A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flo...

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data c...

163904-4747-IJECS-IJENS © August 2016 IJENS I J E N S Abstract— The intelligent water drops (IWD) algorithm was introduced in 2007 by Hamed Shah Hosseini, and has been successfully implemented to solve many types of optimization problems, especially traveling salesman problem (TSP). The algorithm works by imitating the behavior of the natural river water drops. Principally, the algorithm finds ...

The various industries such as automobile industry, aerospace industry, printing industry, and chemical industry operate on job shop system. Job shop scheduling problems are complex and real world. The present work assesses the effect of change in shop utilizations levels on total setups and mean setup time measures. The simulation model of the job shop is developed for investigation purpose. T...

In this paper, we propose a neuro-genetic decision support system coupled with simulation to design a job shop manufacturing system by achieving predetermined values of targeted performance measures such as flow time, number of tardy jobs, total tardiness and machine utilization at each work center. When a manufacturing system is designed, the management has to make decisions on the availabilit...

Job shop scheduling(JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop...

In this paper we present an algorithm for planning in nondeterministic domains. Our algorithm C-SHOP extends the successful classical HTN planner SHOP, by introducing new mechanisms to handle situations where there is incomplete and uncertain information about the state of the environment. Being an HTN planner, C-SHOP supports coding domain-dependent knowledge in a powerful way that describes h...

In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investig...

In this paper, we propose 260 scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems. The types of problems that we propose are : the permutation flow shop, the job shop and the open shop scheduling problems. We restrict us to basic problems : the processing times are fixed, there are neither set-up tim...