Comparison of heuristics for rescheduling in permutation flowshops

NUMERO ESPECIAL / SPECIAL ISSUE XIII Congreso de Ingeniería de Organización 3nd International Conference on Industrial Engineering and Industrial ManagementRescheduling is one of the main consequences of the variability in the shop floor, as a number of unforeseeable disruptions make impossible to follow the original schedule. In this paper we study a flowshop rescheduling problem where a set of jobs arrives to the system and it is scheduled together with jobs already present. The objective is to minimise the makespan of the new jobs and constrained with the fact that the maximum tardiness of the old jobs must be equal to zero. The problem is NP-hard, so we compare heuristic methods in order to select the best option.. La resecuenciación es una de las principales consecuencias de la variabilidad en los talleres, ya que las interrupciones no hacen posible el seguimiento de la secuencia inicial. Este trabajo aborda un problema de resecuenciación en flujo regular donde un conjunto de trabajos llega al sistema, y son secuenciados junto con un conjunto de trabajos que ya está planificado. El objetivo es minimizar el makespan de los nuevos trabajos, restringido a que la máxima tardanza de los trabajos antiguos debe ser cero. El problema es NP, por lo que se comparan diferentes métodos heurísticos para su resolución, y se selecciona la mejor opciónMinisterio de Ciencia e Innovación DPI2007-61345Junta de Andalucía P08-TEP-363

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

An approach to solve job shop scheduling problem

“A biotechnology device manufacturer needs to devise effective scheduling algorithms for its testing devices. A device is a configuration of machines, each of which performs a specific task, such as washing, reading and cleaning. These devices are used to test human samples to diagnose diseases like cholera, malaria etc. Each test is a job, which is to be processed on these machines for a specific amount of time. Every job has its own pre defined sequence. These samples are to be processed simultaneously on machines owing to constraint that as soon as one machine completes processing a sample, it should be immediately processed by another machine. This constraint is significantly known as no- wait constraint. Given a set of jobs the web application assigns an optimal start time for each job owing to no-wait constraint. This results in reducing the overall time taken to process the jobs, which is formally known as makespan. The main objective of the project is to minimize the makespan. The application is specific to laboratory platform, which helps them to test the samples in optimal time. The heuristic, which I have implemented, is designed with future advancements in mind. The application can be extended to test different heuristic procedures by keeping the time tabling intact. The development environment to be used in this project will require Microsoft Visual Studio, C#, ASP.NET, and other real time chart tools like Microsoft Silverlight.

Metaheuristic Approaches for the Two-Machine Flow-Shop Problem with Weighted Late Work Criterion and Common Due Date

Abstract: In this paper, metaheuristic approaches for the two-machine flow shop problem with a common due date and the weighted late work performance measure (F2|dj=d|Yw) are presented. The late work criterion estimates the quality of a solution with regard to the duration of the late parts of jobs, not taking into account the quantity of the delay for the fully late activities. Since, the problem mentioned is known to be NP-hard, three trajectory methods, namely simulated annealing, tabu search and variable neighborhood search are proposed based on the special features of the case under consideration. Then, the results of computational experiments are reported, in which the metaheuristics were compared one to each other, as well to an exact approach and a list scheduling algorithm

A particle swarm optimisation for the no-wait flow shop problem with due date constraints.

Peer ReviewedThis paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature

On the exact solution of the no-wait flow shop problem with due date constraints

Peer ReviewedThis paper deals with the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, the jobs should be completed before their respective due dates; due date constraints are dealt with as hard constraints. The considered performance criterion is makespan. The problem is strongly NP-hard. This paper develops a number of distinct mathematical models for the problem based on different decision variables. Namely, a mixed integer programming model, two quadratic mixed integer programming models, and two constraint programming models are developed. Moreover, a novel graph representation is developed for the problem. This new modeling technique facilitates the investigation of some of the important characteristics of the problem; this results in a number of propositions to rule out a large number of infeasible solutions from the set of all possible permutations. Afterward, the new graph representation and the resulting propositions are incorporated into a new exact algorithm to solve the problem to optimality. To investigate the performance of the mathematical models and to compare them with the developed exact algorithm, a number of test problems are solved and the results are reported. Computational results demonstrate that the developed algorithm is significantly faster than the mathematical models

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approache

Trucks in movement

Beton erzeugende Unternehmen sehen sich täglich vor die Aufgabe gestellt, für die Belieferung der Baustellen eine möglichst effiziente Tourenplanung - unter Berücksichtigung ihrer heterogenen Fahrzeugflotte - zu erstellen. Da der Betonbedarf einer Baustelle die Kapazität eines einzelnen Fahrzeuges übersteigt, muss in der Regel jede Baustelle mehrmals hintereinander mit Beton beliefert werden. Das Planungsproblem ergibt sich nun insbesondere daraus, dass sich aufeinander folgenden Lieferungen nicht überschneiden dürfen, da nicht mehrere Fahrzeuge gleichzeitig entladen werden können. Eventuell entstehenden Lücken zwischen aufeinanderfolgenden Lieferungen jedoch sollten möglichst kurz gehalten werden. Im Rahmen dieser Dissertation werden mehrere Methoden besprochen, mit Hilfe derer eingangs erwähntes Tourenplanungsproblem gelöst werden kann. Die angewendeten Konzepte basieren auf exakten Verfahren, Heuristiken, Metaheuristiken, sowie hybriden Ansätzen. Ein exaktes Modell, beruhend auf einer Erweiterung des klassischen Vehicle Routing Problems (VRP, Tourenplanungsproblem) wurde entwickelt. Allerdings lässt sich die daraus resultierende Formulierung nur für äußerst kleine Instanzen exakt lösen. In der Praxis hingegen, ist dieser Ansatz aufgrund der zu langen Rechenzeiten und des enormen Rechenaufwandes nicht sinnvoll anwendbar. Daher wurde ein von Local Branching (LB) inspiriertes Verfahren konzipiert. Dieser integrativ hybride Ansatz wendet zusätzlich Nachbarschaftstrukturen, wie sie auch bei Variable Neighborhood Search (VNS) angewendet werden, kombiniert an. Darüber hinaus wurden valid inequalities für eine Verbesserung der unteren Schranken herangezogen. Ein weiterer Ansatz beruht auf einer Formulierung für multi-commodity network flow Problemen (MCNF). Anstatt einer globalen Sicht auf das Problem an sich, werden in diesem Zusammenhang nur ausgewählte Subbereiche näher betrachtet. So genannte Muster werden für alle BestellungenCompanies in the concrete industry are facing the following scheduling problem on a daily basis: concrete produced at several plants has to be delivered at customers' construction sites using a heterogeneous fleet of vehicles in a timely, but cost-effective manner. As the ordered quantity of concrete typically exceeds the capacity of a single vehicle several deliveries need to be scheduled to fulfill an order. The deliveries cannot overlap and the time between consecutive deliveries has to be small. This thesis presents a broad range of different ways on how to solve the problem stated above. Various solution methods based on exact, heuristic, meta-heuristic and hybrid approaches have been developed. Exact methods based on a formulation the so called VRP° (a Split Delivery Multi Depot Heterogeneous Vehicle Routing Problem with Time Windows) have been implemented. The resulting problem formulation can be solved to optimality for very small instances. For real-world-sized instances however, even with a steady increase in computational power, just to ``to MIP'' is not the way to success. Hence an algorithm, which controls the solution process of the embedded MIP-formulation, has been developed in order to tackler larger problem instances. This \emph{integrative hybrid} approach is based on Local Branching (LB) which itself is guided by means of Variable Neighborhood Search (VNS). Attention has also been paid to the development of valid inequalities and cuts in order to improve the quality of lower bounds. Another approach has been developed, which is based on a multi-commodity network flow model (MCNF) formulation. Rather than having a comprehensive view on the problem only subparts are considered and solved to optimality. So called \emph{patterns} (options on how orders could be satisfied) are generated heuristically and serve as an input for the MCNF. Given on a set of input pattern it is possible to solve the problem to optimality. Moreover the entir