炼钢-连铸-热轧一体化生产计划编制方法研究

为解决炼钢一连铸一热轧一体化生产计划编制问题,建立了一种一体化生产计划模型体系,将一体化生产计划编制这个复杂优化问题分解为5个局部优化问题。每一个局部优化问题采用多模型协作方式来编制各自范围内的生产计划,5个部分之间采用多系统协作方式完成一体化生产计划的编制。通过实际一体化生产计划软件系统的开发与应用,表明这种模型体系可以很好解决一体化计划编制问题。     

Polynomial approximation technique for dynamic optimization problems

This paper considers the general dynamic optimization problem and describes a polynomial approximation technique for its solution. The proposed procedure utilizes the classical method of Ritz, where the unknown decision function is postulated to be a polynomial of degree n, the coefficients of which are solved for with optimization methods such as the sequential unconstrained minimization technique (SUMT). Thus a series of new transformed minimization problems in finite dimensions is created and solved, instead of the original problem of infinite dimensions. The proposed technique is illustrated with a practical example from the aggregate production planning area.     

生产计划问题的决策模型研究

生产决策问题一直以来都是一个重要的决策问题。它涉及到营销策略、市场预测、风险分析,以及管理经验等方面的知识。文章结合数据仓库和联机分析技术,提出了一种生产计划问题的决策模型,以求提供一个生产计划问题的决策支持解决方案。文中首先分析了生产计划问题的决策模式,在此基础上提出了生产计划问题的决策模型,并讨论了模型的建立方法,以及求解模型过程中所涉及的算法等问题。     

Expert systems and their applications in production and operations management

Recently, the world competitive environment has forced organizations to reexamine and reevaluate their manufacturing strategies. Large capital investments in machine tools and management control systems are being made. The application of expert systems in the production environment has been steadily increasing. This paper examines the concepts of expert systems and reviews the literature pertinent to the areas of application in production management such as scheduling, layout planning and tools used in applying these concepts such as simulation and optimization. It has been concluded that due to increasing automation requirements in manufacturing and the intensified organizational competitive environment, the decision making process itself will have to be automated. It has been suggested that expert systems could be a vehicle in achieving this. This paper examines the applications of expert systems in production and operations management.The technology of expert systems is still in its infancy and many researchers see learning as a major problem. As expert systems become more readily available, the management issues of safety, validity and reliability will become more crucial. The compensation on transferring of knowledge is unclear. Also, the fear of replacement of white collar jobs is growing.     

Energy supply planning and supply chain optimization under uncertainty

In energy supply planning and supply chain design, the coupling between long-term planning decisions like capital investment and short-term operation decisions like dispatching present a challenge, waiting to be tackled by systems and control engineers. The coupling is further complicated by uncertainties, which may arise from several sources including the market, politics, and technology. This paper addresses the coupling in the context of energy supply planning and supply chain design. We first discuss a simple two-stage stochastic program formulation that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse’ decisions that can be varied from one time period to next based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment/policy decisions are made on a time scale orders of magnitude slower than that of operating decisions. The problem of energy capacity planning is introduced as an example. In the particular problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. Use of storage intended to overcome the limitations of intermittent sources puts further demand on the modeling and optimization. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and numerical solution approaches are discussed.     

Production planning and control I. The decision problem

The application of the linear-quadratic-gaussian (LQG) tracking problem to production planning and control systems is discussed. Decision concepts are developed from an examination of the structure of a generalized production control system. The decision problem is modelled as a LQG tracking problem operating in the presence of persistent disturbances—the disturbances representing the demand for finished goods. The development of a solution leads to a linear, time-invariant decision policy which is a decomposition of the decision input into separate and nearly independent decision tasks. Sufficient conditions for the existence of a solution are presented. An open-loop, closed-loop decision system is presented to show how the decision model can be used to analyse relationships among system structure, performance, and information requirements.     

Integrated production planning and preventive maintenance in deteriorating production systems

This paper discusses the issue of integrating production planning and preventive maintenance in manufacturing production systems. In particular, it tackles the problem of integrating production and preventive maintenance in a system composed of parallel failure-prone production lines. It is assumed that when a production line fails, a minimal repair is carried out to restore it to an ‘as-bad-as-old’ status. Preventive maintenance is carried out, periodically at the discretion of the decision maker, to restore the production line to an ‘as-good-as-new’ status. It is also assumed that any maintenance action, performed on a production line in a given period, reduces the available production capacity on the line during that period. The resulting integrated production and maintenance planning problem is modeled as a nonlinear mixed-integer program when each production line implements a cyclic preventive maintenance policy. When noncyclical preventive maintenance policies are allowed, the problem is modeled as a linear mixed-integer program. A Lagrangian-based heuristic procedure for the solution of the first planning model is proposed and discussed. Computational experiments are carried out to analyze the performance of the method for different failure rate distributions, and the obtained results are discussed in detail.     

Soft computing based procurement planning of time-variable demand in manufacturing systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning (ERP).It can be described using the non-analytic mathematical programming model proposed in this paper.To solve the model we propose to use a fuzzy decision embedded genetic algorithm.The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones.Then,a fuzzy decision quantification method is used to quantify experience from planning experts.Thus,decision rules can easily be embedded in the computation of genetic operations.This approach is applied to purchase planning problem in a practical machine tool works,where satisfactory results have been achieved.     

A genetic algorithm approach to multiobjective land use planning

This paper describes a class of spatial planning problems in which different land uses have to be allocated across a geographical region, subject to a variety of constraints and conflicting management objectives. A goal programming/reference point approach to the problem is formulated, which leads however to a difficult nonlinear combinatorial optimization problem. A special purpose genetic algorithm is developed for the solution of this problem, and is extensively tested numerically. The model and algorithm is then applied to a specific land use planning problem in The Netherlands. The ultimate goal is to integrate the algorithm into a complete land use planning decision support system.     

Genetic algorithm modeling for the inspection allocation in reentrant production systems

One of the most important characteristics of reentrant production systems is that the products are manufactured layer-by-layer, so it is difficult to inspect some defects after they are covered by the next layer. This study proposes a genetic algorithm (GA) approach that is very suitable for solving the inspection allocation problem, because the codes used in the chromosome of the GA approach are exactly the same as the representation of the inspection allocation policy for workstations in the production system. Meanwhile, this study shows better performance than the researches done in the literature and is very much closer to the optimization method based on complete enumeration. In addition, a discussion regarding GA parameters is performed to suggest proper parameters used for various production systems. The result obtained in this study is highly practical, extensible and applicable, so it can serve as a production planning tool to solve the inspection allocation problem in reentrant production systems.     

Ant Colony optimization application in bottleneck station scheduling

Finding optimal solutions to production planning and scheduling problems is crucial for surviving in a competitive environment and meeting customer expectations over time. Planning can become complicated in sectors with many different products such as tire production. This study focuses on the bottleneck problem caused by a machine called a Quadruplex Extruder in a tire factory. With this machine, rubber is extruded and transformed into a tread material product, which is critically important in some essential tire features, such as low rolling resistance and brake distance. This study aims to minimize the set-up times in production by optimizing the manufacturing order of the products produced in a quadruplex extruder machine using the Ant Colony Algorithm (ACA), a well-known metaheuristic method to solve polynomial optimization problems. In addition, the second version of the Lin–Kernighan–Helsgaun (LKH-2) algorithm was adapted to this problem. Manually prepared, LKH-2 and ACA-produced schedules were compared in terms of global efficiency. As a result, it has been shown that ACA can provide fast and suitable solutions for decision makers in production planning.     

A resilience optimization approach for workforce-inventory control dynamics under uncertainty

The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.     

Machining process sequencing with fuzzy expert system and genetic algorithms

Traditional process planning systems are usually established in a deterministic framework that can only deal with precise information. However, in a practical manufacturing environment, decision making frequently involves uncertain and imprecise information. This paper describes a fuzzy approach for solving the process selection and sequencing problem under uncertainty. The proposed approach comprises a two-stage process for machining process selection and sequencing. The two stages are called intra-feature planning and inter-feature planning, respectively. According to the feature precedence relationship of a machined part, the intra-feature planning module generates a local optimal operation sequence for each feature element. This is based on a fuzzy expert system incorporated with genetic algorithms for machining cost optimization according to the cost-tolerance relationship. Manufacturing resources such as machines, tools, and fixtures are allocated to each selected operation to form an Operation-Machine-Tool (OMT) unit in the manufacturing resources allocation module. Finally, inter-feature planning generates a global OMT sequence. A genetic algorithm with fuzzy numbers and fuzzy arithmetic is developed to solve this global sequencing problem.     

A particle swarm optimization algorithm for solving unbalanced supply chain planning problems

This paper focuses on developing a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain. In the supply chain system discussed here, multiple products, production loss, transportation loss, quantity discount, production capacity, and starting-operation quantity are considered simultaneously, and the system pattern is ascertained with based on appropriate partners and suitable transportation quantities. To make a quality decision in supply chain planning, we first propose an optimization mathematical model which integrates cost and time criteria. Then, a particle swarm optimization (PSO) solving method is proposed for obtaining acceptable results is called MEDPSO. The MEDPSO introduces the maximum possible quantity strategy into the basic procedure of PSO to generate the initial feasible population in a timely fashion and provides an exchange and disturbance mechanism to prevent particle lapse into the local solution. Finally, one case and two simulated supply chain structures are proposed to illustrate the effectiveness of the MEDPSO method by comparing the results of classical GA and PSO in solving multi-echelon unbalanced supply chain planning problems with quantity discount.     

A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

This paper investigates one of the key decision-making problems referring to the integrated production planning (IPP) for the steelmaking continuous casting-hot rolling (SCC-HR) process in the steel industry. The complexities of the practical IPP problem are mainly reflected in three aspects: large-scale decision variables; multiple objectives and interval-valued uncertain parameters. To deal with the difficulty of large-scale decision variables, we introduce a new concept named “order-set” for modeling. In addition, considering the multiple objectives and uncertainties of the given IPP problem, we construct a multi-objective optimization model with interval-valued objective functions to optimize the throughput of each process, the hot charge ratio of slabs, the utilization rate of tundishes and the additional cost of technical operations. Furthermore, we propose a novel approach based on a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA) to solve the problem. The proposed model and algorithm were tested with daily production data from an iron and steel company in China. Computational experiments demonstrate that the proposed method generates quite effective and practical solutions within a short time. Based on the IPP model and MI-MOEA, an IPP system has been developed and implemented in the company.     

Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption.     

Buffer allocation in flow-shop-type production systems with general arrival and service patterns

This study investigates the buffer allocation strategy of a flow-shop-type production system that possesses a given total amount of buffers and finite buffer capacity for each workstation as well as general interarrival and service times in order to optimize such system performances as minimizing work-in-process, cycle time and blocking probability, maximizing throughput, or their combinations. In theory, the buffer allocation problem is in itself a difficult NP-hard combinatorial optimization problem, it is made even more difficult by the fact that the objective function is not obtainable in closed form for interrelating the integer decision variables (i.e., buffer sizes) and the performance measures of the system. Therefore, the purpose of this paper is to present an effective design methodology for buffer allocation in the production system. Our design methodology uses a dynamic programming process along with the embedded approximate analytic procedure for computing system performance measures under a certain allocation strategy. Numerical experiments show that our design methodology can quickly and quite precisely seek out the optimal or sub-optimal allocation strategy for most production system patterns.Scope and purposeBuffer allocation is an important, yet intriguingly difficult issue in physical layout and location planning for production systems with finite floor space. Adequate allocation and placement of available buffers among workstations could help to reduce work-in-process, alleviate production system's congestion and even blocking, and smooth products manufacturing flow. In view of the problem complexity, we focus on flow-shop-type production systems with general arrival and service patterns as well as finite buffer capacity. The flow-shop-type lines, which usually involve with product-based layout, play an important role in mass production type of manufacturing process organization such as transfer line, batch flow line, etc. The purpose of this paper is to present a design methodology with heuristic search and imbedded analytic algorithm of system performances for obtaining the optimal or sub-optimal buffer allocation strategy. Successful use of this design methodology would improve the production efficiency and effectiveness of flow-shop-type production systems.     

Commitment and dispatch of heat and power units via affinely adjustable robust optimization

The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.     

A feasibility evaluation approach for time-evolving multi-item production–distribution networks

Time-dependent multi-item problems arise frequently in management applications, communication systems, and production–distribution systems. Our problem belongs to the last category, where we wish to address the feasibility of such systems when all network parameters change over time and product. The objective is to determine whether it is possible to have a dynamic production–shipment circuit within a finite planning horizon. And, if there is no such a flow, the goal is to determine where and when the infeasibility occurs and the approximate magnitude of the infeasibility. This information may help the decision maker in their efforts to resolve the infeasibility of the system. The problem in the discrete-time settings is investigated and a hybrid of scaling approach and penalty function method together with network optimality condition is utilized to develop a network-based algorithm. This algorithm is analysed from theoretical and practical perspectives by means of instances corresponding to some electricity transmission-distribution networks and many random instances. Computational results illustrate the performance of the algorithm.