Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

A review of multi-car elevator system

This paper presents a review of a new generation of elevator system, the Multi-Car Elevator System. It is an elevator system which contains more than one elevator car in the elevator shaft. In the introduction, it explains why the Multi-Car Elevator System is a new trend elevator system based on its structural design, cost saving and efficiency in elevator system. Different types of Multi-Car Elevator System such as circulation or loop-type, non-circulation and bifurcate circulation are described in section 2. In section 3, researches on dispatch strategies, control strategies and avoidance of car collision strategies of Multi-Car Elevator System since 2002 are reviewed. In the discussion section, it reveals some drawbacks of the Multi-Car Elevator System in transport capability and the risk of car collision. There are recommendations to the future work as well

Evolutionary design assistants for architecture

In its parallel pursuit of an increased competitivity for design offices and more pleasurable and easier workflows for designers, artificial design intelligence is a technical, intellectual, and political challenge. While human-machine cooperation has become commonplace through Computer Aided Design (CAD) tools, a more improved collaboration and better support appear possible only through an endeavor into a kind of artificial design intelligence, which is more sensitive to the human perception of affairs. Considered as part of the broader Computational Design studies, the research program of this quest can be called Artificial / Autonomous / Automated Design (AD). The current available level of Artificial Intelligence (AI) for design is limited and a viable aim for current AD would be to develop design assistants that are capable of producing drafts for various design tasks. Thus, the overall aim of this thesis is the development of approaches, techniques, and tools towards artificial design assistants that offer a capability for generating drafts for sub-tasks within design processes. The main technology explored for this aim is Evolutionary Computation (EC), and the target design domain is architecture. The two connected research questions of the study concern, first, the investigation of the ways to develop an architectural design assistant, and secondly, the utilization of EC for the development of such assistants. While developing approaches, techniques, and computational tools for such an assistant, the study also carries out a broad theoretical investigation into the main problems, challenges, and requirements towards such assistants on a rather overall level. Therefore, the research is shaped as a parallel investigation of three main threads interwoven along several levels, moving from a more general level to specific applications. The three research threads comprise, first, theoretical discussions and speculations with regard to both existing literature and the proposals and applications of the thesis; secondly, proposals for descriptive and prescriptive models, mappings, summary illustrations, task structures, decomposition schemes, and integratory frameworks; and finally, experimental applications of these proposals. This tripartite progression allows an evaluation of each proposal both conceptually and practically; thereby, enabling a progressive improvement of the understanding regarding the research question, while producing concrete outputs on the way. Besides theoretical and interpretative examinations, the thesis investigates its subject through a set of practical and speculative proposals, which function as both research instruments and the outputs of the study. The first main output of the study is the “design_proxy” approach (d_p), which is an integrated approach for draft making design assistants. It is an outcome of both theoretical examinations and experimental applications, and proposes an integration of, (1) flexible and relaxed task definitions and representations (instead of strict formalisms), (2) intuitive interfaces that make use of usual design media, (3) evaluation of solution proposals through their similarity to given examples, and (4) a dynamic evolutionary approach for solution generation. The design_proxy approach may be useful for AD researchers that aim at developing practical design assistants, as has been examined and demonstrated with the two applications, i.e., design_proxy.graphics and design_proxy.layout. The second main output, the “Interleaved Evolutionary Algorithm” (IEA, or Interleaved EA) is a novel evolutionary algorithm proposed and used as the underlying generative mechanism of design_proxybased design assistants. The Interleaved EA is a dynamic, adaptive, and multi-objective EA, in which one of the objectives leads the evolution until its fitness progression stagnates; in the sense that the settings and fitness values of this objective is used for most evolutionary decisions. In this way, the Interleaved EA enables the use of different settings and operators for each of the objectives within an overall task, which would be the same for all objectives in a regular multi-objective EA. This property gives the algorithm a modular structure, which offers an improvable method for the utilization of domain-specific knowledge for each sub-task, i.e., objective. The Interleaved EA can be used by Evolutionary Computation (EC) researchers and by practitioners who employ EC for their tasks. As a third main output, the “Architectural Stem Cells Framework” is a conceptual framework for architectural design assistants. It proposes a dynamic and multi-layered method for combining a set of design assistants for larger tasks in architectural design. The first component of the framework is a layer-based, parallel task decomposition approach, which aims at obtaining a dynamic parallelization of sub-tasks within a more complicated problem. The second component of the framework is a conception for the development mechanisms for building drafts, i.e., Architectural Stem Cells (ASC). An ASC can be conceived as a semantically marked geometric structure, which contains the information that specifies the possibilities and constraints for how an abstract building may develop from an undetailed stage to a fully developed building draft. ASCs are required for re-integrating the separated task layers of an architectural problem through solution-based development. The ASC Framework brings together many of the ideas of this thesis for a practical research agenda and it is presented to the AD researchers in architecture. Finally, the “design_proxy.layout” (d_p.layout) is an architectural layout design assistant based on the design_proxy approach and the IEA. The system uses a relaxed problem definition (producing draft layouts) and a flexible layout representation that permits the overlapping of design units and boundaries. User interaction with the system is carried out through intuitive 2D graphics and the functional evaluations are performed by measuring the similarity of a proposal to existing layouts. Functioning in an integrated manner, these properties make the system a practicable and enjoying design assistant, which was demonstrated through two workshop cases. The d_p.layout is a versatile and robust layout design assistant that can be used by architects in their design processes

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, DOI: 10.1016/j.ijpe.2014.08.010One of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested

A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type-1), or cycle time is minimised for a given number of workstations (this problem is referred to as type-2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed-model assembly line balancing problem (LB-MALBP), is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single-model assembly line system in the literature, is considered for a mixed-model assembly line system. The mathematical model of the LB-MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large-scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB-MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well-known test problems in this domain and promising results are observed in reasonable CPU times

A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented

Classification systems optimization with multi-objective evolutionary algorithms

L'optimisation des systèmes de classification est une tâche complexe qui requiert l'intervention d'un spécialiste (expérimentateur). Cette tâche exige une bonne connaissance du domaine d'application afin de réaliser l'extraction de l'information pertinente pour la mise en oeuvre du système de classification ou de reconnaissance. L'extraction de caractéristiques est un processus itératif basé sur l'expérience. Normalement plusieurs évaluations de la performance en généralisation du système de reconnaissance, sur une base de données représentative du problème réel, sont requises pour trouver l'espace de représentation adéquat. Le processus d'extraction de caractéristiques est normalement suivi par une étape de sélection des caractéristiques pertinentes (FSS). L'objectif poursuivi est de réduire la complexité du système de reconnaissance tout en maintenant la performance en généralisation du système. Enfin, si le processus d'extraction de caractéristiques permet la génération de plusieurs représentations du problème, alors il est possible d'obtenir un gain en performance en combinant plusieurs classificateurs basés sur des représentations complémentaires. L'ensemble de classificateurs (EoC) permet éventuellement une meilleure performance en généralisation pour le système de reconnaissance. Nous proposons dans cette thèse une approche globale pour l'automatisation des tâches d'extraction, de sélection de caractéristiques et de sélection des ensembles de classificateurs basés sur l'optimisation multicritère. L'approche proposée est modulaire et celle-ci permet l'intégration de l'expertise de l'expérimentateur dans le processus d'optimisation. Deux algorithmes génétiques pour l'optimisation multicritère ont été évalués, le Fast Elitist Non-Dominated sorting Algorithm (NSGA-II) et le Multi-Objective Memetic Algorithm (MOMA). Les algorithmes d'optimisation ont été validés sur un problème difficile, soit la reconnaissance de chiffres manuscrits isolés tirés de la base NIST SD19. Ensuite, notre méthode a été utilisée une seule fois sur un problème de reconnaissance de lettres manuscrites, un problème de reconnaissance provenant du même domaine, pour lequel nous n'avons pas développé une grande expertise. Les résultats expérimentaux sont concluants et ceux-ci ont permis de démontrer que la performance obtenue dépasse celle de l'expérimentateur. Finalement, une contribution très importante de cette thèse réside dans la mise au point d'une méthode qui permet de visualiser et de contrôler le sur-apprentissage relié aux algorithmes génétiques utilisés pour l'optimisation des systèmes de reconnaissance. Les résultats expérimentaux révèlent que tous les problèmes d'optimisation étudiés (extraction et sélection de caractéristiques de même que la sélection de classificateurs) souffrent éventuellement du problème de sur-apprentissage. À ce jour, cet aspect n'a pas été traité de façon satisfaisante dans la littérature et nous avons proposé une solution efficace pour contribuer à la solution de ce problème d'apprentissage

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms