Cyclic Job Shop Scheduling Problem Research Articles

Parallel tabu search for the cyclic job shop scheduling problem

Abstract In this paper, we consider a cyclic job shop problem, consisting of production of a certain set of elements at fixed intervals. Optimization of the process is reduced to a minimization of a cycle time, i.e. the time, after which the next batch of the same elements may be produced. We introduce a new parallel method for the cost function calculation. The parallelization is not trivial and cannot be done automatically by the existing compilers due to the recurrent character of formulas. Since the problem is strongly NP-hard, a heuristic algorithm was designed to solve it. Computational experiments were done in a multiprocessor environment, namely – in Intel Xeon Phi.

Recurrent neural network approach for cyclic job shop scheduling problem

While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system model and the efficiency of neural networks for such a class of problems.

Scheduling for Agile Manufacturing in Response to Change of Product Mix

This paper deals with a cyclic job shop scheduling problem in order to plan an appropriate production schedule corresponding to a change of product mix agilely. The product mix, which is defined as the total composite of products offered by a company, is generally changed to meet demand in a market. We propose a heuristic-based scheduling method for the cyclic job shop scheduling problem to plan the feasible schedules under the different product mix conditions. A re-scheduling method is also introduced in order to shift the production schedules as soon as possible. Numerical simulations are carried out to verify the proposed methods.

A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints

This study concerns the domain of cyclic scheduling. More precisely we consider the cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are cyclic and are subjected to linear precedence constraints. First we review some approaches in the field of cyclic scheduling and present the cyclic job shop scheduling problem definition, which has an open complexity. Then we present a general approach for solving it, based on the coupling of a genetic algorithm and a scheduler. This scheduler utilises a Petri-net modelling the linear precedence constraints between cyclic tasks. The goal of this genetic algorithm is to propose an order of priority for jobs on the machines, to be used by the scheduler for solving resource conflicts. Finally a benchmark and some preliminary results of this approach are presented.

Job shop cyclique à contraintes linéaires : modélisation et résolution par un algorithme génétique

This paper concerns the problem of cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are generic, that is, they have an infinite number of occurrences; moreover, these tasks are constrained by linear precedence constraints. The general approach to solving this scheduling problem, is based on the coupling of a genetic algorithm and a scheduler. The scheduler uses a Petri net to construct a feasible solution step by step, respecting the linear constraints between the tasks. The genetic algorithm aims at proposing heuristics for solving the resource conflicts that occur during the simulation process achieved by the scheduler. Finally, a benchmark and some preliminary results of this approach are presented.