David Alcaide

A tabu search algorithm for the Open Shop problem

In this paper we consider the minimum makespan Open Shop problem without preemption. It is well-known that the case with only two machines can be optimally solved in linear time, whereas the problem with an arbitrary number of machines is NP-hard in the strong sense. We propose a tabu search algorithm for the solution of the problem which uses simple list scheduling algorithms to build the starting solutions. The algorithm is extensively tested on randomly generated instances.

Cyclic multiple-robot scheduling with time-window constraints using a critical path approach

Abstract An automated production system is considered in which several robots are used for transporting parts between workstations following a given route in a carousel mode. The problem is to maximize the throughput rate. Extending previous works treating scheduling problems for a single robot, we consider a more realistic case in which workstations are served by multiple robots. A graph model of the production process is developed, making it possible to apply PERT–CPM solution techniques. The problem is proved to be solvable in polynomial time.

Network flow approaches to pre-emptive open-shop scheduling problems with time-windows

This paper deals with pre-emptive open-shop scheduling problems with time-windows. Network flow procedures to check feasibility and a max-flow parametrical algorithm to minimize the makespan are introduced. Also, computational complexities are evaluated for these feasibility and optimization algorithms. The proposed method is a strongly polynomial algorithm to minimize the makespan of the pre-emptive open-shop scheduling problems with time-windows strictly respected. Finally, the applications of the proposed algorithms are illustrated with numerical examples.

An approach to solve the minimum expected makespan flow-shop problem subject to breakdowns

Abstract Daily, there are multiple situations where machines or workers must execute certain jobs. During a working day it may be that some workers or machines are not available to perform their activities during some time periods. When scheduling models are used in these situations, workers or machines are simply called “machines”, and the temporal absences of availability are known as “breakdowns”. This paper considers some of these cases studying stochastic scheduling models with several machines to perform activities. Machines are specialized and models are flow-shops where breakdowns are allowed. The paper proposes a general procedure that tries to solve these problems. The proposed approach converts breakdowns scheduling problems into a finite sequence of without-breakdowns problems. Thus, we consider random variables, which measure the length of availability periods and repair times, to study availability intervals of machines. We propose partial feasible schedules in these intervals and combine them to offer a final global solution to optimize the expected makespan. Computational experiences are also reported.

Cyclic Scheduling in Robotic Cells: An Extension of Basic Models in Machine Scheduling Theory

There is a growing interest on cyclic scheduling problems both in the scheduling literature and among practitioners in the industrial world. There are numerous examples of applications of cyclic scheduling problems in different industries (see, e.g., Hall (1999), Pinedo (2001)), automatic control (Romanovskii (1967), Cohen et al. (1985)), multi-processor computations (Hanen and Munier (1995), Kats and Levner (2003)), robotics (Livshits et al. (1974), Kats and Mikhailetskii (1980), Kats (1982), Sethi et al. (1992), Lei (1993), Kats and Levner (1997a, 1997b), Hall (1999), Crama et al. (2000), Agnetis and Pacciarelli (2000), Dawande et al. (2005, 2007)), and in communications and transport (Dauscha et al. (1985), Sharma and Paradkar (1995), Kubiak (2005)). It is, perhaps, a surprising thing that many facts in scheduling theory obtained as early as in the 1960s, are re-discovered and rerediscovered by the next generations of researchers. About two decades ago, this fact was noticed by Serafini and Ukovich (1989). The present survey uniformly addresses cyclic scheduling problems through the prism of the classical machine scheduling theory focusing on their features that are common for all aforementioned applications. Historically, the scheduling literature considered periodic machine scheduling problems in two major classes – called flowshop and jobshop in which setup and transportation times were assumed insignificant. Indeed, many machining centers can quickly switch tools, so the setup times for these situations may be small or negligible. There are a lot of results about cyclic flowshop and jobshop problems with negligible setup/transportation times. Advantages of cyclic scheduling policies over conventional (non-cyclic) scheduling in flexible manufacturing are widely discussed in the literature, we refer the interested reader to Karabati and Kouvelis (1996), Lee and Posner (1997), Hall et al. (2002), Seo and Lee (2002), Timkovsky (2004), Dawande et al. (2007), and numerous references therein. At the same time, modern flexible manufacturing systems are supplied by computercontrolled hoists, robots and other material handling devices such that the transportation and setup operation times are significant and should not be ignored. Robots have become a standard tool to serve cyclic transportation and assembling/disassembling processes in manufacturing of airplanes, automobiles, semiconductors, printed circuit boards, food

Fuzzifying clustering algorithms: the case study of majorclust

Among various document clustering algorithms that have been proposed so far, the most useful are those that automatically reveal the number of clusters and assign each target document to exactly one cluster. However, in many real situations, there not exists an exact boundary between different clusters. In this work, we introduce a fuzzy version of the MajorClust algorithm. The proposed clustering method assigns documents to more than one category by taking into account a membership function for both, edges and nodes of the corresponding underlying graph. Thus, the clustering problem is formulated in terms of weighted fuzzy graphs. The fuzzy approach permits to decrease some negative effects which appear in clustering of large-sized corpora with noisy data.

Approximate algorithms for theP‖C max problem

SummaryWe consider one of the problems in deterministic scheduling theory: to schedulen independent and nonpreemptable jobs onm identical parallel machines, so as to minimize overall finishing time. The problem is known to beNP-hard in the strong sense. We propose some heuristic algorithms and will make computational analysis.

Multi-attribute Text Classification Using the Fuzzy Borda Method and Semantic Grades

We consider the problem of automatic classification of text documents, in particular, scientific abstracts and use two types of classifiers: ordinal and numerical. For the first type we use a fuzzy extension of the Borda voting method while for the second type we use a fuzzy Borda method in combination with the semantic grading.

An approach to solve a hierarchical stochastic sequential ordering problem

In this paper, we study a stochastic sequential ordering problem, where a set of jobs must be scheduled on a single machine. The jobs have stochastic processing times and verify certain precedence relations. For each job, there also exists a time window characterized by the release time and the due date. The jobs must be processed into the time-windows. For all the sequences of jobs that verify the precedence relations and the release times, the feasibility probability of a sequence it is defined as the probability that the sequence heeds the due dates. The problem consists of finding a sequence of jobs with the minimum expected makespan among the sequences with maximum feasibility probability. We prove that this problem is NP-hard and we propose an algorithm to find an approximate solution for this problem. The algorithm does not depend on the distribution types of the random input data considered. This procedure looks first for sequences of jobs with a high probability of being feasible. Next, the algorithm selects, among the sequences which have an acceptable feasibility probability level, that sequence with minimum expected makespan. Computational experiences are also reported, and the results show us that the proposed algorithm finds good solutions with short CPU times.