Multiple-Attribute-Based Expert Support System for Customer/Order Evaluation

Sales and marketing people routinely evaluate and rank their customers to develop a marketing strategy and plan of action for utilizing available sales resources. Performing this evaluation informally makes it difficult to take into account all the significant customer attributes in a consistent and objective manner. In this paper an approach based on multiple attributes is developed to assist in evaluating customer performance. This approach is coded primarily in an expert system shell, which is run on a personal computer. The expert system shell was chosen over other software by the marketing people because they preferred its user-friendly capabilities. This system is being used by the Tin Mill Products Marketing office of Bethlehem Steel and is being considered for use by other product-line marketing offices within Bethlehem.     

A hierarchical approach for one-dimensional cutting stock problems in the steel industry that maximizes yield and minimizes overgrading

In the steel industry, as hot steel products exit the producing facility, they are cut at primary saws (hotsaws) into shorter pieces. After these pieces cool, they are inspected for defects and either applied directly to customer orders or are further cut to ordered lengths at secondary saws (cold saws). In this case study, we will describe a hierarchical algorithm, DYNACUT_CS, that efficiently and effectively generates cutting patterns for material that is to be cut at cold saws. DYNACUT_CS strives to maximize yield over all the material cut and simultaneously tries to minimize overgrading (applying higher quality material than specified by the customer). An example will be given to illustrate how the algorithm works. This approach has been implemented for a variety of products at several different Bethlehem Steel Corporation facilities.     

A review of planning and scheduling systems and methods for integrated steel production

Iron and steel industry is an essential and sizable sector for industrialized economies. Since it is capital and energy extensive, companies have been putting consistent emphasis on technology advances in the production process to increase productivity and to save energy. The modern integrated process of steelmaking, continuous casting and hot rolling (SM–CC–HR) directly connects the steelmaking furnace, the continuous caster and the hot rolling mill with hot metal flow and makes a synchronized production. Such a process has many advantages over the traditional cold charge process. However, it also brings new challenges for production planning and scheduling. In this paper we first give a comparative analysis of the production processes and production management problems for the SM–CC–HR and the traditional cold charge process. We then review planning and scheduling systems developed and methods used for SM–CC–HR production. Finally some key issues for further research in this field are discussed.     

A robust optimization model for multi-site production planning problem in an uncertain environment

This paper addresses the multi-site production planning problem for a multinational lingerie company in Hong Kong subject to production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers’ preferences, as well as production capacity, workforce level, storage space and resource conditions at the factories. In this paper, a robust optimization model is developed to solve multi-site production planning problem with uncertainty data, in which the total costs consisting of production cost, labor cost, inventory cost, and workforce changing cost are minimized. By adjusting penalty parameters, production management can determine an optimal medium-term production strategy including the production loading plan and workforce level while considering different economic growth scenarios. The robustness and effectiveness of the developed model are demonstrated by numerical results. The trade-off between solution robustness and model robustness is also analyzed.     

具有惩罚因子的模糊集约生产计划

研究基于模糊环境下的集约生产计划问题,并设计了带有惩罚因子的模糊优化模型,以实现生产费用和惩罚费用之和最小.通过模糊变量和模糊等式定义的描述,简化了模型,并给出机会约束规划方法进行模型求解的整体步骤.通过仿真结果和灵敏度分析,表明模型和方法的有效性,并为决策者在模糊环境下的决策提供支持.     

A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.     

LP modelling of the finishing hot rolling programme for low carbon steel coils

In this paper a mathematical model was developed to optimize the finishing rolling of hot rolled coils by increasing the productivity of the rolling programme, to help achieve the required level of quality assurance and to facilitate production planning and control in the hot rolling mill. A brief account of the technological and planning aspects of the hot rolling processes and mills relevant to strip steel is given. Linear (mixed integer) programming is used to formulate the objective function and the various types of constraints of the model. The model takes into consideration, the general aspects pertinent to hot rolling of low carbon steel and the characteristics of the hot rolling mills as stipulated by the operational codes and guidelines of the relevant establishments. Owing to the flexibility offered by linear programming the model can incorporate any modifications and/or additional requirements, if any, in case of other types of steel and/or other types of mills. The full modelling of the problem required the incorporation of some zero/one variable constraints. Owing to the complexity involved and the need to keep the model as simple as possible, it was decided to exclude these constraints and deal with them externally. HYPER LINDO PC was used to solve the programme. Using available data, in the case under consideration the model showed astonishing results in achieving the objectives. Taking into account the effect on the overall productivity as well as quality improvement, the investigation showed that a net improvement in conforming output to the effect of around 43%, could have been obtained had the model been used in the case under consideration.     

An Expert System Framework for Economic Evaluation of Machining Operation Planning

Recognising the importance of combining manufacturing and management systems for machining operation planning, this paper presents a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satisfies the required specifications, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, fixed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the defined goal. The proposed methodology has been applied in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan. This optimal plan integrates considerations from both managers and production engineers, and balances their needs for efficient machining of a quality product.     

A Practical Solution to a Cargo Loading Problem at Bethlehem Steel

The optimum assignment of structural steel shapes to rail cars is an important logistical problem in the steel industry. In this paper, we discuss an application at Bethlehem Steel that not only involves weight and dimensional constraints, but also customer unloading constraints. The formulation is a generalized bin packing problem which is solved by modifying and extending the first fit decreasing algorithm. The solution algorithm, SOLID (for Structural Optimal Loading IDentification), has been used extensively for one of Bethlehem's high tonnage customers providing very good practical (implementable) results that achieve the desired goals. Bethlehem has enhanced this approach for use with other customers.     

An integer programming approach to short-term production scheduling in a tobacco plant

In this paper, a mathematical model is developed that facilitates daily production scheduling in a tobacco processing plant. The implied objectives are to meet specific horizon production targets (obtained from a master production schedule), to maintain safety stock requirements and to ensure that the demand for labour lies within given limits. The express objective is to minimise the number of machines used in the production process. Additionally, the model incorporates work-in-progress, aspects of the demand for product transportation within the plant and machine capacity (utilisation) reduction effects associated with production sequencing. These aspects are relevant when dealing with time intervals as small as a day but can be averaged out when dealing with monthly time intervals. The developments in this paper represents stage II of the modelling of the tobacco plant, where stage I (already completed) was centred on obtaining a monthly master production schedule for a year ahead and assisting in macro planning activities. This paper also sees the development of a simple user-friendly heuristic which facilitates production sequencing on a daily basis given the master production schedule obtained from Stage I.     

Penalties,rewards, and inspection: provisions for quality in supply chain contracts

Rewards for better quality, penalties for poorer quality, and the type of inspection policy are among the most common quality-related provisions of supply chain contracts. In this paper, we examine the effect of rewards, penalities, and inspection policies on the behaviour of an expected cost minimizing supplier. We assume that the supplier selects a batch size and target quality level in order to meet a buyer's deterministic demand. We show that the reward and/or penalty that motivates a supplier to deliver the buyer's target quality depends upon the inspection policy. We also show that, when sampling inspection is used, penalties and rewards are substitutes for one another in motivating the supplier and that there exists a unique reward/penalty combination at which the buyer's expected cost of quality is zero.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.     

Simulation of strain induced austenite-martensite transformation

Grain refinement due to phase transformation is an effective method for improving the mechanical properties of steel. An approach is proposed in the present work based on the FEM, for numerical simulation of the microstructure evolution as a result of hot rolling and subsequent cold torsion. Grain refinement in 304 stainless steel at four different technological schedules is considered. Results of numerical simulation are compared with experimental data. Coupling of the thermoplastic deformation with microstructure evolution is realized. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Introducing the Microcomputer into the O.R. Department

This paper reviews the typical main-frame computer services that are currently available and by accepting the alleged advantages of microcomputers postulates the ideal computing requirements of O.R. departments. Experience of microcomputers in O.R. departments, within the N.C.B. and elsewhere, is considered in relation to these ideal requirements. It is concluded that although some main-frame computer services will remain essential to O.R. and accepting that the main-frame can give a reasonable general computing service, the micro offers additional advantages. These include: lower cost and/or better service, the ability to configure the equipment exactly to meet O.R. needs; full control over applications and priorities; and portability - which means that computing power can now be taken direct to the manager by the O.R. department.     

A robust optimization model for production planning of perishable products

In this study, a robust optimization model is developed to solve production planning problems for perishable products in an uncertain environment in which the setup costs, production costs, labour costs, inventory costs, and workforce changing costs are minimized. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilize the resources. By adjusting penalty parameters, decision-makers can determine an optimal production loading plan and better utilize resources while considering different economic growth scenarios. A case from a Hong Kong plush toy company is studied and the characteristics of perishable products are discussed. Numerical results demonstrate the robustness and effectiveness of the proposed model. An analysis of the trade-off between solution robustness and model robustness is also presented.     

A neural network system for solving an assortment problem in the steel industry

A neural network model for solving an assortment problem found in the iron and steel industry is discussed in this paper. The problem arises in the yard where steel plate is cut into rectangular pieces. The neural network model can be categorized as a Hopfield model, but the model is expanded to handle inequality constraints. The idea of a penalty function is used. A large penalty is applied to the network if a constraint is not satisfied. The weights are updated based on the penalty values. A special term is added to the energy function of the network to guarantee the convergence of the neural network which has this feature. The performance of the neural network was evaluated by comparison with an existing expert system. The results showed that the neural network has the potential to identify in a short time near-optimal solutions to the assortment problem. The neural network is used as the core of a system for dealing with the assortment problem. In building the neural networks system for practical use, there were many implementation issues. Some of them are presented here, and the fundamental ideas are explained. The performance of the neural network system is compared to that of the expert system and evaluated from the practical viewpoint. The results show that the neural network system is useful in handling the assortment problem.     

Microstructure evolution during rolling and annealing of mild steels

The process chain for components made of sheet metals consists of different forming techniques like hot rolling, cold rolling, and deep drawing as well as heat treatment operations like annealing. For the design and optimization of the whole manufacturing process and the final component behavior, a correct representation of the material behavior and the application of appropriate numerical simulation techniques are required. For our first investigations, a ferritic mild steel DC04, which is a typical steel grade for automotive applications, is analyzed. Therefore, specimens are taken out of a real manufacturing process after hot and cold rolling and after annealing. These specimens are intensively investigated in order to study the texture evolution and the development of other material properties, like yield function during the process chain. In this paper different homogenization methods are used to simulate the texture evolution during cold rolling. The results are compared concerning accuracy and efficiency of the considered models. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Trade-off between capacity invested and inventory needed

A multi-item capacitated make-to-order production system with considerable demand fluctuations is discussed. The relationship between the available capacity and the inventory needed to meet customer requirements with a pre-defined service level is modeled. Furthermore, the total cost for both capacity and inventory is minimized and it is shown that, assuming negligible change-over times, the double of the surplus inventory cost has to be equal to the excess capacity cost to ensure minimum total cost.     

Generation Supply Bidding in Perfectly Competitive Electricity Markets

This paper reports on the development of a comprehensive framework for the analysis and formulation of bids in competitive electricity markets. Competing entities submit offers of power and energy to meet the next day's load. We use the England and Wales Power Pool as the basis for the development of a very general competitive power pool (CPP) framework. The framework provides the basis for solving the CPP dispatcher problem and for specifying the optimal bidding strategies. The CPP dispatcher selects the winning bids for the right to serve load each period of the scheduling horizon. The dispatcher must commit sufficient generation to meet the forecasted load and reserve requirements throughout the scheduling horizon. All the unique constraints under which electrical generators operate including start-up and shut-down time restrictions, reserve requirements and unit output limits must be taken into account. We develop an analytical formulation of the problem faced by a bidder in the CPP by specifying a strategy that maximizes his profits. The optimal bidding strategy is solved analytically for the case of perfect competition. The study in this work takes into account the principal sources of uncertainty—the load forecast and the actions of the other competitors. The formulation and solution methodology effectively exploit a Lagrangian relaxation based approach. We have conducted a wide range of numerical studies; a sample of numerical results are presented to illustrate the robustness and superiority of the analytically developed bidding strategies.     

The synthetic hierarchy method: An optimizing approach to obtaining priorities in the AHP

A new method of synthesizing local and criteria priorities into global priorities is suggested. This approach is a development of the Analytic Hierarchy Process enabling the united consideration of all horizontal and vertical connections of a hierarchical system in a single optimizing objective function based on statistical models of the synthesis process. The solution can be reduced to a linear system or to an eigenproblem of a special matrix constructed as a combination of Kronecker's sums and products of pairwise judgement matrices. A numerical example shows that the optimizing approach produces a ranking of global priorities that may be different from the ranking produced by the classical AHP.