A learning stage of scheduling tends to produce knowledge about a benchmark of priority dispatching rules which allows a scheduler to improve the solution quality for a set of similar job-shop problems. Once trained on the sample job-shop problems (usually with small sizes), the adaptive algorithm solves a similar job-shop problem (with a moderate size or a large size) better than heuristics us...

The paper presents an enterprise application that integrates material requirement planning (MRP) with job shop simulator using distributed object technology. The application aims to realize an integrated system that has rapid response to changing requirements and capability to integrate heterogeneous manufacturing facilities. At first, the application accepts the customer’s order and performs m...

Real world scheduling requirements are related with complex systems operating in dynamic environments. This means that they are frequently subject to several kinds of random occurrences and perturbations, such as new job arrivals, machine breakdowns, employee’s sickness and jobs cancellation causing prepared schedules becoming easily outdated and unsuitable. Scheduling under this environment is...

General job-shop scheduling is a difficult problem to be solved. The complexity of the problem is highlighted by the fact that in a general job-shop having m machines and n jobs the total number of schedules can be as high as (n!), hence if “n=20 m=10” the number of possible solutions is 7.2651x10. Many approaches such as Branch and Bound, Simulated Annealing, Tabu Search and others have been t...

Flexible job-shop scheduling problem (FJSSP) is an extension of the classical job-shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. It is very important in both fields of production management and combinatorial optimisation. This paper presents a new approach based on attribute oriented mining technique to solve the multi-ob...

Production scheduling is the process of allocating the resources and then sequencing of task to produce goods. Allocation and sequencing decision are closely related and it is very difficult to model mathematical interaction between them. The allocation problem is solved first and its results are supplied as inputs to the sequencing problem. High quality scheduling improves the delivery perform...

This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-compl...

The job-shop scheduling problem has attracted many researchers’ attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of par...

Considering the standard particle swarm optimization (PSO) has the shortcomings of low convergence precision in job shop scheduling problems, the job shop scheduling solution is presented based on improved particle swarm optimization (A-PSO). In this paper, the basic theory of A-PSO is described. Also, the coding and the selection of parameters as well as the decoding of A-PSO are studied. It u...

The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the propos...