A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty

A strategy for the integration of production planning and scheduling in refineries is proposed. This strategy relies on rolling horizon strategy and a two-level decomposition strategy. This strategy involves an upper level multiperiod mixed integer linear programming （MILP） model and a lower level simulation system, which is extended from our previous framework for short-term scheduling problems [Luo, C.E, Rong, G, ＂Hierarchical apthis extended framework is to reduce the number of variables and the size of the optimization model and, to quickly find the optimal solution for the integrated planning/scheduling problem in refineries. Uncertainties are also considered in this article. An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.

Integration of real options into short-term mine planning and production scheduling

Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These price falls reflect uncertainties and risks associated with mining projects. In recent years, much work has been published related to the application of real options pricing theory to value life-of-mine plans in response to long term financial uncertainty and risk. However, there are uncertainties and risks associated with medium/short-term mining operations. Real options theory can also be applied to tactical decisions involving uncertainties and risks. This paper will investigate the application of real options in the mining industry and present a methodology developed at University of Queensland, Australia, for integrating real options into medium/short-term mine planning and production scheduling. A case study will demonstrate the validity and usefulness of the methodology and techniques developed.

A new production component method for natural gas development planning

Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.

Robust Optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

Remanufacturing production planning considering quality cost and demand substitution

Aiming to minimize the total production costs in a single planning period, a nonlinear integer programming model for remanufacturing production plans is established considering the influence of different qualities of returns acting on production cost. Three different remanufacturing and discarding strategies are adopted to analyze the change rules of the total production costs. The results returns is greater than indicate that when the number of remanufacturing returns of high the demand, preferentially quality and discarding those of low quality can bring better economic benefits due to manufacturing cost reduction. However, when the number of returns is smaller than the demand, there is no need to consider grading of returns, whereas new demand of remanufacturing. parts are required to satisfy the

A multi-step approach for block-cave production scheduling optimization

Production schedules that provide optimal operating strategies while meeting practical,technical,and environmental constraints are an inseparable part of mining operations.Relying only on manual planning methods or computer software based on heuristic algorithms will lead to mine schedules that are not the optimal global solution.Mathematical mine planning models have been proved to be very effective in supporting decisions on sequencing the extraction of material in mines.The objective of this paper is to develop a practical optimization framework for caving operations’production scheduling.To overcome the size problem of mathematical programming models and to generate a robust practical near-optimal schedule,a multi-step method for long-term production scheduling of block caving is presented.A mixed-integer linear programming(MILP)formulation is used for each step.The formulations are developed,implemented,and verifed in the TOMLAB/CPLEX environment.The production scheduler aims to maximize the net present value of the mining operation while the mine planner has control over defned constraints.Application and comparison of the models for production scheduling using 298 drawpoints over 15 periods are presented.

Saline Water Irrigation Scheduling Through a Crop-Water-Salinity Production Function and a Soil-Water-Salinity Dynamic Model

Using a crop-water-salinity production function and a soil-water-salinity dynamic model, optimal irrigation scheduling was developed to maximize net return per irrigated area. Plot and field experiments were used to obtain the crop water sensitivity index, the salinity sensitivity index, and other parameters. Using data collected during 35 years to calculate the 10-day mean precipitation and evaporation, the variation in soil salinity concentrations and in the yields of winter wheat and cotton were simulated for 49 irrigation scheduling that were combined from 7 irrigation schemes over 3 irrigation dates and 7 salinity concentrations of saline irrigation water (fresh water and 6 levels of saline water). Comparison of predicted results with irrigation data obtained from a large area of the field showed that the model was valid and reliable. Based on the analysis of the investment cost of the irrigation that employed deep tube wells or shallow tube wells, a saline water irrigation schedule and a corresponding strategy for groundwater development and utilization were proposed. For wheat or cotton, if the salinity concentration was higher than 7.0 g L-1 in groundwater, irrigation was needed with only fresh water; if about 5.0 g L-1, irrigation was required twice with fresh water and once with saline water; and if not higher than 3.0 g L-1, irrigation could be solely with saline water.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

An improved cross entropy algorithm for steelmaking-continuous casting production scheduling with complicated technological routes

In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).

A Genetic Algorithm-based Approach to Scheduling of Batch Production with Maximum Profit

The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In addition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.

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.

Multilevel hierarchical production planning architecture for engineer-to-order enterprises

The critical materials and critical parts are keys to the production at Engineer to Order (ETO) enterprises implies that the control of plans for critical materials and critical parts is essential to the control of all plans at ETO enterprises. A mixed mode of hierarchical network planning/MRP (NP/MRP) is proposed to generate network plans for critical materials or critical parts from project networks and the plans for non critical parts produced by MRP are constrained by the related project networks. The multilevel hierarchical network planning/MRP mixed planning (MHNM) architecture proposed is the extension of the hierarchical NP/MRP to the supply chain based on the temporal constraints of multilevel project networks for critical materials or critical parts. A general model is formulated for scheduling tasks on machines at work centers as well.

Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply

Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.

A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.

The Planning and Scheduling of Business Process in Agile Supply Chain System

The rapidly developing global competition is leadin g to the worldwide enterprise alliance, with which the geographical dispersion of production, assembly and distribution operations comes into being. Supply chain system is such a kind of enterprise alliance, managing the material and informat ion flows both in and between enterprises, such as vendors, manufacturing and assembly plants and distribution centers. In the present research work, we can see that supply chain system can quickly respond to customer needs and adapt to the dynamic change of the market so as to improve the competence of enterprises in the chain. Thus, in supply chain system, it’s most important to enhance the speed with which the products are produced and distributed to the customers who order them and reduce the operating costs at the same time. However, because of the special characteristics, such as dynamic and distributed , etc., often in Agile Supply Chain System (ASCS), there are many dynamic tasks, and many urgent changes of the processes, which make the planning work and mana gement become very difficult and complex. Thus, in Agile Supply Chain System, we first need an efficient planning work, which can program the processes properly to get a primary scheme. And then the local scheduling work based on the primar y scheme will play a important role to deal with the dynamic and distributed pro blems in the business process in ASCS. So, this paper will be organized as below . At the first of this paper, we will discuss the situation in which Agile Suppl y Chain System is applied, and then we will elaborate the characteristics of Agi le Supply Chain System. With that, the shortcomings of the process managements t hat are, at present, used in Supply Chain Systems will be displayed clearly. Sec ond, we will introduce the planning methods in our research work. And then, the local scheduling will be discussed in detail, based on the primarily planned wor kflow. To realize the goal, first we build the mathematic model to describe the scheduling goal of system optimum, based on the categories of the cooperating-r elation among the operation nodes, which we defined in our research work, in Agi le Supply Chain System. And then the optimized algorithm to solve the model woul d be introduced, in succession. At the final of this paper, we will introduce some knowledge of the process mana gement and the realization of ASCS and summarize our work.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

Multiproduct and multistage integrated production planning model and algorithm based on an available production capacity network

This research attempts to devise a multistage and multiproduct short-term integrative production plan that can dynamically change based on the order priority and virtual occupancy for application in steel plants. Considering factors such as the delivery time, varietal compatibility between different products, production capacity of variety per hour, minimum or maximum batch size, and transfer time, we propose an available production capacity network with varietal compatibility and virtual occupancy for enhancing production plan implementation and quick adjustment in the case of dynamic production changes. Here available means the remaining production capacity after virtual occupancy.To quickly build an available production capacity network and increase the speed of algorithm solving, constraint selection and cutting methods with order priority were used for model solving. Finally, the genetic algorithm improved with local search was used to optimize the proposed production plan and significantly reduce the order delay rate. The validity of the proposed model and algorithm was numerically verified by simulating actual production practices. The simulation results demonstrate that the model and improved algorithm result in an effective production plan.

Improvement of Lagrangian relaxation performance for open pit mines constrained long-term production scheduling problem

Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.

A mathematical model for open pit mine production scheduling with Grade Engineering and stockpiling

This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework.Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable(i.e.high-grade)and undesirable(i.e.low-grade or uneconomic)materials and ensure the delivery of only selected quantity of high quality(or high-grade)material to energy,water,and cost-intensive processing plant.The model maximizes the net present value under a range of operational and processing constraints.Given that the proposed model is computationally complex,the authors employ a data preprocessing procedure and then evaluate the performance of the model at several practical instances using computation time,optimality gap,and the net present value as valid measures.In addition,a comparison of the proposed and traditional(without Grade Engineering)models reflects that the proposed model outperforms the traditional formulation.