequently find high-quality solutions quickly. To demonstrate the performance of the proposed method, it was tested on several synthetic and real-world benchmark task graphs, and the results were compared with a selection of classic and state-of-the-art algorithms. The method is comprehensively evaluated by performing numerous experiments in terms of execution time, makespan, scalability, and reliability. The results of the experiments on benchmarks show that in terms of the quality of the solutions, GOSA outperforms BGA, HPSO-GA, and FATS by 17%, 13%, and 5%, respectively, and its execution time is considerably less than all competing algorithms. Moreover, according to the experiments done on synthetic graphs, the makespan of the solutions generated by GOSA, Genetic Algorithm (GA), and the Gxhaustive Search over the List Scheduler are improved on average by 7.2%, 8.1%, and 19.1%, respectively. The most significant achievement of the proposed method is its execution time which is 31 times faster than GA. Finally, the results confirm that the proposed method is scalable for large task graphs, and its reliability is superior. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization. [ABSTRACT FROM AUTHOR]

As an important part of container logistics, quay cranes (QCs) are crucial equipment in multimodal container transportation. The scheduling and allocation of QCs determine the operational efficiency of container terminals. By analyzing the way quay cranes are operated, this paper establishes a mixed-integer dynamic rolling-horizon programming model for the scheduling and allocation of QCs and proposes use of a genetic algorithm and two improved firefly algorithms based on segment encoding technology to formulate an optimum QC scheduling scheme. In doing so, the improved approach has made QC control more efficient and balanced. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling. [ABSTRACT FROM AUTHOR]

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms. [ABSTRACT FROM AUTHOR]

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods. [ABSTRACT FROM AUTHOR]

Scheduling is a process of mapping resources to tasks and it's objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time. [ABSTRACT FROM AUTHOR]

The two stage assembly flowshop scheduling problem has a lot of applications, and hence, it has recently received more attention in the scheduling literature. The performance measure of total tardiness is important as the fulfillment of due dates of customers has to be taken into account while making scheduling decisions. To the best of our knowledge, the problem with this performance measure has not been addressed so far, and hence, it is addressed in this paper. Different algorithms are proposed for the problem. The proposed algorithms are; an insertion algorithm, a genetic algorithm, two versions of simulated annealing algorithm ( SA), and two versions of cloud theory-based SA. Moreover, the proposed insertion algorithm ( PIA) is combined with the rest of the algorithms resulting in a total of eleven algorithms. Computational analysis, by using a non-parametric statistical test, indicates that one of the versions of the SA combined with the PIA performs better than the rest of the algorithms. The PIA helps in reducing the error of the SA by about seventy percent. It is worth to state that the performance of the combined algorithm is neither possible to achieve by the insertion algorithm alone nor by the simulated annealing alone no matter how much more computational time is given to the each. [ABSTRACT FROM AUTHOR]

One of the main benefits of using Wireless Sensor Networks (WSNs) is that they can be deployed in remote locations without any prior infrastructure. Because of this nodes are normally battery powered. This limits the lifetime of the network. In this paper, we propose a novel method of scheduling nodes based on a user's sensing coverage requirement. Through the use of our proposed scheduling algorithm (Ncut-GA), it is shown that the duration for which the user's coverage requirement is met can be extended. When compared with a previously published algorithm (Greedy-MSC), the proposed algorithm is able to increase coverage duration by up to 80%. Furthermore it is also shown that the time until the first node dies can be improved by up to 200% through the use of Ncut-GA. [ABSTRACT FROM AUTHOR]

Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters. [ABSTRACT FROM AUTHOR]

This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising. [ABSTRACT FROM AUTHOR]

Open job shop scheduling is a kind of job shop scheduling in which operations can be performed in any order. In this paper an attempt is made to develop a heuristic for the open job shop scheduling problem using genetic algorithm to minimize makespan. Genetic algorithm operators are suitably modified to maintain feasibility. The results are statistically compared and found to be significantly better than the earlier reported results. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks. [ABSTRACT FROM AUTHOR]

This paper considers operational issues that arise in repetitive manufacturing systems served by automated guided vehicles (AGVs) in loops with unidirectional material flow. The objective considered is the minimization of the steady state cycle time required to produce a minimal job set (or equivalently, throughput rate maximization). Our models allow for delays caused by AGV conflicts. We define and analyze three nondominated and widely used AGV dispatching policies. For each policy, we describe algorithms and intractability results for combined job scheduling and material handling problems. We describe a genetic algorithm that estimates the cycle lime within 5% on average for instances with up to 10 machines and four AGVs. Some related fleet sizing and loop decomposition issues are discussed in the companion paper [19]. [ABSTRACT FROM AUTHOR]

The importance of job shop scheduling as a practical problem has attracted the attention of many researchers. However, most research has focused on special cases such as single machine, parallel machine, and flowshop environments due to the ‘hardness’ of general job shop problems. In this paper, a hybrid algorithm based on an Integration of a genetic algorithm and heuristic rules is proposed for a general job shop scheduling problem with sequence-dependent setups (Jm¦sjk¦Cmax). An embedded simulator is employed to implement the heuristic rules, which greatly enhances the flexibility of the algorithm. Knowledge relevant to the problem is inherent in the heuristic rules making the genetic algorithm more efficient, while the optimization procedure provided by the genetic algorithm makes the heuristic rules more effective. Extensive numerical experiments have been conducted and the results have shown that the hybrid approach is superior when compared to recently published existing methods for the same problem. [ABSTRACT FROM AUTHOR]

In this paper, it proposes a multi-population interactive coevolutionary algorithm for the flexible job shop scheduling problems. In the proposed algorithm, both the ant colony optimization and genetic algorithm with different configurations were applied to evolve each population independently. By the interaction, competition and sharing mechanism among populations, the computing resource is utilized more efficiently, and the quality of populations is improved effectively. The performance of our proposed approach was evaluated by a lot of benchmark instances taken from literature. The experimental results have shown that the proposed algorithm is a feasible and effective approach for the flexible job shop scheduling problem. [ABSTRACT FROM AUTHOR]

This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem for the comprehensive description of disruption management problems. This conceptual framework employs the concept of alternative activities to consider both the temporal shift of activities or the reallocation of resources and switches from one valid process variant to another one. Activities can be serialized or parallelized, process steps can be inserted or removed and durations as well as resource requirements can be modified dynamically during optimization. Focusing on the domain of the aircraft turnaround as the most important airport ground process, we illustrate how the Extended RCPSP (x-RCPSP) can be applied for decision support. A specific evolutionary algorithm is presented that identifies good-quality solutions to relatively large disruption management problems within only a few seconds. The results of the evaluation illustrate fast convergence on good or optimal schedules and serve as a basis for the development of future problem solving algorithms. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine a schedule for parallel flow line with bicriteria objective of minimizing the total tardiness and earliness of jobs. An enhancement to its basic greedy randomized adaptive search procedure (GRASP) is used in conjunction with genetic algorithm (GA) and particle swarm optimization (PSO). The feasible solution of GRASP construction phase is used as initial population for both GA and PSO. A number of problems are solved, by varying the number of jobs, lines, and machines, using the hybrid PSO, hybrid GA, PSO, and GA-based methods and the results are compared. [ABSTRACT FROM AUTHOR]

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm's consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

The multi-mode resource-constrained project scheduling problem (MRCPSP) is a very general scheduling model. The MRCPSP covers problems where activities can be executed in several ways or modes, and is affected by parameters such as their duration, temporary relationships with other activities, and renewable and non-renewable resource requirements. The objective of the MRCPSP is to select a combination of time/resources to minimize the duration of the project and complete all activities while satisfying all resource constraints and precedence relationships. Here, we describe a memetic algorithm to solve the MRCPSP. This algorithm uses the components of genetic algorithms and variable neighborhoods search to implement (1) an adaptation of the uniform crossover operator, and (2) a local search to assess agents' performance that appropriately guides the evolution of the algorithm and hence generate better solutions. We implement a metaheuristic strategy and compare its performance for solving different instances of the standard PSPLIB and MMLIB libraries. Overall, our memetic algorithm provides suitable solutions for the MRCPSP and shows outstanding performance in all tested instances. [ABSTRACT FROM AUTHOR]

League championship algorithm is a recently proposed population-based evolutionary algorithm for finding global optimal solutions in continuous optimization problems. The proposed work adopts the algorithm by modifying the team formation step for solving real-time task scheduling problem in heterogeneous multiprocessors. Two different objectives: tardiness and energy consumption were considered for scheduling. Our proposed algorithm is implemented using Java and tested using the graphs generated by the benchmark tools: task graph for free and task graph generator. Simulation results prove the performance of the proposed algorithm is better in terms of the objective functions over the other existing metaheuristic algorithms such as genetic algorithm, ant colony optimization and particle swarm optimization. [ABSTRACT FROM AUTHOR]

We face the job shop scheduling problem with sequence dependent setup times and makespan minimization by memetic algorithm. This algorithm combines a classic genetic algorithm with a local searcher. The performance of the local searcher relies on the combination of a tabu search algorithm with a neighborhood structure termed N that are thoroughly described and analyzed. Also, two evolution models are considered: Lamarckian and Baldwinian evolution. We report results from an experimental study across conventional benchmark instances showing that the proposed algorithm outperforms the current state-of-the-art methods and that Lamarckian evolution is better than Baldwinian evolution. [ABSTRACT FROM AUTHOR]

To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. A heuristic algorithm, called multi-layer genetic algorithm (MLGA) is developed with a view to overcome the computation difficulty for solving the mathematical model. A numerical experimentation is carried out in order to evaluate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method. [ABSTRACT FROM AUTHOR]

Genetic Algorithms (GAs) are a class of evolutionary algorithms that have been successfully applied to scheduling problems, in particular job-shop and flow-shop type problems where a number of theoretical benchmarks exist. This work applies a genetic algorithm to a real-world, heavily constrained scheduling problem of a local chicken factory, where there is no benchmark solution, but real-life needs to produce sensible and adaptable schedules in a short space of time. The results show that the GA can successfully produce daily schedules in minutes, similar to those currently produced by hand by a single expert in several days, and furthermore improve certain aspects of the current schedules. We explore the success of using a GA to evolve a strategy for producing a solution, rather than evolving the solution itself, and find that this method provides the most flexible approach. This method can produce robust schedules for all the cases presented to it. The algorithm itself is a compromise between an indirect and direct representation. We conclude with a discussion on the suitability of the genetic algorithm as an approach to this type of problem. [ABSTRACT FROM AUTHOR]

Local search heuristics are developed for a problem of scheduling a single machine to minimize the total weighted completion time. The jobs are partitioned into families, and a set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. Four alternative neighbourhood search methods are developed: multi-start descent, simulated annealing, threshold accepting and tabu search. The performance of these heuristics is evaluated on a large set of test problems, and the results are also compared with those obtained by a genetic algorithm. The best results are obtained with the tabu search method for smaller numbers of families and with the genetic algorithm for larger numbers of families. In combination, these methods generate high quality schedules at relatively modest computational expense. [ABSTRACT FROM AUTHOR]