In the classical job shop scheduling problem (JSSP), n jobs are processed to completion on m unrelated machines. Each job requires processing on each machine exactly once. For each job, technology constraints specify a complete, distinct routing which is fixed and known in advance. Processing times are sequence-independent, fixed, and known in advance. Each machine is continuously available fro...

In this work we consider a multiobjective open shop scheduling problem with uncertain processing times and flexible due dates, both modelled using fuzzy sets. We adopt a goal programming model based on lexicographic multiobjective optimisation of both makespan and due-date satisfaction and propose a particle swarm algorithm to solve the resulting problem. We present experimental results which s...

Multi-objective open-shop scheduling is definitely significant in practical. However, the research focused on multi-objective open-shop scheduling was relatively scarce. This article proposed a particle swarm optimization to address open-shop scheduling problems with multiple objectives. Originally, particle swarm optimization was invented to treat continuous optimization problems. In this pape...

In view of the difficulty of obtaining the optimal solution to the multi-objective scheduling of flexible job-shop by the general genetic algorithm, this paper takes into account the shortest processing time and the balanced use of machines, and puts forward the multi-population genetic algorithm based on the multi-objective scheduling of flexible job-shop. The method attempts to minimize the l...

This paper addresses the open shop scheduling problem. this problem, due to its complexity, is ranked in the class of NPhard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. So heuristic algorithms such as simulated Annealing (SA), tabu search (TS) genetic algorithm, (GA), ant colony optimization (ACO) used to solve optimization problems s...

This paper represents the metaheuristics proposed for solving a class of Shop Scheduling problem. The Bacterial Foraging Optimization algorithm is featured with Ant Colony Optimization algorithm and proposed as a natural inspired computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop is the combination of Job Shop, Flow Shop and Open Shop scheduling problems. The sample i...

Paradigms in modern production are shifting and pose new demands for optimization techniques. The emergence of new, versatile, reconfigurable and networked machines enables flexible manufacturing scenarios which require, in particular, planning and scheduling methods for cyber-physical production systems to be flexible, reasonably fast, and anytime. This paper presents an approach to flexible j...

Due to the complicated circumstances in workshop, most of the conventional scheduling algorithms fail to meet the requirements of instantaneity, complexity, and dynamicity in job-shop scheduling problems. Compared with the static algorithms, dynamic scheduling algorithms can better fulfill the requirements in real situations. Considering that both flexibility and fuzzy processing time are commo...

Flexible manufacturing systems (FMSs) are nowadays installed in the mechanical industry. In such systems. many different part types are produced simultaneously and it is necessary to take tooling constraints into account for finding an optimal schedule. A heuristic method is presented for solving the m-machine, n-job shop scheduling problem with tooling constraints. This method. named TOMATO, i...