Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe

Analýza dynamiky evolučních algoritmů pomocí komplexních sítí aplikovaných na kombinatorické optimalizační problémy

Import 05/08/2014This thesis explores the connection between Evolutionary Algorithms (EA's) and Complex Networks (CN's). EA's are bio-inspired algorithms which mimic naturally occurring phenomena in order to model and solve complex engineering tasks. One of its features is its population based paradigm. The behaviour of the population over the iterations is analysed in this thesis using CN analysis tools. Four distinct broad attributes are analysed; adjacency matrix, centralities, cliques and communities. Using these attributes, a number of experimentations and analysis were conducted, from which interesting information regarding population development, stagnation, network interconnection and hierarchical development was obtained. These data supported the concept of population dynamics and furthermore could be used for population and evolution control.Tato práce se zabývá spojením mezi evolučními algoritmy (EA) a komplexnímí sítěmi (KS). EA jsou biologicky inspirované algoritmy napodobující přirozené přírodní jevy s cílem modelovat a řešit složité technické problémy. Jednou z jejich funkcí je snaha napodobit evoluční dogma. Chování celé populace je skrze její vývoj sledováno pomocí nástrojů pro analýzu komplexních sítí. Analyzovány jsou tyto čtyři atributy: matice sjednocení, centralita, kliky a komunity. Byla provedena řada experimentů a analýz, ze kterých pomocí těchto atributů, byly získány zajímavé informace týkající se vývoje populace, stagnace, propojení a hierarchie. Získaná data nastínila koncepci populační dynamiky a dala by se využít ke kontrole samotné evoluce.440 - Katedra telekomunikační technikyvýborn

COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS

The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instanc

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

MINIMASI MAKESPAN PADA PERSOALAN PENJADWALAN ORDERED FLOWSHOP MENGGUNAKAN PSO

The production scheduling problem is in the kind of flowshop with n jobs and m machines, to get the order of the schedule for allocating operations of the jobs to the available machines so as to get the minimum total time for completion of all job or commonly called makespan. This study uses an optimization technique approach with the PSO algorithm to get minimum makespan on the ordered flowhop scheduling problem. The performance of the scheduling algorithm presented is evaluated by testing on a benchmark data set of 240 variations in the combination number of jobs and machines. The minimum measure is obtained as a result of scheduling with PSO, whose process stops at a certain iteration when in the last 10 iterations there is no change in the value of a better makespan. The performance of the PSO algorithm is efficient at regular flow scheduling with the use of the most iterations of 19 iterations and the longest execution time of 28.42 seconds or less than half a minute, namely scheduling instances with the largest number of machines and jobs. In this research, only the analysis of the resulting minimal forward and the time of execution was carried out. Further research can be extended by not only measuring the minimum makespan, such as measuring total flowtime, total tardiness, and others

Optimasi Penjadwalan Flow Shop Menggunakan Algoritma Hybrid Differential Evolution

Penjadwalan produksi merupakan bagian integral di dalam sistem manufaktur. Artikel ini menyelesaikan permasalahan penjadwalan flow shop dengan fungsi obyektif total flow time. Dalam penjadwalan, total flow time menghasilkan konsumsi yang stabil terhadap sumber daya, perputaran job yang cepat serta meminimalkan work in process inventory. Permasalahan penjadwalan flow shop tergolong pada permasalahan optimasi kombinatorial yang merupakan NP-hard. Saat ini, penggunaan algoritma metaheuristik banyak digunakan untuk memecahkan kasus optimasi kombinatorial, termasuk penjadwalan flow shop. Salah satu yang memiliki performa yang baik adalah Algoritma Differential Evolution. Untuk meningkatkan kualitas solusinya, Algoritma Differential Evolution akan ditambahkan dengan prosedur local search yang dinamakan Hybrid Differential Evolution. Untuk mengetahui performa dari algoritma tersebut, dilakukan pengujian menggunakan data penjadwalan flow shop yang ada pada OR-Library. Performa Hybrid Differential Evolution akan dibandingkan dengan algoritma lain. Hasil pengujian menunjukkan bahwa Hybrid Differential Evolution memberikan performa yang lebih baik dibandingkan dengan algoritma lain

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