A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.     

Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm

Relative to job-shop scheduling problems that optimize makespan or flow time, due-date-related problems are usually much more computationally complex and are classified as strongly NP-hard. In this paper, a hybrid framework integrating a heuristic and a genetic algorithm (GA) is utilized for job-shop scheduling to minimize weighted tardiness. For each new generation of schedules, the GA determines the first operation of each machine, and the heuristic determines the assignment of the remaining operations. Schedules with inferior tardiness are discarded before the next round of evolution. Extensive numerical experiments were conducted for different levels of due-date tightness. The results show that the hybrid framework performs significantly better than does either a heuristic or GA alone. It is also found to be superior to a well-recognized heuristic improvement procedure (lead-time iterations). Specifically, the combination of the R&M heuristic and a GA outperforms a number of heuristics commonly used to minimize total tardiness and weighted total tardiness; this combination is, however, outperformed by the heuristic of Kreipl [Kreipl, S., 2000. A large step random walk for minimizing total weighted tardiness in a job shop. Journal of Scheduling 3, 125–138]. We also develop a generalized hybrid framework that can adapt to different job-shop problems—with or without sequence-dependent setups and with different objectives (e.g., makespan, tardiness, flow time). The new framework allows the interaction of parallel evolutions, extending the GA-heuristic environment to the solving of multi-objective scheduling problems.     

A hybrid genetic algorithm for the job shop scheduling problem

This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.     

A hybrid genetic algorithm for the resource-constrained project scheduling problem

In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j60 and j120. And that it is competitive with other state-of-the-art heuristics for the instance set j30.     

A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem

In this paper, we study the application of a meta-heuristic to a two-machine flowshop scheduling problem. The meta-heuristic uses a branch-and-bound procedure to generate some information, which in turn is used to guide a genetic algorithm's search for optimal and near-optimal solutions. The criteria considered are makespan and average job flowtime. The problem has applications in flowshop environments where management is interested in reducing turn-around and job idle times simultaneously. We develop the combined branch-and-bound and genetic algorithm based procedure and two modified versions of it. Their performance is compared with that of three algorithms: pure branch-and-bound, pure genetic algorithm, and a heuristic. The results indicate that the combined approach and its modified versions are better than either of the pure strategies as well as the heuristic algorithm.     

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

Traditionally, the permutation flowshop scheduling problem (PFSP) was with the criterion of minimizing makespan. The permutation flowshop scheduling problem to minimize the total flowtime has attracted more attention from researchers in recent years. In this paper, a hybrid genetic local search algorithm is proposed to solve this problem with each of both criteria. The proposed algorithm hybridizes the genetic algorithm and a novel local search scheme that combines two local search methods: the Insertion Search (IS) and the Insertion Search with Cut-and-Repair (ISCR). It employs the genetic algorithm to do the global search and two local search methods to do the local search. Two local search methods play different roles in the search process. The Insertion Search is responsible for searching a small neighborhood while the Insertion Search with Cut-and-Repair is responsible for searching a large neighborhood. Furthermore, the orthogonal-array-based crossover operator is designed to enhance the GA’s capability of intensification. The experimental results show the advantage of combining the two local search methods. The performance of the proposed hybrid genetic algorithm is very competitive. For the PFSP with the total flowtime criterion, it improved 66 out of the 90 current best solutions reported in the literature in short-term search and it also improved all the 20 current best solutions reported in the literature in long-term search. For the PFSP with the makespan criterion, the proposed algorithm also outperforms the other three methods recently reported in the literature.     

An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling

Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems.     

Multiprocessor scheduling: combining LPT and MULTIFIT

We consider the problem of scheduling a set of n independent jobs on m identical machines with the objective of minimizing the total finishing time. We combine the two most popular algorithms, LTP and MULTIFIT, to form a new one. MULTIFIT is well known to have better error bound than LPT with the price paid in running time. Although MULTIFIT provides a better error bound, in many cases LPT still can yield better results. This motivates the development of this new combined algorithm, which uses the result of LPT as the incumbent and then applies MULTIFIT with fewer iterations. The performance of this combined algorithm is better than that of LPT because it uses LPT as an incumbent. The error bound of this combined algorithm is never worse than that of MULTIFIT. For example, the error bound of implementing this combined algorithm to the two-processor problem is , compared to the error bound of in MULTIFIT. Empirical results of the comparison for schedules obtained by the combined algorithm, MULTIFIT and LPT are also provided.     

Job scheduling in virtual manufacturing cells with lot-streaming strategy: a new mathematical model formulation and a genetic algorithm approach

This paper discusses the job scheduling problem in virtual manufacturing cells (VMCs) with the objective of makespan minimization. In the VMC scheduling problem, each job undergoes different processing routes and there is a set of machines to process any operation. Jobs are produced in lot and lot-streaming is permitted. In addition, machines are distributed through the facility, which raises the travelling time issue. For this reason, the decisions are machine assignments, starting times and sub-lot sizes of the operations. We develop a new Mixed Integer Linear Programming (MILP) formulation that considers all aspects of the problem. Owing to the intractability matter, it is unlikely that the MILP could provide solutions for big-sized instances within a reasonable amount of time. We therefore present a Genetic Algorithm (GA) with a new chromosome structure for the VMC environment. Based on a wide range of examinations, comparative results show that GA is quite favourable and that it obtains the optimum solution for any of the instances in the case where sub-lot number equals 1.     

A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties

In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic algorithm and we call it genetic algorithm with dominance properties (GADP). This GADP is a hybrid genetic algorithm. The initial populations of schedules in the genetic algorithm are generated using these dominance properties. GA can further improve the performance of these initial solutions after the evolving procedures. The performances of hybrid genetic algorithm (GADP) have been compared with simple genetic algorithm (SGA) using benchmark instances. It is shown that this hybrid genetic algorithm (GADP) performs very well when compared with DP or SGA alone.     

Maintenance scheduling of rolling stock using a genetic algorithm

We have developed a Genetic algorithm (GA) for the optimisation of maintenance overhaul scheduling of rolling stock (trains) at the Hong Kong Mass Transit Railway Corporation (MTRC). The problem is one of combinatorial optimisation. Genetic algorithms (GAs) belong to the class of heuristic optimisation techniques that utilise randomisation as well as directed smart search to seek the global optima. The workshop at MTRC does have difficulties in establishing good schedules for the overhaul maintenance of the rolling stock. Currently, an experienced scheduler at MTRC performs this task manually. In this paper, we study the problem in a scientific manner and propose ways in which the task can be automated with the help of an algorithm embedded in a computer program. The algorithm enables the scheduler to establish the annual maintenance schedule of the trains in an efficient manner; the objective being to satisfy the maintenance requirements of various units of the trains as closely as possible to their due dates since there is a cost associated with undertaking the maintenance tasks either `too early’ or ‘too late’. The genetic algorithm developed is found to be very effective for solving this intractable problem. Computational results indicate that the genetic algorithm consistently provides significantly better schedules than those established manually at MTRC. More over, we provide evidence that the algorithm delivers close to optimal solutions for randomly generated problems with known optimal solutions. We also propose a local search method to reconfigure the trains in order to improve the schedule and to balance the work load of the overhaul maintenance section of the workshop throughout the planning horizon. We demonstrate that the reconfiguration of trains improves the schedule and reduces cost significantly.     

Minimizing shifts for personnel task scheduling problems: A three-phase algorithm

The personnel task scheduling problem is a subject of commercial interest which has been investigated since the 1950s. This paper proposes an effective and efficient three-phase algorithm for solving the shift minimization personnel task scheduling problem (SMPTSP). To illustrate the increased efficacy of the proposed algorithm over an existing algorithm, computational experiments are performed on a test problem set with characteristics motivated by employee scheduling applications. Experimental results show that the proposed algorithm outperforms the existing algorithm in terms of providing optimal solutions, improving upon most of the best-known solutions and revealing high-quality feasible solutions for those unsolved test instances in the literature.     

A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains

In this paper, we investigate the lot and delivery scheduling problem in a simple supply chain where a single supplier produces multiple components on a flexible flow line (FFL) and delivers them directly to an assembly facility (AF). It is assumed that all of parameters such as demand rates for the components are deterministic and constant over a finite planning horizon. The main objective is to find a lot and delivery schedule that would minimize the average of holding, setup, and transportation costs per unit time for the supply chain. We develop a new mixed integer nonlinear program (MINLP) and an optimal enumeration method to solve the problem. Due to difficulty of obtaining the optimal solution in medium and large-scaled problems, a hybrid genetic algorithm (HGA) is also developed. The proposed HGA incorporates a neighborhood search (NS) into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods are compared on randomly generated problems, and computational results show that the performance of HGA is very promising because it is able to find an optimal or near-optimal solution for majority of the test problems.     

Multiprocessor scheduling under precedence constraints: Polyhedral results

We consider the problem of scheduling a set of tasks related by precedence constraints to a set of processors, so as to minimize their makespan. Each task has to be assigned to a unique processor and no preemption is allowed. A new integer programming formulation of the problem is given and strong valid inequalities are derived. A subset of the inequalities in this formulation has a strong combinatorial structure, which we use to define the polytope of partitions into linear orders. The facial structure of this polytope is investigated and facet defining inequalities are presented which may be helpful to tighten the integer programming formulation of other variants of multiprocessor scheduling problems. Numerical results on real-life problems are presented.     

A polynomial algorithm for some preemptive multiprocessor task scheduling problems

In this paper we consider a problem of preemptive scheduling of multiprocessor tasks on dedicated processors in order to minimize the sum of completion times. Using a standard notation, our problem can be denoted as P ∣ fix_j, pmtn ∣ ∑C_j. We give a polynomial-time algorithm to solve P ∣ fix_j, G = {P₄, dart}-free, pmtn ∣ ∑C_j problem. This result generalizes the following problems: P2 ∣ fix_j, pmtn ∣ ∑C_j, P ∣ ∣fix_j∣ ∈ {1, m}, pmtn ∣ ∑C_j and P4 ∣ fix_j = 2, pmtn ∣ ∑C_j.     

A genetic algorithm for flow shop scheduling problems

Many scheduling problems are NP-hard problems. For such NP-hard combinatorial optimization problems, heuristics play a major role in searching for near-optimal solutions. In this paper we develop a genetic algorithm-based heuristic for the flow shop scheduling problem with makespan as the criterion. The performance of the algorithm is compared with the established NEH algorithm. Computational experience indicates that genetic algorithms can be good techniques for flowshop scheduling problems.     

Fuzzy project scheduling problem and its hybrid intelligent algorithm

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers a type of project scheduling problem with fuzzy activity duration times. According to some management goals, three types of fuzzy models are built to solve the project scheduling problem. Moreover, the technique of fuzzy simulation and genetic algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy models. Finally, some numerical examples are given to illustrate the effectiveness of the algorithm.     

A binary search algorithm for the general coupled task scheduling problem

4OR - The coupled task scheduling problem aims to schedule a set of jobs, each with at least two tasks and there is an exact delay period between two consecutive tasks, on a set of machines to...     

A genetic algorithm for service level based vehicle scheduling

In many practical applications, vehicle scheduling problems involve more complex evaluation criteria than simple distance or travel time minimization. Scheduling to minimize delays between the accumulation and delivery of correspondence represents a class of vehicle scheduling problems, where: the evaluation of candidate solutions is costly, there are no efficient schemes for evaluation of partial solutions or perturbations to existing solutions, and dimensionality is limiting even for problems with relatively few locations. Several features of genetic algorithms (GA's) suggest that they may have advantages relative to alternative heuristic solution algorithms for such problems. These include ease of implementation through efficient coding of solution alternatives, simultaneous emphasis on global as well as local search, and the use of randomization in the search process. In addition, a GA may realize advantages usually associated with interactive methods by replicating the positive attributes of existing solutions in the search process, without explicitly defining or measuring these attributes. This study investigates these potential advantages through application of a GA to a service level based vehicle scheduling problem. The procedure is demonstrated for a vehicle scheduling problem with 15 locations where the objective is to minimize the time between the accumulation of correspondence at each location and delivery to destination locations. The results suggest that genetic algorithms can be effective for finding good quality scheduling solutions with only limited search of the solution space.     

Ship maintenance scheduling by genetic algorithm and constraint-based reasoning

Ship maintenance scheduling is a process to decide start times of maintenance activities that satisfy all precedence and resource constraints and optimize the ship availability. In this paper, ship maintenance scheduling is modelled as a constraint satisfaction problem (CSP). The variables of CSP are the start times and its domain values are the start and horizon of the schedule. To solve the ship maintenance scheduling problem in the Royal Malaysian Navy, we have adopted a constraint-based reasoning (CBR) which requires start times of the first activities of maintenance cycles to solve the problem by the CBR. Thus, we adopt a genetic algorithm (GA) to find the start times of the first activities. The simulation results showed the effectiveness of the present hybrid algorithm.