Abstract: A Quantum Genetic Based Scheduling Algorithm (QGBSA) for stochastic flow shop scheduling with random breakdown and random repair time is proposed in this paper, which combines stochastic programming and stochastic simulation theory, quantum compute and genetic algorithm together. In the QGBSA, the Q-bit based representation in discrete 0-1 hyperspace is employed, which is then convert...

This paper presents a novel, integer-linear programming (ILP) model for an identical parallel-machine scheduling problem with family setup times that minimizes the total weighted flow time (TWFT). Some researchers have addressed parallel-machine scheduling problems in the literature over the last three decades. However, the existing studies have been limited to the research of independent jobs,...

The Economic Lot Scheduling Problem (ELSP) has been well-researched for more than 40 years. As the ELSP has been generally seen as NP-hard, researchers have focused on the development of efficient heuristic approaches. In this paper, we consider the time-varying lot size approach to solve the ELSP. A computational study of the existing solution algorithms, Dobson’s heuristic, Hybrid Genetic alg...

We consider the resource-constrained scheduling of loops with interiteration dependencies. A loop is modeled as a data flow graph (DFG), where edges are labeled with the number of iterations between dependencies. We design a novel and flexible technique, called rotation scheduling, for scheduling cyclic DFG’s using loop pipelining. The rotation technique repeatedly transforms a schedule to a mo...

In the present work, SDST flow shop scheduling with minimizing the weighted sum of total weighted tardiness and makespan have been considered simultaneously. Four modified heuristic have been proposed for preliminary viable sequence. Sequence obtained from the modified heuristics is combined with the initial seed sequence of genetic algorithm and called as Genetic Algorithm (GA). Hence four dif...

We present a model of two-echelon retailer inventory systems, and we cast the problem of generating optimal control strategies into the framework of dynamic programming. We formulate two speci c case studies, for which the underlying dynamic programming problems involve thirty-three and forty-six state variables, respectively. Because of the enormity of these state spaces, classical algorithms ...

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To meet the requirements on time and to minimize the make-span performance of large permutation flow-shop scheduling problems in which there are sequence dep...

This paper delves into the scheduling of two-machine flow-shop problem with step-learning, a scenario in which job processing times decrease if they commence after their learning dates. The objective is to optimize resource allocation and task sequencing ensure efficient time utilization timely completion all jobs, also known as makespan. identified established NP-hard due its reduction single ...

abstract accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. in fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. although, during recent decades, some black box models based on artificial neural networks (ann), have bee...

Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics, which approximate the stochastic dynamic programming algorithm. We s...