Short-term Production Research Articles

SHORT-TERM EGG PRODUCTION AND PHYSICAL EGG CHARACTERISTICS IN A FLOCK OF HYBRID HUBBARD LAYERS

Short-term egg production and characteristics were studied using a total of 200 Hubbard hybrid layers under the battery cage system for four months. Birds were fed on battery layer mash (4080 kcal/kg M.E., 15.31% crude protien). Age at first egg (AFE) was 146 days. The highesthen-day egg production was 60.84% obtained at the 4th week in lay. Average hen-day and hen- housed production percentages for the study period stood at 50.08 and 48.05 respectively. Average egg weight was 53.24g ± 3.07 (Standard) with mean egg-shell thickness of 0.48mm ± 0.02. Average daily feed intake was 153.05g ± 1.82 per bird giving an average feed consumption of 3.7kg per dozen eggs. Mean albumen, yolk and shell weights were 33.81g, 13.52g and 6.06g respectively.

Transfer lines with multi-machine stages and non-deterministic processing time: transient performance analysis and control

Serial transfer lines with parallel machines in each stage are now a fairly common manufacturing system. However, its analysis and predictive-reactive control during transients remains mostly unexplored. This manufacturing system consists of a series of stages where in each stage a finite number of machines work concurrently. A two multi-state stage transfer line is studied in this paper. To describe the probability of one part being completed from the several machines in a stage, an equivalent multi-state, perfectly reliable machine is proposed. This multi-state model enables variance in the processing time between job completions to be captured stochastically. Based on historic data or engineering experience, an equivalent multi-state machine is simulated from the cumulative distribution function of the aggregated behavior of the parallel machines. In this paper, a serial production line with two equivalent, multi-state machines is used to analyze transient performance. Analytical closed-form expressions are derived to evaluate system transient behaviors. This system is also used with a controller to implement practical production control. Using model predictive control, system control parameter values provide decision support for managers to control short-term production. The proposed methodology can be applied to improve resource efficiency in other operation management problems.

PENERAPAN MODEL ERROR CORRECTION MECHANISM: DETERMINAN PRODUKSI KOPI DI INDONESIA PERIODE 2002-2018

Kopi merupakan salah satu komoditas hasil perkebunan yang mempunyai peran cukup penting dalam kegiatan perekonomian di Indonesia. Namun, produksi kopi di Indonesia mengalami kondisi yang berfluktuatif. Sementara itu, kondisi pasar kopi di dalam negeri masih cukup besar dengan diikuti peluang kopi sebagai komoditas ekspor yang semakin terbuka. Dalam jangka panjang, dikhawatirkan produksi kopi tidak dapat memenuhi pasar domestik maupun internasional. Adapun tujuan dari penelitian ini adalah untuk menganalisis faktor-faktor yang berpengaruh dalam jangka panjang dan jangka pendek pada produksi kopi di Indonesia sebagai penentu kebijakan yang tepat untuk meningkatkan produksinya. Metode yang digunakan dalam penelitian ini adalah Error Correction Mechanism (ECM) dengan periode penelitian mulai dari tahun 2002 sampai dengan tahun 2018. Berdasarkan hasil pembahasan, dalam jangka panjang variabel luas tanam menghasilkan dan harga kopi domestik berpengaruh signifikan terhadap produksi kopi sedangkan variabel jumlah petani kopi tidak signifikan dalam mempengaruhi produksi kopi. Selanjutnya, dalam jangka pendek hanya variabel jumlah petani kopi yang berpengaruh secara signifikan terhadap produksi kopi sedangkan variabel luas tanam menghasilkan dan harga kopi domestik tidak signifikan dalam mempengaruhi produksi kopi.

Emergent Ceramics and Identity at the Fifteenth-Century Iroquoian Keffer Village

The recent interpretation of ceramic types as fluid and relational (Fowler 2017) has facilitated their use in the exploration of relational identity. In this study, ceramics from the fifteenth-century southern Ontario Iroquoian Keffer (AkGv-14) village are employed in the exploration of matrilineal, matrilocal household self-identification as seen through ceramic communities of practice. The Keffer assemblage is separated into two categories; local tradition ceramics which I suggest represent genealogies of family practice, and non-local tradition pottery, which I propose communicates contemporary relations and long distance interaction. In addition, a new, third category of ceramics is proposed “emergent vessels.” Emergent ceramics are materialized in two separate and distinct vessel forms in the collection, the Everted Lip and North Shore Durfee Underlined. Their sudden and geographically restricted materialization reflects the equally sudden appearance of newly emergent facets of the polyvalent identities of potting communities as seen at Keffer and other north shore sites. The short-term production and use of these emergent ceramics attests to the quickly diminishing importance of these new emergent aspects of identity while the ceramics of the latest village occupations verify the endurance and gradual transformation of those facets of identity tied to family genealogy and long distance interaction.

A Digital Twin-based approach for multi-objective optimization of short-term production planning

Abstract Short-term multi-product production planning strongly depends on manifold considerations which can be related to backlog production, product-type prioritization, machine capacity, and set-ups. Different manufacturing systems conditions may lead to different objectives. In this context, it is rather difficult to define a-priori optimal production plans. This paper presents an effective approach for short-term production planning of multi-product systems based on the integration of a Digital Twin and a multi-objective optimization method. The proposed approach has been implemented in a real industrial case of the railway sector. Numerical results show that useful insights can be inferred from the proposed methodology.

Model-based production optimization under geological and economic uncertainties using multi-objective particle swarm method

Optimization of the water-flooding process in the oilfields is inherently subject to several uncertainties arising from the imperfect reservoir subsurface model and inadequate data. On the other hand, the uncertainty of economic conditions due to oil price fluctuations puts the decision-making process at risk. It is essential to handle optimization problems under both geological and economic uncertainties. In this study, a Pareto-based Multi-Objective Particle Swarm Optimization (MOPSO) method has been utilized to maximize the short-term and long-term production goals, robust to uncertainties. Some modifications, including applying a variable in the procedure of leader determination, namely crowding distance, a corrected archive controller, and a changing boundary exploration, are performed on the MOPSO algorithm. These corrections led to a complete Pareto front with enough diversity on the investigated model, covering the entire solution space. Net Present Value (NPV) is considered the first goal that represents the long-term gains, while a highly discounted NPV (with a discount rate of 25%) has been considered short-term gains since economic uncertainty risk grows with time. The proposed optimization method has been used to optimize water flooding on the Egg benchmark model. Geological uncertainty is represented with ensembles, including 100 model realizations. The k-means clustering method is utilized to reduce the realizations to 10 to reduce the computing cost. The Pareto front is obtained from Robust Optimization (RO) by maximizing average NPV over the ensembles, as the conservative production plan. Results show that optimization over the ensemble of a reduced number of realizations by the k-means technique is consistent with all realizations’ ensembles results, comparing their cumulative density functions. Furthermore, 10 oil price functions have been considered to form the economic uncertainty space. When SNPV and LNPV are optimized, considering uncertainty in oil price scenarios, the Pareto front’s production scenarios are robust to oil price fluctuations. Using the robust Pareto front of LNPV versus SNPV in both cases, one can optimize production strategy conservatively and update it according to the current reservoir and economic conditions. This approach can help a decision-maker to handle unexpected situations in reservoir management.

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Effect of work-force availability on manufacturing systems operations of job shops

In manufacturing systems characterized by job shop architectures, long lead times, and multiple part types, operators are important resources of the system, because they supervise and actively support the technical operations of machines. However, Industry 4.0 tools, as digital twins of manufacturing systems, rarely consider the human aspect. As a consequence, the real impact of workforce on production performance cannot be assessed precisely. In this paper, the effect of work-force availability on manufacturing systems operations is shown, with a special focus on job-shop systems. Results show that there is a strong relation between the operators management and the short-term production planning. The method is applied and validated within a real industrial case in the aeronautics sector, characterized by high value-added complex parts.

Method for quantifying the value of information for production control in cross-company value-adding networks

The advancing digitalisation of production processes increases data availability and enables real-time information sharing between value-adding partners. However, despite the technological feasibility, information relevant for production control to initiate target-oriented countermeasures against disturbances are rarely shared with value-adding partners. To create incentives for companies to exchange information for short-term production control, the respective values of information must be determined. This publication presents a method for quantifying the value of information for production control in cross-company value-adding networks on a monetary basis. The input for the required performance measurement system is derived from a simulation model of the production system. Applying the approach to a simulative scenario validates the method’s functionality and the potential of information sharing to reduce disturbance cost.

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs.

Cooperative nature of viral replication.

The ability of viruses to infect their hosts depends on rapid dissemination following transmission. The notion that viral particles function as independent propagules has been challenged by recent observations suggesting that viral aggregates show enhanced infectivity and faster spread. However, these observations remain poorly understood. Here, we show that viral replication is a cooperative process, such that entry of multiple viral genome copies into the same cell disproportionately increases short-term viral progeny production. This cooperativity arises from the positive feedback established between replication templates and virus-encoded products involved in replication and should be a general feature of viruses. We develop a simple model that captures this effect, verify that cooperativity also emerges in more complex models for specific human viruses, validate our predictions experimentally using different mammalian viruses, and discuss the implications of cooperative replication for viral fitness.

ОПТИМІЗАЦІЯ ЖИТТЄВОГО ЦИКЛУ СТВОРЕННЯ НОВОЇ ТЕХНІКИ В УМОВАХ КОНКУРЕНЦІЇ ТА СТОХАСТИЧНОЇ ПОВЕДІНКИ РИНКУ ЗБУТУ ВИСОКОТЕХНОЛОГІЧНОЇ ПРОДУКЦІЇ

The task to optimize the life cycle of new technique (aerospace, engineering products, etc.) in difficult economic conditions is stated and solved. The aim of the study is to develop a method to reduce the life cycle of complex technique creation. The subject of the research is the planning and management of the life cycle of complex technique in a highly competitive environment and stochastic behavior of high-tech products market. The paper shows the contradictions between the planned nature of modern production, which operates in conditions of Industry 4.0 and the stochastic behavior of the market. This contradiction leads to the relevance of short-term production plans with minimal risks. Therefore, the planning of the production system is carried out based on the portfolio that can be done in the short term. When planning new orders, it is necessary to shorten the life cycle of new equipment creation by analyzing the main stages: design, preparation of production, production. The optimization model to select the measures (project actions) at the initial stage of the life cycle is proposed. In order to generate the set from which the alternative options for activities are selected, expert assessments being ordered on the basis of the importance of time indicators, competitiveness, innovation, costs and risks are used. Simulation of the shortening of the new technique creation life cycle at a time of capacity constraints of production that creates high-tech products is carried out. The following mathematical models and methods are used: system analysis, optimization using integer programming, multi-criteria optimization, expert assessments, simulation modeling, agent-based modeling, risk assessment. The method allows to create competitive products at a time of capacity constraints by means of planning to shorten the life cycle of new technique creation and resources management.

Benefits of short-term photovoltaic power production forecasting to the power system

The impact of intermittent power production by Photovoltaic (PV) systems to the overall power system operation is constantly increasing and so is the need for advanced forecasting tools that enable understanding, prediction, and managing of such a power production. Solar power production forecasting is one of the enabling technologies, which can accelerate the transition to sustainable energy environment. Short-term forecast information on the expected power production can assist existing forecasting techniques and enable efficient integration of renewable energy sources through the efficient energy trading, power system control and management of energy storage units. The paper presents an approach to predict local PV power output based on short-term solar forecasting using ground-based camera and analyzes the benefits of such forecast to the power system operation. PV power plant production data collected over 216 days is used to analyze the magnitude and energy contained in transients caused by changes in sky cover. Cost-effectiveness was calculated with different scales of a power plant. An overview of the benefits for the transmission system operator is given. This overview considers the ways in which short-term forecasting can improve the efficiency of power management in an electric grid. A system cost-effectiveness analysis was carried out for electricity producers that can use this system to generate more precise forecasts and thus reduce penalties for non-compliance with the anticipated production.

Short-term oil production global optimization with operational constraints: A comparative study of nonlinear and piecewise linear formulations

The short-term oil production optimization of offshore platforms which use continuous gas lift and consider several operational constraints is a challenging task, specially when decision variables include both continuous and integer ones. The intrinsic non-linearity of the Gas Lift Performance Curves (GLPC) naturally yields to Mixer-Integer Nonlinear Program formulation. Picewise linear approximations of the GLPC are usually applied in order to reduce the complexity of the optimization problem and to ensure an approximate solution, leading to Mixer-Integer Linear Program formulation. However, a comprehensive and comparative study of these two formulations for the oil production optimization problem is scarce in the literature. This paper provides a deep performance evaluation of these two approaches, showing the limitations of both strategies, and proposes a hybrid two-stage optimization strategy to achieve the global optimal operating condition which maximizes the oil production subject to safety, environmental and processing capacity constraints.

Review of Shale Gas Sorption and Its Models

Shale is a self-sourced reservoir rock that contains tremendous natural gas resources. Shale incorporates unique gas storage mechanisms, and both free gas and adsorbed gas contribute significantly to the total gas resource. Because a considerable portion of the gas is adsorbed at the pore surfaces of the shale matrix, the study on gas adsorption is critical to the reservoir assessment and production forecasting of shale plays. This work presents a comprehensive literature survey of gas adsorption on shales and the interrelationship with gas well recovery. This review revisits the fundamental knowledge of adsorption theory and theoretical adsorption models commonly encountered in the shale gas adsorption community. This study also summarizes the relation of pore structural characteristics and geochemical properties (i.e., total organic carbon, kerogen type, thermal maturity, and mineral content) to the sorption capacity of shale. Because shale incorporates a highly heterogeneous structure, its ability to adsorb gas varies from basin to basin and no universal relationship persists between porosity and permeability. This study also outlines the influence of moisture on gas sorption in shale reservoirs, where the imbibed water from hydraulic fracturing and internal moisture content negatively impact gas storage and transport capacity of shale. Most importantly, this work carefully articulates the contribution of adsorbed gas to overall gas recovery from shale reservoirs based on the different stages of depletion. Because the pay zone of shale reservoirs has a deep burial depth with high initial pressure, especially for shale in thermogenic origin, short-term production mainly comes from gas in the free phase. In contrast, long-term production is extensively supplied by adsorbed gas as the reservoir pressure approaches the critical desorption pressure and beyond. The relative role that free gas and adsorbed gas play in shale gas production is still obscure. It is recommended that further studies will be required to analyze the role of sorption on gas transport processes, including desorption, diffusivity, and permeability.

A new mathematical formulation for a potash-mine shift scheduling problem with a simultaneous assignment of machines and workers

In this paper, we introduce a mixed-integer linear program for a shift scheduling problem in a German potash mine. In particular, we consider a short-term (work shift) production scheduling problem, where drill-and-blast mining operations have to be assigned to machines and workers simultaneously. Since we deal with several sequence-dependent setup, changeover, and removal times, TSP-variables are used in the mathematical program to determine the processing-sequence of the operations on each worker and each machine, respectively. In addition, several mining-specific requirements are taken into account to obtain a solution that can be put into practice. Computational experiments are conducted on problem instances of realistic size derived from real-world data. The results show that our new mixed-integer linear formulation outperforms both existing solution procedures for the problem at hand.

Frazil ice growth and production during katabatic wind events in the Ross Sea, Antarctica

Abstract. Katabatic winds in coastal polynyas expose the ocean to extreme heat loss, causing intense sea ice production and dense water formation around Antarctica throughout autumn and winter. The advancing sea ice pack, combined with high winds and low temperatures, has limited surface ocean observations of polynyas in winter, thereby impeding new insights into the evolution of these ice factories through the dark austral months. Here, we describe oceanic observations during multiple katabatic wind events during May 2017 in the Terra Nova Bay and Ross Sea polynyas. Wind speeds regularly exceeded 20 m s−1, air temperatures were below −25 ∘C, and the oceanic mixed layer extended to 600 m. During these events, conductivity–temperature–depth (CTD) profiles revealed bulges of warm, salty water directly beneath the ocean surface and extending downwards tens of meters. These profiles reflect latent heat and salt release during unconsolidated frazil ice production, driven by atmospheric heat loss, a process that has rarely if ever been observed outside the laboratory. A simple salt budget suggests these anomalies reflect in situ frazil ice concentration that ranges from 13 to 266×10-3 kg m−3. Contemporaneous estimates of vertical mixing reveal rapid convection in these unstable density profiles and mixing lifetimes from 7 to 12 min. The individual estimates of ice production from the salt budget reveal the intensity of short-term ice production, up to 110 cm d−1 during the windiest events, and a seasonal average of 29 cm d−1. We further found that frazil ice production rates covary with wind speed and with location along the upstream–downstream length of the polynya. These measurements reveal that it is possible to indirectly observe and estimate the process of unconsolidated ice production in polynyas by measuring upper-ocean water column profiles. These vigorous ice production rates suggest frazil ice may be an important component in total polynya ice production.

Investigation of crowdshipping delivery trip production with real-world data

Crowd-shipping (CS) is an innovative logistics service that occasional and professional couriers sign up for via an online platform to deliver packages upon requests by senders. Currently the demand and supply of CS is not yet well understood, largely due to the limited real-world data. This study aims to first fill this gap by analyzing the real-world CS data from the city of Atlanta, GA between April 2015 and August 2018. We first present an overview of the real-world CS data in three aspects: (1) the CS pricing scheme; (2) the CS spatial and temporal delivery patterns; and (3) comparison of preferences between the senders’ requests and the couriers’ bids. The analysis finds that the CS service has a clear price advantage over FedEx in the same-day and express service, as well as in the large, extra large, and huge size package delivery. The data analysis also reveals considerable discrepancies between senders’ and couriers’ preferences. We then compare two classes of the state-of-the-art Deep Learning (DL) methods in their ability to predict short-term CS delivery trip production. One class captures only the temporal features, namely the Long Short-term Memory Neural Network (LSTM), the Bidirectional Long Short-term Memory Neural Network (BDLSTM), and the Gated Recurrent Unit (GRU). The other class considers both spatial and temporal features, namely Convolutional Neural Network (CNN), CNN-LSTM, and ConvLSTM. The results show that ConvLSTM has overall the best predictive performance among the six DL methods considered, proving the importance of capturing both the spatial and temporal features of the delivery trip production data, as well as the convolutional nature of the spatial and temporal features in the data.

Design of electrical submersible pumps in methane hydrate production wells: A case study in Nankai trough methane hydrates

Gas hydrate industry aims to conduct long-term gas hydrate production trials after gaining the experiences in short-term gas hydrate production trials. Electrical submersible pumps (ESP) were mostly chosen in gas hydrate production trials to depressurize gas hydrate reservoirs. However, there is a knowledge gap about the usage and design of ESP systems in gas hydrate wells. Therefore, in this study, it is aimed to design ESP systems in methane hydrate production well in the conditions of Nankai Trough (Japan) methane hydrate reservoirs. For this purpose, a set of python codes was written to design ESP in the case study gas hydrate well and also HEP simulator was used to predict gas hydrate formation risks along the wellbore during gas production from methane hydrates via ESP production string. It was shown that high variances in water production rates (50–794 m3/day) affect the pump performance negatively, especially in the outside of suggested pump working flow rates. Moreover, pump efficiencies decrease from 70 s% to 20 s% in the outside of pump working flow rates due to huge variances in water flow rates during production. Different than conventional gas wells, the temperature rise generated by the motor is important to avoid any gas hydrate formation in gas hydrate well, which was affected by the operating frequency. Above 40 Hz of operating frequency, well temperature increases (nearly 0.65–1.75°C) by the motor with increasing frequency, which is good for the prohibition of gas hydrate formation in the well. In terms of pump power requirements, there is no difference of producing water at sea surface and releasing produced water to the seafloor. According to the methane hydrate equilibrium predictions in the wellbore during production with ESP, methane hydrate is not like to form in the design conditions. However, with ESP malfunction, methane hydrate might form inside the well due to increasing wellbore pressure and decreasing well temperature.