Permutation flow shop scheduling problem (PFSP) is a well-known NP-hard with extensive engineering relevance. Consequently, various meta-heuristics have been proposed to obtain near optimum solutions. However, most of them involve tuning algorithm-specific parameters, which leads excessive computational complexities. A recently developed meta-heuristic named Jaya algorithm simple yet efficient ...

In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and metaheuristics alg...

This paper deals with performance evaluation and scheduling problems in m machine stochastic flow shop with unlimited buffers. The processing time of each job on each machine is a random variable exponentially distributed with a known rate. We consider permutation flow shop. The objective is to find a job schedule which minimizes the expected makespan. A classification of works about stochastic...

scheduling is an important subject of production and operations management area. in flow-shop scheduling, the objective is to obtain a sequence of jobs which when processed in a fixed order of machines, will optimize some well defined criteria. the concept of transportation time is very important in scheduling. transportation can be done by robots. in situations that robots are used to transpor...

Flexible flow shop scheduling problem (also called blended flow shop scheduling problem) is a sort of complex Job Shop Scheduling problem. For the Flexible Flow Shop scheduling problem, only in very special cases, there are polynomial optimal algorithms. In most of the other cases, the problems are NP-Hard. It is a simplification of the original problem to solve Flexible flow shop scheduling wi...

Uncertainty plays a significant role in modeling and optimization of real world systems. Among uncertain approaches, fuzziness describes impreciseness while for ambiguity another definition is required. Vagueness is a probabilistic model of uncertainty being helpful to include ambiguity into modeling different processes especially in industrial systems. In this paper, a vague set based on dista...