Open Shop Environment Research Articles

No-Wait Open-Shop Scheduling Problems with Reverse Flows

Open shop scheduling has a special place in both production and optimization problems. This study investigates an assembly (direct flow) and disassembly (reverse flow) scheduling problem in an open shop environment where both are processed on the same machine, but only one is a no-wait operation. The objective is to minimize makespan of jobs on all machines. A mixed-integer linear program is developed. GAMS 25.1.1.2 is used to solve small size test problems. Differential Evolution (DE) Algorithm is applied to large-scale problems. A comparison of the computing results of this algorithm and the Genetic algorithm (GA), Whale Optimization Algorithm (WOA), Bat algorithm (BA), Artificial bee colony algorithm (ABC), and vibration damping optimization algorithm (VDO) is presented. Based on the results obtained, it has been found that there are no obvious differences between the performance of the algorithms, but the DE algorithm produces better solutions.

Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization

Reduction of carbon emissions is receiving growing attention in manufacturing industry. In the real-life flexible open-shop manufacturing environments, the workpieces generally need frequent transport between machines so that the limitation of the transport resource is non-negligible. Joint scheduling is a better way to achieve global optimum than sequential scheduling due to the interactive relationship between production and transport. However, owing to the extreme complexity, joint scheduling has received little attention over the past years. In this paper, the low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle (LCJS-FOSCA) is investigated. A mixed-integer programming (MIP) model is formulated with the objectives to minimize the total carbon emission and makespan. Facing the extreme complexity of LCJS-FOSCA, an enhanced multi-objective particle swarm optimization (EMOPSO) is developed. In EMOPSO, several improvement strategies including the initialization method for high-quality solutions, chaotic position updating and mutation for global exploration, and problem-knowledge-based neighborhood search for local exploitation, are proposed. Finally, the following results are obtained through a comprehensive case study: (1) the proposed strategies make significant promotion on the convergence and comprehensive quality, and EMOPSO is verified to be effective for solving LCJS-FOSCA; (2) compared with the two sequential scheduling methods, the makespan obtained by joint scheduling is reduced by approximately 27.7% and 25.5%, and the total carbon emission is reduced by 3.3% and 4.2%; (3) the optimal numbers of AGV in various scales of transport time are provided based on the analysis of optimization results. This work presents a significance on promoting cleaner production of the modern manufacturing industry.

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

Workshop Training: Union Membership Mobilization and Collective Bargaining in an Open Shop Environment

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.

O(log m)-approximation for the routing open shop problem

We consider the routing open shop problem which is a generalization of the open shop and the metric travelling salesman problems. The jobs are located in some transportation network, and the machines travel on the network to execute the jobs in the open shop environment. The machines are initially located at the same node (depot) and must return to the depot after completing all jobs. The goal is to find a non-preemptive schedule with the minimum makespan. We present a new polynomial-time approximation algorithm with worst-case performance guarantee O (log m ), where m is the number of machines.

An Algorithm for an Optimal Staffing Problem in Open Shop Environment

The paper addresses a problem of optimal staffing in open shop environment. The problem is to determine the optimal number of operators serving a given number of machines to fulfill the number of independent operations while minimizing staff idle. Using a Gantt chart presentation of the problem it is modeled as two- dimensional cutting stock problem. A mixed-integer programming model is used to get minimal job processing time (makespan) for fixed number of machines' operators. An algorithm for optimal open- shop staffing is developed based on iterative solving of the formulated optimization task. The execution of the developed algorithm provides optimal number of machines' operators in the sense of minimum staff idle and optimal makespan for that number of operators. The proposed algorithm is tested numerically for a real life staffing problem. The testing results show the practical applicability for similar open shop staffing problems.

Efficient approximation algorithms for the routing open shop problem

We consider the routing open shop problem being a generalization of two classical discrete optimization problems: the open shop scheduling problem and the metric traveling salesman problem. The jobs are located at nodes of some transportation network, and the machines travel on the network to execute the jobs in the open shop environment. The machines are initially located at the same node (depot) and must return to the depot after completing all the jobs. It is required to find a non-preemptive schedule with the minimum makespan. The problem is NP-hard even on the two-node network with two machines. We present new polynomial-time approximation algorithms with worst-case performance guarantees.

The two-machine open-shop problem with unit-time operations and time delays to minimize the makespan

This paper addresses the problem of scheduling unit-time operations with integral and non-negative time delay considerations on a two-machine open-shop environment. The criterion to minimize is the makespan. Two well solvable cases and two approximation algorithms, with their worst-case analyses, are presented.

Polynomial time algorithms for two special classes of the proportionate multiprocessor open shop

This paper describes a complex scheduling problem taken from a hospital diagnostic testing center that schedules hundreds of patients in an open shop environment consisting of multiple facilities and multiple processors. This scheduling problem, known as the multiprocessor open shop (MPOS) problem, is strongly NP-hard with few published results. Realizing that in many MPOS environments processing times are stage-dependent, not both job and stage-dependent, this paper examines a new class of problems for the MPOS—proportionate ones. This paper exploits the structural nature of the proportionate MPOS and defines new terms. Despite the enormous complexity of the MPOS problem, this work demonstrates that polynomial time algorithms exist for two special cases. Since other applications of this problem exist in service and manufacturing environments, solving the proportionate MPOS problem is not only significant in the theory of optimization, but also in many real-world applications.

Heuristic constructive algorithms for open shop scheduling to minimize mean flow time

In this paper, we consider the problem of scheduling n jobs on m machines in an open shop environment so that the sum of completion times or mean flow time becomes minimal. For this strongly NP-hard problem, we develop and discuss different constructive heuristic algorithms. Extensive computational results are presented for problems with up to 50 jobs and 50 machines, respectively. The quality of the solutions is evaluated by a lower bound for the corresponding preemptive open shop problem and by an alternative estimate of mean flow time. We observe that the recommendation of an appropriate constructive algorithm strongly depends on the ratio n/ m.

Simulated annealing and genetic algorithms for minimizing mean flow time in an open shop

This paper considers the problem of scheduling n jobs on m machines in an open shop environment so that the sum of completion times or mean flow time becomes minimal. It continues recent work by Bräsel et al. [H. Bräsel, A. Herms, M. Mörig, T. Tautenhahn, T. Tusch, F. Werner, Heuristic constructive algorithms for open shop scheduling to minmize mean flow time, European J. Oper. Res., in press ( doi.10.1016/j.ejor.2007.02.057)] on constructive algorithms. For this strongly NP-hard problem, we present two iterative algorithms, namely a simulated annealing and a genetic algorithm. For the simulated annealing algorithm, several neighborhoods are suggested and tested together with the control parameters of the algorithm. For the genetic algorithm, new genetic operators are suggested based on the representation of a solution by the rank matrix describing the job and machine orders. Extensive computational results are presented for problems with up to 50 jobs and 50 machines, respectively. The algorithms are compared relative to each other, and the quality of the results is also estimated partially by a lower bound for the corresponding preemptive open shop problem. For most of the problems, the genetic algorithm is superior when fixing the same number of 30 000 generated solutions for each algorithm. However, in contrast to makespan minimization problems, where the focus is on problems with an equal number of jobs and machines, it turns out that problems with a larger number of jobs than machines are the hardest problems.

The routing open-shop problem on a network: Complexity and approximation

In the routing open-shop problem, jobs are located at nodes of an undirected transportation network, and the machines travel on the network to execute jobs in the open-shop environment. The machines are initially located at the same node (depot) and must return to the depot after completing all the jobs. It is required to find a nonpreemptive schedule that minimizes the makespan. We prove that the problem is NP-hard even on a two-node network with two machines, and even on a two-node network with two jobs and m machines. We develop polynomial-time approximation heuristics and obtain bounds on their approximation performance.

A COMPARISON OF HEURISTICS FOR MEAN FLOW TIME OPEN SHOP SCHEDULING

In this paper, the problem of scheduling n jobs on m machines in an open shop environment is considered so that mean flow time becomes minimal. Since this problem is strongly NP-hard, different constructive and iterative heuristic algorithms are developed and discussed. Computational results are presented for problems with up to 50 jobs and 50 machines, respectively. The quality of the solutions is estimated by a lower bound for the corresponding preemptive open shop problem and by an alternative estimation of mean flow time.

On the complexity and some properties of multi-stage scheduling problems with earliness and tardiness penalties

In this paper, the complexity and some other properties of several multi-stage problems with a non-regular performance measure are investigated. Mainly, we deal with two-machine problems. The problems are extensions of a one-machine problem with a common due date and a non-regular optimization criterion, where the sum of absolute deviations of the completion times from the due date is to be minimized. There are two reasonable generalizations: Either, the objective function is kept the same as in the one-machine problem, where the completion time is now the completion time of the last operation of the job, or a second penalty is added to this term which takes into account the storage costs of the intermediate. For both types of problems, we study the flow shop, the job shop, and the open shop environment. In the case of intermediate storage costs, we distinguish the cases of a due date d=0 (which leads to a regular optimization criterion) and a non-restrictive due date. We obtain the following results. The two-machine flow shop problem with intermediate storage costs defined in this paper is NP-hard in the strong sense for d=0 as well as for a non-restrictive due date. We prove the similar results for the two-machine open shop problem. In absence of intermediate storage costs, the open shop and the job shop problem are polynomially solvable for a non-restrictive due date. Scope and purpose The just-in-time production philosophy has lead to a growing interest in scheduling problems considering both earliness and tardiness penalties. Most publications considering these so-called non-regular objective functions are devoted to single-stage problems. This paper describes the two main approaches to extend the objective function to multi-stage environments and investigates the computational complexity of the problems obtained. The investigations in this paper may serve as a starting point for the development of appropriate algorithms for the type of problem considered.

Scheduling Batches with Sequential Job Processing for Two-Machine Flow and Open Shops

In this paper, we study a problem of scheduling and batching on two machines in a flow-shop and open-shop environment. Each machine processes operations in batches, and the processing time of a batch is the sum of the processing times of the operations in that batch. A setup time, which depends only on the machine, is required before a batch is processed on a machine, and all jobs in a batch remain at the machine until the entire batch is processed. The aim is to make batching and sequencing decisions, which specify a partition of the jobs into batches on each machine, and a processing order of the batches on each machine, respectively, so that the makespan is minimized. The flow-shop problem is shown to be strongly NP-hard. We demonstrate that there is an optimal solution with the same batches on the two machines; we refer to these as consistent batches. A heuristic is developed that selects the best schedule among several with one, two, or three consistent batches, and is shown to have a worst-case performance ratio of 4/3. For the open-shop, we show that the problem is NP-hard in the ordinary sense. By proving the existence of an optimal solution with one, two or three consistent batches, a close relationship is established with the problem of scheduling two or three identical parallel machines to minimize the makespan. This allows a pseudo-polynomial algorithm to be derived, and various heuristic methods to be suggested.

The impact of lot splitting on due date performance

AbstractThe primary purpose of the research project was to determine if the flow time reductions that occur from splitting jobs into smaller transfer batches result in improved due data performance. The research was performed using a computer simulation of a five machine open job shop. In the open shop environment, each job is unique, and the flow time reductions are solely a result of overlapping operations rather than saved setups from processing similar jobs in sequence. To determine the impact of lot splitting on due date performance, two questions had to be answered. First, how does the impact of lot splitting vary with the magnitude of setup times? Second, how does the size of the transfer batch affect lot splitting performance?This study shows the benefits from lot splitting improve monotonically with decreasing transfer batch size and that lot splitting provides improvements that are essentially independent of the setup time as long as the total utilization (ratio of setup and processing time to available time) is constant. As for the primary research question, lot splitting can provide substantial reductions in mean tardiness (up to 39%) and number of jobs tardy in the open shop environment.The repetitive lots logic developed by Jacobs and Bragg (1988) is reformulated to clearly show that it can be used in combination with any dispatching rule. This rule demonstrates that the repetitive lots logic makes surprisingly good scheduling decisions in spite of its simplicity.Lot splitting is shown to be a simple and effective means of improving the performance of an open job shop. Based on its robust performance in this study, the benefits should be able to be realized in combination with other shop floor control techniques, such as order review release and order expediting.