Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

A study on flexible flow shop and job shop scheduling using meta-heuristic approaches

Scheduling aims at allocation of resources to perform a group of tasks over a period of time in such a manner that some performance goals such as flow time, tardiness, lateness, and makespan can be minimized. Today, manufacturers face the challenges in terms of shorter product life cycles, customized products and changing demand pattern of customers. Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time and tardiness. Since all the scheduling criteria are important from business operation point of view, it is vital to optimize all the objectives simultaneously instead of a single objective. It is also essentially important for the manufacturing firms to improve the performance of production scheduling systems that can address internal uncertainties such as machine breakdown, tool failure and change in processing times. The schedules must meet the deadline committed to customers because failure to do so may result in a significant loss of goodwill. Often, it is necessary to reschedule an existing plan due to uncertainty event like machine breakdowns. The problem of finding robust schedules (schedule performance does not deteriorate in disruption situation) or flexible schedules (schedules expected to perform well after some degree of modification when uncertain condition is encountered) is of utmost importance for real world applications as they operate in dynamic environments

Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Exact and non-exact procedures for solving the response time variability problem (RTVP)

Premi extraordinari doctorat curs 2009-2010, àmbit d’Enginyeria IndustrialCuando se ha de compartir un recurso entre demandas (de productos, clientes, tareas, etc.) competitivas que requieren una atención regular, es importante programar el derecho al acceso del recurso de alguna forma justa de manera que cada producto, cliente o tarea reciba un acceso al recurso proporcional a su demanda relativa al total de las demandas competitivas. Este tipo de problemas de secuenciación pueden ser generalizados bajo el siguiente esquema. Dados n símbolos, cada uno con demanda di (i = 1,...,n), se ha de generar una secuencia justa o regular donde cada símbolo aparezca di veces. No existe una definición universal de justicia, ya que puede haber varias métricas razonables para medirla según el problema específico considerado. En el Problema de Variabilidad en el Tiempo de Respuesta, o Response Time Variability Problem (RTVP) en inglés, la injusticia o irregularidad de una secuencia es medida como la suma, para todos los símbolos, de sus variabilidades en las distancias en que las copias de cada símbolo son secuenciados. Así, el objetivo del RTVP es encontrar la secuencia que minimice la variabilidad total. En otras palabras, el objetivo del RTVP es minimizar la variabilidad de los instantes en que los productos, clientes o trabajos reciben el recurso necesario. Este problema aparece en una amplia variedad de situaciones de la vida real; entre otras, secuenciación en líneas de modelo-mixto bajo just-in-time (JIT), en asignación de recursos en sistemas computacionales multi-hilo como sistemas operativos, servidores de red y aplicaciones mutimedia, en el mantenimiento periódico de maquinaria, en la recolección de basura, en la programación de comerciales en televisión y en el diseño de rutas para agentes comerciales con múltiples visitas a un mismo cliente. En algunos de estos problemas la regularidad no es una propiedad deseable por sí misma, si no que ayuda a minimizar costes. De hecho, cuando los costes son proporcionales al cuadrado de las distancias, el problema de minimizar costes y el RTVP son equivalentes. El RTVP es muy difícil de resolver (se ha demostrado que es NP-hard). El tamaño de las instancias del RTVP que pueden ser resueltas óptimamente con el mejor método exacto existente en la literatura tiene un límite práctico de 40 unidades. Por otro lado, los métodos no exactos propuestos en la literatura para resolver instancias mayores consisten en heurísticos simples que obtienen soluciones rápidamente, pero cuya calidad puede ser mejorada. Por tanto, los métodos de resolución existentes en la literatura son insuficientes. El principal objetivo de esta tesis es mejorar la resolución del RTVP. Este objetivo se divide en los dos siguientes subobjetivos : 1) aumentar el tamaño de las instancias del RTVP que puedan ser resueltas de forma óptima en un tiempo de computación práctico, y 2) obtener de forma eficiente soluciones lo más cercanas a las óptimas para instancias mayores. Además, la tesis tiene los dos siguientes objetivos secundarios: a) investigar el uso de metaheurísticos bajo el esquema de los hiper-heurísticos, y b) diseñar un procedimiento sistemático y automático para fijar los valores adecuados a los parámetros de los algoritmos. Se han desarrollado diversos métodos para alcanzar los objetivos anteriormente descritos. Para la resolución del RTVP se ha diseñado un método exacto basado en la técnica branch and bound y el tamaño de las instancias que pueden resolverse en un tiempo práctico se ha incrementado a 55 unidades. Para instancias mayores, se han diseñado métodos heurísticos, metaheurísticos e hiper-heurísticos, los cuales pueden obtener soluciones óptimas o casi óptimas rápidamente. Además, se ha propuesto un procedimiento sistemático y automático para tunear parámetros que aprovecha las ventajas de dos procedimientos existentes (el algoritmo Nelder & Mead y CALIBRA).When a resource must be shared between competing demands (of products, clients, jobs, etc.) that require regular attention, it is important to schedule the access right to the resource in some fair manner so that each product, client or job receives a share of the resource that is proportional to its demand relative to the total of the competing demands. These types of sequencing problems can be generalized under the following scheme. Given n symbols, each one with demand di (i = 1,...,n), a fair or regular sequence must be built in which each symbol appears di times. There is not a universal definition of fairness, as several reasonable metrics to measure it can be defined according to the specific considered problem. In the Response Time Variability Problem (RTVP), the unfairness or the irregularity of a sequence is measured by the sum, for all symbols, of their variabilities in the positions at which the copies of each symbol are sequenced. Thus, the objective of the RTVP is to find the sequence that minimises the total variability. In other words, the RTVP objective is to minimise the variability in the instants at which products, clients or jobs receive the necessary resource. This problem appears in a broad range of real-world areas. Applications include sequencing of mixed-model assembly lines under just-in-time (JIT), resource allocation in computer multi-threaded systems such as operating systems, network servers and media-based applications, periodic machine maintenance, waste collection, scheduling commercial videotapes for television and designing of salespeople's routes with multiple visits, among others. In some of these problems the regularity is not a property desirable by itself, but it helps to minimise costs. In fact, when the costs are proportional to the square of the distances, the problem of minimising costs and the RTVP are equivalent. The RTVP is very hard to be solved (it has been demonstrated that it is NP-hard). The size of the RTVP instances that can be solved optimally with the best exact method existing in the literature has a practical limit of 40 units. On the other hand, the non-exact methods proposed in the literature to solve larger instances are simple heuristics that obtains solutions quickly, but the quality of the obtained solutions can be improved. Thus, the solution methods existing in the literature are not enough to solve the RTVP. The main objective of this thesis is to improve the resolution of the RTVP. This objective is split in the two following sub-objectives: 1) to increase the size of the RTVP instances that can be solved optimally in a practical computing time; and 2) to obtain efficiently near-optimal solutions for larger instances. Moreover, the thesis has the following two secondary objectives: a) to research the use of metaheuristics under the scheme of hyper-heuristics, and b) to design a systematic, hands-off procedure to set the suitable values of the algorithm parameters. To achieve the aforementioned objectives, several procedures have been developed. To solve the RTVP an exact procedure based on the branch and bound technique has been designed and the size of the instances that can be solved in a practical time has been increased to 55 units. For larger instances, heuristic, heuristic, metaheuristic and hyper-heuristic procedures have been designed, which can obtain optimal or near-optimal solutions quickly. Moreover, a systematic, hands-off fine-tuning method that takes advantage of the two existing ones (Nelder & Mead algorithm and CALIBRA) has been proposed.Award-winningPostprint (published version

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions