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...

A frequently occurring operational problem is one of processing a given number of jobs (commodities) on a specified number of machines (facilities) referred to by various investigators as scheduling, dispatching, sequencing, or combinations thereof (Gupta and Stafford, 2006). In most manufacturing environments, a set of processes is needed to be serially performed in several stages before a job...

This paper presents a mathematical model for a flow shop scheduling problem consisting of m machine and n jobs with fuzzy processing times that can be estimated as independent stochastic or fuzzy numbers. In the traditional flow shop scheduling problem, the typical objective is to minimize the makespan). However,, two significant criteria for each schedule in stochastic models are: expectable m...

This paper proposes a distributed manufacturing scheduling framework at the shop floor level. The shop floor is modeled as a collection of multiple workcells, each of which is modeled as a flexible manufacturing system. The framework consists of a distributed shop floor control structure, dynamic distributed scheduling algorithms, multi-agent system modeling of workcell, and service oriented in...

Rapid development of the Internet of Things not only provides large amounts of data to the job-shop scheduling, but also proposes a great challenge for dynamic job shop scheduling. A dynamic job shop scheduling approach is proposed based on the data-driven genetic algorithm. Application examples suggest that this approach is correct, feasible and available. This approach can provide the technic...

Reactive scheduling is often been criticized for its inability to provide timely optimized and stable schedules. So far, the extant literature has focused on generating schedules that optimize shop floor efficiency. Only a few have considered optimizing both shop floor efficiency and schedule stability. This paper applies a unique selfadaptation mechanism of the ant colony optimization (ACO) al...

In this paper we study specially structured two stage flow shop scheduling problem with jobs in a string of disjoint job blocks having sequence independent setup times separated from processing times each associated with their respective probabilities including job weightage. In flow shop scheduling optimization of elapsed time may not always result in optimization of utilization time. Here, th...

This research introduces three heuristic algorithms for solving job-shop scheduling problems, job-shop scheduling problems with multi-purpose machines, and open-shop scheduling problems. All these algorithms are based on the particle swarm optimization algorithm, and generate solutions that belong to the class of parameterized active schedules via their specific decoding procedures. Comparison ...

In the multi-operation scheduling that arises in industrial engineering, each job contains multiple tasks (operations) that require execution in different shops. It is assumed that in each shop there is only one machine to perform the required operations. In this paper, a parallel model of multi-operation scheduling is proposed, in which multiple machines are available in each shop to perform t...

In this paper, we propose two efficient scheduling algorithms for the total exchange, which is a basic communication pattern used on the GRID environment. Both algorithms are used with the open shop algorithm. The first algorithm is a static scheduling algorithm which partitions messages before the open shop algorithm, and the second algorithm is a dynamic algorithm used in the execution of the...