Toolpath Planning Methodology for Multi-Gantry Fused Filament Fabrication 3D Printing

Additive manufacturing (AM) has revolutionized the way industries manufacture and prototype products. Fused filament fabrication (FFF) is one of the most popular processes in AM as it is inexpensive, requires low maintenance, and has high material utilization. However, the biggest drawback that prevents FFF printing from being widely implemented in large-scale production is the cycle time. The most practical approach is to allow multiple collaborating printheads to work simultaneously on different parts of the same object. However, little research has been introduced to support the aforementioned approach. Hence a new toolpath planning methodology is proposed in this paper. The objectives are to create a collision-free toolpath for each printhead while maintaining the mechanical performance of the printed model. The proposed method utilizes the Tabu Search heuristic and a combination of two subroutines: collision checking and collision resolution (TS-CCR). A computer simulation was used to compare the performance of the proposed method with the industry-standard approach in terms of cycle time. Physical experimentation is conducted to validate the mechanical strength of the TS-CCR specimens. The experiment also validated that the proposed toolpath can be executed on a custom multi-gantry setup without a collision. Experimental results indicated that the proposed TS-CCR can create toolpaths with shorter makespans than the current standard approach while achieving better ultimate tensile strength (UTS). This research represents opportunities for developing general toolpath planning for concurrent 3D printing

USING THE VEHICLE ROUTING PROBLEM (VRP) TO PROVIDE LOGISTICS SOLUTIONS IN AGRICULTURE

Agricultural producers consider utilizing multiple machines to reduce field completion times for improving effective field capacity. Using a number of smaller machines rather than a single big machine also has benefits such as sustainability via less compaction risk, redundancy in the event of an equipment failure, and more flexibility in machinery management. However, machinery management is complicated due to logistics issues. In this work, the allocation and ordering of field paths among a number of available machines have been transformed into a solvable Vehicle Routing Problem (VRP). A basic heuristic algorithm (a modified form of the Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed to solve the VRP. The solution considered optimization of field completion time as well as improving the field efficiency. Both techniques were evaluated through computer simulations with 2, 3, 5, or 10 vehicles working simultaneously to complete the same operation. Furthermore, the parameters of the VRP were changed into a dynamic, multi-depot representation to enable the re-route of vehicles while the operation is ongoing. The results proved both the Clarke-Wright and Tabu Search algorithms always generated feasible solutions. The Tabu Search solutions outperformed the solutions provided by the Clarke-Wright algorithm. As the number of the vehicles increased, or the field shape became more complex, the Tabu Search generated better results in terms of reducing the field completion times. With 10 vehicles working together in a real-world field, the benefit provided by the Tabu Search over the Modified Clarke-Wright solution was 32% reduction in completion time. In addition, changes in the parameters of the VRP resulted in a Dynamic, Multi-Depot VRP (DMDVRP) to reset the routes allocated to each vehicle even as the operation was in progress. In all the scenarios tested, the DMDVRP was able to produce new optimized routes, but the impact of these routes varied for each scenario. The ability of this optimization procedure to reduce field work times were verified through real-world experiments using three tractors during a rotary mowing operation. The time to complete the field work was reduced by 17.3% and the total operating time for all tractors was reduced by 11.5%. The task of a single large machine was also simulated as a task for 2 or 3 smaller machines through computer simulations. Results revealed up to 11% reduction in completion time using three smaller machines. This time reduction improved the effective field capacity

High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

A MULTI-OBJECTIVE GENETIC ALGORITHM APPROACH TO THE PROBABILISTIC MANUFACTURING CELL FORMATION PROBLEM

ABSTRACT Due to customised products, shorter product life-cycles, and unpredictable patterns of demand, manufacturing industries are faced with stochastic production requirements. It is unlikely that the production requirements (product mix and demand) are known exactly at the time of designing the manufacturing cell. However, a set of possible production requirements (scenarios) with certain probabilities are known at the design stage. Though a large number of research works on manufacturing cells have been reported, very few have considered random product mix constraints at the design stage. This paper presents a nonlinear mixed-integer mathematical model for the cell formation problem with the uncertainty of the product mix for a single period. The model incorporates real-life parameters like alternate routing, operation sequence, duplicate machines, uncertain product mix, uncertain product demand, varying batch size, processing time, machine capacity, and various cost factors. A solution methodology for best possible cell formation using a genetic algorithm (GA) is presented, and the computational procedure is illustrated for the case study undertaken. OPSOMMING Vanweë doelgemaakte produkte, korter produklewensiklusse en onvoorspelbare vraagpatrone, staar vervaardigingsindustrieë stochastiese produksiebehoeftes in die gesig. Dit is onwaarskynlik dat produksiebehoeftes (produkmengsel en vraag) presies bekend sal wees wanneer die vervaardigingsel ontwerp word. Desnieteenstaande sal 'n stel moontlike produksiebehoeftes (scenarios) met bepaalde waarskynlikhede tog op hierdie stadium bekend wees. Alhoewel heelwat navorsing reeds op vervaardigingselle gedoen is, is daar weinig gerapporteer waar lukraak produkmengselrandvorwaardes by die ontwerpfase oorweeg is. Hierdie artikel hou 'n nie-lineêre gemengde-heeltal-wiskundige model voor vir die selformasieprobleem met onsekerheid oor die produkmengsel in 'n enkelperiode. Die model inkorporeer werklike parameters soos alternatiewe roetes, bewerkingsvolgordes, duplikaat toerusting, onsekere produkmengsels, onsekere produkvraag, wisselende lotgroottes, prosesseertye, toerustingkapasiteit en verskeie kostefaktore. 'n Oplossingsmetodologie aan die hand van 'n genetiese algoritme vir die beste moontlike selformasie word voorgehou en die prosedure word by wyse van 'n gevallestudie geïllustreer

A Mathematical Approach to the Design of Cellular Manufacturing System Considering Dynamic Production Planning and Worker Assignments

Due to increasing international competition, shorter product life-cycles, variable demand, diverse customer needs and customized products, manufacturers are forced from mass production to the production of a large product mix. Traditional manufacturing systems, such as job shops and flow lines, cannot provide such requirements efficiently coupled with flexibility to handle these changes. Cellular Manufacturing (CM) is an alternate manufacturing system combining the high throughput rates of line layouts with the flexibility offered by functional layouts (job shops). The benefits include reduced set-up times, material handling, in-process inventory, better product quality, and faster response time. The benefits of CM can only be achieved by sufficiently incorporating the real-life structural and operational features of a manufacturing plant when creating the cellular layout. This research presents integrated CM models, with an extensive coverage of important manufacturing structural and operational features. The proposed Dynamic Cellular Manufacturing Systems (DCMSs) model considers several manufacturing attributes such as multiperiod production planning, dynamic system relocation, duplicate machines, machine capacities, available time for workers, worker assignments, and machine breakdowns. The objective is to minimize total manufacturing cost comprised of holding cost, outsourcing cost, intercell material handling cost, maintenance and overhead cost, machine relocation cost as well as salary, hiring, and firing costs of the workers. Numerical examples are presented to show the performance of the model

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Cell formation problem - A Lagrangean relaxation to mathematical programming approach and a linear performance measure

Two topics in the part-machine cell formation problem are discussed: In the first part, a Lagrangean relaxation in a mathematical programming model is proposed to simultaneously set machines into groups and parts into families in a cellular manufacturing system. The objective of this model is to find the optimal number of cells while minimizing inter-cellular part moves and increasing utilization of machines within the cells. The method uses a 0-1 integer programming model. The Lagrangean relaxation relaxes the model through an iterative search. In the second part, we introduce a new performance measure and compare it to some known performance measures. The new measure preserved some important features of previous performance measures and overcomes a number of drawbacks. Both the measure and the model are applied to benchmark problems as well as randomly generated problems. The new measure and model are comparable to the existing models and measures

Selection of return channels and recovery options for used products

Due to legal, economic and socio-environmental factors, reverse logistics practices and extended producer responsibility have developed into a necessity in many countries. The end results and expectations may differ, but the motivation remains the same. Two significant components in a reverse logistics system -product recovery options and return channels - are the focus of this thesis. The two main issues examined are allocation of the returned products to recovery options, and selection of the collection methods for product returns. The initial segment of this thesis involves the formulation of a linear programming model to determine the optimal allocation of returned products differing in quality to specific recovery options. This model paves the way for a study on the effects of flexibility on product recovery allocation. A computational example utilising experimental data was presented to demonstrate the viability of the proposed model. The results revealed that in comparison to a fixed match between product qualities and recovery options, the product recovery operation appeared to be more profitable with a flexible allocation. The second segment of this thesis addresses the methods employed for the initial collection of returned products. A mixed integer nonlinear programming model was developed to facilitate the selection of optimal collection methods for these products. This integrated model takes three different initial collection methods into consideration. The model is used to solve an illustrative example optimally. However, as the complexity of the issue renders this process ineffective in the face of larger problems, the Lagrangian relaxation method was proposed to generate feasible solutions within reasonable computational times. This method was put to the test and the results were found to be encouraging