A polynomial time algorithm for solving a quality control station configuration problem

AbstractWe study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations; existing machines and optional quality control stations (QCS). Our aim is to simultaneously decide where and if to install the QCSs along the line and to determine the production rate, so as to maximize the steady state expected net profit per time unit from the system.We use dynamic programming to solve the cost minimization auxiliary problem where the aim is to minimize the time unit production cost for a given production rate. Using the above developed O(N2) dynamic programming algorithm as a subroutine, where N stands for the number of machines in the line, we present an O(N4) algorithm to solve the Profit Maximization QCS Configuration Problem

An efficient MILP formulation for the parallel load retrieval in puzzle based storage systems with simultaneous load movements

Puzzle-based storage (PBS) is one of the most spaceefficient types of storage systems. In a PBS unit, loads are stored in a grid of cells, where each cell may be empty or contain a load. A load can move only to adjacent empty cells using four-way conveyors. When loads are requested for retrieval, a sequence of load movements is performed in order to bring them to the input/output (I/O) points of the unit. In this paper, we present a time-expanded-graph based integer-linear-programming (MILP) formulation that aims to minimize the time to retrieve a set of target loads (makespan). Note that other objectives, such as the flowtime or the number of moves, can be minimized as well. Experiments show that the proposed formulation can solve small to medium size instances, especially when the storage density is not extremely high. Additionally, we show that parallel load retrieval significantly decreases the makespan compared to serial load retrieval

Butanol by Two Stage Fermentation

Current techniques for producing butanol tend to have a low yield and form a large amount of other solvents, because there is only one stage for fermentation. Having one stage limits the type of bacteria that can be used, because the chosen bacteria must be able to both convert glucose to butyric acid, and then convert butyric acid to butanol. The only types of bacteria that can perform both these tasks also create a lot of other acids, which are turned to other solvents in the product stream. This is most prevalent in ABE fermentation, which creates significant amounts of acetone and ethanol along with the butanol. David Ramey, of ButylFuel LLC, has created a distinct process that generates butanol, without significant amounts of acetone or ethanol, using a two-stage fermentation process. The first stage converts glucose to butyric acid through acidogenesis, while the second stage converts the butyric acid to butanol via solventogenesis. This process optimizes the efficiency and specific production of the desired solvent, butanol. The purpose of this report is to scale-up Ramey’s process and build a plant based on a two-stage fermentation procedure. The economical viability of producing 50 million gallons of butanol per year, at a purity of 99.5% from the plant will also been discussed. These results will allow the organization to determine the worth of licensing the technology from ButylFuel. Additionally, because this process will compete with many ethanol plants, it is necessary for the design to mirror a typical ethanol plant as much as possible. Because of this, aspects of the current production of ethanol were implemented in the design, including the Dry Grind process and the Dried Distillers Grain Drying process. These implementations allow the process to be constructed from modified ethanol plants, rather than having to rebuild a new plant. The fermentation phase of the design utilizes a series of fibrous bed reactors and two different strands of Clostridium bacteria for each stage. The product stream out of the second fermentation stage, containing butanol, is separated using a liquid-liquid extractor, and a series of distillation columns, to extract the butanol from water. Different separation options were researched, including pervaporation, decanters, and stripping. The liquid-liquid extractor with distillation columns was chosen in the end, because it was the simplest and most economical process for dealing with a product stream that was over 90% water. Also, a butanol/water azeotrope surfaces during the separations process that is efficiently dealt with by the extractor. For the economic analysis, this report uses 50 million gallons per year producing ethanol plant as a comparison with the butanol process. The total capital investment for the ethanol plant is about $74.1 million with an investment rate of return (IRR) of 33.1$1.48/gallon of ethanol produced. Since the design specifications involved the modification of an existing ethanol plant, it was assumed that some existing ethanol equipment would be integrated into the system. Specifically, the Dried Distiller’s Grains (DDGS) dryer and the Dry Grind process are assumed to be installed and operational in year one. Additionally, it was assumed this equipment had been fully depreciated by the time of construction of the butanol plant. The results of this report were based on 54.3 million gallons per year producing butanol plant. , For this design, a total capital investment of $219 million was determined. This is a substantial investment cost highlighted by the fact that the overall net present value (NPV) of the design, after 15 years, was found to be a negative$3.55 billion. The poor investment opportunity stems from the high cost of utilities needed to run the plant. Of the total annual costs, 94.5% is derived from the overall utility costs. The profitability analysis and a review of current market conditions indicate that this investment should not be undertaken due to its high degree of unprofitability. Serious consideration of external factors and of the design itself must be taken before pursuing any investment. These factors, such as the price of corn, will be outlined more thoroughly at the end of the report

Microparticles from tumors exposed to radiation promote immune evasion in part by PD-L1

Radiotherapy induces immune-related responses in cancer patients by various mechanisms. Here, we investigate the immunomodulatory role of tumor-derived microparticles (TMPs)-extracellular vesicles shed from tumor cells-following radiotherapy. We demonstrate that breast carcinoma cells exposed to radiation shed TMPs containing elevated levels of immune-modulating proteins, one of which is programmed death-ligand 1 (PD-L1). These TMPs inhibit cytotoxic T lymphocyte (CTL) activity both in vitro and in vivo, and thus promote tumor growth. Evidently, adoptive transfer of CTLs pre-cultured with TMPs from irradiated breast carcinoma cells increases tumor growth rates in mice recipients in comparison with control mice receiving CTLs pre-cultured with TMPs from untreated tumor cells. In addition, blocking the PD-1-PD-L1 axis, either genetically or pharmacologically, partially alleviates TMP-mediated inhibition of CTL activity, suggesting that the immunomodulatory effects of TMPs in response to radiotherapy is mediated, in part, by PD-L1. Overall, our findings provide mechanistic insights into the tumor immune surveillance state in response to radiotherapy and suggest a therapeutic synergy between radiotherapy and immune checkpoint inhibitors

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101

The data-driven time-dependent orienteering problem with soft time windows

In this paper, we study an extension of the orienteering problem where travel times are random and time-dependent and service times are random. Additionally, the service at each selected customer is subject to a soft time window; that is, violation of the window is allowed but subject to a penalty that increases in the delay. A solution is a tour determined before the vehicle departs from the depot. The objective is to maximize the sum of the collected prizes net of the expected penalty. The randomness of the travel and service times is modeled by a set of scenarios based on historical data that can be collected from public geographical information services. We present an exact solution method for the problem based on a branch-and-bound algorithm enhanced by a local search procedure at the nodes. A numerical experiment demonstrates the merits of the proposed solution approach. This study is the first to consider an orienteering problem with stochastic travel times and soft time windows, which are more relevant than hard time windows in stochastic settings