Eva Vallada

A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times

In this work a genetic algorithm is presented for the unrelated parallel machine scheduling problem in which machine and job sequence dependent setup times are considered. The proposed genetic algorithm includes a fast local search and a local search enhanced crossover operator. Two versions of the algorithm are obtained after extensive calibrations using the Design of Experiments (DOE) approach. We review, evaluate and compare the proposed algorithm against the best methods known from the literature. We also develop a benchmark of small and large instances to carry out the computational experiments. After an exhaustive computational and statistical analysis we can conclude that the proposed method shows an excellent performance overcoming the rest of the evaluated methods in a comprehensive benchmark set of instances.

Minimising total tardiness in the m -machine flowshop problem: A review and evaluation of heuristics and metaheuristics

In this work, a review and comprehensive evaluation of heuristics and metaheuristics for the m-machine flowshop scheduling problem with the objective of minimising total tardiness is presented. Published reviews about this objective usually deal with a single machine or parallel machines and no recent methods are compared. Moreover, the existing reviews do not use the same benchmark of instances and the results are difficult to reproduce and generalise. We have implemented a total of 40 different heuristics and metaheuristics and we have analysed their performance under the same benchmark of instances in order to make a global and fair comparison. In this comparison, we study from the classical priority rules to the most recent tabu search, simulated annealing and genetic algorithms. In the evaluations we use the experimental design approach and careful statistical analyses to validate the effectiveness of the different methods tested. The results allow us to clearly identify the state-of-the-art methods.

Cooperative metaheuristics for the permutation flowshop scheduling problem

In this work, we propose cooperative metaheuristic methods for the permutation flowshop scheduling problem considering two objectives separately: total tardiness and makespan. We use the island model where each island runs an instance of the algorithm and communications start when the islands have reached certain level of evolution, that is, communication is not allowed from the beginning of the execution. Subsequent ones occur when new better solutions are found. We carry out an exhaustive comparison of the cooperative methods against the sequential counterparts running in completely comparable scenarios. Results have been carefully analysed by means of statistical procedures and we can conclude that the cooperative methods yield much better results than the sequential algorithms and state-of-the-art methods running in the same number of processors but without communications. The proposed cooperative schemes are easy to apply to other algorithms and problems.

Flow shop rescheduling under different types of disruption

Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.

New hard benchmark for flowshop scheduling problems minimising makespan

In this work a new benchmark of hard instances for the permutation flowshop scheduling problem with the objective of minimising the makespan is proposed. The new benchmark consists of 240 large instances and 240 small instances with up to 800 jobs and 60 machines. One of the objectives of the work is to generate a benchmark which satisfies the desired characteristics of any benchmark: comprehensive, amenable for statistical analysis and discriminant when several algorithms are compared. An exhaustive experimental procedure is carried out in order to select the hard instances, generating thousands of instances and selecting the hardest ones from the point of view of a gap computed as the difference between very good upper and lower bounds for each instance. Extensive generation and computational experiments, which have taken almost six years of combined CPU time, demonstrate that the proposed benchmark is harder and with more discriminant power than the most common benchmark from the literature. Moreover, a website is developed for researchers in order to share sets of instances, best known solutions and lower bounds, etc. for any combinatorial optimisation problem.

A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times

Abstract This paper describes a simulation–optimization algorithm for the Permutation Flow shop Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with the stochastic behavior of the problem. Using the expected makespan as initial minimization criterion, our simheuristic approach is based on the assumption that high-quality solutions (permutations of jobs) for the deterministic version of the problem are likely to be high-quality solutions for the stochastic version – i.e., a correlation will exist between both sets of solutions, at least for moderate levels of variability in the stochastic processing times. No particular assumption is made on the probability distributions modeling each job-machine processing times. Our approach is able to solve, in just a few minutes or even less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper proposes the use of reliability analysis techniques to analyze simulation outcomes or historical observations on the random variable representing the makespan associated with a given solution. This way, criteria other than the expected makespan can be considered by the decision maker when comparing different alternative solutions. A set of classical benchmarks for the deterministic version of the problem are adapted and tested under several scenarios, each of them characterized by a different level of uncertainty – variance level of job-machine processing times.

Scheduling in Flowshops with No-Idle Machines

This chapter deals with an interesting and not so well studied variant of the classical permutation flowshop problem with makespan criterion. In the studied variant, no idle time is allowed on machines. In order to ensure this no-idle constraint, the start times of jobs on machines must be delayed until all assigned jobs can be processed without incurring in idle times. This is a real situation arising in practice when expensive machinery is operated or when specific machines cannot be easily started and stopped due to technological constraints.

Scheduling Unrelated Parallel Machines with Sequence Dependent Setup Times and Weighted Earliness–Tardiness Minimization

This work deals with the unrelated parallel machine scheduling problem with machine and job-sequence dependent setup times. The studied objective is the minimization of the total weighted earliness and tardiness. We study existing Mixed Integer Programming (MIP) mathematical formulations. A genetic algorithm is proposed, which includes a procedure for inserting idle times in the production sequence in order to improve the objective value. We also present a benchmark of small and large instances to carry out the computational experiments. After an exhaustive computational and statistical analysis, the conclusion is that the proposed method shows a good performance.

Rescheduling flowshops under simultaneous disruptions

Production planning and scheduling systems are pervasive in manufacturing systems to increase productivity, variety and customization without incurring in high costs. A large number of impromptu disruptions frequently affect the scheduled operations and invalidate the original schedules. Rescheduling actions have to be triggered in order to reduce the impact on the performance of the system. A large body of research covering miscellaneous problem characteristics can be found in the scheduling literature. Even so, the application of scheduling techniques is still infrequent in real-life scheduling problems. In this work we generate eight types of disruptions that affect the original schedules simultaneously. We apply three rescheduling methods and compare their results with simple rule based repair actions typically employed in production environments. Statistical analysis is used to demonstrate that the proposed methods outperform the rule based approaches by a significant margin, highlighting their effectiveness in real life manufacturing settings.

Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

Abstract In this paper, we study the unrelated parallel machine scheduling problem with one scarce additional resource to minimise the maximum completion time of the jobs or makespan. Several heuristics are proposed following two strategies: the first one is based on the consideration of the resource constraint during the whole solution construction process. The second one starts from several assignment rules without considering the resource constraint, and repairs the non feasible assignments in order to obtain a feasible solution. Several computation experiments are carried out over an extensive benchmark. A comparative evaluation against previously proposed mathematical models and matheuristics (combination of mathematical models and heuristics) is carried out. From the results, we can conclude that our methods outperform the existing ones, and the second strategy performs better, especially for large instances.