No Optimisation Without Representation: A Knowledge Based Systems View of Evolutionary/Neighbourhood Search Optimisation

Centre for Intelligent Systems and their ApplicationsIn recent years, research into ‘neighbourhood search’ optimisation techniques such as simulated annealing, tabu search, and evolutionary algorithms has increased apace, resulting in a number of useful heuristic solution procedures for real-world and research combinatorial and function optimisation problems. Unfortunately, their selection and design remains a somewhat ad hoc procedure and very much an art. Needless to say, this shortcoming presents real difficulties for the future development and deployment of these methods. This thesis presents work aimed at resolving this issue of principled optimiser design. Driven by the needs of both the end-user and designer, and their knowledge of the problem domain and the search dynamics of these techniques, a semi-formal, structured, design methodology that makes full use of the available knowledge will be proposed, justified, and evaluated. This methodology is centred around a Knowledge Based System (KBS) view of neighbourhood search with a number of well-defined knowledge sources that relate to specific hypotheses about the problem domain. This viewpoint is complemented by a number of design heuristics that suggest a structured series of hillclimbing experiments which allow these results to be empirically evaluated and then transferred to other optimisation techniques if desired. First of all, this thesis reviews the techniques under consideration. The case for the exploitation of problem-specific knowledge in optimiser design is then made. Optimiser knowledge is shown to be derived from either the problem domain theory, or the optimiser search dynamics theory. From this, it will be argued that the design process should be primarily driven by the problem domain theory knowledge as this makes best use of the available knowledge and results in a system whose behaviour is more likely to be justifiable to the end-user. The encoding and neighbourhood operators are shown to embody the main source of problem domain knowledge, and it will be shown how forma analysis can be used to formalise the hypotheses about the problem domain that they represent. Therefore it should be possible for the designer to experimentally evaluate hypotheses about the problem domain. To this end, proposed design heuristics that allow the transfer of results across optimisers based on a common hillclimbing class, and that can be used to inform the choice of evolutionary algorithm recombination operators, will be justified. In fact, the above approach bears some similarity to that of KBS design. Additional knowledge sources and roles will therefore be described and discussed, and it will be shown how forma analysis again plays a key part in their formalisation. Design heuristics for many of these knowledge sources will then be proposed and justified. This methodology will be evaluated by testing the validity of the proposed design heuristics in the context of two sequencing case studies. The first case study is a well-studied problem from operational research, the flowshop sequencing problem, which will provide a through test of many of the design heuristics proposed here. Also, an idle-time move preference heuristic will be proposed and demonstrated on both directed mutation and candidate list methods. The second case study applies the above methodology to design a prototype system for resource redistribution in the developing world, a problem that can be modelled as a very large transportation problem with non-linear constraints and objective function. The system, combining neighbourhood search with a constructive algorithm which reformulates the problem to one of sequencing, was able to produce feasible shipment plans for problems derived from data from the World Health Organisation’s TB programme in China that are much larger than those problems tackled by the current ‘state-of-the-art’ for transportation problems

Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

Due to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness. The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice. The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. A series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied. Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtaiUrlings ., T. (2010). Heuristics and metaheuristics for heavily constrained hybrid flowshop problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8439Palanci

Réagir et s’adapter à son environnement: Concevoir des méthodes autonomes pour l’optimisation combinatoire à plusieurs objectifs

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself.While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms.We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.Les problèmes d’optimisation à grande échelle sont généralement difficiles à résoudre de façon optimale. Des algorithmes d’approximation tels que les métaheuristiques, capables de trouver rapidement des solutions sous-optimales, sont souvent préférés. Cette thèse porte sur les algorithmes de recherche locale multi-objectif (MOLS), des métaheuristiques capables de traiter l’optimisation simultanée de plusieurs critères. Comme de nombreux algorithmes, les MOLS exposent de nombreux paramètres qui ont un impact important sur leurs performances. Ces paramètres peuvent être soit prédits et définis avant l’exécution de l’algorithme, soit ensuite modifiés dynamiquement.Alors que de nombreux progrès ont récemment été réalisés pour la conception automatique d’algorithmes, la grande majorité d’entre eux ne traitent que d’algorithmes mono-objectif et l’optimisation d’un unique indicateur de performance. Dans cette thèse, nous étudions les relations entre la conception automatique d’algorithmes et l’optimisation multi-objective.Nous passons d’abord en revue les stratégies MOLS possibles et présentons un framework MOLS général et hautement configurable. Nous proposons également MO-ParamILS, un configurateur automatique spécialement conçu pour gérer plusieurs indicateurs de performance. Nous menons ensuite plusieurs études sur la conception automatique de MOLS sur de multiples problèmes combinatoires bi-objectifs. Enfin, nous discutons deux extensions de la configuration d’algorithme classique : d’abord l’intégration des mécanismes de contrôle de paramètres, pour bénéficier de multiples prédictions de configuration; puis l’utilisation séquentielle de plusieurs configurations

Effective and efficient estimation of distribution algorithms for permutation and scheduling problems.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Rámec pro plánování problémy

Import 22/07/2015Scheduling problems form an important subclass of combinatorial optimisation problems with many applications in manufacturing and logistics. Predominately these problems are NP-complete (decision based) and NP-hard (optimisation based), hence the main course of research in solving them concentrates on the design of efficient heuristic algorithms. Two main categories of these algorithms exist: deterministic algorithms and evolutionary metaheuristics. The deterministic algorithms comprise local improvement techniques, such as k-opt algorithm, which try to improve existing feasible solution, and constructive heuristics, such as NEH, which build a solution starting from scratch, adding one job at a time. Evolutionary metaheuristics have prospered in the past decades, owing to their efficiency and flexibility. Drawing inspiration from the theory of natural evolution or swarm behavioural patterns, the most popular of these algorithms in practice include for instance Genetic Algorithms, Differential Evolution, Particle Swarm Optimisation, amongst others. However, even though these heuristics provide in most cases close to optimal solution at reasonable execution time, this time is still impractically long for many applications. Therefore much effort has been dedicated to accelerating these algorithms. Since the development of hardware turns away from increasing the clock speed towards the parallel processing units, owing to reaching the limits of technology due to the increased power consumption and heat dissipation, this effort goes into parallelisation of the existing algorithms, to enable exploitation of the computing power of multi-core or many-core platforms. This is the goal of the first part of the thesis, accelerating two of the deterministic algorithms, NEH and 2-opt, with interesting results. Another approach has been taken in the second part, with the core premise of exploring the influence of stochasticity on the performance of an evolutionary algorithm, selecting the relatively recent and promising Discrete Artificial Bee Colony algorithm. The pseudo-random number generator has been replaced with the different types of dissipative chaos maps, with some of them improving the algorithm significantly. It has been shown that the population based evolutionary algorithms often form complex networks, taken from the point of view of the information exchange between individual solutions during the course of population development. The final part of this thesis puts this observation into practice by embedding the complex network analysis based self-adaptive mechanism into the ABC algorithm, a continuous optimisation problems solving evolutionary algorithm, which is however the basis for the afore mentioned DABC algorithm, and proving the effectiveness for some of the developed versions, currently on the standard continuous optimisation test functions, with the possibility to extend this modification to the combinatorial optimisations problems in the future being discussed in the conclusion.Rozvrhovací problémy jsou důležitou podtřídou úloh kombinatorické optimalizace s řadou aplikací ve výrobě a logistice. Většina těchto problémů je NP-úplných (rozhodovací forma) a NP-těžkých (optimalizační forma), proto se výzkum zaměřuje na návrh efektivních heuristických algoritmů. Dvě hlavní kategorie těchto algoritmů jsou deterministické algoritmy a evoluční metaheuristiky. Deterministické algoritmy zahrnují techniky lokálního prohledávání, například algoritmus k-opt, jejichž cílem je zlepšení existujícího přípustného řešení problému, dále pak konstruktivní heuristiky, jejichž příkladem je algoritmus NEH, které hledané řešení vytvářejí inkrementálně, bez potřeby znalosti vstupního bodu v prohledávaném prostoru řešení. Evoluční metaheuristiky mají za sebou historii úspěšného vývoje v posledních desetiletích, zejména díky jejich efektivitě a flexibilitě. Jejich inspirací jsou poznatky převzaté z biologie, teorie evoluce a inteligence hejna. Mezi nejpopulárnějšími z těchto algoritmů jsou, mimo jiné, genetické algoritmy, diferenciální evoluce, rojení částic (Particle Swarm Optimisation). Ačkoli tyto heuristiky nalézají ve většině případů řešení blížící se globálnímu optimu v přípustném výpočetním čase, pro řadu aplikací mohou být stále ještě nepřijatelně pomalé. Velké úsilí bylo věnováno zrychlení těchto algoritmů. Protože se vývoj hardware díky dosažení technologických limitů, vzhledem ke zvyšující se spotřebě energie a tepelnému vyzařování, obrací od zvyšování frekvence jednojádrového procesoru k vícejádrovým procesorům a paralelnímu zpracování, je tato snaha většinou orientovaná na paralelizaci existujících algoritmů, aby bylo umožněno využití výpočetní síly vícejádrových platforem (multi-core a many-core). Prvním cílem této práce je tudíž akcelerace dvou deterministických algoritmů, NEH a 2-opt, přičemž bylo dosaženo zajímavých výsledků. Jiný přístup byl zvolen ve druhé části, s hlavní myšlenkou prozkoumání vlivu náhodnosti na výkon evolučního algoritmu. Za tímto účelem byl zvolen relativně nový a slibný algoritmus Discrete Artificial Bee Colony. Generátor pseudonáhodných čísel byl nahrazen několika různými chaotickými mapami, z nichž některé znatelně zlepšily výsledky algoritmu. Bylo ukázáno, že evoluční algoritmy založené na populaci často formují komplexní sítě, vzato z pohledu výměny informací mezi jednotlivými řešeními v populaci během jejího vývoje. Závěrečná část práce aplikuje toto pozorování vložením samo přizpůsobivého mechanismu založeném na analýze komplexní sítě do algoritmu ABC, který je evolučním algoritmem pro spojitou optimalizaci a zároveň základem dříve zmíněného DABC algoritmu. Efektivita několika verzí algoritmu založeném na této myšlence je dokázána na standardní sadě testovacích funkcí pro spojitou optimalizaci. Možnost rozšíření této modifikace na kombinatorické optimalizační problémy je diskutována v závěru práce.460 - Katedra informatikyvýborn

Multi-Objective Discrete Particle Swarm Optimisation Algorithm for Integrated Assembly Sequence Planning and Assembly Line Balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set