Aplicación del algoritmo lompen a los problemas Fm | prmu | Cmac y Fm | block | Cmax

In this paper we face the permutation flow-shop scheduling problem with a makespan objective function in two variants, with and without storage space between machines. We use an improved branch and bound algorithm, suitable for parallel computation, to solve these problems, and auxiliary heuristics to attain an initial good solution. The auxiliary heuristics proposed are built by two steps: in the first step a permutation is obtained; in the second step a local search procedure is applied. The improvement obtained by the local search procedure on NEH heuristic as first step is shown. Since the flow-shop scheduling problem with storage space is a relaxation of the problem without storage space, some elements and procedures developed for that problem can be used in both problems. In particular, some bounding procedures, for instance Nabeshima or Lageweg bounding schema, can be adapted. Moreover, the reversibility property holds on both problems. Consequently the branch and bound algorithm can be applied simultaneously to the direct and the inverse instances. The same sets of data are submitted to heuristics and to the double branch-and-bound algorithm, LOMPEN, assuming first they are instances of flow-shop scheduling problem with storage space and later they are instances of flow-shop scheduling problem without storage space. The algorithms are coded in a similar way; therefore the behaviour and performance can be compared

On tie-breakers and other exotic Tools for the NEH Algorithm

Este es el primero de tres trabajos dedicados a procedimientos de resolución de los problemas flowshop con criterio minimización del makespan, basados en la clásica heurística de Nawaz, Enscore y Ham. Consideramos dos casos el Fm|prmu|Cmax y el Fm|block|Cmax. En el presente trabajo analizamos los procedimientos de desempate en la intercalación del segundo paso del NEH. Basamos las conclusiones en los experimentos realizados tomando como base los 120 ejemplares de las colecciones de Taillard. Son suficientemente conocidos y ejercen cierta fascinación sobre los investigadores. En el anexo recogemos resultados sobre otras colecciones, más numerosas, generadas siguiendo los principios establecidos por Taillard.Preprin

New insights on the blocking flow shop problem

We present some results attained with different algorithms for the Fm|block|Cmax problem using as experimental data the well-known Taillard instances.Preprin

Note on the blocking flow shop problem

We present some results attained with different algorithms for the Fm|block|Cmax problem using as experimental data the well-known Taillard instances

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

New insights on the blocking flow shop problem best solutions updated. Vol.5

Preprin

Modelado y resolución de problemas de secuenciación en contexto JIT/DS mediante BDP

This document develops the doctoral thesis entitled "Modelado y resolución de problemas de secuenciación en contexto JIT/DS mediante BDP". Through a literature review we explain the problems, within the environment of the sequence problems in production systems. Then, we describe the problems under study, the MMSP-W (Mixed-Model Sequencing Problem with Work overload Minimization), the BFSP (Blocking Flow Shop Problem) and the ORV (Output Rate Variation Problem). Then, a resolution procedure based on BDP (Bounded Dynamic Programming) to solve the problems is proposed. Finally, the results of the proposed procedure are compared with others based on for example linear programming or heuristics.Este documento desarrolla la tesis doctoral titulada “Modelado y resolución de problemas de secuenciación en contexto JIT/DS mediante BDP”. A través de una revisión de la literatura se explican los problemas objeto de estudio, enmarcándolo dentro del entorno de la secuenciación en los sistemas productivos. A continuación se describen dichos problemas, el MMSP-W (Mixed-Model Sequencing Problem with Work overload Minimization), el BFSP (Blocking Flow Shop Problem) y el ORV (Output Rate Variation Problem). Finalmente se proponen algoritmos de resolución para dichos problemas basados en BDP (Bounded Dynamic Programming o programación dinámica acotada) y se compara la calidad de los procedimientos propuestos con otros basados en programación lineal o heurística

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

A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem

International audienceIn this work we propose an efficient branch-and-bound (B&B) algorithm for the permutation flow-shop problem (PFSP) with makespan objective. We present a new node decomposition scheme that combines dynamic branching and lower bound refinement strategies in a computationally efficient way. To alleviate the computational burden of the two-machine bound used in the refinement stage, we propose an online learning-inspired mechanism to predict promising couples of bottleneck machines. The algorithm offers multiple choices for branching and bounding operators and can explore the search tree either sequentially or in parallel on multi-core CPUs. In order to empirically determine the most efficient combination of these components, a series of computational experiments with 600 benchmark instances is performed. A main insight is that the problem size, aswell as interactions between branching and bounding operators substantially modify the trade-off between the computational requirements of a lower bound and the achieved tree size reduction. Moreover, we demonstrate that parallel tree search is a key ingredient for the resolution of largeproblem instances, as strong super-linear speedups can be observed. An overall evaluation using two well-known benchmarks indicates that the proposed approach is superior to previously published B&B algorithms. For the first benchmark we report the exact resolution – within less than20 minutes – of two instances defined by 500 jobs and 20 machines that remained open for more than 25 years, and for the second a total of 89 improved best-known upper bounds, including proofs of optimality for 74 of them