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Stigler Milton Friedman. Kates George A. Miller Eleanor J. Gibson Robert K. Merton Roger N. Shepard Paul Samuelson William K. Bower Michael I. Posner Mortimer Mishkin. Nirenberg Francis P. Rous George G. Simpson Donald D. Van Slyke Edward F. Rose Sewall Wright Kenneth S. Cole Harry F. Harlow Michael Heidelberger Alfred H. Sturtevant Horace Barker Bernard B. Brodie Detlev W. Sabin Daniel I. Arnon Earl W. Sutherland Jr. Wilson Robert H. Burris Elizabeth C. Burton Mildred Cohn Howard L. Bachrach Paul Berg Wendell L.

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Witkin J. Michael Bishop Solomon H. Snyder Charles Yanofsky Norman E. Borlaug Phillip A. Sharp Thomas E. Starzl Anthony S. Fauci Torsten N. Wiesel Rita R. Lefkowitz Bert W. O'Malley Francis S. Collins Elaine Fuchs J. Craig Venter Susan L. Lindquist Stanley B. Pimentel Richard N. Zare Harry B. Marvel Frank H. Westheimer William S. Johnson Walter H. Stockmayer Max Tishler William O. Baker Konrad E. Bloch Elias J. Corey Richard B. Bernstein Melvin Calvin Rudolph A. Marcus Harden M. Roberts Ronald Breslow Gertrude B.

Elion Dudley R. Herschbach Glenn T. Seaborg Howard E. Simmons Jr. Cram Norman Hackerman George S. Hammond Thomas Cech Isabella L. Karle Norman Davidson Darleane C. Hoffman Harold S. Johnston John W. Cahn George M. Whitesides Stuart A. Rice John Ross Susan Solomon. Baldeschwieler Ralph F. Hirschmann Ernest R. Somorjai John I. Brauman Stephen J. Lippard Marvin H. Caruthers Peter B. Dervan Mostafa A. Benkovic Marye Anne Fox.

Barton Peter J. Stang Allen J. Bard M. Frederick Hawthorne Judith P. Klinman Jerrold Meinwald A. Paul Alivisatos Geraldine L. Draper Hugh L. Dryden Clarence L. Johnson Warren K. Lewis Claude E. Shannon Edwin H. Land Igor I. Sikorsky J. Presper Eckert Nathan M. Newmark Jack St.

Clair Kilby. Mueller Harold E. Edgerton Richard T. Leith Raymond D. Mindlin Robert N. Noyce Earl R. Parker Simon Ramo. Heinemann Donald L. Oliver Robert Byron Bird H. Drucker Willis M. Hawkins George W. Heilmeier Luna B. Leopold H. Guyford Stever Calvin F. Cho Ray W. Clough Hermann A. Haus James L. Flanagan C. Kumar N. Patel Eli Ruckenstein Kenneth N. Prausnitz Edwin N. Lightfoot Jan D. Achenbach Tobin J. Marks Robert S. Langer David J. Wineland Rudolf E. Goodenough Thomas Kailath. Mathematical, statistical, and computer sciences.

Doob Donald E. Gomory Joseph B. Rao Elias M. Stein James G. Glimm Carl R. Viterbi David B. Tapia S. Srinivasa Varadhan Solomon W. Van Vleck Vladimir K. Brown Wolfgang Panofsky. Dicke Allan R. Sandage John C. Slater John A. Bethe Joseph O. Feynman Herman Mark Edward M. Townes E. Rossi J. Robert Schrieffer Solomon J. Buchsbaum H.

Lauterbur George Pake James A. Van Allen D. Ramsey Jack Steinberger Arnold O. Cormack Edwin M. Anderson John N. Bahcall James Cronin Leo Kadanoff. Lamb Jeremiah P. Ostriker Gilbert F. White Marvin L. Cohen Raymond Davis Jr. Charles Keeling Richard Garwin W. Jason Morgan Edward Witten G. Clayton Ralph A. Slichter Berni Alder James E. Gunn Yakir Aharonov Esther M. Conwell Warren M. Solomon Shirley Ann Jackson. Thorndike Award recipients. Pressey William Brownell B. Thorndike John C. Flanagan Benjamin Bloom Robert M. McKeachie Frank Farley. Snow Herbert Klausmeier Robert L.

Anderson Lauren Resnick Albert Bandura. Mayer John D. Social and political philosophy. Jurisprudence Philosophy and economics Philosophy of education Philosophy of history Philosophy of love Philosophy of sex Philosophy of social science Political ethics Social epistemology. Namespaces Article Talk. Views Read Edit View history. In other projects Wikimedia Commons Wikiquote. By using this site, you agree to the Terms of Use and Privacy Policy. The food service provider will secure a professional chef and supervise and manage the chef, who will meet the SafeServ hygiene standards we have incorporated and the standards of meal quality and service that the undergrads desire.

These companies exist and are quite good. The undergraduate Sage, steward, and RA are a part of this interview and selection process. The committee will work with the vendor we select to request that they hire our existing cook, Patrick. How often do the brothers have dinner together during the week? Lunch and dinner are served daily at Sigma Pi. At lunch, 35 brothers come down the hill. Roughly 45 brothers have dinner daily at the house. In sum, approximately 80 meals are served daily.

Sigma Pi alumni should be aware that the traditional p. The primary reason for this change is that Cornell University adjusted its class and lab hours, leaving most students with evening classes or other evening commitments, including athletics, clubs, associations, outside jobs, etc. The brothers still dine together one or two nights each week, including a Sunday meal after the weekly house meeting.

A buffet-style dinner is offered Monday through Friday, giving the undergrads the flexibility to eat dinner at the house at a time that accommodates their individual schedule. Given this change, the brothers have also asked for access to light meals, including paninis, salads, fruit, dry goods, etc. This is partly the inspiration for establishing the new Learning Commons. Will the brotherhood have access to the kitchen during off hours? The kitchen will not be locked. The brotherhood will always have access to and use of the dishwasher, but certain equipment is restricted during off hours.

The donor honor roll will be updated regularly throughout the campaign. Check out the top-ten giving classes and the naming opportunities claimed. Click the icon to see what a donor has to say about his or her gift! If you have already made a gift and would like to add a comment, please email alumnirecords sigmapicornell. Donate here or contact Steve Pirozzi to learn more about how to serve as a class agent. The Dining and Learning for the 21st Century campaign continues to progress well. The committee has selected a contractor for the construction phase of the project.

The target date for work to begin is June 4, , one week after Cornell commencement ceremonies, and we anticipate final completion of the project later in the summer. We expect to continue our successful fundraising up to and through the beginning of construction. We are asking all Sigma Pi alumni to contribute, especially those from class years that have yet to participate in the project. With new contributors and the continued hard work of many alumni and friends of Sigma Pi, we look forward to being able to fully fund this project before the work begins.

We would like to thank all of our generous supporters and everyone who has worked on the campaign! We will continue to keep you updated on our progress throughout the coming year. For those who have yet to contribute, we would really appreciate your support at whatever level you feel comfortable with. These projects and equipment can also be funded in memory of a deceased Sigma Pi brother. We define the future state of a system is a set of uncertain values, which can be modeled by fuzzy numbers.

The future machine state is not very dissimilar to the current status, and the next event is a sort of behavior repetition. The PI also justifies the system being in a trend to achieve a goal, and it counts the unplanned contextual reactions of the system. In this paper, we come up with a fuzzy data-driven predictor application to foretell the system behavior.

The optimized controller reaches the reference speed at the same time it keeps the temperature in the semiconductors of the BLDC within minimum variations to preserve the loses in a minimum value in the power electronics stage, so the lifetime of the power electronic stage is incremented. A co-simulation of the BLDC, which is a powerful simulation tool for getting similar result to experimental tests is presented.

The results validate the proposed optimized controller as well as show the increment in the lifetime of the power electronic stage in the BLDC. This phenomenon has allowed fashion and product designers to promote their merchandise more easily. However, these approaches require new methodologies for developing their products, as well as consider the sustainable aspects in their campaigns. In the era of Information 4. This paper proposes a product analysis methodology for the fashion industry, based on the S3 model, for evaluating how useful internet videos influence the customer's emotional response and the tendency for using certain products.

All of this while analyzing their responses through monitoring their brain activity during the view of the videos using electroencephalography EEG readings, and the emotional response they generate in them by implementing the "Emocard" model, all of this to analyze the media influence for using or not the promoted product. Therefore, companies must offer products that can be competitive in the market and solve social problems through the use of emerging technologies and practices that allow high customisation and better performance of products.

Thus, the information generated by using the proposed taxonomy will support the design process and obtain an early identification of the value proposition of products to be developed. Sometimes, the products are extremely limited in the social communication between products to consumers and products to products, and the primary communication system is the interface, but typically, this interface is not designed as a social element.

As a result, social communication is minimal. Thus, a basic design methodology that allows product designers to incorporate social factors between products and consumers and products to products is presented in this paper. Also, the term social communication PCPP Product-Consumer and Product-Product is coined as a part of the product design methodology that allows defining the social features as primary elements in the process of designing products.

Besides, a conceptual method for smart thermostats is proposed for a social communication PCPP to demonstrate how this conceptual proposal could be implemented using the sensing, smart and sustainable S3 features. In the same time, modern supply chain systems are increasingly predisposed to complexity and uncertainty.

Therefore, making strategic decisions requires well informed risk analysis, control and mitigation. When comparing with other resources, a WS is a long-term asset as the building often has an amortization period of 20 to 25 years and the equipment 5 to 10 years. Accordingly, strategic warehousing decisions when designing have to anticipate changes for long terms. The literature proposes 4 indicator dimensions and 45 warehouse indicators.

One of the most important issues in this scope is the contradictory evolution between these indicators. In presented research, a methodical study of this indicators is represented in form of contradictions that allow learning systematically about future characteristics of designed system. Intervention research was conducted in a French 3PL. The scope of the study consists of 25 WS. The working group included 6 individuals. The group had 26 meetings of 4 working hours over a month period. The result of the working group activities is characterised by: 21 trends, 48 drivers, 49 barriers and desired results.

The formulation of the measurable expected results indicated 88 performance indicators and to facilitate the connection of contradictions within a map, the performance indicators were classified into 6 dimensions. The map of contradictions represents the conflicts of interest between 7 major stakeholders of supply chain. The achieved result is described by a map of 58 contradictions.

The map of contradictions is obtained by linking desired results with performance indicators that are classified into 6 dimensions The prospects of this study is the development of a systematic method designed to build map of contradictions. The two approaches have been combined to achieve the objective of Justin- Time JIT scheduling in an open-shop system.

This approach makes the two algorithms work together aimed with combining the main advantages of each of them, to obtain a reactive control strategy. The SA algorithm plays a role as the machine-route explorer, while the DATC algorithm is inserted into the SA loop, allowing searching for the best arrival times of jobs to satisfy the JIT objective wanted. The performance of this new hybrid SA-DATC approach is evaluated with quadratic linear program solutions to test its relative performance in a static environment.

Computational results show that the algorithm performs well on the most of the test problems generated randomly in this paper with an interesting computational time, proving that our approach is favorable for an open-shop scheduling problem. The purpose of this article is to focus on developing a hybrid approach to address the supply chain planning problem. The proposed approach combines simulation and mixed integer linear programming.

The supply chain system studied includes discrete and continuous processes. These two aspects should be always considered together in any modelling approach. This article, first, focuses on developing a discrete event simulation model of a hybrid supply chain, which is used to best describe the system and therefore to evaluate the system behavior over time.

This simulation model permits to verify the feasibility of a given optimal planning proposed by a mixed integer programming model. The objective of this approach is to evaluate the solution of the optimization model by the simulation model. The efficacy and the usefulness of the approach are illustrated with a complex real industrial supply chain, consisting of several logistic activities in a chemical fertilizer plant. After being trained on normal data, the networks are used to predict interested steps in time series.

The difference between the predicted values and observed values is calculated as prediction errors. Then we use a kernel estimator of the quantile function to compute a threshold, which is used to determine anomalous observations.

The performance of proposed method is illustrated through an example of anomaly detection of consumer demand in supply chain management. Our experiments show that our approach achieved a high level of detection accuracy and a low percentage of false alarm rate in comparison with One-Class Support Vector Machine method. It becomes important for such systems to be able to face this dynamism and respond to different foreseen or unforeseen disruptions in the current industrial, societal and environmental contexts. From our perspective, one can identify different kinds of relevant scientific challenges depending on the time at which the complex system is studied, depending on the occurrences dates of disruptions and depending on the phase of a decision-making process.


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This paper suggests structuring the different works coping with disruptions in complex systems using a generic framework organized according to the occurrence time of disruptions and on the concerned decision levels in the complex system. Three examples from different areas health care, physical internet and manufacturing systems are presented to illustrate the framework. This paper concludes with a set of research prospects.

The hybrid supply chain is formed as a chain by multi-echelon manufacturers and service providers who work together to offer product service system to customers. To tackle this problem, we establish a system dynamics model, by taking the multi-echelon hybrid supply chain for a logistics equipment company in China as an example. Firstly, we analyzed the oscillation characteristics of service flow and product flow.

Then, we proposed the performance metrics of bullwhip effect in multi-echelon hybrid supply chain.

Finally, based on the model, we found that the bullwhip effect of multi-echelon hybrid supply chain could be smoothened by incorporating forecast smooth factor, control percentage coefficient and order lead time. Findings: The authors defend that appropriate partner selection is a vital success factor in any collaboration. Existing supplier partner selection tools do not include the unified approach for business processes modelling of partners organisations and do not apply the lean methodology for sustainable partner selection.

Existing collaborative networks spend considerable time and money to search for suitable partner decrease the number of successfully initiated and completed collaborative projects. After implementation, solution applies the lean based approach for partner selection, to decrease the time and money spend with simultaneous improvement of collaborative project sustainability. Nevertheless, the proposed approach is adaptable to other fields. The paper includes a feasibility case study for the approval of findings, where twenty several small and medium enterprises SMEs from the manufacturing and logistics fields collaborate to achieve a common goal.

The time required for collaborative partner selection in conventional supply chain is compared to the partner selection in Sustainable Partner Network. In this study, we extend an existing SCN model by elaborating on stochastic price and demand and integrating economic uncertainty. Each of these components focuses on a different aspect of investment attractiveness under various economic conditions, and in turn, facilitates access to capital for the companies involved.

We introduce a weighted sum method and a novel genetic algorithm GA solution approach to solve the resultant bi-objective mixed integer nonlinear programming problem. We illustrate the representation scheme required for the GA in this paper. However, little is known about the effect of trust on supply network resilience, i.

This paper overcomes this gap by adopting a recent conceptualization of network-level trust and by developing an agent-based model of supply network, simulating its resilient performance. A simulation analysis is carried out to assess the effect of trust on the resilience of supply networks showing different interdependence structures. Results confirm that trust positively affects supply network resilience. However, we find that the benefit varies across interdependence structures, being almost neglected for supply networks exhibiting a dependent structure. Furthermore, we achieve that the higher the frequency of disruptions, the lower the beneficial effect of trust for supply network resilience.

The system is intended to analyze complex decision problems in production networks including questions related to trust, pricing and resilience. Industry 4. Manufacturing analytics helps in making an efficient production system by gaining productivity along with cost minimization and quality products. The applications of data analytics are categorized in several domains of manufacturing systems such as engineering design, decision support systems, shop floor control, fault detection, machine failures, predictive maintenance, and cyber-security.

To envision the goal of the fourth industrial revolution, real-time data needs to be collected by upgrading the manufacturing units with emerging technologies. Manufacturing analytics envision the manufacturing future beyond shop floor automation by deploying digital twins, sensors based technologies, and data science to achieve optimized manufacturing units and it delivers the personalized and small batch products.

A digital factory model, created with the material handling simulation toolkit, can help test and optimize production, transportation, and inventory policies, as well as reduce possible errors while also introducing innovations to existing operations, or integrating modern technologies. In conveyor network models, created with the Material Handling Library, users can apply default or custom routing strategies for material items, industrial robots, manufacturing machines, and operators. Simulated AGVs and other transporters automatically avoid collisions, detect possible deadlocks, and resolve them.

See it in action and find out more. The main role of BHS is to sort and transport all transfer and check-in bags to each destination, that is to the right plane, at the right time. The airport in charge of BHS has to minimize the number of failed bags in the system. A failed bag is defined to be a bag that is not delivered in time for the assigned flight. These extensions makes it possible to model complex processes like BHS. Simulation results show that one of the main causes of the failed bags is the Manual Scanning process, especially the percentage of barcode misread.

Based on the technical properties of single technologies and process requirements a classification of IoT technologies provides the basis for a use case specific evaluation of the technical feasibility. This developed classification is further combined with cost-benefit evaluation approaches. The current status of these developments is observed in the context of a logistics use cases. The paper concludes with an outlook on the further development works and discussing the contribution of IoT solutions to the creation of Smart Logistics Zones. However, automated micro-vehicles for delivery purposes might be more lucrative and even earlier avail-able on the market.

Until now, limited research is available about the global development of automated micro-vehicles for urban logistics. This paper conducts a market analysis on them to gain knowledge about the state of the art in respect to application, business model and technical set-up. This study identifies 39 automated micro-vehicles. We furthermore developed a classification dividing automated micro-vehicles by infrastructure road, non-road , vehicle delivery robot, bicycle and human role. The analysis of the technical equipment shows, that the technical set-up between vehicle classes differs greatly due to different fields of application and infrastructure used.

State detection is performed via sensor-based technologies that are attached to logistics objects or to elements of the logistics system. Besides other technologies, image-based sensor systems have already proven their potential to fulfill this task. Within the development of image-based sensor systems, a suitable combination of processing modules e.

This complexity results in a high number of test cases, which are typically performed in laboratory environments within a prototyping stage. This approach involves considerable effort. In order to mitigate this problem, the paper proposes the application of virtual commissioning of image-based information systems for state detection in logistics processes.

Virtual commissioning can simplify data acquisition of different sensor configurations and subsequent early evaluation of algorithmic models for state detection in logistics processes and thus accelerate design, implementation and testing of image-based information systems for specific environments. However, major challenges such as the variety of tools and expert roles and the integration of virtual commissioning into a process model remain and must be solved in order to establish virtual commissioning of image-based information systems in logistics.

Therefore, this study utilizes a power measurement device, which is composed of voltage and current sensors. The cutting power is the multiplication of cutting force the load information and the speed information. However, in this study, the cutting power is monitored by estimating the induced voltage of the drive motor and correcting the motor current. The method is experimentally validated in a two-dimensional milling test and compared to a dynamometer-based signal.

Results of the comparisons showed that the Kaizen multi-CONWIP system outperformed all the other systems on the average lead time with up to The demand is a compound Poisson process with a price sensitive intensity and a continuous batch size distribution. A diffusion approximation of the demand process is used to find the probabilistic characteristics of the selling process and the expected revenue.

The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The ID study considers the elements of the Industry 4. These four groups of the I4 elements are examined in the industrial cases covering from mature industries to laboratory level systems to determine their current technological states and gaps into the I4 stage. The examples highlighted are crushed-ore stockpile level control in the mining field, resin bed cleaning timetabling in water demineralisation treatment, compositional data-driven real-time optimisation of hydrocarbon streams and diverse I4 basics in the next generation of biorefineries.

In the main example, supported by a high-end sensing apparatus to measure crushed-ore stockpile levels in real-time live inventory as a controlled variable by a target , a hybrid dynamic control prescribes every 4 minutes discrete positions and time-slots of the shuttle-conveyor tripper car mechatronics that creates the stockpiles. From such IDoI4 methodology, a table on the MSA, ICT, HPC and MEC ground bases summarises how such technologies are integrated to the industrial examples considering research, development and deployment in stable, demanded and highly demanded stages of the technologies into the I4 mandate.

Thus, by following the traditional perception, smart contracts target to reduce transaction costs including arbitration and enforcement costs by realising trackable and irreversible transactions by using blockchain technology for distributed databases. However, the potential of smart contracts goes far beyond cost reductions by facilitating the entrepreneurial collaboration of cross-organisational businessprocesses that are characteristic for smart supply chains. A closer look to existing or ongoing smart contract projects reveals that the majority of smart-contract applications in business life are linked to supply chain management, Internet of Things and Industry 4.

The author participated in several EU projects related to transnational entrepreneurial networks and smart supply chains. Thus, the paper discusses the research question of how and to which extent smart contracting and blockchain technology can facilitate the implementation of collaborative business structures for sustainable entrepreneurial activities in smart supply chains. The research is based on expert interviews, surveys and case studies, which took place in the context of the EU projects with a focus on the Baltic Sea Region.

However, making legacy manufacturing equipment compliant with this new standard is still a difficult task. Regarding additive manufacturing machines, this issue has been poorly studied. To address the subject, this work introduces an MTConnect-compliant framework for shopfloor data access and monitoring of RepRap additive manufacturing machines based on Arduino open technology controllers.

A web-client application was also developed to validate the system by performing real-time machine monitoring over the Internet. The proposed MTConnect solution allows retrieving data of axes position, heat-bed temperature, hot-end temperature, material extrusion, current layer number and elapsed time from the web application, demonstrating feasibility to operate in cloud manufacturing environments. A solution of the JPSR is not a left-shifted solution where all operations start the earliest possible, but a solution minimizing the makespan and maximizing the QoS.

Gondran et al. This paper is a comparison between two approaches using the TLH evaluation, the first approach is an integrated approach permitting to find high quality solutions in terms of makespan but requiring high computational time. The second approach is a sequential approach in which the TLH evaluation is performed only on the best solution according to the makespan criteria, the TLH evaluation included in a sequential approach permits to obtain a solution maximizing the QoS with a low computational time.

In this paper, we developed a highly efficient Two-Stage Genetic Algorithm 2SGA that in the first stage, GA coding only determines the order of operations for assignment. But machines are assigned through an evaluation process that starts from the first operation in the chromosome and chooses machines with the shortest completion time considering current machine load and process time.

At the end of the first stage, we have a high-quality solution population that will be fed to the second stage. The second stage follows the regular GA approach for FJSP and searches the entire solution space to explorer solutions that might have been excluded at the first stage because of its greedy approach.

The efficiency of proposed 2SGA has been successfully tested using published benchmark problems and also generated examples of different sizes. The problem encompasses several identical parallel machines with sequence-dependent setup times and a release date for each job. A Mixed Integer Linear Programming model is used to generate the initial schedule and then, every time a new order arrives, the MIP is executed again but considering the new order plus the orders not completed so far executed.

The results produced for a tested of 16 different problems are analyzed according to two parameters: 1 the number of new jobs arriving to the system, and 2 the relative weight granted to each of the objectives. These capabilities contribute to increased throughput and capacity utilization and reduced storage and retrieval time. We address concurrent storage location assignment and storage and retrieval sequence scheduling under the shared storage policy and the modified 2n-command cycle pattern.

Owing the problem complexity to large-scale instances, a hybrid solution procedure that combines an Ant colony algorithm and Adaptive Large Neighborhood Search is proposed. In addition, a mathematical model, based on the allocation problem, is proposed to improve the quality of the solutions. By means of a brief survey of the current trends, a set of recommendations are provided in order to open discussions for the foreseen evolutions of the ISO standard. A focus is made on the role of the humans in their increasingly closer collaboration with the pervasive autonomous intelligent entities in the context of a human-centered manufacturing.

It is contended that there is still some room for a review of the definitions of the performance indicators and their structure that should aim to reflect human and intelligent automation aspects in an integrated framework. Differences between workers in terms of age, gender, physical measures, culture and skills have a large impact on production systems performance. This study investigates workforce diversity factors in production system modeling and design in order to highlight strengths and weakness in the present published literature.

The paper categorizes a selection of papers in the last ten years and discusses how human factors are incorporated into manufacturing systems optimization and design approaches. Finally, a discussion on future research steps is provided. The development human-robot collaborative workplaces is a complex multidisciplinary problem.

Efficient measure, evaluation and interpretation of a significant number of variables is therefore necessary without incurring in excessive computation power demands. A number of advancements to hierarchical task analysis HTA are proposed in order to improve Hierarchical Task decomposition and complexity evaluation. A simple predictive ergonomic evaluation approach is proposed.

Together with PTMS, this simpler ergonomic evaluation method can more easily be integrated in more complex collaborative workplace design methods. If AM hold these promises, firms which take the risk today to invest will be recognized. The decision-maker must dispose of a methodology to define the optimal design strategy under uncertainty which considers the risk taken by the decision-maker and his confidence on the suppliers and on the future.

This problem matches a capacity planning problem with uncertainties on demand, on resource development, on cost evolution and on technologic evolution. In this article, we propose, in a first approach, to solve a deterministic problem of capacity planning. First, we formalize a mixed integer program. The chosen objective function is the maximization of the net profit.

A case of study is presented and solved thanks to a solver. Smoothing facilitates planning the workforce and optimizing the resource allocation problems. We test the effectiveness of the algorithm with experimental study. The main task is to improve the comfort of passengers using public transport. A number of indicators help toevaluate the convenience of the transport system of a metropolis. To compute these indicators, we analyzed the data obtained from the municipal information systems: the public transport payment system and the transport tracking system.

We evaluated the following indicators: rush hours during the day, average amount of trips made by passengers of each social group per month, transportation comfort index, the most and the least accessible districts of the city, interchange coefficient for multimodal trips average number of single trips within multimodal trips and etc. This method uses linguistic expressions and has good information about cause-effect chains. However, it lacks probabilistic information. Transforming it into a Bayesian Network BN makes it possible to be used in maintenance for both diagnosis and prediction. The purpose of this paper is to develop a method that uses as much information as possible from FMECA, including frequency and detection to precisely make the configuration of the BN.

Moreover, we develop an algorithm to set the parameters of a BN obtained. Elicitation methods based on expert knowledge are used when data is not sufficient.

A case study of FMECA in the automotive industry is introduced to verify the applicability of the proposed method in an industrial environment. In this work we want to expand the current literature basis relating chassis terminals and propose a mathematical model that aids the design process of the terminal.

We develop cycle time models for gantry cranes and tractor units which allow computing the expected equipment cycle time in a single command cycle. Next, we integrate the cycle time models into an optimization framework that seeks to minimize the investment and operation costs of the terminal. The results show that container dwell time has a substantial impact on total cost, especially for a large terminal. We also found that optimal configuration uses only a small number of gantry cranes due to high investment costs. This model contributes to address mobility and logistics requirements for stakeholders with the rise of Smart Cities.

Good solutions are found thanks to a hybrid metaheuristic based on an evolutionary algorithm. Our results are compared on small instances for which optimal solutions have been found using MILP. Future work are discussed and next challenges such the model scalability are addressed. On the other hand, urban goods delivery systems can be held accountable for the same negative externalities they suffer. The complexity of last-mile delivery systems arises from the heterogeneity of stakeholders and their objectives. In this context, local retailers might be called upon to adopt innovative last-mile delivery services offered by Logistics Service Providers LSPs.

With this paper, we aim to build on this stream of literature by analyzing the perception of local retailers regarding the value proposition of a wider set of LM delivery innovations, including the ones that do not comprise retailers among their paying customers. To this end, a survey is submitted to retailers of different sizes and type located in the limited traffic zone LTZ of Turin Italy.

Through the survey, we aim to assess the relative importance of nine service requirements retrieved from literature, and cluster them into factors, i. Results show that retailers are able to accept higher costs for the delivery for deliveries that are more reliable and for the reduction of stock. Retailers also appear to correlate punctuality and flexibility of the LM delivery service, because flexible and on time deliveries allow for better inventory management, higher control and in turn improved customer service level by the retailer.

The convenience of the delivery service is seen as correlated with its sustainability, because logistics activities can be carried out with small and low impact vehicles that allowing easier deliveries into the city center. The highlighted factors serve as a basis for the value propositions to be taken into account by practitioners in the design of LM innovations.

The objective of the cooperation between these health institutions is to provide a better treatment offer. To do so, these entities pool their means together. Our goal is to propose efficient methods to assign the different operations to the periods and resources, considering resources compatibilities and due dates. We consider this problem as an extension of the classical Bin Packing Problem. The results show the interest of the proposed PSO for this kind of problem. Shorter delivery lead times provide better service to customers. This paper investigates the bid generation problem of a carrier in collaborative transportation realized via a combinatorial auction.

In this paper, we propose a multi-period bid generation problem with two types of pickup and delivery requests, namely reserved and selective requests. This problem is an extension of pickup and delivery problem with time windows.

This problem arises when a shipper applies an auction for the procurement of transportation services from carriers. In each period, the carrier may have reserved requests that must be served by itself. This carrier wants to determine within a time horizon of multi periods which requests to bid and serve among a set of selective requests open for bid and its multi-period routing plan to maximize its profit and minimize delivery lead times.

This problem is NP-hard. The algorithm is evaluated on instances with 20 to requests. The computational results show that the proposed algorithm significantly outperforms CPLEX with much shorter computation times. Universidad De La Sabana Keywords: Supply Chain Management , Supply chains and networks , Heuristic and Metaheuristics Abstract: Collaboration in transportation activities is becoming more and more important in urban last-mile logistics. This growing interest is not only motivated by transportation cost reduction and load capability improvements, but also by customer service level and stakeholder coordination enhancement.

Computational experiments are carried out on a standard benchmark as well as new instances derived from the literature. The results show that the hybrid metaheuristic provide good solutions in comparison with state of the art methods. The effect of the hybridization is also assessed in this study. Despite the researches and technological progress, it is still not possible to predict when or where the natural disaster will occur beforehand.

Natural disasters cause severe loss of lives and damages. In addition, they cause physical, financial, social and environmental losses. Pre-disaster, during disaster and post-disaster activities are significant in order to decrease the losses caused by natural disasters. This study is about one of the pre-disaster activities. In the pre-disaster management process, a new activity is being tried in Turkey. It is important to keep items in these stations usable at all times.

Since these commodities have expiration dates, they need to be replenished periodically in order to remain useable at any time. The proper time for the replenishment should be determined by considering the probability of reselling or re-using the commodities with the maximum return as much as possible. However, replenishing frequently will result in large operational costs. Therefore, there is a trade-off between routing costs and replenishment.