Search

This paper presents an efficient heuristic method to minimize total flow time in no-wait flow shop scheduling. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. Empirical results demonstrate the superiority of the proposed method over the best-known heuristics in the literature, while remaining the same complexity order of O( n2). [ABSTRACT FROM AUTHOR]

New distributed computing platforms (grids) are based on interconnections of a large number of processing elements. A most important issue for their effective utilization is the optimal use of resources through proper task scheduling. It consists of allocating the tasks of a parallel program to processors on the platform and to determine at what time the tasks will start their execution. As data may be subject to uncertainties or disturbances, it is practically impossible to precisely predict the input parameters of the task scheduling problem. We briefly survey existing approaches for dealing with data uncertainties and discuss their relevance in the context of grid computing. We describe the stabilization process and analyze a scheduling algorithm that is intrinsically stable (i.e., it mitigates the effects of disturbances in input data at runtime). This algorithm is based on a decomposition of the application graph into convex sets of vertices. Finally, it is compared experimentally to pure on-line and well-known off-line algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the permutation flow shop scheduling problem with the objective of minimizing the makespan. A new hybrid heuristic, based on simulated annealing and an improvement heuristic, is presented. The proposed hybrid heuristic uses simulated annealing in conjunction with the constructive heuristic of Nawaz et al. ( Omega 11:91–95, ). Computational experiments carried out with the benchmark problems of Taillard ( Eur J Oper Res 64:278–285, ) show that the proposed method produces solutions that are mostly superior to those obtained with five state-of-the-art approaches. Statistical tests of significance are used to verify the improvement in solution quality. [ABSTRACT FROM AUTHOR]

This paper presents a new constructive heuristic, based on the principle of job insertion, for minimizing makespan in no-wait permutation flow shop scheduling problems. Empirical results demonstrate the superiority of the proposed approach over four of the best-known methods in the literature. Analytical expressions for the total number of partial and complete sequences generated by the algorithms are derived. Statistical tests of significance substantiate the improvement in solution quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general model for various scheduling problems that occur in container terminal logistics. The scheduling model consists of the assignment of jobs to resources and the temporal arrangement of the jobs subject to precedence constraints and sequence-dependent setup times. We demonstrate how the model can be applied to solve several different real-world problems from container terminals in the port of Hamburg (Germany). We consider scheduling problems for straddle carriers, automated guided vehicles (AGVs), stacking cranes, and workers who handle reefer containers. Subsequently, we discuss priority rule based heuristics as well as a genetic algorithm for the general model. Based on a tailored generator for experimental data, we examine the performance of the heuristics in a computational study. We obtain promising results that suggest that the genetic algorithm is well suited for application in practice. [ABSTRACT FROM AUTHOR]

The classical deterministic scheduling problem of minimizing the makespan on unrelated parallel processors is known to be NP-hard in the strong sense. Given the mixed integer linear model with binary decision variables, this paper presents heuristic algorithms based on partial enumeration. Basically, they consist in the construction of mixed integer subproblems, considering the integrality of some subset of variables, formulated using the information obtained from the solution of the linear relaxed problem. Computational experiments are reported for a collection of test problems, showing that some of the proposed algorithms achieve better solutions than other relevant approximation algorithms published up to now. [ABSTRACT FROM AUTHOR]