A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

Primary logistics planning of oil products under the imbalance of supply and demand

This paper intends to complete the primary logistics planning of oil products under the imbalance of supply and demand. An integrated mathematical programming model is developed to simultaneously find the balance between supply and demand, and optimize the logistics scheme. The model takes minimum logistics cost and resource adjustment cost as the objective function, and takes supply and demand capacity, transportation capacity, mass balance, and resource adjustment rules as constraints.Three adjustment rules are considered in the model, including resource adjustment within oil suppliers,within oil consumers, and between oil consumers. The model is tested on a large-scale primary logistics of a state-owned petroleum enterprise, involving 37 affiliated refineries, 31 procurement departments,286 market depots and dedicated consumers. After the unified optimization, the supply and demand imbalance is eased by 97% and the total cost is saved by 7%, which proves the effectiveness and applicability of the proposed model.

A Quadratic Programming Model for Blast Scheduling

A quadratic programming model is established to choose the blocks to be blasted in a given period. The length of this period depends on the production planning requirements. During the given period, the blocks' parameters are available from the geological database of the mine. The objective is to minimize the deviation of the average ore grade of blasted blocks from the standard ore grade required by the mill. Transportation ability constraint. production quantity demand constraint. minimum safety bench constraint. block size constraint and block, bench precedence constraints are considered in forming the programming model. This model has more practical objective function and reasonable constraints compared with the existing model for this kind of problems.

Optimum blending of iron ore from Choghart stockpiles by using goal programming

Iron and steel industry is an important sector of Iran's economy.Choghart iron ore mine is an important iron ore producer of Iran steel industry.Phosphorous contained in the iron ore concentrates of Choghart mine has a detrimental effect on the steel making process,whereby this causes cracks to form in the refractory lining of blast furnaces.In the past,about 1.43 Mt of low-grade and 4.53 Mt of high-phosphorous materials had been transported to low grade and high phosphorous stockpiles,respectively,for future beneficiation.As a result of the progressive depletion of high-grade ore and establishment of beneficiation plant in Choghart,exploitation of these two stockpiles in this mine became an important issue.In this work,a linear goal programming(GP) model was developed in order to determine the optimum iron ore blend in terms of quality from low grade and high phosphorous stockpiles of Choghart mine.The model was solved by the SOLVER V.9 program.Results show that feeding with acceptable quality(w(Fe)≥50% and w(P)≤1.2%,mass fraction) materials can be blended from stockpiles that satisfy the needs of the Choghart processing line.

HEURISTIC MODELING FOR A DYNAMIC AND GOAL PROGRAMMING IN PRODUCTION PLANNING OF CONTINUOUS MANUFACTURING SYSTEMS

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production, batch production and job shop systems, but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely, because of initial inadequate plant layout, stage by stage development of production lines, the purchase of second hand machineries from various countries, plurality of customers. A case of production system planning is proposed for a chemical company in which the above mentioned conditions are almost presented. The goals and constraints in this issue are as follows： （1） Minimizing deviation of customer＇s requirements. （2） Maximizing the profit. （3） Minimizing the frequencies of changes in formula production. （4） Minimizing the inventory of final products. （5） Balancing the production sections with regard to rate in production. （6） Limitation in inventory of raw material. The present situation is in such a way that various techniques such as goal programming, linear programming and dynamic programming can be used. But dynamic production programming issues are divided into two categories, at first one with limitation in production capacity and another with unlimited production capacity. For the first category, a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero- one model. At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that＇s how it is solved.

The role of natural products in regulating pyroptosis

In recent years,large numbers of novel cell death types have been reported such as autophagic death,paraptosis,mitosis,oncosis and pyroptosis.As a new type of proinflammatory programmed cell death,pyroptosis has attracted increasing attentions gradually,and its morphological characteristics and molecular mechanisms are significantly different from other cell death types such as necrosis and apoptosis.Many research groups have demonstrated the association between pyroptosis and various human diseases including immunological disease,cancer,atherosclerosis,infectious disease,and cardiovascular and cerebrovascular disease.Natural products are small molecules synthetized in organisms including primary and secondary metabolites.Natural products are important sources of modern innovative drugs discovery and can be used as key tools to explore the molecular mechanism of cell fate.The aim of this study is to review the molecular mechanisms and pathways of pyroptosis,and to categorize and conclude research results on the correlation between different natural products and pyroptosis in recent years.In this study,a total of 39 papers were enrolled in analyses.The molecular pathways and mechanisms of pyroptosis were clearly described.Fourteen types of natural products,their sources,effects,mechanisms and therapeutic potentials are categorized and illuminated.It is showed that a variety of natural products and pyroptosis have close correlations.They negatively or positively affect or act on different positions of pyroptosis inflammatory pathways,indicating that they may have certain potential therapeutic effects on pyroptosis-related diseases.Pyroptosis,a relatively new way of cell death,is closely associated with a variety of diseases.Natural products can have obvious effects on the process of pyroptosis as potential sources of new drugs.In-depth studies using natural products to investigate pyroptosis will help to enhance our understandings of human diseases and establish effective prevention and treatment strategies.

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning.It influences activities such as purchasing,manufacturing,and selling,and quick adaption is required.In production planning,for reasons such as reducing costs and obtaining supplier discounts,many decisions must be made in the initial stage when demand has not been realized.The effects of non-optimal decisions will propagate to later stages,which can lead to losses due to overstocks or out-of-stocks.To find the optimal solutions for the initial and later stage regarding demand realization,this study proposes a stochastic two-stage linear program-ming model for a multi-supplier,multi-material,and multi-product purchasing and production planning process.The objective function is the expected total cost after two stages,and the results include detailed plans for purchasing and production in each demand scenario.Small-scale problems are solved through a deterministic equivalent transformation technique.To solve the problems in the large scale,an algorithm combining metaheuristic and sample average approximation is suggested.This algorithm can be implemented in parallel to utilize the power of the solver.The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given,then the problems of the first and second stages can be decomposed.

A Review of Quantitative Analysis (QA) in Production Planning Decisions Using the Linear Programming Model

The purpose of this paper was to examine the role of quantitative analysis in production planning decisions. This draws from the observed imperatives of quantitative analysis in business decisions and its capacity for predictability and enhanced decision making given the increasingly complex nature of the business environment. The paper therefore addressed the historical evolution of quantitative technique as an efficient and effective decision-making tool. The content of the paper addressed commonly applied quantitative technique in manufacturing firms today which is, linear programming and its subsequent impact on production planning decisions. The results based on a congruence of views revealed that the “best-fit” application of quantitative analysis models and tools can untangle the complexities of production and planning decision making process in order to achieve the organizational goal. This is, as literature also showed that there is obviously no consensus or integrated model that is capable of solving all managerial problem, different models such as the linear programming model have however been developed to cater for different problems as they arise. The workability or suitability of quantitative analysis is actually premised on its appropriate application. The paper recommends the application of quantitative analysis using linear programming in solving various resource allocation related issues in the primary production planning function of manufacturing firms.

Inner Product Laplacian Embedding Based on Semidefinite Programming

This paper proposes an inner product Laplacian embedding algorithm based on semi-definite programming, named as IPLE algorithm. The new algorithm learns a geodesic distance-based kernel matrix by using semi-definite programming under the constraints of local contraction. The criterion function is to make the neighborhood points on manifold as close as possible while the geodesic distances between those distant points are preserved. The IPLE algorithm sufficiently integrates the advantages of LE, ISOMAP and MVU algorithms. The comparison experiments on two image datasets from COIL-20 images and USPS handwritten digit images are performed by applying LE, ISOMAP, MVU and the proposed IPLE. Experimental results show that the intrinsic low-dimensional coordinates obtained by our algorithm preserve more information according to the fraction of the dominant eigenvalues and can obtain the better comprehensive performance in clustering and manifold structure.

Models for Ordering Multiple Products Subject to Multiple Constraints, Quantity and Freight Discounts

One of the most important responsibilities of a supply chain manager is to decide “how much” (or “many”) of inventory items to order and how to transport them. This paper presents four mixed-integer linear programming models to help supply chain managers make these decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts. Each model deals with one of the possible combinations of all-units, incremental quantity discounts, all-weight and incremental freight discounts. The models are based on a piecewise linear approximation of the number of orders function. They allow any number of linear constraints and determine if independent or common (fixed) cycle ordering has a lower total cost. Results of computational experiments on an example problem are also presented.

Application of Linear Programming Algorithm in the Optimization of Financial Portfolio of Golden Guinea Breweries Plc, Nigeria

In this study, Simplex Method, a Linear Programming technique was used to create a mathematical model that optimized the financial portfolio of Golden Guinea Breweries Plc, Nigeria. This work was motivated by the observed and anticipated miscalculations which Golden Guinea Breweries was bound to face if appropriate linear programming techniques were not applied in determining the profit level. This study therefore aims at using Simplex Method to create a Mathematical Model that will optimize the production of brewed drinks for Golden Guinea Breweries Plc. The first methodology involved the collection of sample data from the company, analyzed and the relevant coefficients were deployed for the coding of the model. Secondly, the indices collected from the first method were deployed in the software model called PHP simplex, an online software for solving Linear Programming Problem to access the profitability of the organization. The study showed that Linear Programming Model would give a high profit coefficient of N9,190,862,833 when compared with the result obtained from the manual computation which gave a profit coefficient of N7,172,093,375. Also, Bergedoff Lager, Eagle Stout and Bergedoff Malta were found not to contribute to overall profitability of the company and it was therefore recommended that their productions should be discontinued. It also recommends that various quantities of Golden Guinea Lager (1 × 12) and Golden Guinea Lager (1 × 24) should be produced.

A DESIGN OPTIMIZATION FRAMEWORK TO ESTIMATE ENVIRONMENTAL IMPACT OF DESIGN DECISIONS IN CONSUMER PRODUCTS

Most products have the potential to negatively impact the environment during all life-cycle stages.However,most environmental impact assessment methods focus on a single product life-cycle and on a specific life-cycle stage.Product design plays a significant role by determining traditional environmental impacts,such as embodied energy of materials,but also by influencing market adoption and production volumes.The main objective of this work is to develop a design optimization framework that estimates the environmental impact of design decisions(e.g.materials choice,etc.)across all life-cycle stages in consumer products.The methodology relies on quality function deployment(QFD),multi-attribute utility theory,non-linear mathematical programming,and life-cycle assess-ment tools to estimate the utility of the design options to the customer,the producer,and the environment.The proposed framework allows designers and other decision makers to select options that are environmentally sound and also aligned with the business objectives.

不确定环境下预制构件生产鲁棒性调度研究

在确定了预制构件方生产工艺和资源约束情况的基础上,分析了预制构件生产调度计划对鲁棒值的影响;基于关键链技术制定了考虑二次资源冲突的集中缓冲进度计划,构建了以预制构件生产完工时间最短和鲁棒值最大为目标函数的双层规划模型,并通过嵌套式遗传算法对该模型进行求解。实证结果表明:基于该模型得出的调度计划鲁棒值最大且在面对不确定环境时更为稳定。

计及电制氢和碳捕集的园区综合能源系统动态规划

“碳达峰,碳中和”目标对综合能源系统提出了低碳高效的新要求。针对园区综合能源系统的源-网-荷-储联合规划问题,基于Bellman最优理论将系统净收益最大化的规划模型进行分解,建立“风光储协同-多能互联耦合-能源消纳制氢-最优碳捕集”的多阶段动态规划模型并求解设备的最佳配置方案。结果表明,该模型在经济性和环保性方面具有显著优势,电制氢收益可观并促进电能就地消纳,碳捕集降低了系统碳排放并具有售卖CO_(2)气体和碳配额的双重收益,同时在动态规划过程中达到了最佳的电制氢配置和最优的碳捕集比例。

“十三五”时期我国水产品流通与加工科技创新进展和展望

水产食品对全球粮食安全和膳食营养意义重大,据统计,2020年全球渔业和水产养殖总产量创历史新高,达到2.14亿t,包括1.78亿t水生动物和3600万t藻类,其中有超过1.57亿t水生动物直接供人食用,相当于人均每年供应约20.2kg,提供了约17%的动物蛋白^([1])。我国是人口大国,改善国民膳食结构,提升全民营养健康水平,是关系国计民生的重大战略问题。开辟以水域为基础生产优质蛋白,构建覆盖内陆水域和海洋的蓝色粮仓,是发展我国现代渔业的迫切需求,也是实现优质蛋白持续高效供给的重要载体^([2])。

基于云-TOPSIS-灰关联分析模型的产品服务系统方案评价技术

方案评价是产品服务系统设计的关键环节之一,评价结果的合理性对企业发展至关重要。基于最小平方和法,构建了云-TOPSIS-灰关联分析模型,对产品服务系统方案进行评价。引入云模型和相对偏好关系量化离散型指标,解决专家评价的模糊性和随机性问题。运用TOPSIS灰关联分析法确定正、负理想灰关联系数矩阵,集成指标权重和正、负理想灰关联系数矩阵建立正、负理想加权灰关联系数矩阵,兼顾了几何方案序列曲线的位置接近度和形状相似度。运用最小平方和法求解灰关联投影系数,确定方案排序。以某农业装备企业拖拉机产品服务系统方案评价为实例,验证了所提模型的科学性和可行性。

基于一种互联网+喷粉加工设备的研制

喷粉机采用三菱PLC、触摸屏、无线服务器CMT-SVR-200等组成上下位机控制,通过手机或触屏控制喷粉炉温、喷粉量、流化气量、雾化气量、喷枪清洗气量、高压电流电压的大小等7种加工参数来进行喷粉调试加工。根据不同材质的金属制品喷涂需求,最多可预设编辑30种不同产品的组合加工参数,通过触屏或手机输入PLC寄存器中。喷粉加工时,只需选择对应的加工产品参数程序,便可由PLC程序将预设的7种加工参数调出来,提高产品喷涂的工作效率。

“双高计划”背景下“都市蔬菜生产”课程改革的实施路径

在“双高计划”背景下,探讨如何对专业群核心课程进行构建与实施,从而提高人才培养的质量和效果。“都市蔬菜生产”课程从都市蔬菜就业岗位群出发,结合产业技术发展及企业需求,通过重构课程内容、修订课程标准、创建思政链条、完善课程资源、升级教学条件、改进评价方法等方面展开探索,构建了“2条主线,3多评价、4种能力、5个环节”为核心的“2345”教学模式,有利于培养新时代“一懂两爱”的农业工作者,对涉农专业课程改革具有示范作用和借鉴意义。

基于数字孪生的呼叫机生产线虚拟调试技术分析

阐述基于数字孪生技术的呼叫机生产线虚拟调试技术特点。结合系统设计目标和需要,分析关键技术应用,并通过MCD内部编程,结合仿真技术集成,实现基于孪生技术的生产线设计和验证。

我国城市区域“三生”空间优化配置理论与方法探索

本文结合“十四五”国家重点研发计划项目“城市群都市圈空间优化关键技术”指南任务要求,从实现“三生”空间优化的角度,对我国城市区域的人居环境建设理论,产业链、供应链和创新链的耦合,提高生态空间的安全韧性水平,构建区域生活圈的技术路径,以及实现城市群都市圈多情景空间优化配置方案,提出了研究框架和重点方向。