A beam-search-based constructive heuristic for the PFSP to minimise total flowtime

In this paper we present a beam-search-based constructive heuristic to solve the permutation flowshop scheduling problem with total flowtime minimisation as objective. This well-known problem is NP-hard, and several heuristics have been developed in the literature. The proposed algorithm is inspired in the logic of the beam search, although it remains a fast constructive heuristic. The results obtained by the proposed algorithm outperform those obtained by other constructive heuristics in the literature for the problem, thus modifying substantially the state-of-the-art of efficient approximate procedures for the problem. In addition, the proposed algorithm even outperforms two of the best metaheuristics for many instances of the problem, using much lesser computation effort. The excellent performance of the proposal is also proved by the fact that the new heuristic found new best upper bounds for 35 of the 120 instances in Taillard’s benchmark.Ministerio de Ciencia e Innovación DPI2013-44461-PMinisterio de Ciencia e Innovación DPI2016-80750-

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimize total flowtime

This paper focuses on the blocking flow shop scheduling problem with the objective of total flowtime minimisation. This problem assumes that there are no buffers between machines and, due to its application to many manufacturing sectors, it is receiving a growing attention by researchers during the last years. Since the problem is NP-hard, a large number of heuristics have been proposed to provide good solutions with reasonable computational times. In this paper, we conduct a comprehensive evaluation of the available heuristics for the problem and for related problems, resulting in the implementation and testing of a total of 35 heuristics. Furthermore, we propose an efficient constructive heuristic which successfully combines a pool of partial sequences in parallel, using a beam-search-based approach. The computational experiments show the excellent performance of the proposed heuristic as compared to the best-so-far algorithms for the problem, both in terms of quality of the solutions and of computational requirements. In fact, despite being a relative fast constructive heuristic, new best upper bounds have been found for more than 27% of Taillard’s instances.Ministerio de Ciencia e Innovación DPI2013-44461-P/DP

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-

Iterative beam search algorithms for the permutation flowshop

We study an iterative beam search algorithm for the permutation flowshop (makespan and flowtime minimization). This algorithm combines branching strategies inspired by recent branch-and-bounds and a guidance strategy inspired by the LR heuristic. It obtains competitive results, reports many new-best-so-far solutions on the VFR benchmark (makespan minimization) and the Taillard benchmark (flowtime minimization) without using any NEH-based branching or iterative-greedy strategy. The source code is available at: https://gitlab.com/librallu/cats-pfsp

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-

Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms

Exploring the benefits of scheduling with advanced and real-time information integration in Industry 4.0: A computational study

The technological advances recently brought to the manufacturing arena (collectively known as Industry 4.0) offer huge possibilities to improve decision-making processes in the shop floor by enabling the integration of information in real-time. Among these processes, scheduling is often cited as one of the main beneficiaries, given its data-intensive and dynamic nature. However, in view of the extremely high implementation costs of Industry 4.0, these potential benefits should be properly assessed, also taking into account that there are different approaches and solution procedures that can be employed in the scheduling decision-making process, as well as several information sources (i.e. not only shop floor status data, but also data from upstream/downstream processes). In this paper, we model various decision-making scenarios in a shop floor with different degrees of uncertainty and diverse efficiency measures, and carry out a computational experience to assess how real-time and advance information can be advantageously integrated in the Industry 4.0 context. The extensive computational experiments (equivalent to 6.3 years of CPU time) show that the benefits of using real-time, integrated shop floor data and advance information heavily depend on the proper choice of both the scheduling approach and the solution procedures, and that there are scenarios where this usage is even counterproductive. The results of the paper provide some starting points for future research regarding the design of approaches and solution procedures that allow fully exploiting the technological advances of Industry 4.0 for decision-making in scheduling.Ministerio de Ciencia e Innovación PID2019-108756RB-I0Junta de Andalucía P18-FR-1149, 5835 and US-12645

The permutation flowshop scheduling problem: analysis, solution procedures and problem extensions

During the past twenty five years, following the massive use of internet and the EU single Market, European manufacturing companies struggle in a more competitive market, where firms from different countries must fight for common customers. As a consequence, prices of the products have decreased and the efficiency in the production processes of the companies have become more and more important. Nowadays, this fact is also increasing due to the competition from companies in developing countries whose labour cost is substantially lower. Therefore, production management is a key element for companies to survive. Production management involves decision making over several issues such as master scheduling, material requirements planning, capacity planning, manufacturing scheduling, ... Among these decisions, manufacturing scheduling plays an essential role on resource productivity and customer service. Its role is also increasing in many service industries as transportation, computer and communications industries, which are moving towards manufacture-to-order and virtual environments. Manufacturing scheduling deals with the determination of the jobs which are processed for each resource in each instant of time, i.e. establishes the schedules of the resources along the horizon under consideration. In order to determine the best schedule for the shop floor, both the specific constraints and the goal of the shop have to be considered. In these environments, the difficulty of the scheduling problem increases and becomes NP-hard even for the most simple scheduling problems, being extremely complex for real manufacturing scenarios. Additionally, scheduling decisions should be made in short time intervals requiring a rapid response time, due to several aspects such as the lifetime of a schedule, the delay in the suppliers, arrivals of new jobs to be processed, rescheduling due to failures while processing a job, .... All these issues strongly stress the need to find fast and efficient solution procedures (i.e. heuristics and metaheuristics) for solving manufacturing scheduling problems. In practice, several processing layouts have been adopted by companies to manufacture their products. Among them, the Permutation Flowshop Scheduling Problem (PFSP in the following), which is the problem addressed in this Thesis, stands out as the most relevant, being one of the most studied problems in Operations Research. There are several reasons for this fact: On the one hand, the flow shop layout is the common configuration in many real manufacturing scenarios, as it presents several advantages over more general job shop configuration, and, in addition, many job shops are indeed a flow shop for most of the jobs. On the other hand, many models and solution procedures for different constraints and layouts have their origins in the flowshop scheduling problem, which increases the importance to find efficient algorithms for this scheduling problem. Despite the huge number of research conducted on the PFSP, we believe that there is room for improving the current state of the art in the topic by: 1. deepening the understanding of the problem with respect to their input parameters, 2. devising new approximate solution procedures for the common employed objectives, and 3. addressing problem extensions to capture more realistic situations. To carry out this goal, the following general research objectives are identified: 1. To review the PFSP literature for the most common objectives, i.e. makespan, total completion time and due-date-based objectives (total tardiness, and total earliness and tardiness). 2. To analyse the influence of the processing times and due dates of the jobs on the PFSP. 3. To provide schedulers with faster and more efficient heuristics and metaheuristics to solve the PFSP for makespan, total completion time, total tardiness, and total earliness and tardiness minimisation. 4. To demonstrate the efficiency and good performance of the solution procedures developed in Goal 3. 5. To extend the proposals in Goal 3 to some constrained PFSP based on real manufacturing environments. To achieve these objectives, the Thesis have been structured in five parts as follows: - Part I is divided into two chapters. In Chapter 1.1, we introduce this Thesis and discuss its main contributions. In Chapter 2, the problem under consideration is stated. The measures to compare approximated algorithms are discussed in Chapter 3. There, the benchmarks used to evaluated the algorithms are introduced and an alternative indicator is proposed to overcome some problems detected using the traditional ones. - In Part II, we analyse the problem in detail along three chapters. Dealing with Objective 1, the main contributions in the literature are review for the most-common objective functions in Chapter 4. Additionally, in Chapter 5, we extensively study the behaviour of the problem depending on the configuration of the shops, i.e. processing times and due dates of the jobs (see Goal 2). - In Part III, we propose new novelties efficient algorithms to solve the PFSP under several objectives. The procedures, constructive and improvement heuristics and metaheuristics, exploit the specific structure of the problem to both reduce the computational times of them and improve the quality of the solutions. Additionally, they are validated in extensive computational evaluations, comparing them with the state-of-the-art algorithms under the same conditions. More specifically, this part is divided in four chapters and addresses the general research objectives GO3 and GO4. Firstly, a new tie-breaking mechanism to minimise makespan, which can be incorporated in the two most efficient algorithms for the problem, is proposed in Chapter 6. In Chapter 7, two efficient constructive heuristics are proposed to minimise total flowtime. Several tie-breaking mechanisms are proposed and compared to minimise total tardiness in Chapter 8. Finally, four procedures to minimise total earliness and tardiness are proposed in Chapter 9. - In Part IV, focused in more real manufacturing environment, new constraints are added to the traditional problem as well as different consideration and interaction between factories are taken into account. The proposed environments are solved using efficient approximate methods taken into consideration ideas of the traditional PFSP. More specifically, an iterated non-population algorithm to minimise makespan subject to a maximum tardiness is proposed in Chapter 10. In the Chapter 11, we add the blocking constraints to the traditional PFSP. These constraints take into consideration limited buffers between the machines. This problem, of permutation nature, is solved by means of an efficient beam-search-based constructive heuristic trying to minimise the total completion time. In Chapter 12, we consider the parallel flowshop scheduling problem also denoted as distributed PFSP where several identical flowshop or even flowshop factories are available in parallel to assign the jobs. The problem is solved using a bounded-search iterated greedy algorithm - Finally, in Part V, the conclusions of this research and future research lines are discussed.Premio Extraordinario de Doctorado U

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing

Bounded dynamic programming approach to minimize makespan in the blocking flowshop problem with sequence dependent setup times

This paper aims at presenting an algorithm for solving the blocking flow shop problem with sequence dependent setup times (BFSP-SDST) with minimization of the makespan. In order to do so, we propose an adapted Bounded Dynamic Programming (BDP-SN) algorithm as solution method, since the problem itself does not present a significant number of sources in the state-of-art references and also because Dynamic Programming and its variants have been resurfacing in the flowshop literature. Therefore, we apply the modified method to two sets of problems and compare the results computationally and statistically for instances with a MILP and a B&B method for at most 20 jobs and 20 machines. The results show that BDP-SN is promising and outperforms both MILP and B&B within the established time limit. In addition, some suggestions are made in order to improve the method and employ it in parallel research regarding other branches of machine scheduling