Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness

The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed towards the satisfaction of customers in a make-to-order scenario. Although several approximate algorithms have been proposed for this problem in the literature, recent contributions for related problems suggest that there is room for improving the current available algorithms. Thus, our contribution is twofold: First, we propose a fast beam-search-based constructive heuristic that estimates the quality of partial sequences without a complete evaluation of their objective function. Second, using this constructive heuristic as initial solution, eight variations of an iterated-greedy-based algorithm are proposed. A comprehensive computational evaluation is performed to establish the efficiency of our proposals against the existing heuristics and metaheuristics for the problem.Ministerio de Ciencia e Innovación DPI2013-44461-PMinisterio de Ciencia e Innovación DPI2016-80750-

Flowshop scheduling problems with due date related objectives: A review of the literature

3rd International Conference on Industrial Engineering and Industrial Management XIII Congreso de Ingeniería de Organización Barcelona-Terrassa, September 2nd-4th 200

Deterministic Assembly Scheduling Problems: A Review and Classification of Concurrent-Type Scheduling Models and Solution Procedures

Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.Ministerio de Ciencia e Innovación español DPI2016-80750-

Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems

Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis. The first method employs an EA to search for problem instances on which a given dispatching rule performs badly. These instances can then be analysed to reveal weaknesses of the tested rule, thereby providing guidelines for the design of a better rule. The other two methods are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming (GP), and generates a single rule from basic job and machine attributes, while the other generates a set of work centre-specific rules by selecting a (potentially) different rule for each work centre from a number of existing rules. Each of the three methods is applied to some complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested against benchmark rules from the literature. In each case, the benchmark rules are shown to be outperformed by a rule (set) that results from the application of the respective method, which demonstrates the effectiveness of the proposed methods

Aproximações heurísticas para um problema de escalonamento do tipo flexible job-shop

Mestrado em Engenharia e Gestão IndustrialEste trabalho aborda um novo tipo de problema de escalonamento que pode ser encontrado em várias aplicações do mundo-real, principalmente na indústria transformadora. Em relação à configuração do shop floor, o problema pode ser classificado como flexible job-shop, onde os trabalhos podem ter diferentes rotas ao longo dos recursos e as suas operações têm um conjunto de recursos onde podem ser realizadas. Outras características de processamento abordadas são: datas possíveis de início, restrições de precedência (entre operações de um mesmo trabalho ou entre diferentes trabalhos), capacidade dos recursos (incluindo paragens, alterações na capacidade e capacidade infinita) e tempos de setup (que podem ser dependentes ou independentes da sequência). O objetivo é minimizar o número total de trabalhos atrasados. Para resolver o novo problema de escalonamento proposto um modelo de programação linear inteira mista é apresentado e novas abordagens heurísticas são propostas. Duas heurísticas construtivas, cinco heurísticas de melhoramento e duas metaheurísticas são propostas. As heurísticas construtivas são baseadas em regras de ordenação simples, onde as principais diferenças entre elas dizem respeito às regras de ordenação utilizadas e à forma de atribuir os recursos às operações. Os métodos são designados de job-by-job (JBJ), operation-by-operation (OBO) e resource-by-resource (RBR). Dentro das heurísticas de melhoramento, a reassign e a external exchange visam alterar a atribuição dos recursos, a internal exchange e a swap pretendem alterar a sequência de operações e a reinsert-reassign é focada em mudar, simultaneamente, ambas as partes. Algumas das heurísticas propostas são usadas em metaheurísticas, nomeadamente a greedy randomized adaptive search procedure (GRASP) e a iterated local search (ILS). Para avaliar estas abordagens, é proposto um novo conjunto de instâncias adaptadas de problemas de escalonamento gerais do tipo flexible job-shop. De todos os métodos, o que apresenta os melhores resultados é o ILS-OBO obtendo melhores valores médios de gaps em tempos médios inferiores a 3 minutos.This work addresses a new type of scheduling problem which can be found in several real-world applications, mostly in manufacturing. Regarding shop floor configuration, the problem can be classified as flexible job-shop, where jobs can have different routes passing through resources and their operations have a set of eligible resources in which they can be performed. The processing characteristics addressed are release dates, precedence constraints (either between operations of the same job or between different jobs), resources capacity (including downtimes, changes in capacity, and infinite capacity), and setup times, which can be sequence-dependent or sequence-independent. The objective is to minimise the total number of tardy jobs. To tackle the newly proposed flexible job-shop scheduling problem (FJSP), a mixed integer linear programming model (MILP) is presented and new heuristic approaches are put forward. Three constructive heuristics, five improvement heuristics, and two metaheuristics are proposed. The constructive heuristics are based on simple dispatching rules, where the main differences among them concern the used dispatching rules and the way resources are assigned. The methods are named job-by-job (JBJ), operation-by-operation (OBO) and resource-by-resource (RBR). Within improvement heuristics, reassign and external exchange aim to change the resources assignment, internal exchange and swap intend changing the operations sequence, and reinsert-reassign is focused in simultaneously changing both parts. Some of the proposed heuristics are used within metaheuristic frameworks, namely greedy randomized adaptive search procedure (GRASP) and iterative local search (ILS). In order to evaluate these approaches, a new set of benchmark instances adapted from the general FJSP is proposed. Out of all methods, the one which shows the best average results is ILS-OBO obtaining the best average gap values in average times lower than 3 minutes

NEW HEURISTICS FOR MINIMISING TOTAL COMPLETION TIME AND THE NUMBER OF TARDY JOBS CRITERIA ON A SINGLE MACHINE WITH RELEASE TIME

<p>ENGLISH ABSTRACT: This paper considers the bi-criteria scheduling problem of simultaneously minimising the total completion time and the number of tardy jobs with release dates on a single machine. Since the problem had been classified as NP-Hard, two heuristics (HR9 and HR10) were proposed for solving this problem. Performance evaluations of the proposed heuristics and selected solution methods (HR7 and BB) from the literature were carried out on 1,100 randomly generated problems ranging from 3 to 500 jobs. Experiment results show that HR7 outperformed HR10 when the number of jobs (n) is less than 30, while HR10 outperformed HR7 for n≥ 30.</p><p>AFRIKAANSE OPSOMMING: In hierdie artikel word die bi-kriteria-skeduleringsprobleem bestudeer waar die totale voltooiingstyd en die aantal take wat laat is op ‘n enkele masjien geminimiseer moet word. Verskeie heuristieke word voorgestel en getoets om sodoende die beste benadering te identifiseer.</p&gt

Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows

[EN] In practice due dates usually behave more like intervals rather than specific points in time. This paper studies hybrid flowshops where jobs, if completed inside a due window, are considered on time. The objective is therefore the minimization of the weighted earliness and tardiness from the due window. This objective has seldom been studied and there are almost no previous works for hybrid flowshops. We present methods based on the simple concepts of iterated greedy and iterated local search. We introduce some novel operators and characteristics, like an optimal idle time insertion procedure and a two stage local search where, in the second stage, a limited local search on a exact representation is carried out. We also present a comprehensive computational campaign, including the reimplementation and comparison of 9 competing procedures. A thorough evaluation of all methods with more than 3000 instances shows that our presented approaches yield superior results which are also demonstrated to be statistically significant. Experiments also show the contribution of the new operators in the presented methods. (C) 2016 Elsevier Ltd. All rights reserved.The authors would like to thank Professors Lofti Hidri and Mohamed Haouari for sharing with us the source codes and explanations of the lower bounds. Quan-Ke Pan is supported by the National Natural Science Foundation of China (Grant No. 51575212), Program for New Century Excellent Talents in University (Grant No. NCET-13-0106), Science Foundation of Hubei Province in China (Grant No. 2015CFB560), Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130042110035), Key Laboratory Basic Research Foundation of Education Department of Liaoning Province (LZ2014014), Open Research Fund Program of the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China. Ruben Ruiz and Pedro Alfaro-Fernandez are supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Pan, Q.; Ruiz García, R.; Alfaro-Fernandez, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research. 80:50-60. https://doi.org/10.1016/j.cor.2016.11.022S50608

A new hybrid meta-heuristic algorithm for solving single machine scheduling problems

A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution algorithms. While there is a plethora of research into various aspects of scheduling problems, little has been done in evaluating the performance of the Simulated Annealing algorithm for the fundamental problem, or using it in combination with other techniques. Speci cally, this has not been done for minimising total weighted earliness and tardiness, which is the optimisation objective of this work. If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible solution schedules. It is thus of de nite practical necessity to reduce the search space in order to nd an optimal or acceptable suboptimal solution in a shorter time, especially when scaling up the problem size. This is of particular importance in the application area of packet scheduling in wireless communications networks where the tolerance for computational delays is very low. The main contribution of this work is to investigate the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo methods before running the Simulated Annealing algorithm on the pruned search space can result in overall reduced running times. The search space is divided into a number of sections and Metropolis-Hastings Markov Chain Monte Carlo is performed over the sections in order to reduce the search space for Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time and number of sections of the pre-sampling algorithm, and the run time of Simulated Annealing for minimising the percentage deviation of the nal result from the optimal solution cost. Algorithm performance is determined both by computational complexity and the quality of the solution (i.e. the percentage deviation from the optimal). We nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation from the optimal, as compared to the basic Simulated Annealing algorithm on the full search space. More importantly, we are able to reduce the complexity of nding the optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis- Hastings iterations, r inner samples and m sections.MT 201

Self-organisation of mobile robots in large structure assembly using multi-agent systems

Competition between manufacturers in large structure assembly (LSA) is driven by the need to improve the adaptability and versatility of their manufacturing systems. The lack of these qualities in the currently used systems is caused by the dedicated nature of their fixtures and jigs. This has led to their underutilisation and costly changeover procedures. In addition to that, modern automation systems tend to be dedicated to very specific tasks. This means that such systems are highly specialised and can reach obsolescence once there is a substantial change in production requirements. In this doctoral thesis, a dynamic system consisting of mobile robots is proposed to overcome those limitations. As a first knowledge contribution in this doctoral thesis, it is investigated under which conditions using mobile robots instead of the traditional, fixed automation systems in LSA can be advantageous. In this context, dynamic systems are expected to be more versatile and adaptive than fixed systems. Unlike traditional, dedicated automation systems, they are not constrained to gantry rails or fixed to the floor. This results in an expanded working envelope and consequently the ability to reach more workstations. Furthermore, if a product is large enough, the manufacturer can choose how many mobile robots to deploy around it. Accordingly, it was shown that the ability to balance work rates on products and consequently meet their due times is improved. For the second knowledge contribution, two fundamentally different decision-making models for controlling mobile agents in the complex scheduling problem are investigated. This is done to investigate ways of taking full advantage from the potential benefits of applying mobile robots. It is found that existing models from related academic literature are not suited for the given problem. Therefore, two new models had to be proposed for this purpose. It was plausible to use an agent-based approach for self-organisation. This is because similarly to agents, mobile robots can perform independently of one-another; and have limited perception and communication abilities. Finally, through a comparison study, scenarios are identified where either model is better to use. In agreement with much of the established literature in the field, the models are shown to exhibit the common advantages and disadvantages of their respective architecture types. Considering that the enabling technologies are nearing sufficient maturity for deploying mobile robots in LSA, it is concluded that this approach can have several advantages. Firstly, the granularity and freedom of movement enables much more control over product completion times. Secondly, the increased working envelope enables higher utilisation of manufacturing resources. In the context of LSA, this is a considerable challenge because products take a very long time to get loaded and unloaded from workstations. However, if the product flow is steady, there are rare disruptions and rare production changes, fixed automation systems have an advantage due to requiring much less time (if any) for moving and localising. Therefore, mobile systems become more preferred to fixed systems in environments where there is an increasing frequency of disruptions and changes in production requirements. The validation of agent-based self-organisation models for mobile robots in LSA confirms the expectations based on existing literature. Also, it reveals that with relatively low amounts of spare capacity (5%) in the manufacturing systems, there is little need for sophisticated models. The value of optimised models becomes apparent when spare capacity approaches 0% (or even negative values) and there is less room for inefficiencies in scheduling

New efficient constructive heuristics for the hybrid flowshop to minimise makespan: A computational evaluation of heuristics

This paper addresses the hybrid flow shop scheduling problem to minimise makespan, a well-known scheduling problem for which many constructive heuristics have been proposed in the literature. Nevertheless, the state of the art is not clear due to partial or non homogeneous comparisons. In this paper, we review these heuristics and perform a comprehensive computational evaluation to determine which are the most efficient ones. A total of 20 heuristics are implemented and compared in this study. In addition, we propose four new heuristics for the problem. Firstly, two memory-based constructive heuristics are proposed, where a sequence is constructed by inserting jobs one by one in a partial sequence. The most promising insertions tested are kept in a list. However, in contrast to the Tabu search, these insertions are repeated in future iterations instead of forbidding them. Secondly, we propose two constructive heuristics based on Johnson’s algorithm for the permutation flowshop scheduling problem. The computational results carried out on an extensive testbed show that the new proposals outperform the existing heuristics.Ministerio de Ciencia e Innovación DPI2016-80750-