A spreadsheet-based optimization model for the integrated problem of producing and distributing a major weekly newsmagazine

In this paper, we address the problem of producing and distributing the Brazilian newsmagazine Época, a major weekly publication with one of the 10 largest circulations in the world. This real-world problem had been puzzling magazine publishers in Brazil and remained unsolved for many years. We propose an innovative mixed-integer-linear programming model to determine the number and location of the industrial facilities that should produce the magazines, what destinations should be assigned to each selected facility; the production sequencing and the modes of transportation (air or truck). Our model aims to minimize the total cost while adhering to production capacity and time constraints. The model was implemented in an electronic spreadsheet environment and yielded a savings of 7.1% of the total costs. Given that despite their huge popularity, little has been written on the issues of implementing full-scale optimization models in spreadsheets; thus we also provide the details of the model’s implementation in Excel.     

A two-echelon inventory model for a deteriorating item with stock-dependent demand,partial backlogging and capacity constraints

This study investigates a two-echelon supply chain model for deteriorating inventory in which the retailer’s warehouse has a limited capacity. The system includes one wholesaler and one retailer and aims to minimise the total cost. The demand rate in retailer is stock-dependent and in case of any shortages, the demand is partially backlogged. The warehouse capacity in the retailer (OW) is limited; therefore the retailer can rent a warehouse (RW) if needed with a higher cost compared to OW. The optimisation is done from both the wholesaler’s and retailer’s perspectives simultaneously. In order to solve the problem a genetic algorithm is devised. After developing a heuristic a numerical example together with sensitivity analysis are presented. Finally, some recommendations for future research are presented.     

A comprehensive analytical approach for policy analysis of system dynamics models

Formal tools to link system dynamics model’s structure to the system modes of behavior have recently become available. In this paper, we aim to expand the use of these tools to perform the model’s policy analysis in a more structured and formal way than the exhaustive exploratory approaches used to date. We consider how a policy intervention (a parameter change) affects a particular behavior mode by affecting the gains of particular feedback loops as well as how it affects the presence of that mode in the variable of interest. The paper demonstrates the utility of considering both of these aspects since the analysis provides an assessment of the overall impact of a policy on a variable and explains why the impact occurs in terms of structural changes in the model. Particularly in the context of larger models, this method enables a much more efficient search for leverage policies, by ranking the influence of each model parameter without the need for multiple simulation experiments.     

A deterministic resource scheduling model in epidemic control: A case study

The resources available to tackle an epidemic infection are usually limited, while the time and effort required to control it are increasing functions of the starting time of the containment effort. The problem of scheduling limited available resources, when there are several areas where the population is infected, is considered. A deterministic model, appropriate for large populations, where random interactions can be averaged out, is used for the epidemic’s rate of spread. The problem is tackled using the concept of deteriorating jobs, i.e. the model represents increasing loss rate as more susceptibles become infected, and increasing time and effort needed for the epidemic’s containment. A case study for a proposed application of the model in the case of the mass vaccination against A(H1N1)_v influenza in the Attica region, Greece and a comparative study of the model’s performance vs. the applied random practice are presented.     

Using Bayesian networks for bankruptcy prediction: Some methodological issues

This study provides operational guidance for building naïve Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naïve Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naïve Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of first-order variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model’s performance. Our results show that the model’s performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model’s performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction.     

A fuzzy stochastic model for telecommunications bandwidth brokers under probabilistic QoS measures

In this paper, we model and solve profit maximization problem of a telecommunications Bandwidth Broker (BB) under uncertain market and network infrastructure conditions. The BB may lease network capacity from a set of Backbone Providers (BPs) or from other BBs in order to gain profit by leasing already purchased capacity to end-users. BB’s problem becomes harder to deal with when bandwidth requests of end-users, profit and cost margins are not known in advance. The novelty of the proposed work is the development of a mechanism via combining fuzzy and stochastic programming methodologies for solving complex BP selection and bandwidth demand allocation problem in communication networks, based on the fact that information needed for making these decisions is not available prior to leasing capacity. In addition, suggested model aims to maximize BB’s decision maker’s satisfaction ratio rather than just profit. As a solution strategy, the resulting fuzzy stochastic programming model is transformed into deterministic crisp equivalent form and then solved to optimality. Finally, the numerical experiments show that on the average, proposed approach provides 14.30% more profit and 69.50% more satisfaction ratio compared to deterministic approaches in which randomness and vagueness in the market and infrastructure are ignored.     

Technology selection and capacity investment under uncertainty

We analyze the problem of technology selection and capacity investment for electricity generation in a competitive environment under uncertainty. Adopting a Nash-Cournot competition model, we consider the marginal cost as the uncertain parameter, although the results can be easily generalized to other sources of uncertainty such as a load curve. In the model, firms make three different decisions: (i) the portfolio of technologies, (ii) each technology’s capacity and (iii) the technology’s production level for every scenario. The decisions related to the portfolio and capacity are ex-ante and the production level is ex-post to the realization of uncertainty. We discuss open and closed-loop models, with the aim to understand the relationship between different technologies’ cost structures and the portfolio of generation technologies adopted by firms in equilibrium. For a competitive setting, to the best of our knowledge, this paper is the first not only to explicitly discuss the relation between costs and generation portfolio but also to allow firms to choose a portfolio of technologies. We show that portfolio diversification arises even with risk-neutral firms and technologies with different cost expectations. We also investigate conditions on the probability and cost under which different equilibria of the game arise.     

Optimal enlistment age: A cost–benefit analysis and some simulations

Enlistment at the earliest viable age maximizes the country’s wartime army size and thereby the country’s attack-deterrence capacity. Injuries and death generate a loss of quantity and quality of life that reduces the benefit from early-age enlistment. The benefit from any age of recruitment is also affected by the rise and decline of the individual’s military performance and civilian productivity and by the changes in the individual’s adjustment costs over his lifecycle. The simulations of an optimization model incorporating these cost and benefit elements suggest that if the intensity of the rise and decline of the individual’s military performance is sufficiently larger than the intensity of the rise and decline of his civilian productivity, there exists an interior optimal enlistment age that is greater than the commonly practiced 18. In such a case, most of the simulation results are closely scattered around 21 despite large parameter changes.     

Two-stage production scheduling with an outsourcing option

This paper considers a two-stage production scheduling problem in which each activity requires two operations to be processed in stages 1 and 2, respectively. There are two options for processing each operation: the first is to produce it by utilizing in-house resources, while the second is to outsource it to a subcontractor. For in-house operations, a schedule is constructed and its performance is measured by the makespan, that is, the latest completion time of operations processed in-house. Operations by subcontractors are instantaneous but require outsourcing cost. The objective is to minimize the weighted sum of the makespan and the total outsourcing cost. This paper analyzes how the model’s computational complexity changes according to unit outsourcing costs in both stages and describes the boundary between NP-hard and polynomially solvable cases. Finally, this paper presents an approximation algorithm for one NP-hard case.     

Horizontal coordinating contracts in the semiconductor industry

Integrated device manufacturers (IDMs) and foundries are two types of manufacturers in the semiconductor industry. IDMs integrate both design and manufacturing functions whereas foundries solely focus on manufacturing. Since foundries often have cost advantage over IDMs due to their specialization and economies of scale, IDMs have incentives to source from foundries for the purpose of avoiding excessive capacity investment risk. As the IDM is also a potential capacity source, the IDM and foundry are in a horizontal setting rather than a purely vertical setting. In the absence of sophisticated contracts, the benchmark contract for the IDM and foundry is a wholesale price contract. We define “coordinating” contracts as those that improve both the IDM’s and foundry’s expected profits over the benchmark wholesale price contract and also lead to the maximum system profit. This paper examines if there exist coordinating capacity reservation contracts. It is found that wholesale price contracts in the horizontal setting cannot achieve the maximum system profit due to either double marginalization effect, or “misalignment of capacity-usage-priority”. In contrast, if the IDM’s capacity investment risk is not too low, there always exist coordinating capacity reservation contracts. Furthermore, under coordinating contracts, the IDM’s sourcing structure, either sole sourcing from the foundry or dual sourcing, is contingent on the firms’ cost structures.     

A hidden Markov regime-switching model for option valuation

We investigate two approaches, namely, the Esscher transform and the extended Girsanov’s principle, for option valuation in a discrete-time hidden Markov regime-switching Gaussian model. The model’s parameters including the interest rate, the appreciation rate and the volatility of a risky asset are governed by a discrete-time, finite-state, hidden Markov chain whose states represent the hidden states of an economy. We give a recursive filter for the hidden Markov chain and estimates of model parameters using a filter-based EM algorithm. We also derive predictors for the hidden Markov chain and some related quantities. These quantities are used to estimate the price of a standard European call option. Numerical examples based on real financial data are provided to illustrate the implementation of the proposed method.     

Asset-selling problem with an uncertain deadline,quitting offer,and search skipping option

This paper presents a discrete-time sequential stochastic asset-selling problem with an infinite planning horizon, where the process of selling the asset may reach a deadline at any point in time with a probability. It is assumed that a quitting offer is available at every point in time and search skipping is permitted. Thus, decisions must be made as to whether or not to accept the quitting offer, to accept an appearing buyer’s offer, and to conduct a search for a buyer. The main purpose of this paper is to clarify the properties of the optimal decision rules in relation to the model’s parameters.     

Multicriteria sorting using a valued indifference relation under a preference disaggregation paradigm

Most multicriteria decision methods need the definition of a significant amount of preferential information from a decision agent. The preference disaggregation analysis paradigm infers the model’s parameter values from holistic judgments provided by a decision agent. Here, a new method for inferring the parameters of a fuzzy outranking model for multicriteria sorting is proposed. This approach allows us to use most of the preferential information contained in a reference set. The central idea is to characterize the quality of the model by measuring discrepancies and concordances amongst (i) the preference relations derived from the outranking model, and (ii) the preferential information contained in the reference set. The model’s parameters are inferred from a multiobjective optimization problem, according to some additional preferential information from a decision agent. Once the model has been fitted, sorting decisions about new objects are performed by using a fuzzy indifference relation. This proposal performs very well in some examples.     

A discrete model for optimal operation of fossil-fuel generators of electricity

This paper presents a new discrete approach to the price-based dynamic economic dispatch (PBDED) problem of fossil-fuel generators of electricity. The objective is to find a sequence of generator temperatures that maximizes profit over a fixed-length time horizon. The generic optimization model presented in this paper can be applied to automatic operation of fossil-fuel generators or to prepare market bids, and it works with various price forecasts. The model’s practical applications are demonstrated by the results of simulation experiments involving 2009 NYISO electricity market data, branch-and-bound, and tabu-search optimization techniques.     

Analyzing online B2B exchange markets: Asymmetric cost and incomplete information

This research applies the discriminating auction to analyze the online B2B exchange market in which a single buyer requests multiple items and several suppliers having equal capacity and asymmetric cost submit bids to compete for buyer demand. In the present model, we examine the impact of asymmetric cost and incomplete information on the participants in the market. Given the complete cost information, each supplier randomizes its price and the lower bound of the price range is determined by the highest marginal cost. In addition, the supplier with a lower marginal cost has a larger considered pricing space but ultimately has a smaller equilibrium one than others with higher marginal costs. When each supplier’s marginal cost is private information, the lowest possible price is determined by the number of suppliers and the buyer’s reservation price. Comparing these two market settings, we find whether IT is beneficial to buyers or suppliers depends on the scale of the bid process and the highest marginal cost. When the number of suppliers and the difference between the highest marginal cost and the buyer’s reservation price are sufficiently large, each supplier can gain a higher profit if the marginal costs are private information. On the contrary, when the highest marginal cost approaches the buyer’s reservation price, complete cost information benefits the suppliers.     

Make-or-buy service capacity decision in a supply chain providing after-sales service

We consider a supply chain comprising a manufacturer and a retailer. The manufacturer supplies a product to the retailer, while the retailer sells the product bundled with after-sales service to consumers in a fully competitive market. The sales volume is affected by the retailer’s service-level commitment. The retailer can build service capacity in-house at a deterministic price before service demand is realized, or buy the service from an outsourcing market at an uncertain price after service demand realization. We find that the outsourcing market encourages the retailer to make a higher level of service commitment, while prompting the manufacturer to reduce the wholesale price, resulting in more demand realization. We analyze how the expected cost of the service in the outsourcing market and the retailer’s risk attitude affect the decisions of both parties. We derive the conditions under which the retailer is willing to build service capacity in-house and under which it will buy the service from the outsourcing market. Moreover, we find that the manufacturer’s sharing with the retailer the cost to build service capacity improves the profits of both parties.     

Identifying winners of competitive events: A SVM-based classification model for horserace prediction

The aim of much horserace modelling is to appraise the informational efficiency of betting markets. The prevailing approach involves forecasting the runners’ finish positions by means of discrete or continuous response regression models. However, theoretical considerations and empirical evidence suggest that the information contained within finish positions might be unreliable, especially among minor placings. To alleviate this problem, a classification-based modelling paradigm is proposed which relies only on data distinguishing winners and losers. To assess its effectiveness, an empirical experiment is conducted using data from a UK racetrack. The results demonstrate that the classification-based model compares favourably with state-of-the-art alternatives and confirm the reservations of relying on rank ordered finishing data. Simulations are conducted to further explore the origin of the model’s success by evaluating the marginal contribution of its constituent parts.     

A new model for estimating mass transfer coefficients for the extraction of ethanethiol with alkaline solutions in packed columns

The mathematical model for estimating the mass transfer coefficients for the ethanethiol extraction from gasoline, with alkaline solutions was established by processing the data obtained in a laboratory and in a pilot plant experiment. It allows the estimation of overall mass transfer coefficients, taking into account the dispersed phase velocity, the concentration of NaOH solution, the specific area and the porosity of the packing. The model’s parameters were statistical tested in order to confirm the accuracy of the model.     

Dual role of price and myopia in a marketing channel

In this paper we study a dynamic two-player channel where the manufacturer controls the wholesale price and the investment in quality and the retailer chooses the retail price. We consider that the retail price affects both the demand and the perceived quality of the brand and that its variations contribute to the building of an internal reference price. One of the model’s distinctive features is that it accounts for the two meanings of price, i.e., its classical objective measure of the cost of acquiring a particular quantity of the product, and its subjective roles as an assessment of the quality of the product and an evaluation of gains or losses (deal vs. sacrifice) resulting from buying a “cheap” or an “expensive” product. This dual computation is done with respect to the internal reference price.     

Evaluating dredging and offshore loading locations for U.S. coal exports using the local logistics system

The formulation and use of a mixed integer mathematical programming location-allocation model, the Coal Logistics System (COLS), is presented in this study. COLS is used to evaluate the potential for reducing water-borne coal transportation costs, and concomitantly the costs of delivering coal to European markets. This cost reduction is accomplished through the use of supercolliers which would require the dredging of channels at selected ports or the use of offshore loading sites at East and Gulf Coast ports or both. The model developed and the analysis presented in this paper are intended to aid in the determination of the location and extent of these activities, and to indicate the size of the potential reduction in the costs of U.S. export coal. In order to be able to accommodate these deeper draft vessels at East and Gulf Coast ports, expensive improvements would need to be undertaken which may include the deepening of harbor channels to the depths required for 120 000 dead weight ton (dwt) or larger supercolliers. Since dredging requires large initial investments and has significant long-term maintenance costs, excess capacity could represent an inefficient use of both U.S. revenues and the ports' own funds. The use of offshore loading moorings to permit the topping off of supercolliers by self-unloading colliers at the deepwater sections of harbor channels has been proposed as a way of reducing trans-ocean transportation costs and avoiding the large investments and time delays associated with dredging activities. The Coal Logistics System was modified and extended to enable the evaluation of these two port improvement options to be undertaken in a consistent and realistic manner.