Organizational, systems and human issues in production planning, scheduling and control

With global markets and global competition, pressures are placed on manufacturing organizations to compress order fulfillment times, meet delivery commitments consistently and also maintain efficiency in operations to address cost issues. This chapter argues for a process perspective on planning, scheduling and control that integrates organizational planning structures, information systems as well as human decision makers. The chapter begins with a reconsideration of the gap between theory and practice, in particular for classical scheduling theory and hierarchical production planning and control. A number of the key studies of industrial practice are then described and their implications noted. A recent model of scheduling practice derived from a detailed study of real businesses is described. Socio-technical concepts are then introduced and their implications for the design and management of planning, scheduling and control systems are discussed. The implications of adopting a process perspective are noted along with insights from knowledge management. An overview is presented of a methodology for the (re-)design of planning, scheduling and control systems that integrates organizational, system and human perspectives. The most important messages from the chapter are then summarized.

Órbitas ordenadas del mapeo shift, raíz cuadrada y la escalera del diablo

Nuestro principal objetivo en este trabajo será seguir el artículo en el que consideran una órbita del “mapeo doblamiento”, shift: σ t → 2t en R=Z (este es el mapeo cuadrático cuando pensamos a R=Z como el círculo unitario en el plano complejo). Llamaremos a un subconjunto cerrado A de R=Z ordenado bajo σ si A es invariante (esto es σ (A) = A) y si σ preserva el orden cíclico de los puntos de A. Tales conjuntos tienen asignado un número de rotación, que lo llamamos así porque se parece mucho al que definimos en homeomorfismos del círculo, otra manera de ver el número de rotación es tomar la expansión decimal de cualquier t en A y luego calcular la frecuencia con la cual el dígito ’1’ se produce en esta expansión binaria. En este trabajo nos preocuparemos por dar una clasificación completa de los subconjuntos A que cumplen con ser ordenados, explícitamente daremos un algoritmo para su construcción, algunas propiedades de teoría de números, una generalización de la noción de orden y una caracterización del orden de todos los puntos alrededor de R=Z

A Mapping-Scheduling Algorithm for Hardware Acceleration on Reconfigurable Platforms

Reconfigurable platforms are a promising technology that offers an interesting trade-off between flexibility and performance, which many recent embedded system applications demand, especially in fields such as multimedia processing. These applications typically involve multiple ad-hoc tasks for hardware acceleration, which are usually represented using formalisms such as Data Flow Diagrams (DFDs), Data Flow Graphs (DFGs), Control and Data Flow Graphs (CDFGs) or Petri Nets. However, none of these models is able to capture at the same time the pipeline behavior between tasks (that therefore can coexist in order to minimize the application execution time), their communication patterns, and their data dependencies. This paper proves that the knowledge of all this information can be effectively exploited to reduce the resource requirements and the timing performance of modern reconfigurable systems, where a set of hardware accelerators is used to support the computation. For this purpose, this paper proposes a novel task representation model, named Temporal Constrained Data Flow Diagram (TCDFD), which includes all this information. This paper also presents a mapping-scheduling algorithm that is able to take advantage of the new TCDFD model. It aims at minimizing the dynamic reconfiguration overhead while meeting the communication requirements among the tasks. Experimental results show that the presented approach achieves up to 75% of resources saving and up to 89% of reconfiguration overhead reduction with respect to other state-of-the-art techniques for reconfigurable platforms.

An evolutionary squeaky wheel optimisation approach to personnel scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation’s effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.

A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

BOA for nurse scheduling

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA)for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.

Train scheduling in high speed railways: considering competitive effects

The railway planning problem is usually studied from two different points of view: macroscopic and microscopic. We propose a macroscopic approach for the high-speed rail scheduling problem where competitive effects are introduced. We study train frequency planning, timetable planning and rolling stock assignment problems and model the problem as a multi-commodity network flow problem considering competitive transport markets. The aim of the presented model is to maximize the total operator profit. We solve the optimization model using realistic probleminstances obtained from the network of the Spanish railwa operator RENFE, including other transport modes in Spain

Train scheduling and rolling stock assignment in high speed trains

The Train Timetabling Problem (TTP) has been widely studied for freight and passenger rail systems. A lesser effort has been devoted to the study of high-speed rail systems. A modeling issue that has to be addressed is to model departure time choice of passengers on railway services. Passengers who use these systems attempt to travel at predetermined hours due to their daily life necessities (e.g., commuter trips). We incorporate all these features into TTP focusing on high-speed railway systems. We propose a Rail Scheduling and Rolling Stock (RSch-RS) model for timetable planning of high-speed railway systems. This model is composed of two essential elements: i) an infrastructure model for representing the railway network: it includes capacity constraints of the rail network and the Rolling-Stock constraints; and ii) a demand model that defines how the passengers choose the departure time. The resulting model is a mixed-integer programming model which objective function attempts to maximize the profit for the rail operator

Irrigation scheduling using ICSWAB model.

The paper presents a simple method of irrigation scheduling using ICSWAB model for dry land crops. The main inputs to this approache are daily precipitation or irrigation amounts and open pan evaporation (US class 'A' pan-mesh covered). The fixed cumulative evapotranspiration procedure is better than fixed days or fixed percentage soil moisture procedures of irrigation scheduling. Fixed days procedures could be reasonably applied during nonrainy season.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment

Effective and efficient implementation of intelligent and/or recently emerged networked manufacturing systems require an enterprise level integration. The networked manufacturing offers several advantages in the current competitive atmosphere by way to reduce, by shortening manufacturing cycle time and maintaining the production flexibility thereby achieving several feasible process plans. The first step in this direction is to integrate manufacturing functions such as process planning and scheduling for multi-jobs in a network based manufacturing system. It is difficult to determine a proper plan that meets conflicting objectives simultaneously. This paper describes a mobile-agent based negotiation approach to integrate manufacturing functions in a distributed manner; and its fundamental framework and functions are presented. Moreover, ontology has been constructed by using the Protégé software which possesses the flexibility to convert knowledge into Extensible Markup Language (XML) schema of Web Ontology Language (OWL) documents. The generated XML schemas have been used to transfer information throughout the manufacturing network for the intelligent interoperable integration of product data models and manufacturing resources. To validate the feasibility of the proposed approach, an illustrative example along with varied production environments that includes production demand fluctuations is presented and compared the proposed approach performance and its effectiveness with evolutionary algorithm based Hybrid Dynamic-DNA (HD-DNA) algorithm. The results show that the proposed scheme is very effective and reasonably acceptable for integration of manufacturing functions.

Riesgo biomecánico y dolor lumbar en operarios y personal administrativo en una fábrica de jabón en Bogotá 2016

Introducción: El dolor lumbar y los desórdenes músculo esqueléticos comprometen la salud y la calidad de vida de los trabajadores, pueden poner en riesgo el futuro laboral de las personas. bjetivo: Estimar la prevalencia de dolor lumbar y los posibles factores biomecánicos asociados en el personal operativo y administrativo en una empresa manufacturera de jabón en Bogotá, en el año 2016 Metodología: Estudio de corte transversal donde se evaluó el riesgo biomecánico y la prevalencia del dolor lumbar en personal administrativo (138) y operativo (165); se utilizó como instrumento el ERGOPAR validado en España. Se revisó la asociación utilizando la prueba Chi Cuadrado de Pearson, con un nivel de significación α 0.05 Resultados: 303 trabajadores de una empresa manufacturera de jabón en Bogotá, donde predominó el género masculino (51,82%) y la población adulta media entre 30-39 años (57,42%). La prevalencia del dolor lumbar en la población fue de 61,39% (186). La edad no se asoció estadísticamente al dolor lumbar. Se encontró asociación estadística entre el síntoma dolor lumbar y extensión de cuello (p=0,05 OR1.95 IC 1.33-2.88), así como con agarrar o sujetar objetos (p= 0,036. OR 2.3 IC 1.59-3.51) y con las exigencias físicas laborales (p= 0.001 OR 1.99 IC 1.31-3.02). Conclusiones: La población estudiada presentó una alta prevalencia de dolor lumbar, con predominio en personal que realiza labores operativas, y del género femenino. La adopción de posturas de extensión del cuello y la sujeción o agarre de objetos son factores asociados directamente con la aparición de lumbalgia.