Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

[EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz García, R.; Pagnozzi, F.; Stützle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. 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Automatic Design of Hybrid Stochastic Local Search Algorithms - analysis and application

Combinatorial optimization problems can be found in many aspects ofmanufacturing, computer science, logistics and many more. These problemsconsist in combining a finite set of elements so that a cost measure isminimized or a quality measure is maximized. Despite the great interestgenerated by the many practical applications, combinatorial optimizationproblems can be quite hard to solve. In fact, many combinatorial optimizationproblems, like the traveling salesman problem and the permutation flowshopproblem, belong to a class of problems called NP-hard. The techniques used tosolve these problems can be grouped in two classes, exact methods andapproximate methods. Exact methods are guaranteed to eventually find theoptimal solution. Yet, the time needed to find the optimal solution may beimpractical. On the contrary, approximate methods are not guaranteed to findthe optimal solution but, in most cases, can find solutions with a qualityclose to the optimal in little time. Among these methods, stichastic localsearch (SLS) algorithms have been proved to be very successful. In fact, SLSalgorithms comprehend many of the most widely known high performance algorithmsto solve hard combinatorial optimization problems. These algorithms are oftenobtained after a significant, manual algorithm engineering effort. It ispossible to automatize this process by using automatic configuration tools witha configurable algorithmic framework. Such frameworks implement one or more SLSalgorithms in a modular way, where an algorithm is composed of differentalgorithmic components. This process is called automatic algorithm design(AAD). In this thesis, we expand the work done on grammar based automaticdesign of stochastic local search algorithms. In particular, we present a newalgorithmic framework, EMILI. This new framework improves over previousframeworks thanks to its modular design and its ability to instantiatealgorithms at run time. Using AAD, we present new state-of- the-art algorithmsfor the major objectives of the permutation flowshop problem (PFSP) and PFSPvariants with additional constraints. While working on PFSP we introduced a newspeed-up mechanism for the calculation of the objective function for PFSP withthe weighted tardiness objective as well as a new state-of-the-art algorithmfor PFSP with the makespan objective. Finally, we analyze how algorithmcomplexity affects the performances of automatically generated SLS algorithms.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Speeding up Local Search for the Insert Neighborhood in the Weighted Tardiness Permutation Flowshop Problem

Many algorithms for minimizing the weighted tardiness in the permutation flowshop problem rely on local search procedures. An increase in the efficiency of evaluating the objective function for neighboring candidate solutions directly also improves the performance of such algorithms. In this paper, we introduce a speed up of the evaluation of the weighted tardiness while exploring the insert neighborhood of a solution. To discard non-improving neighbors and to avoid the full computation of the objective function, we use an approximation of the weighted tardiness. The experimental results show that the technique delivers a consistent speed-up that increases with instance size. Furthermore, we show that it is possible to apply the same approximation technique to the exchange neighborhood achieving again a consistent, but smaller speed-up.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems

Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks; (ii) define the rules of how to compose algorithms from the building blocks; and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints

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Off-Policy Evaluation of the Performance of a Robot Swarm: Importance Sampling to Assess Potential Modifications to the Finite-State Machine That Controls the Robots

Due to the decentralized, loosely coupled nature of a swarm and to the lack of a general design methodology, the development of control software for robot swarms is typically an iterative process. Control software is generally modified and refined repeatedly, either manually or automatically, until satisfactory results are obtained. In this paper, we propose a technique based on off-policy evaluation to estimate how the performance of an instance of control software—implemented as a probabilistic finite-state machine—would be impacted by modifying the structure and the value of the parameters. The proposed technique is particularly appealing when coupled with automatic design methods belonging to the AutoMoDe family, as it can exploit the data generated during the design process. The technique can be used either to reduce the complexity of the control software generated, improving therefore its readability, or to evaluate perturbations of the parameters, which could help in prioritizing the exploration of the neighborhood of the current solution within an iterative improvement algorithm. To evaluate the technique, we apply it to control software generated with an AutoMoDe method, Chocolate − 6 S   .In a first experiment, we use the proposed technique to estimate the impact of removing a state from a probabilistic finite-state machine. In a second experiment, we use it to predict the impact of changing the value of the parameters. The results show that the technique is promising and significantly better than a naive estimation. We discuss the limitations of the current implementation of the technique, and we sketch possible improvements, extensions, and generalizations.info:eu-repo/semantics/publishe

An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem

Permutation flowshop scheduling problems (PFSPs) and, in particular, the variant with the objective of minimizing makespan have received an enormous attention in scheduling research and combinatorial optimization. As a result, the algorithmic approaches to this PFSP variant have reached extremely high performance. Currently, one of the most effective algorithm for this problem is a structurally rather simple iterated greedy algorithm. In this paper, we explore the possibility of re-optimizing partial solutions obtained after the solution destruction step of the iterated greedy algorithm. We show that with this extension, the performance of the state-of-the-art algorithm for the PFSP under makespan criterion can be significantly improved and we give experimental evidence that the local search on partial solutions is the key component for the high performance of the algorithm.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

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