Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups

A lot sizing and scheduling problem from a foundry is considered in which key materials are produced and then transformed into many products on a single machine. A mixed integer programming (MIP) model is developed, taking into account sequence-dependent setup costs and times, and then adapted for rolling horizon use. A relax-and-fix (RF) solution heuristic is proposed and computationally tested against a high-performance MIP solver. Three variants of local search are also developed to improve the RF method and tested. Finally the solutions are compared with those currently practiced at the foundry.

Meta-heuristic approaches for a soft drink industry problem

The present paper evaluates meta-heuristic approaches to solve a soft drink industry problem. This problem is motivated by a real situation found in soft drink companies, where the lot sizing and scheduling of raw materials in tanks and products in lines must be simultaneously determined. Tabu search, threshold accepting and genetic algorithms are used as procedures to solve the problem at hand. The methods are evaluated with a set of instance already available for this problem. This paper also proposes a new set of complex instances. The computational results comparing these approaches are reported. © 2008 IEEE.

Engaging environments: Tacit knowledge sharing on the shop floor

Purpose: The purpose of this paper is to identify factors that facilitate tacit knowledge sharing in unstructured work environments, such as those found in automated production lines. Design/methodology/approach: The study is based on a qualitative approach, and it draws data from a four-month field study at a blown-molded glass factory. Data collection techniques included interviews, informal conversations and on-site observations, and data were interpreted using content analysis. Findings: The results indicated that sharing of tacit knowledge is facilitated by an engaging environment. An engaging environment is supported by shared language and knowledge, which are developed through intense communication and a strong sense of collegiality and a social climate that is dominated by openness and trust. Other factors that contribute to the creation of an engaging environment include managerial efforts to provide appropriate work conditions and to communicate company goals, and HRM practices such as the provision of formal training, on-the-job training and incentives. Practical implications: This paper clarifies the scope of managerial actions that impact knowledge creation and sharing among blue-collar workers. Originality/value: Despite the acknowledgement of the importance of blue-collar workers' knowledge, both the knowledge management and operations management literatures have devoted limited attention to it. Studies related to knowledge management in unstructured working environments are also not abundant. © Emerald Group Publishing Limited.

Integrated pulp and paper mill planning and scheduling

This article describes a real-world production planning and scheduling problem occurring at an integrated pulp and paper mill (P&P) which manufactures paper for cardboard out of produced pulp. During the cooking of wood chips in the digester, two by-products are produced: the pulp itself (virgin fibers) and the waste stream known as black liquor. The former is then mixed with recycled fibers and processed in a paper machine. Here, due to significant sequence-dependent setups in paper type changeovers, sizing and sequencing of lots have to be made simultaneously in order to efficiently use capacity. The latter is converted into electrical energy using a set of evaporators, recovery boilers and counter-pressure turbines. The planning challenge is then to synchronize the material flow as it moves through the pulp and paper mills, and energy plant, maximizing customer demand (as backlogging is allowed), and minimizing operation costs. Due to the intensive capital feature of P&P, the output of the digester must be maximized. As the production bottleneck is not fixed, to tackle this problem we propose a new model that integrates the critical production units associated to the pulp and paper mills, and energy plant for the first time. Simple stochastic mixed integer programming based local search heuristics are developed to obtain good feasible solutions for the problem. The benefits of integrating the three stages are discussed. The proposed approaches are tested on real-world data. Our work may help P&P companies to increase their competitiveness and reactiveness in dealing with demand pattern oscillations. (C) 2012 Elsevier Ltd. All rights reserved.

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Implementation of a non ground meta interpreter for defeasible logic

Human reasoning is a fascinating and complex cognitive process that can be applied in different research areas such as philosophy, psychology, laws and financial. Unfortunately, developing supporting software (to those different areas) able to cope such as complex reasoning it’s difficult and requires a suitable logic abstract formalism. In this thesis we aim to develop a program, that has the job to evaluate a theory (a set of rules) w.r.t. a Goal, and provide some results such as “The Goal is derivable from the KB5 (of the theory)”. In order to achieve this goal we need to analyse different logics and choose the one that best meets our needs. In logic, usually, we try to determine if a given conclusion is logically implied by a set of assumptions T (theory). However, when we deal with programming logic we need an efficient algorithm in order to find such implications. In this work we use a logic rather similar to human logic. Indeed, human reasoning requires an extension of the first order logic able to reach a conclusion depending on not definitely true6 premises belonging to a incomplete set of knowledge. Thus, we implemented a defeasible logic7 framework able to manipulate defeasible rules. Defeasible logic is a non-monotonic logic designed for efficient defeasible reasoning by Nute (see Chapter 2). Those kind of applications are useful in laws area especially if they offer an implementation of an argumentation framework that provides a formal modelling of game. Roughly speaking, let the theory is the set of laws, a keyclaim is the conclusion that one of the party wants to prove (and the other one wants to defeat) and adding dynamic assertion of rules, namely, facts putted forward by the parties, then, we can play an argumentative challenge between two players and decide if the conclusion is provable or not depending on the different strategies performed by the players. Implementing a game model requires one more meta-interpreter able to evaluate the defeasible logic framework; indeed, according to Göedel theorem (see on page 127), we cannot evaluate the meaning of a language using the tools provided by the language itself, but we need a meta-language able to manipulate the object language8. Thus, rather than a simple meta-interpreter, we propose a Meta-level containing different Meta-evaluators. The former has been explained above, the second one is needed to perform the game model, and the last one will be used to change game execution and tree derivation strategies.

Comparing Haemophilus influenzae type b conjugate vaccine schedules: a systematic review and meta-analysis of vaccine trials

BACKGROUND The optimal schedule and the need for a booster dose are unclear for Haemophilus influenzae type b (Hib) conjugate vaccines. We systematically reviewed relative effects of Hib vaccine schedules. METHODS We searched 21 databases to May 2010 or June 2012 and selected randomized controlled trials or quasi-randomized controlled trials that compared different Hib schedules (3 primary doses with no booster dose [3p+0], 3p+1 and 2p+1) or different intervals in primary schedules and between primary and booster schedules. Outcomes were clinical efficacy, nasopharyngeal carriage and immunological response. Results were combined in random-effects meta-analysis. RESULTS Twenty trials from 15 countries were included; 16 used vaccines conjugated to tetanus toxoid (polyribosylribitol phosphate conjugated to tetanus toxoid). No trials assessed clinical or carriage outcomes. Twenty trials examined immunological outcomes and found few relevant differences. Comparing polyribosylribitol phosphate conjugated to tetanus toxoid 3p+0 with 2p+0, there was no difference in seropositivity at the 1.0 μg/mL threshold by 6 months after the last primary dose (combined risk difference -0.02; 95% confidence interval: -0.10, 0.06). Only small differences were seen between schedules starting at different ages, with different intervals between primary doses, or with different intervals between primary and booster doses. Individuals receiving a booster were more likely to be seropositive than those at the same age who did not. CONCLUSIONS There is no clear evidence from trials that any 2p+1, 3p+0 or 3p+1 schedule of Hib conjugate vaccine is likely to provide better protection against Hib disease than other schedules. Until more data become available, scheduling is likely to be determined by epidemiological and programmatic considerations in individual settings.

An MBLP model for scheduling assessment centers

Firms aim at assigning qualified and motivated people to jobs. Human resources managers often conduct assessment centers before making such personnel decisions. By means of an assessment center, the potential and skills of job applicants can be assessed more objectively. For the scheduling of such assessment centers, we present a formulation as a mixed-binary linear program and report on computational results for four real-life examples.

Efficient list-generation techniques for scheduling assessment centers

Human resources managers often conduct assessment centers to evaluate candidates for a job position. During an assessment center, the candidates perform a series of tasks. The tasks require one or two assessors (e.g., managers or psychologists) that observe and evaluate the candidates. If an exercise is designed as a role-play, an actor is required who plays, e.g., an unhappy customer with whom the candidate has to deal with. Besides performing the tasks, each candidate has a lunch break within a prescribed time window. Each candidate should be observed by approximately half the number of the assessors; however, an assessor may not observe a candidate if they personally know each other. The planning problem consists of determining (1) resource-feasible start times of all tasks and lunch breaks and (2) a feasible assignment of assessors to candidates, such that the assessment center duration is minimized. We present a list-scheduling heuristic that generates feasible schedules for such assessment centers. We propose several novel techniques to generate the respective task lists. Our computational results indicate that our approach is capable of devising optimal or near-optimal schedules for real-world instances within short CPU time.

A heuristic approach for scheduling assessment centers

Human resources managers often use assessment centers to evaluate candidates for a job position. During an assessment center, the candidates perform a series of exercises. The exercises require one or two assessors (e.g., managers or psychologists) that observe and evaluate the candidate. If an exercise is designed as a role-play, an actor is required as well which plays, e.g., an unhappy customer with whom the candidate has to deal with. Besides performing the exercises, the candidates have a lunch break within a prescribed time window. Each candidate should be observed by approximately half the number of the assessors. Moreover, an assessor cannot be assigned to a candidate if they personally know each other. The planning problem consists of determining (1) resource-feasible start times of all exercises and lunch breaks and (2) a feasible assignment of assessors to candidates, such that the assessment center duration is minimized. We propose a list-scheduling heuristic that generates feasible schedules for such assessment centers. We develop novel procedures for devising an appropriate scheduling list and for incorporating the problem-specific constraints. Our computational results indicate that our approach is capable of devising optimal or near-optimal solutions to real-world instances within short CPU time.

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

Preliminary job descriptions for the domestic & personal service industry.

"Published by the Minnesota State Department of Education and the Division of Standards and Research of the United States Employment Service as a report on official projects 5196 ... 5201, [4155 and 4184] conducted under the auspices of the Works Progress Administration."

Job Corps Intergroup Relations Program.

Shipping list no.: 91-665-P.

A meta-analysis of the validity of training and experience ratings in personnel selection /

"April 1985."

A longitudinal investigation of coping processes during a merger: Implications for job satisfaction and organizational identification

This study tested the utility of a stress and coping model of employee adjustment to a merger Two hundred and twenty employees completed both questionnaires (Time 1: 3 months after merger implementation; Time 2: 2 years later). Structural equation modeling analyses revealed that positive event characteristics predicted greater appraisals of self-efficacy and less stress at Time 1. Self-efficacy, in turn, predicted greater use of problem-focused coping at Time 2, whereas stress predicted a greater use of problem-focused and avoidance coping. Finally, problem-focused coping predicted higher levels of job satisfaction and identification with the merged organization (Time 2), whereas avoidance coping predicted lower identification.