Decision analysis in the energy sector

An optimization model has been developed primarily for the planning of the national energy system. It is also shown, however, that the model can be used as a tool for decision analysis.

The dynamic linear programming mini-model includes four primary energy sources and three forms of secondary energy. Decision variables include investment as well as production activities in the refining, conversion, and transmission of energy. The model contains 186 columns and 226 rows. The objective function is the discounted sum of costs of fuels, investments, operating costs, and under-utilized capacity. The model has been used to analyse decisions in the Turkish energy sector for the years 1960-1975, and possible developments for the period 1980-1995.

Through the use of this model, the underlying assumptions concerning the import prices of petroleum have been analysed, as well as the impact of a delay in the construction of a large hydro project. Furthermore, the effect of the shadow costs of foreign currency, the consequences of rapid industrialization, rapid economic growth, and substitution among fuels have been investigated. The model is currently being used for the analysis of possible nuclear strategies for Turkey.     

Oligopoly games under asymmetric costs and an application to energy production

Oligopolies in which firms have different costs of production have been relatively under-studied. In contrast to models with symmetric costs, some firms may be inactive in equilibrium. (With symmetric costs, the results trivialize to all firms active or all firms inactive.) We concentrate on the linear demand structure with constant marginal but asymmetric costs. In static one-period models, we compare the number of active firms, i.e. the number of firms producing a positive quantity in equilibrium, across four different models of oligopoly: Cournot and Bertrand with homogeneous or differentiated goods. When firms have different costs, we show that, for fixed good type, Cournot always results in more active firms than Bertrand. Moreover, with a fixed market type, differentiated goods result in more active firms than homogeneous goods. In dynamic models, asymmetric costs induce different entry times into the market. We illustrate with a model of energy production in which multiple producers from costly but inexhaustible alternative sources such as solar or wind compete in a Cournot market against an oil producer with exhaustible supply.     

Numerical Approach of Multi-Objective Optimal Control Problem in Imprecise Environment

In this paper, realistic production-inventory models without shortages for deteriorating items with imprecise holding and production costs for optimal production have been formulated. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the demand is time dependent and known. The imprecise holding and production costs are assumed to be represented by fuzzy numbers which are transformed to corresponding interval numbers. Following interval mathematics, the objective function is changed to respective multi-objective functions and thus the single-objective problem is reduced to a multi-objective decision making(MODM) problem. The MODM problem is then again transformed to a single objective function with the help of weighted sum method and then solved using global criteria method, calculus method, the Kuhn–Tucker conditions and generalized reduced gradient(GRG) technique. The models have been illustrated by numerical data. The optimum results for different objectives are obtained for different types of production function. Numerical values of demand, production function and stock level are presented in both tabular and graphical forms     

A Dynamic Programming Approach to Determine Optimal Manpower Recruitment Policies

The manpower planning models available in the literature have dealt with how changes take place in a manpower planning system, under various operating and policy constraints. However, none of these models has identified the manpower system costs. In this paper we have identified various manpower system costs. Further, we have developed a manpower planning model with the objective of minimizing the manpower system costs. The model has been found to be analogous to the Wagner-Whitin model in production/inventory management. A numerical example has been given to illustrate the model.     

Evaluation of Lead Time in Production/Inventory Systems with Non-stationary Stochastic Demand

For the past decade, new techniques for production management–such as kanban (pull-through) systems, internal set-ups, zero inventory, etc–have gained tremendous interest. However, research indicates that the production environment is more important than specific techniques. One aspect of this environment is the production lead time. This paper examines the impact of lead time, in conjunction with the characteristics of the demand process, on costs through the use of a periodic-review production/inventory model with a non-stationary stochastic demand process. Our results indicate that costs are affected by a combination of production lead time and demand variances. While the demand means have no effect on costs, both the shapes of the demand distributions and the relative magnitude of the penalty costs do have an effect.     

Production Planning in the New Zealand Dairy Industry

From 1977 onwards, D.S.I.R. has been developing an L.P. model to help the largest N.Z. dairy company plan its medium-term production. This time-staged model finds the most profitable daily production schedule of the various powder, casein, cheese and butter products. Computer reports and graphics show Company management how to allocate the milk available in the various farming regions to the Company's factories, and where to send the by-products for further processing.The L.P. models seasonal fluctuations in wholemilk availability and quality, as well as transport costs, factory capacities and costs, and product-yields and prices. Comparison runs have shown that the L.P. increases the profitability of the manual plan by over $5000 per day (because the computer can handle changing yields, factory costs and by-product processing). The model gives management the ability to adjust production plans quickly in response to improved information, new market opportunities and emergencies like plant breakdown.     

Modeling and analysis of build-to-order supply chains

The build-to-order supply chain (BTO-SC) or make-to-order (MTO) system has received a great deal of attention in recent years because of the success of high-tech companies such as Dell, BMW, Compaq, and Gateway. Some auto companies have also implemented BTO-SC. Quite a few research articles have been written on BTO-SC and MTO. However, those that explicitly address the problems of BTO-SCM with modeling are rather limited in number. Considering the growing importance of more informed and timely decision making in these areas, there is a need to encourage further research on the modeling and analysis of global outsourcing, optimization between product variants and the cost of production, the point of differentiation along the production/assembly process, the selection of suppliers, logistics costs, and customer relationship management. Traditional operations research models have been used to solve supply chain management problems. Considering the importance of BTO or MTO, an attempt has been made to review the selected literature on the modeling and analysis of BTO-SC with the objectives of providing assistance to and motivating both researchers and practitioners to design, develop, and manage BTO-SC effectively; and suggesting some future research directions on BTO supply chain management (BTO-SCM). The literature available on BTO-SCM has been classified based on the nature of the decision-making areas and then sub-classified to focus on solving problems with modeling and analysis. We have focused mostly on the modeling aspect of the BTO-SC, but have not extended our efforts to empirical research. We have developed a unified framework for modeling and analyzing BTO-SCM and suggest some future research directions.     

An Application of Branch and Cut to Open Pit Mine Scheduling

The economic viability of the modern day mine is highly dependent upon careful planning and management. Declining trends in average ore grades, increasing mining costs and environmental considerations will ensure that this situation will remain in the foreseeable future. The operation and management of a large open pit mine having a life of several years is an enormous and complex task. Though a number of optimization techniques have been successfully applied to resolve some important problems, the problem of determining an optimal production schedule over the life of the deposit is still very much unresolved. In this paper we will critically examine the techniques that are being used in the mining industry for production scheduling indicating their limitations. In addition, we present a mixed integer linear programming model for the scheduling problems along with a Branch and Cut solution strategy. Computational results for practical sized problems are discussed.     

现代管理科学热点——浅析精益生产方式

本文介绍了新型的精益生产方式组织与管理的特点以及与传统的大指量生产方式的区别。并指出了精益生产方式在目前我国社会主义市场经济中对企业降低生产成本，提高产品质量，增强企业竞争能力和应变能力的重要作用和潜在优势。     

Facility location with increasing production costs

Facility location models determine the set of locations on a network that minimize the sum of the costs of investment, production, and distribution to meet a known set of demands. In this paper, we introduce a new type of facility location model, which combines aspects of the well-studied simple uncapacitated and capacitated facility location problems. Its distinctive feature is that unit production costs are modeled as increasing with scale of output. Such cost functions have practical value in handling cases in which capacity can be “stretched” by incurring some additional cost (e.g., by adding workers). Indeed, it is shown that average total costs are minimized at a point where average production costs are rising. Four different formulations for this problem are proposed. Using linear programming plus branch-and-bound as a solution method, the four formulations are tested and compared on a set of 216 problems with randomly generated data.     

Energy-aware workload management models for operation cost reduction in data centers

In the last century, the costs of powering datacenters have increased so quickly, that datacenter power bills now dwarf the IT hardware bills. Many large infrastructure programs have been developed in the past few years to reduce the energy consumption of datacenters, especially with respect to cooling requirements. Although these methods are effective in lowering the operation costs they do require large upfront investments. It is therefore not surprising that some datacenters have been unable to utilize the above means and as a result are still struggling with high energy bills. In this work we present a cheap addition to or an alternative to such investments as we propose the use of intelligent, energy efficient, system allocation mechanisms in place of current packaged system schedulers available in modern hardware infrastructure cutting server power costs by 40%. We pursue both the quest for (1) understanding the energy costs generated in operation as well has how to utilize this information to (2) allocate computing tasks efficiently in a cost minimizing optimization approach. We were able to underline the energy savings potential of our models compared to current state-of-the-art schedulers. However, since this allocation problem is complex (NP-hard) we investigated various model approximations in a trade-off between computational complexity and allocative efficiency. As a part of this investigation, we evaluate how changes in system configurations impact the goodness of our results in a full factorial parametric evaluation.     

Economic Batch-Size Determination for Multi-Product Scheduling

In the determination of batch-sizes in batch production, different criteria can be used as guiding principles, depending on the specified objectives. In a production schedule consisting of several products, an optimum solution for the whole production schedule is sought. At the same time it is necessary to ensure that the total production costs of each individual product will not exceed a certain pre-determined value. Solutions for overall optimization of the schedule with respect to maximum profit per batch, or with respect to maximum return on the total cost of production of the batch, have already been published. This paper suggests a method for determining the economic batch-sizes when it is desirable to maximize the rate of return per unit time for a multi-product schedule.     

Coordinated supply chain management

Historically, the three fundamental stages of the supply chain, procurement, production and distribution, have been managed independently, buffered by large inventories. Increasing competitive pressures, and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive, these firms must reduce operating costs while continuously improving customer service. With recent advances in communications and information technology, as well as a rapidly growing array of logistics options, firms have an opportunity to reduce operating costs by coordinating the planning of these stages. In this paper, we review the literature addressing coordinated planning between two or more stages of the supply chain, placing particular emphasis on models that would lend themselves to a total supply chain model. Finally, we suggest directions for future research.     

A learning curve model with knowledge depreciation

Empirical studies in several industries have verified that unit costs decline as organizations gain experience or knowledge in production, which is referred to as the learning curve effect. In the past two decades, there has also been analytical work on the relationship between a firm's learning curve effects and its pricing and output decisions. Learning rates differ significantly across firms in the same industry and recent empirical evidence has shown that knowledge depreciation may be an important reason for these differences. We propose and analyze a learning curve model with knowledge depreciation and provide several new insights. First, we show that there exists a steady state where knowledge level and unit cost remain constant over time and there exists an optimal path to this steady state. Many empirical researchers have observed this ‘plateau’ phenomenon, whereby unit costs decline but reach saturation after some time. While this has been traditionally modeled exogenously in the learning curve literature by assuming that cost reduction stops at some level of knowledge through a convex, decreasing unit cost function, we provide an alternative endogenous explanation. We are also able to show that, unlike in the model without knowledge depreciation, the production rate along the optimal path to the steady state may decrease over time. Also, the knowledge level along the optimal path may actually decline over time. Finally, we show that the optimal production rate decreases at higher interest rates and increases at higher knowledge depreciation rates. In turn, this implies that a high interest rate environment discourages firms from achieving high knowledge levels and results in higher prices. On the other hand, higher knowledge depreciation rates result in higher production rates and lower prices.     

The Optimization of Paper Machine Scheduling

A method for determining schedules in a paper mill is described and applied to real production data. The problem is to allocate products to machines and determine their sequence so as to minimize total costs. The costs include production losses through set-ups, stock holding and penalties for delayed delivery. The method is based on a permutation representation of the production schedule and the determination of a locally optimal solution. A permutation is locally optimal if it cannot be improved by transferring groups of products to a different place in the schedule. The method has been programmed and the results show a considerable improvement over alternative manual procedures. The paper has been written in two parts, the first dealing with the development of the method and the second discussing its application to a specific problem.     

Tactical level planning in float glass manufacturing with co-production,random yields and substitutable products

We investigate tactical level planning problems in float glass manufacturing. Float glass manufacturing is a process that has some unique properties such as uninterruptible production, random yields, partially controllable co-production compositions, complex relationships in sequencing of products, and substitutable products. Furthermore, changeover times and costs are very high, and production speed depends significantly on the product mix. These characteristics render measurement and management of the production capacity difficult. The motivation for this study is a real life problem faced at Trakya Cam in Turkey. Trakya Cam has multiple geographically separated production facilities. Since transportation of glass is expensive, logistics costs are high. In this paper, we consider multi-site aggregate planning, and color campaign duration and product mix planning. We develop a decision support system based on several mixed integer linear programming models in which production and transportation decisions are made simultaneously. The system has been fully implemented, and has been in use at Trakya Cam since 2005.     

MIP models for production lot sizing problems with distribution costs and cargo arrangement

This study presents mixed integer programming (MIP) models for production lot sizing problems with distribution costs using unit load devices such as pallets and containers. Problems that integrate production lot sizing decisions and loading of the products in vehicles (bins) are also modelled, in which constraints such as weight limits, volume restrictions or the value of the cargo loaded in the bins are considered. In general, these problems involve a trade-off between production, inventory and distribution costs. Lot sizing decisions should take into account production capacity and product demand constraints. Distribution decisions are related to the loading and transport of products in unit load devices. The MIP models are solved by the branch-and-cut method of an optimization package and the results show that these approaches have the potential to address different practical situations.     

Two aspects of optimal diet determination for pig production: efficiency of solution and incorporation of cost variation

For many decades linear programming has been used to find minimum cost diets, notably in the chicken and pig meat industries. More recently, animal growth models together with nonlinear optimisation methods have been used to find feeding schedules which simultaneously minimise feed costs and maximise market return, so maximising gross margin. Genetic algorithms can handle these problems, albeit slowly. In this paper we study the particular nature of the objective function (for pig meat production) and develop a global optimisation algorithm tailored to its discontinuous structure. We also demonstrate the use of stochastic programming to cope with changing feed costs and changing price at slaughter.     

Environmental integrated production and recycling management

Environmental integrated production and recycling planning is of great importance for the competitive position of production enterprises. Due to increasing disposal costs for industrial byproducts and waste as well as stronger emission standards, companies will be required to set up and control advanced, environmental friendly production technologies, so that emissions and byproducts will be reduced drastically. Nonavoidable byproducts and used products at the end of their lifetime have to be recycled by the producers. The complexity of the resulting decision problems requires adequate operations research methods. The following paper deals with the development of sophisticated operations research models for two selected planning problems: recycling of industrial byproducts and dismantling and recycling of products at the end of their lifetime. The models have been applied successfully to large industrial problems in practice in the fields of recycling of demolition waste in a German-French region and byproduct management in the steel industry. The presentations of these two applications follow a case study point of view.     

A two-bottleneck system with binomial yields and rigid demand

This study considers multistage production systems where production is in lots and only two stages have non-zero setup costs. Yields are binomial and demand, needing to be satisfied in its entirety, is “rigid”. We refer to a stage with non-zero setup cost as a “bottleneck” (BN) and thus to the system as “a two-bottleneck system” (2-BNS). A close examination of the simplest 2-BNS reveals that costs corresponding to a particular level of work in process (WIP) depend upon costs for higher levels of WIP, making it impossible to formulate a recursive solution.For each possible configuration of intermediate inventories a production policy must specify at which stage to produce next and the number of units to be processed. We prove that any arbitrarily “fixed” production policy gives rise to a finite set of linear equations, and develop algorithms to solve the two-stage problem. We also show how the general 2-BNS can be reduced to a three-stage problem, where the middle stage is a non-BN, and that the algorithms developed can be modified to solve this problem.