Constructing a fuzzy flow-shop sequencing model based on statistical data

AbstractThis study investigated an approach for incorporating statistics with fuzzy sets in the flow-shop sequencing problem. This work is based on the assumption that the precise value for the processing time of each job is unknown, but that some sample data are available. A combination of statistics and fuzzy sets provides a powerful tool for modeling and solving this problem. Our work intends to extend the crisp flow-shop sequencing problem into a generalized fuzzy model that would be useful in practical situations. In this study, we constructed a fuzzy flow-shop sequencing model based on statistical data, which uses level (1−α,1−β) interval-valued fuzzy numbers to represent the unknown job processing time. Our study shows that this fuzzy flow-shop model is an extension of the crisp flow-shop problem and the results obtained from the fuzzy flow-shop model provides the same job sequence as that of the crisp problem

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II)

Robust multiobjective optimisation for fuzzy job shop problems

Abstract In this paper we tackle a variant of the job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. Our goal is to simultaneously minimise the schedule's fuzzy makespan and maximise its robustness. To this end, we consider two measures of solution robustness: a predictive one, prior to the schedule execution, and an empirical one, measured at execution. To optimise both the expected makespan and the predictive robustness of the fuzzy schedule we propose a multiobjective evolutionary algorithm combined with a novel dominance-based tabu search method. The resulting hybrid algorithm is then evaluated on existing benchmark instances, showing its good behaviour and the synergy between its components. The experimental results also serve to analyse the goodness of the predictive robustness measure, in terms of its correlation with simulations of the empirical measure.This research has been supported by the Spanish Government under Grants FEDER TIN2013-46511-C2-2-P and MTM2014-55262-P

Development of a decision support system for decision-based part/fixture assignment and fixture flow control = Ukusungulwa kohlelo lokuxhaswa kwezinqumo mayelana nokwabiwa kwezingxenye ezakhiwayo kanye nokuhanjiswa kwazo.

Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT: An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach. Keywords: Decision support systems, case-based reasoning, analytic hierarchy process, fuzzy set theory, object-oriented methods, discrete-event simulation, fixtures. IQOQA LOCWANINGO : Ukuncintisana okunezinhlelo eziguquguqukayo kulesi sikhathi samanje sekwenze ukuthi kube nezidingo ezintsha ezinhlelweni zokukhiqiza. Phakathi kwakho konke lokhu izingxenye (fixtures) zingezinye zezinto ezidala izinkinga. Intengo yokwakha uhlaka lwengxenye kanye nokuyikhiqiza kubiza amaphesenti ayi-10 kuya kwangama-20 entengo yokukhiqiza. Amafemu akhiqizayo avamise ukusebenzisa izindlela ezindala zomsebenzi wokwaba izingxenye. Lezi zindlela zidla kakhulu izinsizangqangi futhi kuthatha isikhathi eside ukubala izingxenye ezikhona nokuqinisekisa ukuthi kunesibalo esanele kulokho okumele kube yikho ohlelweni lokusebenza. Inhloso yalolu cwaningo bekungukucwaninga nokusungula i-Decision Support System (DSS) ebe lusizo ekwenzeni umsebenzi wokuthatha izinqumo ngokwabiwa kwezingxenye kanye nokuhanjiswa kwazo ngezikhathi ezimiselwe ukukhiqiza. I-DSS yakhelwa ukusiza labo abayisebenzisayo ukuze basebenzise noma bazisebenzise lapho zingakaze zisetshenziswe khona lezo zingxenye ezibuyisiwe, noma kwakhiwe ezintsha kuya ngokuthi zibuyiswe zinjani lezi ezibuyisiwe nokuthi ziyafana yini nalezo ezintsha. I-DSS isebenzise amasu ahlanganise i-Case-Based Reasoning (CBR), injulalwazi echazwa ngokuthi i-fuzzy, ne-Analytic Hierarchy Process (AHP) ne-Discrete-Event Simulation (DES). I-Artificial Intelligence (AI) eyingxenye ye-DSS isebenzise kakhulu uhlelo lwe-fuzzy CBR luhlangene ne-fuzzy AHP kulandelwa imithetho yolwazi olumayelana nohlobo lomsebenzi. I-CBR isetshenziswe ukumelela lezo zimo zamanani ezingaqondakali nezingaphelele kulezo zingxenye. I-AHP e-fuzzy yasetshenziswa ukuze kutholakale ulwazi kochwepheshe olubeka phambili lezo zingxenye. Ama-oda ezingxenye ezintsha kanye namasampuli asetshenziselwa ukuqeqesha avezwe njengamasha kanye nabekade evele ekhona ngokulandelana kusetshenziswa indlela eyaziwa ngokuthi yi-Object-Oriented (OO) method lapho kubuyiswa izinto noma kunezinqumo eziphakanyiswayo. Izindlela ezijwayelekile zokulandelanisa nokufanisa zisetshenziswe ohlelweni lokubuyisa izinto. Kusetshenziswe isu eliyi-DES ukuhlaziya ukusebenza kwezisombululo eziphakanyiswe yindlela ye-CBR e-fuzzy. Le ndlela iphinde yaveza izimo ezintathu eziphakanyiswe ukuba zibe yisisombululo esibalweni sezingxenye ezihlongozwayo. Ukusebenza kwalezi zimo kuhlungwe ngokusebenzisa indlela ye-DES kwase kuvela inqubo engcono. Ukungajwayeleki kwalolu cwaningo kusebenzise ingxube yezindlela ze-fuzzy CBR ne-DES ngoba lolu hlobo lwengxube belungakaze lusetshenziswe. Kusetshenziswe isibonelo sezibalo ekwethuleni ukusebenza kwale nqubo yokusebenza ehlongozwayo

Development of a decision support system for decision-based part/fixture assignment and fixture flow control.

Doctoral Degree. University of KwaZulu-Natal, Durban.An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach.Please refer to the PDF for author's keywords

Benchmarks for fuzzy job shop problems

The fuzzy job shop scheduling problem with makespan minimisation is a problem with a significant presence in the scientific literature. However, a common meaningful comparison base is missing for such problem. This work intends to fill the gap in this domain by reviewing existing benchmarks as well as proposing new benchmark problems. First, we shall survey the existing test beds for the fuzzy job shop, analysing whether they are sufficiently varied and, most importantly, whether there is room for improvement on these instances - an essential requirement if the instances are to be useful for the scientific community in order to compare and develop new solving strategies. In the light of this analysis, we shall propose a new family of more challenging benchmark problems and provide lower bounds for the expected makespan of each instance as well as reference makespan values obtained with a memetic algorithm from the literature. The resulting benchmark will be made available so as to facilitate experiment reproducibility and encourage research competition

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II)

Fleet dimensioning and scheduling in the Brazilian ethanol industry: a fuzzy logic approach

This work solves a real-world multi-depot vehicle routing problem (MDVRP) with a homogeneous fleet and capacitated depots. A pipeline company wants to establish a vehicle policy in order to own part of its fleet and serve its customers for a period of one year. The company also wants to know the schedule of the visits for collecting ethanol from 261 producers and taking it to their three terminals located in Brazil. This problem presents uncertain demand, since weather conditions impact the final crop and uncertain depot capacity. Due to the vagueness of managers’ speech, this problem also presents uncertain travel time. In this paper, fuzzy logic is used to model uncertainty and vagueness and to split the initial instance into smaller ones. Besides solving a real-world problem with fuzzy demand, fuzzy depot capacity and fuzzy travel time, this paper contributes with a decision making tool that reports different solutions for different uncertainty levels.Este trabalho resolve um problema de roteamento de veículos multi-depósito do mundo real (MDVRP) com frota homogênea e depósitos capacitados. Uma empresa de pipeline deseja estabelecer uma política de veículos para possuir parte de sua frota e atender seus clientes por um período de um ano. A empresa também quer saber o agendamento das visitas para coleta de etanol de 261 produtores e retirada para seus três terminais localizados no Brasil. Este problema apresenta incertezas de demanda, já que as condições climáticas impactam a safra final e depósito de capacidade incerta. Devido à imprecisão do discurso dos gerentes, este problema também apresenta tempo de viagem incerto. Neste artigo, a lógica fuzzy é usada para modelar a incerteza e vagueza e dividir a instância inicial em outras menores. Além de resolver um problema do mundo real com demanda difusa, capacidade de depósito difusa e tempo de viagens difusas, este artigo contribui com uma ferramenta de tomada de decisão que relata diferentes soluções para diferentes níveis de incerteza

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

This paper proposes a fuzzy slack-diversifying fluctuation-smoothing rule to enhance the scheduling performance in a wafer fabrication factory. The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. An important input to the proposed rule is the job remaining cycle time. To this end, this paper proposes a self-adjusted fuzzy back propagation network (SA-FBPN) approach to estimate the remaining cycle time of a job. In addition, a systematic procedure is also established, which can solve the problem of slack overlapping in a nonsubjective way and optimize the overall scheduling performance. The simulation study provides evidence that the proposed rule can improve the four performance measures simultaneously

A general framework integrating techniques for scheduling under uncertainty

Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof