Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations

This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Improvement of constructive heuristics for combinatorial optimisation problems in operations management.

Операциони менаџер користи скуп поступака чији је циљ да се послови ураде брже, јефтиније и квалитетније. Научници из области операционог менаџмента имају задатак да ови поступци буду изводљиви и практични. Скоро увек, менаџери покушавају да нешто оптимизују – или је то минимизација трошкова и потрошње енергије, или пак, максимизација профита, резултата, перформанси и ефикасности. Међутим, није увек могуће пронаћи оптимална решења. У пракси, менаџер мора да се задовољи решењима која можда нису оптимална, али су допустива, задовољавајућа, робустна, и достижна у разумном времену. Оваква решења се добијају применама хеуристика, које могу бити конструктивне, побољшавајуће или хибридне. Област истраживања у докторској дисертацији су конструктивне хеуристике за проблеме комбинаторне оптимизације у операционом менаџменту који припадају класи сложености НП. Представљен је нови генерализовани конструктивни алгоритам који омогућава да се разноврсне хеуристике формирају избором његових аргумената. Такође је уведено опште окружење за генерисање пермутација, које формира везу између енумерације пермутација и корака у конструктивним хеуристикама уметања. Предложен је скуп аргумената генерализованог алгоритма који омогућује паралелно праћење више парцијалних решења за време извршавања алгоритма. Могућности и предности генерализованог алгоритма су представљене кроз његову примену на проблем формирања ћелија у производним системима, проблем распореда производних ћелија и проблем редоследа послова у линији. Нови приступ даје решења која на испитиваним примерима надмашују најбоље познате резултате из литературе.Operations manager deals with a collection of methods for getting things done more quickly, more cheaply or to a higher standard of quality. It is the job of the management scientist to make sure that these methods are practical and relevant. Almost always managers try to optimize something - whether to minimize the cost and energy consumption, or to maximize the profit, output, performance and efficiency. Subsequently, it is not always possible to find the optimal solutions. In practice, managers have to settle for suboptimal solutions or even feasible ones that are satisfactory, robust, and practically achievable in a reasonable time scale. These kind of solutions are obtained with heuristics, which can be constructive, improvement heuristics or hybrid. The field of research in the doctoral thesis are constructive heuristics for NP-hard combinatorial optimization problems in operations management. A new generalized constructive algorithm is presented which makes it possible to select a wide variety of heuristics just by the selection of its arguments values. A general framework for generating permutations of integers is presented. This framework forms a link between the numbering of permutations and steps in the insertion-based heuristics. A number of arguments controlling the operation of the generalized algorithm tracking multiple partial solutions, are identified. Features and benefits of the generalized algorithm are presented through the implemetations to the Cell Formation Problem, the Quadratic Assignment Problem and the Permutation Flowshop Problem. The new approach produces solutions that outperform, on the tested instances, the best known results from literature

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum