Strangeness production in pA and AA collisions at 158A GeV

LUCIAE, a hadronic and string cascade model and its corresponding event generator are used to analyse strangeness production singly and multiply in p-Pb and Pb-Pb collisions at 158 A GeV. Spectra of multiplicity and transverse mass for single $(\Lambda ,\overline \Lambda )$ and multiple $(\Xi ^ - ,\overline {\Xi ^ - } ,\Omega ^ - ,\overline {\Omega ^ - } )$ strangeness are given. In LUCIAE model it suggests a physical mechanism, i.e. the dependence of the strange quark suppression factor on incident energy, projectile mass and centrality of colliding system might result in increase of yield of strange particles with increasing the above three parameters. Calculations from the model reconstruct well the WA97 experimental data: increase of yield of strange particles with increasing centrality and increase of strangeness enhancement with increasing number of strange quarks, in relativistic nucleus-nucleus collisions.     

Systematic study for grey particles in relativistic nucleus + emulsion (Ag) interactions

The hadronic and string cascade model and the corresponding event generator LUCIAE are used to study systematically the production of the grey particles in (14.6, 60 and 200A GeV)0 + Em, (2004 GeV)Si + Em, (200A GeV)S + Em and (11.6A GeV)Au + Em reactions. The dependences of the mean multiplicity, the multiplicity distributions and the polar angular distributions of the grey particles on the incident energy, the projectile mass and the collision centrality observed in emulsion experiments, respectively, are all reproduced by LUCIAE calculations. In addition the effects of the rescattering on the grey particle production are also investigated. Project supported by the National Natural Science Foundation of China (Grant Nos. 19575071 and 19875019) and DFG of Germany.     

Enhancement of φ meson production in p-p and Pb-Pb collisions at SPS energy

The φ meson yield, rapidity and transverse mass distributions in p-p and Pb-Pb collisions at 158 A GeV were studied by the hadron-string cascade model, LUCIAE. By adjusting the width parameter in q-q Gaussian like transverse momentum distribution in string fragmentation to fit the NA49 data ofφ meson transverse mass distribution in p-p collisions, the obtained φ meson rapidity distribution in p-p collisions, the rapidity and transverse mass distributions in Pb-Pb collisions, and theφ meson enhancement factor in Pb-Pb relative to p-p collisions were compatible with corresponding NA49 data, which might be attributed to the collective effect in gluon emission of string and the reduction of strange quark suppression mechanisms involved in the LUCIAE model.     

Qualities of goods in the models of production

In this paper the models of production in which the qualities of goods are considered are presented. Such models are completely described by the families of matrices or by a function which has some properties which are natural from the economic point of view. The special cases of the models are the well known linear model of production and the van Neumann-Gale's model.     

Oil production: A probabilistic model of the Hubbert curve

The specific distribution of hydrocarbon field sizes has a deep impact on the dynamics of the production inside a basin. This paper proposes a probabilistic model based on the field size distribution, taking into account the launching production process of the fields. This model can be seen as a ‘bottom‐up’ probabilistic method to simulate and predict oil production. The asymptotic results obtained allow us to propose relevant fitting to real production curves. Copyright © 2010 John Wiley & Sons, Ltd.     

A solution approach for optimizing long- and short-term production scheduling at LKAB’s Kiruna mine

We present a mixed-integer program to schedule long- and short-term production at LKAB’s Kiruna mine, an underground sublevel caving mine located in northern Sweden. The model minimizes deviations from monthly preplanned production quantities while adhering to operational constraints. Because of the mathematical structure of the model and its moderately large size, instances spanning a time horizon of more than a year or two tend to be intractable. We develop an optimization-based decomposition heuristic that, on average, obtains better solutions faster than solving the model directly. We show that for realistic data sets, we can generate solutions with deviations that comprise about 3-6% of total demand in about a third of an hour.     

A fresh approach to performance evaluation in a multi-item production scenario

In a truly flexible production environment, it is feasible to interchange the production rate of two items, as long as this does not affect the constraint imposed on total manufacturing time. Performance evaluation in a multi-item production scenario has many facets, and its improvement is possible in many ways. After adopting certain benchmarking or analytical process, usually a facility runs at certain performance level with respect to total system cost. It may be a unique approach to explore the possibility of interchanging the production rate of any item with that of another appropriate item in the group. This is discussed in the present paper with the objective of further improvement in the total cost.     

A multi-stage stochastic programming approach in master production scheduling

Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions.     

A production–inventory model in an imperfect production process

The paper develops a model to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process. The basic assumption of the classical Economic Manufacturing Quantity (EMQ) model is that all manufacturing items are of perfect quality. The assumption is not true in practice. Most of the production system produces perfect and imperfect quality items. In some cases the imperfect quality (non conforming) items are reworked at a cost to restore its quality to the original one. Rework cost may be reduced by improvements in product reliability (i.e., decreasing in product reliability parameter). Lower value of product reliability parameter results in increase development cost of production and also smaller quantity of nonconforming products. The unit production cost is a function of product reliability parameter and production rate. As a result, higher development cost increases unit production cost. The problem of optimal planning work and rework processes belongs to the broad field of production–inventory model which deals with all kinds of reuse processes in supply chains. These processes aim to recover defective product items in such a way that they meet the quality level of ‘good item’. The benefits from imperfect quality items are: regaining the material and value added on defective items and improving the environment protection. In this point of view, a model is introduced here to guide a firm/industry in addressing variable product reliability factor, variable unit production cost and dynamic production rate for time-varying demand. The paper provides an optimal control formulation of the problem and develops necessary and sufficient conditions for optimality of the dynamic variables. In this purpose, the Euler–Lagrange method is used to obtain optimal solutions for product reliability parameter and dynamic production rate. Finally, numerical examples are given to illustrate the proposed model.     

A simulated annealing approach for buffer allocation in reliable production lines

We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximizing their average throughput. The latter is calculated utilizing a decomposition method. The allocation plan is calculated subject to a given amount of total buffer slots in a computationally efficient way.     

Aggregation and disaggregation in hierarchical production planning

This paper reports on a simulation study of hierarchical planning procedures, which can support a material requirements planning system. Data for this study have been obtained from a Swedish manufacturing company. The three final products considered in the simulations represent a major part of this company.An aggregate plan in terms of product groups and machine groups is derived with the aid of an aggregate model. This plan is then disaggregated by changing order release times obtained from material requirements planning, and by distributing extra capacity among individual machines.The results indicate that our methods in general perform significantly better than a comparable reference case without the supporting hierarchical planning process. In our simulation experiments we evaluate different design features, like disaggregation procedures and methods for aggregating items and machines.     

A deterministic inventory/production model with general inventory cost rate function and piecewise linear concave production costs

We present a thorough analysis of the economic production quantity model with shortages under a general inventory cost rate function and piecewise linear concave production costs. Consequently, an effective solution procedure, particularly useful for an approximation scheme, is proposed. A computational study is appended to illustrate the performance of the proposed solution procedure.     

Stochastic production frontiers and panel data: A latent variable framework

This paper deals with the issue of estimating production frontier and measuring efficiency from a panel data set. First, it proposes an alternate method for the estimation of a production frontier on a short panel data set. The method is based on the so-called mean-and-covariance structure analysis which is closely related to the generalized method of moments. One advantage of the method is that it allows us to investigate the presence of correlations between individual effects and exogenous variables without the requirement of some available instruments uncorrelated with the individual effects as in instrumental variable estimation. Another advantage is that the method is well suited to a panel data set with a short number of periods. Second, the paper considers the question of recovering individual efficiency levels from the estimates obtained from the mean-and-covariance structure analysis. Since individual effects are here viewed as latent variables, they can be estimated as factor scores, i.e., weighted sums of the observed variables. We illustrate the proposed methods with the estimation of a stochastic production frontier on a short panel data of French fruit growers.     

Optimal pricing and production in an inventory model

This paper deals with the problem of simultaneously determining the optimal price policy and production rate over a given planning horizon. For nonlinear demand functions and convex inventory and shortage cost functions the optimal solution paths are derived by using optimal control theory. The treatment of linear nonsmooth cost functions requires the use of a generalized maximum principle. The solution method is a phase portrait analysis providing insight into the optimal pricing and production policies as well as the resulting inventory paths. Moreover, it is shown that in the case of nonsmooth piecewise linear cost functions the equilibrium is approached within finite time although the model is nonlinear in the control variables. Finally it is illustrated that exogenous fluctuations in the demand rate (seasonal demand pattern) amount to cyclical optimal solutions.     

A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks

A production network can be described as a collection of production processes performed by several interdependent groups of sub decision-making units (SDMUs) within a DMU. Dynamic effects pertain to the situation where intermediate outputs consumed by one SDMU may also dynamically influence its output level in the future. Without considering these effects in efficiency measurement, we would obtain biased efficiency measurement, because the measure could not faithfully reflect the underlying performance. Hence the result would provide misleading information to decision-makers. This paper proposes a network-DEA model with new efficiency measures to systematically cope with the dynamic effect within a production network. Various interconnections between the new measure and the DEA-efficiency have also been established. Additionally, we also formalize the relationship between returns-to-scale properties of DMUs and those of its constituting SDMUs. This paper presents a unified framework to analyze performances in a dynamic production network.     

A mathematical programming model and solution for scheduling production orders in Shanghai Baoshan Iron and Steel Complex

In this paper, we investigate the production order scheduling problem derived from the production of steel sheets in Shanghai Baoshan Iron and Steel Complex (Baosteel). A deterministic mixed integer programming (MIP) model for scheduling production orders on some critical and bottleneck operations in Baosteel is presented in which practical technological constraints have been considered. The objective is to determine the starting and ending times of production orders on corresponding operations under capacity constraints for minimizing the sum of weighted completion times of all orders. Due to large numbers of variables and constraints in the model, a decomposition solution methodology based on a synergistic combination of Lagrangian relaxation, linear programming and heuristics is developed. Unlike the commonly used method of relaxing capacity constraints, this methodology alternatively relaxes constraints coupling integer variables with continuous variables which are introduced to the objective function by Lagrangian multipliers. The Lagrangian relaxed problem can be decomposed into two sub-problems by separating continuous variables from integer ones. The sub-problem that relates to continuous variables is a linear programming problem which can be solved using standard software package OSL, while the other sub-problem is an integer programming problem which can be solved optimally by further decomposition. The subgradient optimization method is used to update Lagrangian multipliers. A production order scheduling simulation system for Baosteel is developed by embedding the above Lagrangian heuristics. Computational results for problems with up to 100 orders show that the proposed Lagrangian relaxation method is stable and can find good solutions within a reasonable time.     

二阶段加法DEA模型的生产可能集与生产前沿面

针对二阶段加法DEA模型的中间要素的特殊性,构造生产可能集及其公理体系,由此定义生产前沿面,并建立DEA有效和生产前沿面之间的等价关系.通过构造一个多目标规划模型,建立该问题的Pareto有效解与DEA有效之间的等价关系.     

A stochastic optimal control approach to a class of production and inventory problems

A comprehensible and unified system control approach is presented to solve a class of production/inventory smoothing problems. A nonstationary, non-Gaussian, finite-time linear optimal solution with an attractive computation scheme is obtained for a general quadratic and linear cost structure. A complete solution to a classical production/inventory control problem is given as an example. A general solution to the discrete-time optimal regulator with arbitrary but known disturbance is provided and discussed in detail. A computationally attractive closed-loop suboptimal scheme is presented for problems with constraints or nonquadratic costs. Implementation and interpretation of the results are discussed.     

Reversibility and dependence in a U-shaped production line

We deal with a U-shaped production line with multiple machines and a multifunction worker. The worker visits machines in a cyclic fashion and operates an item on each machine. The operation consists of detaching the processed item from the machine, sending it to the next machine, putting the new item on the machine and switching it on. Walking times of the worker and processing and operation times are i.i.d. random variables for each machine. We show that the sum of the firstn cycle times is stochastically the same as that in the reversed system where machines are arranged in the reversed order. We give examples where the distributions of waiting and cycle times depend on the machines, called starting ones, where the worker begins operation at time 0. A sufficient condition is derived under which the limiting expected waiting and cycle times do not depend on the starting machines.