Optimal Production Rate Research Articles

Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli

The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.

Rivalry Restraint as Equilibrium Behavior

Rivalry restraint has received a lot of attention as a theory of profits in recent research on business strategy. Its economic rationale is explained as the consequences of either exogenous or endogenous anticompetitive forces present in different industries. In this paper, we use a dynamic oligopolistic industry model and show that rivalry restraint emerges as equilibrium behavior among firm owners who delegate decisions to managers. In the corresponding two‐stage game, managers choose optimal production rates in a dynamic Cournot market and owners set incentives for managers, acting sequentially rational. Equilibrium incentives correspond to rivalry restraint, that is, managers are less aggressive in the product market with lower outputs and increasing profits for all firms in the industry.

Optimal production and preventive maintenance rate in a failure-prone manufacturing system using discrete event simulation

Production control in a failure-prone manufacturing system (FPMS) is studied in the present paper. FPMS are those which are subjected to random breakdown assumption and corrective and preventive maintenance are considered for them too. In order to prevent shortage, buffer is used in these systems. Determining the inventory level of the buffer is one of the most important parameter. Another effective parameter is determining the time of period for preventive maintenance which is important to discover breakdowns before their occurrence and thus minimise the cost of corrective maintenance. The purpose of this paper is to determine the optimal production rate and time of period for preventive maintenance for an FPMS to minimise sum of holding shortage, corrective and preventive maintenance cost. To this end, discrete event simulation is used. Studying the system under conditions where analytical solution is not possible is the most important advantage of simulation method.

Determination of optimum of production rate of Network failure-prone manufacturing systems with perishable items using discrete event simulation and Taguchi design of experiment

In today's competitive world, due to factors such as stochastic demand and failure of machines, managers have been attention more to the importance of production and inventory control. Network Failure Prone Manufacture Systems are such as production systems that include failure of machine. This paper considers multi-products network failure manufacturing system with perishable items. The production control policy is based on the hedging point policy that consists in producing and maintaining of products in order to respond to demand and to avoid shortages. Demand is constant and shortages (backlog and lost sale) have been allowed. The main goal of this paper is to determine of optimal production rates that minimizes average total costs consist of shortage, production, holding and perishable costs. Because of uncertainly and complexity of this system, simulation optimization of this system using ARENA software has been done. A numerical example will show the efficiency of the proposed approach.

Determination of optimal production rate in stochastic manufacturing systems by simulation optimisation approach

Production planning is one of the most important functions in the process of production and operation management. The production planning should be made under uncertainty and considering these uncertainties will result in more realistic production planning model. Simulation optimisation methods have been proved to be useful for analysis of different system configurations and/or alternative operating procedures for complex logistic or manufacturing systems under uncertainty. The present article suggested a multi-period multi-product production planning model in a stochastic situation. Due to the probability of three problem parameters, including demand rate, process time and also setup time of every product in each period, the simulation optimisation technique was employed. In fact, through simulation, the system response rate (the mathematical expectation of objective) for various numbers of production rates in different periods was determined, and consequently, we approached the optimal response by simulated annealing algorithm. Finally, a numeral exam was presented and solved through the suggested approach.

Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms

This paper addresses the optimization of economic lot scheduling problem, where multiple items are produced on a single machine in a cyclical pattern. It is assumed that each item can be produced more than once in every cycle, each product has a shelf-life restriction, and backordering is permitted. The aim is to determine the optimal production rate, production frequency, cycle time, as well as a feasible manufacturing schedule for the family of items, and to minimize the long-run average costs. Efficient search procedures are presented to obtain the optimum solutions by employing four well-known metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and artificial bee colony (ABC). Furthermore, to make the algorithms more effective, Taguchi method is employed to tune various parameters of the proposed algorithms. The computational performance and statistical optimization results show the effectiveness and superiority of the metaheuristic algorithms over other reported methods in the literature.

Optimal control of decoupling point with deteriorating items

Purpose: The aim of this paper is to develop a dynamic model to simultaneously determine the optimal position of the decoupling point and the optimal path of the production rate as well as the inventory level in a supply chain. With the objective to minimize the total cost of the deviation from the target setting, the closed forms of the optimal solution are derived over a finite planning horizon with deterioration rate under time-varying demand rate. Design/methodology/approach: The Pontryagin's Maximum Principle is employed to explore the optimal position of decoupling point and the optimal production and inventory rate for the proposed dynamic models. The performances of parameters are illustrated through analytical and numerical approaches. Findings: The results denote that the optimal production rate and inventory level are closely related to the target setting which are highly dependent on production policy; meanwhile the optimal decoupling point is exist and unique with the fluctuating of deteriorating rate and product life cycle. The further analyses through both mathematic and numerical approaches indicate that the shorten of product life cycle shifts the optimal decoupling point forward to the end customer meanwhile a backward shifting appears when the deterioration rate increase. Research limitations/implications: There is no shortage allowed and the replacement policy is not taken into account. Practical implications: Solutions derived from this study of the optimal production-inventory plan and decoupling point are instructive for operation decision making. The obtained knowledge about the performance of different parameters is critical to deteriorating supply chains management. Originality/value: Many previous models of the production-inventory problem are only focused on the cost. The paper introduces the decoupling point control into the production and inventory problem such that a critical element-customer demand, can be taken into account. And the problem is solved as dynamic when the production rate, inventory level and the position of the decoupling point are all regarded as decision variables.

Optimal control of a stochastic production–inventory system under deteriorating items and environmental constraints

In this paper, we present an optimal control model of a stochastic production–inventory with deteriorating items, emission tax and pollution abatement investment. In our model, the emission tax is levied on the firm’s environmental obsolescence rate of technology rather than the total amount of the environmental externality. Our objective is to apply Hamilton–Jacobi–Bellman (HJB) equation to solve the stochastic production–inventory system with deteriorating items, emission tax and pollution abatement investment; and derive the optimal production rate and pollution abatement investment rate that maximise the objective function value. The results are discussed with some illustrative examples for different demand rate functions, and sensitivity analysis is conducted to study the effect of changing the parameters and coefficients on the objective function value.

Joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products

This paper considers the joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products. The inventory system, with random disturbance, is modelled as a continuous-time stochastic differential equation. Combined dynamic pricing and production control, a stochastic dynamic optimisation problem that maximises the total discounted profit is developed. By applying the stochastic optimal control method, we formulate the problem of finding the optimal joint dynamic pricing and production schedule as the problem of solving a Hamilton–Jacobi–Bellman (HJB) equation. It is shown that the optimal dynamic pricing and production rate take the linear feedback form of the inventory level, which allows the decision-maker to effectively adjust the strategies as time evolves. In addition, to highlight the advantage of the joint dynamic pricing and production strategy, the case of the optimal production with static price is considered. Numerical examples are given to illustrate the validness of the theoretical results.

Optimization of mixed integer nonlinear economic lot scheduling problem with multiple setups and shelf life using metaheuristic algorithms

This paper addresses the economic lot scheduling problem where multiple items produced on a single facility in a cyclical pattern have shelf life restrictions. A mixed integer non-linear programming model is developed which allows each product to be produced more than once per cycle and backordered. However, production of each item more than one time may result in an infeasible schedule due to the overlapping production times of various items. To eliminate the production time conflicts and to achieve a feasible schedule, the production start time of some or all the items must be adjusted by either advancing or delaying. The objective is to find the optimal production rate, production frequency, cycle time, as well as a feasible manufacturing schedule for the family of items, in addition to minimizing the long-run average cost. Metaheuristic methods such as the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms are adopted for the optimization procedures. Each of such methods is applied to a set of problem instances taken from literature and the performances are compared against other existing models in the literature. The computational performance and statistical optimization results shows the superiority of the proposed metaheuristic methods with respect to lower total costs compared with other reported procedures in the literature.

Enhancement of Biogas Production in Two-Phase Anaerobic Fermentation System for Lower-Temperature Applications

For the purpose of enhancing the biogas production and operation stability of system at low temperature, a two-phase anaerobic fermentation facility for rural household energy generation was proposed. In this facility, the quantity of the fermented materials could be balanced by controlling the hydraulic retention time, which based on pH of the acidification tank and the fermentation tank. In addition, a portion of the biogas generated could be used to heat the acidification tank and the fermentation tank. Results shows that the optimal daily production rate of biogas for 1 m3 fermentation liquid was about 1.47 L/L d-1 at a mixing ratio of cow: swine: chicken manure was 3: 1: 0.5. The production rate obtained in this study was more than four times higher than that from traditional single-phase processes (0.35 L/L d-1). About 5.43 m3 biogas can be produced daily per household with an average CH4 content of 76.8%. The two-phase process developed in this work will also reduce environmental pollution and increase energy production efficiencies.

Optimizing mining rates under financial uncertainty in global mining complexes

This paper presents a distributed and dynamic programming framework to the mining production rate target tracking of multiple metal mines under financial uncertainty. A single mine׳s target tracking is stated as a stochastic optimization problem and the solution is obtained by solving the dynamic program which gives the optimal production rate schedule of each mine as a Markovian feedback control on the price process. The global solution is distributed on multiple mines by a policy iteration method, and this iterative method is shown to provide the unique equilibrium among Markovian strategies. Numerical results confirm the efficacy of the proposed global method when compared to individual optimization of mining rate target tracking.

Effects of cell density, carbon dioxide and molybdenum concentration on biohydrogen production by Anabaena variabilis ATCC 29413

This paper aims to determine the effects of cell density, carbon dioxide, and molybdenum concentration towards hydrogen production rate. Batch cultures of Anabaenavariabilis sp. were incubated in anaerobic environment under continuous indoor illumination of 70μEm−2s−1 at 35°C. The optimal volumetric hydrogen production rate obtained was 44μmolH2mgchl a−1h−1 occurred at cells density of 110mgL−1, 5% carbon dioxide headspace concentration, and molybdenum concentration of 1.6mM. The effect of organic carbon source (glucose) was also evaluated in the present study and it was found that the additional carbon produced the highest hydrogen production rate in all conditions. An increased concentration of molybdenum significantly enhanced the hydrogen productivity rate almost to that of glucose-supplemented culture at 49μmolH2mgchl a−1h−1. However, further increase in molybdenum concentration beyond 1.6mM showed no further improvement in the amount of hydrogen produced.

Oxide content optimized ZnS–ZnO heterostructures via facile thermal treatment process for enhanced photocatalytic hydrogen production

The ZnS–ZnO heterostructured photocatalysts are synthesized by thermal treatments of the ZnS materials at various thermal processing temperatures (150 °C – 550 °C) with controlling O2 partial pressures (7.8 kPa – 33.8 kPa). The ZnS–ZnO composite structure shows much higher photocatalytic hydrogen production than those from the ZnS and ZnO pure substances. This phenomenon is mainly caused by effective charge separation between the photoexcited electrons and holes. The thermal oxidation of ZnS materials proceeds at temperatures higher than 500 °C. In addition to the thermal processing temperature, O2 partial pressure is also chosen for an experimental variable in order to control the atomic composition minutely. The ZnS–ZnO photocatalyst composite fabricated at 500 °C under 16.9 kPa of O2 partial pressure shows the highest hydrogen production rate of 494.8 μmol g−1 h−1 under 1 sun irradiation condition, and it is 37 times higher than that (13.5 μmol g−1 h−1) from the ZnS pure substance. At this optimized production rate, the Zn/S/O atomic compositions are measured as 45.9/46.9/7.2 (XPS) and 53.3/42.1/4.6 (ICP-AES), respectively.

Integrated Optimization Models and Problems of Gas Production, Distribution, and Storage

The authors consider the mathematical modeling of flow distribution in gas transmission networks, which take into account the possibility of establishing reserves in underground storage resources and different operation strategies of gas companies. Taking into account the basic technical and economic prerequisites, the problem of determining the optimal production rates for gas fields is analyzed. The proposed mathematical tools are aimed at solving problems of long-term planning, but it can also be applied to the cases of operational control of gas transmission systems.

Optimal pricing, production, and sales policies for new product under supply constraint

This paper considers a firm that sells a durable product with a given market potential. The purpose of the firm is to maximize its profit by determining how much capacity to install before the sales horizon, how many products to produce in accordance with the capacity, and how many products to sell by pricing. Appealing to Pontryagin maximum principle in control theory, the authors obtain the closed-forms of all optimal decisions the firm should make. Furthermore, the optimal production rate and optimal sales rate are both equal to the demand rate, which is caused by the optimal pricing policy during the whole horizon, and the optimal pricing path is increasing with the cost of installing a unit of capacity. Furthermore, numerical analysis reveals the visual impression of the relationship of the parameters.

A production-inventory model for a two-echelon supply chain when demand is dependent on sales teams׳ initiatives

This paper investigates the issue of channel coordination for a two echelon supply chain consisting of one manufacturer and one retailer. In this supply chain, the demand is sensitive to promotional efforts/sales teams׳ initiatives. A production-inventory model is developed that considers the procurement cost per unit as a function of the production rate. To resolve the issues of channel coordination and promotion-based demand, a variety of centralized coordinating systems are explored. An analytical method is employed to achieve optimal production rate, production lot size, backlogging and the initiatives of sales teams so that the profits of both manufacturer and retailer are maximized. Some numerical examples are solved in order to better understand the proposed production-inventory model. The results of a sensitivity analysis are also provided. Finally, some conclusions and future researches are included.

Predicting Soil CO2 Dynamics in Arable Land of Andisol Using the SOILCO2 Model

AbstractRecently, carbon capture and storage by soils is recognized as an important function of agricultural fields. When discussing global warming mitigation such as proper agricultural practices and/or an appropriate depth of composting in agricultural lands for soil carbon storage, not only CO2 efflux, but also profiles of CO2 concentrations in soil air, are important. This study evaluated the performance of the SOILCO2 model in predicting soil CO2 dynamics, especially CO2 concentrations at various depths in an arable bare land of Andisol. We attempted to experimentally determine two soil parameters required for the CO2 production model, the optimal CO2 production rate and the parameter of a depth-dependent CO2 production function by measuring CO2 efflux from the surface and a vertical distribution of concentration of dissolved organic carbon, respectively. Soil CO2 concentrations, water contents and temperatures were continuously monitored for approximately one year at three different depths, and simu...

Optimal control of a continuous review production inventory system with Gamma distributed deterioration

This paper develops the production inventory models with and without Gamma distributed deteriorating items. The production system with inventory level dependent demand is considered here and the Pontryagin maximum principle is used to determine the optimal control, which is the production rate that minimizes the optimal inventory control model, while satisfying the system dynamics. The necessary optimality conditions associated with and without Gamma distributed deteriorating items are derived from the Hamiltonian functions. The optimal inventory levels and the optimal production rates are derived and numerical simulations are used to illustrate the theoretical results.

Coordination policy for a three echelon supply chain considering imperfect quality items

Article history: Received October 26 2013 Received in Revised Format July 5 2014 Accepted July 5 2014 Available online July 7 2014 In this study, we develop a three echelon supply chain model for items to determine the optimal reliability and production rate, which achieves the biggest total integrated profit for an imperfect manufacturing process. Here, we have taken a supplier, a manufacturer and a retailer in which supplier supplies raw materials to manufacturer, manufacturer produces perfect and imperfect quality items because practically it happens and manufacturer supplies perfect quality items to the retailers. In production system, production facility may shift from an in-control state to an out-ofcontrol state at any random time. The basic assumption of classical economic manufacturing quantity model is that all manufacturing items are of perfect quality but the assumption is not true in practice. The proposed study is formulated assuming that a certain percent of total product is defective. This percentage also varies with production rate and production run time. The defective items are restored in original quality by reworked at some costs to maintain the quality of products in a competitive market. Finally, numerical example and its graphical representation are given to illustrate the proposed model. Sensitivity analysis is also provided to test feasibility of the model. © 2014 Growing Science Ltd. All rights reserved