Fawaz S. Al-Anzi

A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

The assembly flowshop scheduling problem has been addressed recently in the literature. There are many problems that can be modeled as assembly flowshop scheduling problems including queries scheduling on distributed database systems and computer manufacturing. The problem has been addressed with respect to either makespan or total completion time criterion in the literature. In this paper, we address the problem with respect to a due date-based performance measure, i.e., maximum lateness. We formulate the problem and obtain a dominance relation. Moreover, we propose three heuristics for the problem: particle swarm optimization (PSO), Tabu search, and EDD. PSO has been used in the areas of function optimization, artificial neural network training, and fuzzy system control in the literature. In this paper, we show how it can be used for scheduling problems. We have conducted extensive computational experiments to compare the three heuristics along with a random solution. The computational analysis indicates that Tabu outperforms the others for the case when the due dates range is relatively wide. It also indicates that the PSO significantly outperforms the others for difficult problems, i.e., tight due dates. Moreover, for difficult problems, the developed dominance relation helps reduce error by 65%.

The two-stage assembly scheduling problem to minimize total completion time with setup times

We address the two-stage assembly scheduling problem where there are m machines at the first stage and an assembly machine at the second stage. The objective is to schedule the available n jobs so that total completion time of all n jobs is minimized. Setup times are treated as separate from processing times. This problem is NP-hard, and therefore we present a dominance relation and propose three heuristics. The heuristics are evaluated based on randomly generated data. One of the proposed heuristics is known to be the best heuristic for the case of zero setup times while another heuristic is known to perform well for such problems. A new version of the latter heuristic, which utilizes the dominance relation, is proposed and shown to perform much better than the other two heuristics.

Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimize makespan with setup times

In this paper we address the two-stage assembly flowshop scheduling problem with respect to the makespan criterion where setup times are considered as separate from processing times. We formulate the problem and obtain a dominance relation. Moreover, we propose two evolutionary heuristics: a Particle Swarm Optimization (PSO) and a Tabu search. We also propose a simple and yet efficient algorithm with negligible computational time. We have conducted extensive computational experiments to compare the two heuristics and the algorithm along with a random solution. The computational analysis indicates that both heuristics and the algorithm perform significantly well. The computational analysis also indicates that PSO is the best and that the difference between the average errors of PSO and the algorithm becomes small as the number of jobs increases, while the computational time of PSO becomes much larger. Moreover, the difference between the two errors becomes even smaller as the number of machines (at the first stage) and the ratio of setup times to processing times becomes smaller. Therefore, PSO is recommended for a number of jobs up to 50, whereas the algorithm is suggested for larger numbers of jobs and larger numbers of machines at the first stage.

Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan

We consider a two-stage assembly flowshop scheduling problem with the objective of minimizing a weighted sum of makespan and maximum lateness. The problem is known to be NP-hard, and therefore, we propose heuristics to solve the problem. The proposed heuristics are Tabu search (Tabu), particle swarm optimization (PSO), and self-adaptive differential evolution (SDE). An extensive computational experiment is conducted to compare performances of the proposed heuristics. The computational experiment reveals that both PSO and SDE are much superior to Tabu. Moreover, it is statistically shown that PSO performs better than SDE. The computation times of both PSO and SDE are close to each other and they are less than 40 and 45s, respectively, for the largest size problem considered.

Using two-machine flowshop with maximum lateness objective to model multimedia data objects scheduling problem for WWW Applications

The multimedia data objects scheduling problem for WWW applications is modeled using the two-machine flowshop problem of minimizing maximum lateness with separate setup times. We establish three dominance relations, and propose four heuristics. Also, we conduct computational experiments to compare the performance of the proposed heuristics and that of existing ones in the literature. The results of the computational experiments show that the proposed heuristics are quite efficient.

Scheduling multi-stage parallel-processor services to minimize average response time

The problem of scheduling on a multi-stage parallel-processor architecture in computer centres is addressed with the objective of minimizing average completion time of a set of requests. The problem is modelled as a flexible flowshop problem for which few heuristics exist in the flowshop scheduling literature. A new three-phase heuristic is proposed in this paper. An extensive computational experiment has been conducted to compare the performance of the existing heuristics and the proposed heuristic. The results indicate that the proposed heuristic significantly outperforms the existing ones. More specifically, the overall average error of the best existing heuristic is about five times that of the proposed heuristic while the overall average CPU time of the proposed heuristic is about half of the best existing one. More importantly, as the number of requests increases, the CPU time of the proposed heuristic decreases considerably (compared to the best existing heuristic) while the ratio of the error (of the best existing to the proposed heuristic) of about five times remains almost the same.

Using mixed graph coloring to minimize total completion time in job shop scheduling

The problem of scheduling a set of jobs with unit operation times in a job shop to minimize total completion time is addressed. It is shown that this problem can be modeled as finding the optimal coloring of a special mixed graph. Subgraph of such a mixed graph without edges represents union of paths, and subgraph without arcs represents union of cliques. Finding the optimal coloring of the mixed graph with the criterion of minimizing the sum of maximal colors (used for the paths) is shown to determine a schedule for minimizing total completion time for processing jobs in a job shop. Since the problem is NP-hard, we develop a branch and bound algorithm for obtaining the optimal coloring of such a mixed graph. We develop a color-based branching scheme, dominance of the vertices in the solution tree, and three lower bounds of the objective function. The computational experiments indicate that the branch and bound algorithm performs well for randomly generated mixed graphs of order up to 200, if all the paths in the directed subgraph (job routes) have the same length, and of order up to 750 otherwise. Since in the considered job shop problem, machine repetition and absence of some machines in the job route are allowed, the developed methodology can be used to minimize total completion time in the job shop with arbitrary integer operation durations.

Batching deteriorating items with applications in computer communication and reverse logistics

We study two deterministic scheduling problems that combine batching and deterioration features. In both problems, there is a certain demand for identical good quality items to be produced in batches. In the first problem, each batch is assigned an individual machine that requires a cost and a time to be activated. All the machines are identical, work in parallel, and always produce good quality items. All the items are available at time zero and they deteriorate while waiting for production. Deterioration results in a linear increase of time and cost of production. In the second problem, there is a single machine that produces good quality as well as defective items in batches. Each batch is preceded by a setup time and requires a setup cost. Defective items have to be reworked on the same machine. They deteriorate while waiting for rework. At a time to be decided, the machine switches from production to rework defective items of the current batch. After rework, every defective item has the required good quality. In both problems, the objective is to find batch partitioning such that a linear combination of the production cost and production completion time is minimized. The two problems are observed at computer service providers and also reverse logistics. In computer service providers, machines and items correspond to communication service channels and information transfer tasks, respectively. We reduce both problems to minimizing a function of one variable representing the number of batches. In an optimal solution of either problem, there are at most two different batch sizes. Linear time algorithms are proposed for both problems.

A branch-and-bound algorithm for three-machine flowshop scheduling problem to minimize total completion time with separate setup times

In this paper, we address the three-machine flowshop scheduling problem. Setup times are considered separate from processing times, and the objective is to minimize total completion time. We show that the three-site distributed database scheduling problem can be modeled as a three-machine flowshop scheduling problem. A lower bound is developed and a dominance relation is established. Moreover, an upper bound is developed by using a three-phase hybrid heuristic algorithm. Furthermore, a branch-and-bound algorithm, incorporating the developed lower bound, dominance relation, and the upper bound is presented. Computational analysis on randomly generated problems is conducted to evaluate the lower and upper bounds, the dominance relation, and the branch-and-bound algorithm. The analysis shows the efficiency of the upper bound, and, hence, it can be used for larger size problems as a heuristic algorithm.

Stemming impact on Arabic text categorization performance: A survey

The significant growth of online textual information has increased the demand for effective content-based Arabic text categorization methods. The categorization of Arabic texts has some challenges that need to be addressed specially when using stemming. In the literature, we found a debate among researchers about the benefits of using stemming in Arabic text categorization. Hence, we performed a study of this feature reduction method to clarify the impact of this widely used method in text mining and document classification. We also presented some Arabic text cases to deny the importance of stemming in Arabic text categorization.