On-line scheduling with delivery time on a single batch machine

AbstractWe consider a single batch machine on-line scheduling problem with jobs arriving over time. A batch processing machine can handle up to B jobs simultaneously as a batch, and the processing time for a batch is equal to the longest processing time among the jobs in it. Each job becomes available at its arrival time, which is not known in advance, and its characteristics, such as processing time and delivery time, become known at its arrival. Once the processing of a job is completed we deliver it to the destination. The objective is to minimize the time by which all jobs have been delivered. In this paper, we deal with two variants: the unbound model where B is sufficiently large and the bounded model where B is finite. We provide on-line algorithms with competitive ratio 2 for the unbounded model and with competitive ratio 3 for the bounded model. For when each job has the same processing time, we provide on-line algorithms with competitive ratios (5+1)/2, and these results are the best possible

A new mathematical model for single machine batch scheduling problem for minimizing maximum lateness with deteriorating jobs

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model

Scheduling en línea minimizando la tardanza total ponderada para una máquina mediante metaheurística Grasp

Programar actividades es una tarea que consciente o inconscientemente todos aplicamos para planear nuestras actividades diarias. Para cualquier persona se hace necesario plantear y evaluar el orden en las que éstas deben ser desarrolladas dependiendo de factores externos y el criterio único del individuo. De igual forma, la planeación de la producción en la industria requiere organizar los trabajos de una manera óptima para aprovechar al máximo unos recursos limitados (máquinas por ejemplo) y cumplir con objetivos enmarcados por la estrategia de la organización (un ejemplo es la política Just-in-Time). Sin embargo, a diferencia de un individuo, la industria cuenta con especializadas herramientas analíticas cuyo único propósito es apoyar a la toma de decisiones, y a lo que se conoce comúnmente dentro del campo de la investigación de operaciones como scheduling.Ingeniero (a) IndustrialPregrad