Open Shop Scheduling Research Articles

Development of efficient genetic algorithm for open shop scheduling problem to minimise makespan

Scheduling problem deals with the management of the resources in most optimal manner. In this research, open shop scheduling problem with an objective of minimising the makespan is considered. This problem comes under combinatorial category. Hence, development of an efficient heuristic is inevitable to minimise the makespan of the open shop scheduling problem. Meta-heuristic genetic algorithm (GA) is considered as it has the scope of improving performance measure of the problem. The performance of the genetic algorithm is influenced by selection method, crossover operator and mutation probability. Four different genetic algorithms are developed by varying selection method and crossover operator where three of this algorithm use newly proposed crossover operator while the fourth uses existing one-point crossover operator. A complete factorial experiment with three factors and three replications for each experimental combination is carried out on a set of problem instances with all the four genetic algorithm methods.

Development of efficient genetic algorithm for open shop scheduling problem to minimise makespan

Scheduling problem deals with the management of the resources in most optimal manner. In this research, open shop scheduling problem with an objective of minimising the makespan is considered. This problem comes under combinatorial category. Hence, development of an efficient heuristic is inevitable to minimise the makespan of the open shop scheduling problem. Meta-heuristic genetic algorithm (GA) is considered as it has the scope of improving performance measure of the problem. The performance of the genetic algorithm is influenced by selection method, crossover operator and mutation probability. Four different genetic algorithms are developed by varying selection method and crossover operator where three of this algorithm use newly proposed crossover operator while the fourth uses existing one-point crossover operator. A complete factorial experiment with three factors and three replications for each experimental combination is carried out on a set of problem instances with all the four genetic algorithm methods.

Single Objective for an Integer Partial Flexible Open Shop Scheduling Problem Using Developed Ant Colony Optimization

Single Objective for an Integer Partial Flexible Open Shop Scheduling Problem Using Developed Ant Colony Optimization

Stochastic group shop scheduling with fuzzy due dates

The group shop scheduling (GSS) problem is a general formulation that includes the job shop and the open shop scheduling problems. This paper is the first dealing with a GSS problem with random release and processing times and fuzzy due dates. While release and processing times are assumed to be ra ndom variables with known distributions, as in many real-world situations, job due dates are considered to be fuzzy tolerating a certain amount of delay. The objective is to maximize the expected total degree of satisfaction with respect to the fuzzy due dates. The problem is formulated as a stochastic disjunctive program and then two solution approaches are developed for it, both of which act within an ant colony optimization (ACO) framework. In the first approach, to estimate the performance of a constructed schedule, each random variable is replaced by its expectation, whereas in the other approach following a simulation optimization procedure, a discrete event simulation model is employed to estimate the performance of a schedule. The proposed approaches are then compared through computational experiments.

Open shop scheduling problems with conflict graphs

This paper deals with open shops subject to the constraint that some conflicting jobs cannot be processed simultaneously on different machines. In the context of our study, these conflicts are given by an undirected graph, called the conflict graph. We seek a schedule that minimizes the maximum completion time (makespan). We first prove the NP-hardness of different versions of this problem. Then, we present polynomial-time solvable cases, for which we devise simple and efficient algorithms. On the other hand, we present heuristics and lower bounds for the general case. The heuristics are based on the construction of matchings in bipartite graphs as well as on a specific insertion technique combined with beam search. Finally, we test the efficiency of the heuristics and report our computational experiments that display satisfying results.

Partially concurrent open shop scheduling with integral preemptions

Partially-concurrent open shop scheduling (PCOSS) was recently introduced as a common generalization of the well-known open shop scheduling model and the concurrent open shop scheduling model. PCOSS was shown to be NP-hard even when there is only one machine and all operations have unit processing time. In the present paper we study PCOSS problems with integral processing times that allow preemptions at integral time points. A special and simple subclass of the problems at focus is that of unit processing times, which is considered separately. For these two cases a schedule is related to the colouring of a graph called the conflict graph, which represents the operations that cannot be performed concurrently. This enables us to extract insights and solutions from the well-studied field of graph colouring and apply them to the recently introduced PCOSS model. We then focus on two special cases of the problem—the case where the conflict graph is perfect, and the case of uniform PCOSS, in which all the jobs, including their conflicts, are identical. The development of the PCOSS model was motivated from a real-life timetabling project of assigning technicians to a fleet of airplanes. The latter case of uniform PCOSS correlates to instances in which the fleet of airplanes is homogeneous.

English

English

A simulation optimisation approach for real-time scheduling in an open shop environment using a composite dispatching rule

In the last decades, many researchers have studied open shop scheduling (OSS) problem by considering deterministic parameters using mathematical modelling, heuristics and meta-heuristics. However, it is important to study the problem as close as possible to real world conditions which consists of uncertainty and stochastic parameters. In this study, dispatching rules, as accepted tools for real-time scheduling, are applied for optimising the OSS problem. Since none of conventional dispatching rules performs well for all performance measures, a simulation-based real-time scheduling composite dispatching rule is developed. For this purpose, a multi response optimisation approach based on computer simulation for scheduling a non-preemptive open shop with stochastic ready times is presented in order to minimise the mean waiting time of jobs. The presented approach composed of design of experiments, discrete event simulation, multi-layer perceptron artificial neural network, radial basis function and data envelopment analysis to determine the most efficient dispatching rule for each machine.

A Critical Improvement on Open Shop Scheduling Algorithm for Routing in Interconnection Networks

In the past years, Interconnection Networks have been used quite often and especially in applications where parallelization is critical. Message packets transmitted through such networks can be interrupted using buffers in order to maximize network usage and minimize the time required for all messages to reach their destination. However, preempting a packet will result in topology reconfiguration and consequently in time cost. The problem of scheduling message packets through such a network is referred to as PBS and is known to be NP-Hard. In this paper we have improved, critically, variations of polynomially solvable instances of Open Shop to approximate PBS. We have combined these variations and called the induced algorithm IHSA, Improved Hybridic Scheduling Algorithm. We ran experiments to establish the efficiency of IHSA and found that in all datasets used it produces schedules very close to the optimal. In addition, we tested IHSA with datasets that follow non-uniform distributions and provided statistical data which illustrates better its this http URL further establish the efficiency of IHSA we ran tests to compare it to SGA, another algorithm which when tested in the past has yielded excellent results.

Adaptive Open-Shop Scheduling for Optical Interconnection Networks

This paper deals with resource management in optical interconnection networks. It first proposes an optical resource management framework as a platform to develop and evaluate efficient solutions for multipoint-to-multipoint optical communication systems with a centralized controller. The paper then focuses on studying the optical resource scheduling (ORS) problem as a core element in the framework by applying the classical open-shop scheduling theory. The ORS problem can therefore be solved by adopting the existing preemptive and nonpreemptive open-shop scheduling algorithms. In an optical network with nonnegligible reconfiguration delay, a preemptive algorithm may incur high reconfiguration overhead resulting in worse performance compared to the nonpreemptive strategy. Motivated by this fact, this paper proposes an adaptive open-shop scheduling (AOS) algorithm that dynamically decides the optimal scheduling strategy according to traffic condition and system parameters, such as reconfiguration delay, nonpreemptive approximation ratio, and number of involved optical interfaces. The solution is assessed by means of an analytical model that allows to quantify the network performance in terms of packet delay and potential energy savings obtained by the sleep mode operation. As a possible application scenario, the inter- and intrarack optical interconnection networks in data centres are considered. Analytical results demonstrate how the proposed AOS outperforms the nonpreemptive and preemptive scheduling strategies for typical configurations used in data center networks. In addition, the reconfiguration delay and wake-up time of optical devices are identified as performance-determining factors.

A Co-evolutionary Algorithm for Open-shop Scheduling with Disassembly Operations

In recent years, many used products are returned from customers through reverse supply chains in order to be reused and recycled. Therefore, it is necessary to provide suitable disassembly procedures from the view point of economic efficiency. This research proposes a scheduling method for open-shop scheduling problems containing both disassembly operations and post-processing operations. A co-evolutionary algorithm is proposed to modify not only the sequences of disassembly and post-processing operations of products but also the sequences of products loaded on disassembly and post-processing machines. Two different kinds of individuals are modeled for both the sequences of operations and the sequences of products in the co-evolutionary algorithm. These individuals alternately repeat evolution at short time intervals, and affect their fitness values each other. We developed a prototype of scheduling system and applied it to some computational experiments for open-shop type scheduling problems including disassembly and post-processing operations. The experimental results of the proposed method were compared with the one of another scheduling method with various heuristic rules in order to evaluate the effectiveness of the proposed method.

Open Shop Scheduling with Synchronization

In this paper, we study open shop scheduling problems with synchronization. This model has the same features as the classical open shop model, where each of the n jobs has to be processed by each of the m machines in an arbitrary order. Unlike the classical model, jobs are processed in synchronous cycles, which means that the m operations of the same cycle start at the same time. Within one cycle, machines which process operations with smaller processing times have to wait until the longest operation of the cycle is finished before the next cycle can start. Thus, the length of a cycle is equal to the maximum processing time of its operations. In this paper, we continue the line of research started by Weiß et al. (Discrete Appl Math 211:183–203, 2016). We establish new structural results for the two-machine problem with the makespan objective and use them to formulate an easier solution algorithm. Other versions of the problem, with the total completion time objective and those which involve due dates or deadlines, turn out to be NP-hard in the strong sense, even for m=2 machines. We also show that relaxed models, in which cycles are allowed to contain less than m jobs, have the same complexity status.

Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem

Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem

A Featured Tuning of the Simulated Annealing Applied to the Open Shop Scheduling

A Featured Tuning of the Simulated Annealing Applied to the Open Shop Scheduling

Open shop scheduling problem to minimize total weighted completion time

ABSTRACTA given number of jobs in an open shop scheduling environment must each be processed for given amounts of time on each of a given set of machines in an arbitrary sequence. This study aims to achieve a schedule that minimizes total weighted completion time. Owing to the strong NP-hardness of the problem, the weighted shortest processing time block (WSPTB) heuristic is presented to obtain approximate solutions for large-scale problems. Performance analysis proves the asymptotic optimality of the WSPTB heuristic in the sense of probability limits. The largest weight block rule is provided to seek optimal schedules in polynomial time for a special case. A hybrid discrete differential evolution algorithm is designed to obtain high-quality solutions for moderate-scale problems. Simulation experiments demonstrate the effectiveness of the proposed algorithms.

A Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem

Open Shop Scheduling Problem (OSSP) is a combinatorial optimization problem for more than two machines and n jobs. Open Shop Scheduling Problem is another kind of scheduling problem along with flow shop and job shop scheduling problems. The open shop scheduling problem involves scheduling of jobs, where the sequence of the operations of each job can be arbitrarily chosen and need not be same. This means that the operations of the jobs can be performed in any sequence. In the absence of sequences for the jobs, for a given set of jobs, finding different parameters like maximum completion time Cmax becomes highly difficult and complex. One can use complete enumeration method or branch and bound method to solve this problem optimally for small and medium size problems. The large size problems of open shop problem with more than two machines and with n jobs can be solved by either a heuristic or meta-heuristics such as genetic algorithm, simulated annealing algorithm, etc. to obtain very near optimal solution. The performance of the genetic algorithm is affected by crossover operator performed between two parent chromosomes. Hence, this paper explores various crossover operators used, while using evolutionary based genetic algorithm to solve open shop scheduling problems. It further attempts to propose a new crossover operator using three chromosomes.

A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints

This paper presents an open shop scheduling problem based on a real mechanical workshop made of m machines that process n jobs. It deals with different resource constraints related to the tools allocation and the multi-skills staff assignment. Resource skills and their availability are required to execute one process task. In this work, we expose a multi-objective problem where the idea is to minimize three objectives simultaneously. The first one considers the minimization of the total flow time of jobs in the production system, then the workload balancing concerning both, humans and machines is addressed. We propose and compare two multi-objective methods: NSGA-II and NSGA-III. Computational experiments are designed according to the literature, we expose the small and large sized instances to present the general performance of these algorithms using the hyper-volume metric to compare them.

A two-level particle swarm optimisation algorithm for open-shop scheduling problem

This paper proposes a two-level PSO algorithm for open-shop scheduling problem. In this algorithm, the upper-level PSO algorithm fine-tunes the parameter values for the lower-level PSO algorithm; the lower-level PSO algorithm then uses the given parameter values to generate the solutions for open-shop scheduling problem. The proposed two-level PSO-based algorithm performs very well on the benchmark instances.

A two-level particle swarm optimisation algorithm for open-shop scheduling problem

This paper proposes a two-level PSO algorithm for open-shop scheduling problem. In this algorithm, the upper-level PSO algorithm fine-tunes the parameter values for the lower-level PSO algorithm; the lower-level PSO algorithm then uses the given parameter values to generate the solutions for open-shop scheduling problem. The proposed two-level PSO-based algorithm performs very well on the benchmark instances.

Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach

The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant colony optimisation (ACO) method to solve large size problems. Finally, the best method (ACO) has been used to solve a real industrial case that is presented at the end.