An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and noidling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.This research is partially supported by the National Science Foundation of China (60874075, 70871065), and Science Foundation of Shandong Province in China under Grant BS2010DX005, and Postdoctoral Science Foundation of China under Grant 20100480897. Ruben Ruiz is partially funded by the Spanish Ministry of Science and Innovation, under the project "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI and by the IMPIVA-Institute for the Small and Medium Valencian Enterprise, for the project OSC with references IMIDIC/2008/137, IMIDIC/2009/198 and IMIDIC/2010/175.Pan, Q.; Ruiz García, R. (2012). An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega. 40(2):166-180. https://doi.org/10.1016/j.omega.2011.05.002S16618040

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Lot streaming in hybrid flow shop scheduling

Production planning and scheduling play significant roles in manufacturing system operations and different techniques have been used to enhance their performance. Lot streaming has been studied for decades and is shown to accelerate production flow. This research deals with lot streaming in hybrid flow shops. Multiple products are processed in a multi-stage hybrid flow shop with non-identical machines. Sublots can be constant or consistent and intermingling is not allowed. Setups are attached and sequence independent. The problem is to simultaneously determine product sequence and sublots sizes so that the makespan is minimized. The model presented in this thesis is a mixed integer linear programming formulation for solving this problem. Several variations of the model are presented to incorporate different problem settings such as exploitation of variable sublots in the single product problem. Numerical examples are presented to validate the proposed model and to compare it to similar example problems in the literature. Furthermore, an example of a lot streaming problem in a general multi-stage hybrid flow shop is concerned and discussions and analysis are presented. Keywords . Production planning; Scheduling; Lot streaming; Hybrid flow shop; Integer programmin

Makespan Minimization in Re-entrant Permutation Flow Shops

Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

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 memetic algorithm to minimize the total sum of earliness tardiness and sequence dependent setup costs for flow shop scheduling problems with job distinct due windows

The research considers the flow shop scheduling problem under the Just-In-Time (JIT) philosophy. There are n jobs waiting to be processed through m operations of a flow shop production system. The objective is to determine the job schedule such that the total cost consisting of setup, earliness, and tardiness costs, is minimized. To represent the problem, the Integer Linear Programming (ILP) mathematical model is created. A Memetic Algorithm (MA) is developed to determine the proper solution. The evolutionary procedure, worked as the global search, is applied to seek for the good job sequences. In order to conduct the local search, an optimal timing algorithm is developed and inserted in the procedure to determine the best schedule of each job sequence. From the numerical experiment of 360 problems, the proposed MA can provide optimal solutions for 355 problems. It is obvious that the MA can provide the good solution in a reasonable amount of time

Permutation Flow Shop Scheduling unter Einbezug von Lot Streaming bei auftragsspezifischen Lieferterminvektoren für Due Window-bezogene Zielfunktionen

In dieser Arbeit wird eine Untersuchung vorgestellt zur Aufteilung von Auftragslosen mit mehreren identischen Einheiten in mehrere sog. Sublots, angewandt auf mehrere Liefertermine pro Auftrag. Hierfür werden zwei Zielsetzungen verfolgt, die Minimierung von Terminabweichungen sowie die Minimierung der nicht termingerecht fertiggestellten Menge. Diese Problemstellung wurde bislang in der Literatur nicht untersucht, hat aber praktische Relevanz in allen Fragestellungen, bei denen mehrere identische Einheiten zu fertigen und an verschiedenen Zeitpunkten auszuliefern sind. Die bisherige Forschung hat in den vergangenen knapp fünfzig Jahren die Aufteilung von Auftragslosen intensiv für die Problemstellung einer Minimierung der Gesamtdurchlaufzeit untersucht und hierzu eine Reihe optimierender wie heuristischer Verfahren vorgestellt. Es wurden in dieser Zeit jedoch nur wenige Untersuchungen unter Einbezug von Lieferterminzielsetzungen publiziert, welche zudem alle auf nur einen Liefertermin pro Auftrag ausgelegt waren. Es ist somit die Frage bislang offen geblieben, inwiefern eine Aufteilung von Aufträgen geeignet ist, mehrere Liefertermine pro Auftrag mit geringeren Terminabweichungen zu bedienen. In der vorliegenden Arbeit werden erstmalig auftragsspezifische Lieferterminvektoren und damit verbunden die Zuordnung von Sublots zu diesen Lieferterminen untersucht, angewandt auf Reihenfertigungsprozesse unter Einbezug von Maschinenrüstzeiten. Hierzu wird ein gemischt-ganzzahliges Modell zur Bestimmung der Sublot-Anzahlen sowie ihrer -Größen vorgestellt. Dieses setzt im Rahmen eines zweistufigen Lösungsverfahrens auf einer zuvor bestimmten Zuordnungsmatrix von Auftrags-Sublots in sog. Einlastungspositionen auf den Maschinen auf. Die Bestimmung der Positionen erfolgt zunächst mit Hilfe von Prioritätsregeln und wird durch ein heuristisches Verfahren in Form eines Genetischen Algorithmus anschließend verbessert. Das vorgestellte Verfahren wurde in einer numerischen Untersuchung validiert. In dieser konnte aufgezeigt werden, dass mit steigendem Rüstaufwand mehrere Liefertermine durch ein Sublot bedient werden, während die Zuordnung mehrerer Sublots zu einem Liefertermin abnahm. In allen Testinstanzen führte das Verfahren zu besseren Zielfunktionswerten im Vergleich zu einer Produktion ohne Aufteilung in Teillose. Mit der vorliegenden Arbeit wird die bisherige Forschung zu Lot Streaming um eine neue Richtung erweitert und ein neues Lösungsverfahren vorgestellt.The present thesis introduces a study concerning the splitting of jobs consisting of several identical items into sublots under the assumption of several due windows per job. The two objectives regarded are minimizing the time deviation from due windows and minimizing the number of parts not finished on time. This research question has not been addressed before, but is highly important for any practical situation in which several identical items have to be produced and delivered to customers in various time slots. Previous research within the past fifty years has focused intensively on splitting jobs into sublots to minimize the makespan. Therefore, optimizing and heuristic solution procedures were provided. During this time research involving due dates has received only little attention, which all focused on a single due date per job. Therefore the question remains open if splitting a job into sublots is appropriate to serve several due dates per job to reduce deviations from due dates. This thesis introduces for the first time several due windows per job und investigates the allocation of sublots to due windows, under the assumption of flow shop environments including setups. To achieve this, a mixed integer programming formulation is presented to simultaneously determine sublot number and sizes per job. This approach is based on a two-stage solution method which provides an allocation of job sublots into so-called dispatching positions on the machines in the first stage. The dispatchment of positions is firstly done by using priority rules and afterwards improved by a heuristic procedure based on a Genetic Algorithm. To prove the effectiveness of the proposed method, numerical examples were calculated. These experiments are presented to show that higher setup durations lead to more due windows being served by a single sublot, whereas the number of sublots serving only a single due window diminishes. All of the test instances prove the solution procedure presented in this thesis to be effective to reduce the objective function value compared to a production without using the splitting possibility. The present thesis extends the previously published work on lot streaming to a new research direction which has not been explored before

New Solution Approaches for Scheduling Problems in Production and Logistics

The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process