Screening location strategies to reduce exchange rate risk

A firm that markets its products and services world-wide may face significant financial risk due to exchange rate fluctuations unless it maintains an international sourcing/production network. Thus, the firm must evaluate various sourcing/production network designs. The selection of the appropriate design is not straight-forward since decisions are made in an environment of uncertainty and involve multiple time periods. We propose a two-phase approach to screen various alternative designs (configurations) utilizing a number of criteria, and illustrate it using a popular Harvard Business School case. As by-products, we introduce a new criterion for making a pairwise stochastic comparison of alternatives and demonstrate the value of maintaining excess geographically-dispersed production capacity.     

Multi-period traffic routing in satellite networks

We introduce a traffic routing problem over an extended planning horizon that appears in geosynchronous satellite networks. Unlike terrestrial (e.g., fiber optic) networks, routing on a satellite network is not transparent to the customers. As a result, a route change is associated with significant monetary penalties that are usually in the form of discounts (up to 40%) offered by the satellite provider to the customer that is affected. The notion of these rerouting penalties requires the network planners to explicitly consider these penalties in their routing decisions over multiple time periods and introduces novel challenges that have not been considered previously in the literature. We develop a branch-and-price-and-cut procedure to solve this problem and describe an algorithm for the associated pricing problem. Our computational work demonstrates that the use of a multi-period optimization procedure as opposed to a myopic period-by-period approach can result in cost reductions up to 13% depending on problem characteristics and network size considered. These cost reductions correspond to potential savings of several hundred million dollars for large satellite providers.     

Time-dependent discrete road network design with both tactical and strategic decisions

This paper aims to model and investigate the discrete urban road network design problem, using a multi-objective time-dependent decision-making approach. Given a base network made up with two-way links, candidate link expansion projects, and candidate link construction projects, the problem determines the optimal combination of one-way and two-way links, the optimal selection of capacity expansion projects, and the optimal lane allocations on two-way links over a dual time scale. The problem considers both the total travel time and the total CO emissions as the two objective function measures. The problem is modelled using a time-dependent approach that considers a planning horizon of multiple years and both morning and evening peaks. Under this approach, the model allows determining the sequence of link construction, the expansion projects over a predetermined planning horizon, the configuration of street orientations, and the lane allocations for morning and evening peaks in each year of the planning horizon. This model is formulated as a mixed-integer programming problem with mathematical equilibrium constraints. In this regard, two multi-objective metaheuristics, including a modified non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective B-cell algorithm, are proposed to solve the above-mentioned problem. Computational results for various test networks are also presented in this paper.     

MANAGING THE CLIMATE RENT: HOW CAN REGULATORS IMPLEMENT INTERTEMPORALLY EFFICIENT MITIGATION POLICIES?

This paper provides a formal framework to analyze informational and commitment requirements of several intertemporal price and quantity instruments for mitigating global warming. We ask under what conditions and to what extend the regulator can shift the complex and daunting intertemporal optimization of fossil resource use to markets. Mitigation always generates an intertemporal climate rent which reflects the stock‐dependent damages and emerging scarcities of the atmospheric carbon deposit. In order to calculate and to manage this climate rent appropriately, common policy instruments like Pigouvian taxes or emissions trading presume perfect information about resource demand, extraction costs, reserve sizes, and damages for the entire planning horizon. To reduce these informational requirements we develop an alternative policy approach—a state dependent tax rule—that relies only on current observations of cumulative extraction (or atmospheric carbon concentration). Within a cost–benefit analysis, this instrument is capable to shift the complex intertemporal optimization problem completely to the resource sector when resource owners are homogeneous. Under a cost‐effective carbon budget approach, emissions trading with banking and borrowing can also unburden the regulator from solving the intertemporal social planner optimization problem. Additionally, we discuss which instruments can obtain an optimal allocation even if resource owners employ discount rate mark‐ups (i.e., due to imperfect commitment or insecure property rights). While an emissions trading scheme without banking and borrowing is robust against discount rate mark‐ups, resource taxes have to be modified in order to achieve an optimal allocation.     

A scenario-based stochastic model for supplier selection in global context with multiple buyers,currency fluctuation uncertainties,and price discounts

Suppliers network in the global context under price discounts and uncertain fluctuations of currency exchange rates have become critical in today’s world economy. We study the problem of suppliers’ selection in the presence of uncertain fluctuations of currency exchange rates and price discounts. We specifically consider a buyer with multiple sites sourcing a product from heterogeneous suppliers and address both the supplier selection and purchased quantity decision. Suppliers are located worldwide and pricing is offered in suppliers’ local currencies. Exchange rates from the local currencies of suppliers to the standard currency of the buyer are subject to uncertain fluctuations overtime. In addition, suppliers offer discounts as a function of the total quantity bought by the different customer’ sites over the time horizon irrespective of the quantity purchased by each site.     

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms.     

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.     

Robust delay-constrained routing in telecommunications

In telecommunications, operators usually use market surveys and statistical models to estimate traffic evolution in networks or to approximate queuing delay functions in routing strategies. Many research activities concentrated on handling traffic uncertainty in network design. Measurements on real world networks have shown significant errors in delay approximations, leading to weak management decisions in network planning. In this work, we introduce elements of robust optimization theory for delay modeling in routing problems. Different types of data uncertainty are considered and linked to corresponding robust models. We study a special case of constraints featuring separable additive functions. Specifically, we consider that each term of the sum is disturbed by a random parameter. These constraints are frequent in network based problems, where functions reflecting real world measurements on links are summed up over end-to-end paths. While classical robust formulations have to deal with the introduction of new variables, we show that, under specific hypotheses, the deterministic robust counterpart can be formulated in the space of original variables. This offers the possibility of constructing tractable robust models. Starting from Soyster’s conservative model, we write and compare different uncertainty sets and formulations offering each a different protection level for the delay constrained routing problem. Computational experiments are developed in order to evaluate the “price of robustness” and to assess the quality of the new formulations.     

A Neural Network Application in Personnel Scheduling

In this paper, we present and evaluate a neural network model for solving a typical personnel-scheduling problem, i.e. an airport ground staff rostering problem. Personnel scheduling problems are widely found in servicing and manufacturing industries. The inherent complexity of personnel scheduling problems has normally resulted in the development of integer programming-based models and various heuristic solution procedures. The neural network approach has been admitted as a promising alternative to solving a variety of combinatorial optimization problems. While few works relate neural network to applications of personnel scheduling problems, there is great theoretical and practical value in exploring the potential of this area. In this paper, we introduce a neural network model following a relatively new modeling approach to solve a real rostering case. We show how to convert a mixed integer programming formulation to a neural network model. We also provide the experiment results comparing the neural network method with three popular heuristics, i.e. simulated annealing, Tabu search and genetic algorithm. The computational study reveals some potential of neural networks in solving personnel scheduling problems.     

Metaheuristic hybridizations for the regenerator placement and dimensioning problem in sub-wavelength switching optical networks

Physical layer impairments severely limit the reach and capacity of optical systems, thereby hampering the deployment of transparent optical networks (i.e., no electrical signal regenerators are required). Besides, the high cost and power-consumption of regeneration devices makes it unaffordable for network operators to consider the opaque architecture (i.e., regeneration is available at every network node). In this context, translucent architectures (i.e., regeneration is only available at selected nodes) have emerged as the most promising short term solution to decrease costs and energy consumption in optical backbone networks. Concurrently, the coarse granularity and inflexibility of legacy optical technologies have re-fostered great interest in sub-wavelength switching optical networks, which introduce optical switching in the time domain so as to further improve resources utilization. In these networks, the complex regenerator placement and dimensioning problem emerges. In short, this problem aims at minimizing the number of electrical regenerators deployed in the network. To tackle it, in this paper both a greedy randomized adaptive search procedure and a biased random-key genetic algorithm are developed. Further, we enhance their performance by introducing both path-relinking and variable neighborhood descent as effective intensification procedures. The resulting hybridizations are compared among each other as well as against results from optimal and heuristic mixed integer linear programming formulations. Illustrative results over a broad range of network scenarios show that the biased random-key genetic algorithm working in conjunction with these two intensification mechanisms represents a compelling network planning algorithm for the design of future sub-wavelength optical networks.     

Simultaneity and non‐linear variability in financial markets: simulation and forecasting

Non‐linear variability in financial markets can emerge from several mechanisms, including simultaneity and time‐varying coefficients. In simultaneous equation systems, the reduced‐form coefficients that determine the behaviour of jointly dependent variables are products and ratios of the original structural coefficients. If the coefficients are stochastic, the resulting multiplicative interactions will result in high degrees of non‐linearity. Processes generated in this way will scale as fractals: they will exhibit intermittent outliers and scaling symmetries, i.e. proportionality relationships between fluctuations at different separation distances. A model is specified in which both the exchange rate itself and the exchange rate residual exhibit simultaneity. The exchange rate depends on other exchange rates, while the residual depends on the other residuals. The model is then simulated using embedding noise from a t‐distribution. The simulations replicate the observed properties of exchange rates, heavy‐tailed distributions and long memory in the variance. A forecasting algorithm is specified in two stages. The first stage is a model for the actual process. In the second stage the residuals are modelled as a function of the predicted rate of change. The first and second stage models are then combined. This algorithm exploits the scaling symmetry: the residual is proportional to the predicted rate of change at separation distances corresponding to the forecast horizon. The procedure is tested empirically on three exchange rates. At a daily frequency and a 1‐day forecast horizon, two‐stage models reduce the forecast error by one fourth. At a 5‐day horizon, the improvement is 10–15 percent. At a weekly frequency, the improvement at the 1‐week horizon is on the order of 30–40 percent. Copyright © 2006 John Wiley & Sons, Ltd.     

Models for planning capacity expansion in local access telecommunication networks

The rapid progress of communications technology has created new opportunities for modeling and optimizing the design of local telecommunication systems. The complexity, diversity, and continuous evolution of these networks pose several modeling challenges. In this paper, we present an overview of the local telephone network environment, and discuss possible modeling approaches. In particular, we (i) discuss the engineering characteristics of the network, and introduce terminology that is commonly used in the communications industry and literature; (ii) describe a general local access network planning model and framework, and motivate different possible modeling assumptions; (iii) summarize various existing planning models in the context of this framework; and (iv) describe some new modeling approaches. The discussion in this paper is directed both to researchers interested in modeling local telecommunications systems and to planners interested in using such models. Our goal is to present relevant aspects of the engineering environment for local access telecommunication networks, and to discuss the relationship between engineering issues and the formulation of economic decision models. We indicate how changes in the underlying switching and transmission technology affect the modeling of the local telephone network. We also review various planning issues and discuss possible optimization approaches for treating them.This research was initiated through a grant from GTE Laboratories, IncorporatedSupported in part by an AT&T research award.Supported in part by Grant No. ECS-8316224 from the Systems Theory and Operations Research Program of the National Science Foundation.     

Multistage stochastic programming in strategic telecommunication network planning

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.     

Formation of a strategic manufacturing and distribution network with transfer prices

We propose a profit maximization model for the decision support system of a firm that wishes to establish or rationalize a multinational manufacturing and distribution network to produce and deliver finished goods from sources to consumers. The model simultaneously evaluates all traditional location factors in a manufacturing and distribution network design problem and sets intra-firm transfer prices that take account of tax and exchange rate differentials between countries. Utilizing the generalized Benders decomposition approach, we exploit the partition between the product flow and the cash allocation (i.e., the pricing and revenue assignment) decisions in the supply chain to find near optimal model solutions. Our proposed profit maximizing strategic planning model produces intuitive results. We offer computational experiments to illustrate the potential valuable guidance the model can provide to a firm's supply chain design strategic planning process.     

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.     

A multi-period bank portfolio management program

We describe a bank portfolio management program based on the complete Markowitz model, which explicitly treats risk due to unanticipated fluctuations in interest rate. Our program takes into account both inter-temporal and intra-temporal covariance. The major result of this approach is that, for the same expected return, our model yields a portfolio with significantly smaller risk than that determined by an index model. For the same risk level, our method yields a portfolio with higher expected yield. The model employs a rolling planning horizon, with time periods in the planning horizon of arbitrary length. A novelty in the model is that it permits inter-temporal transactions in the portfolio's securities by generating dummy securities to represent every possible transaction over the planning horizon. The output from the model consists of a list of portfolio strategies showing the expected after-tax return and the 1% worst case yield for each strategy. We also present an illustrative example, using real data from a large Pennsylvania bank, and compare the results from our model to the simpler variance-only and index models. The principles upon which the model is based are sufficiently general to allow the program to be expanded into a general asset-liability balance sheet management program.     

Asset allocation under threshold autoregressive models

We discuss the asset allocation problem in the important class of parametric non‐linear time series models called the threshold autoregressive model in (J. Roy. Statist. Soc. Ser. A 1977; 140 :34–35; Patten Recognition and Signal Processing. Sijthoff and Noordhoff: Netherlands, 1978; and J. Roy. Statist. Soc. Ser. B 1980; 42 :245–292). We consider two specific forms, one self‐exciting (i.e. the SETAR model) and the other smooth (i.e. the STAR) model developed by Chan and Tong (J. Time Ser. Anal. 1986; 7 :179–190). The problem of maximizing the expected utility of wealth over a planning horizon is considered using a discrete‐time dynamic programming approach. This optimization approach is flexible enough to deal with the optimal asset allocation problem under a general stochastic dynamical system, which includes the SETAR model and the STAR model as particular cases. Numerical studies are conducted to demonstrate the practical implementation of the proposed model. We also investigate the impacts of non‐linearity in the SETAR and STAR models on the optimal portfolio strategies. Copyright © 2011 John Wiley & Sons, Ltd.     

Urban rapid transit network capacity expansion

This paper examines a multi-period capacity expansion problem for rapid transit network design. The capacity expansion is realized through the location of train alignments and stations in an urban traffic context by selecting the time periods. The model maximizes the public transportation demand using a limited budget and designing lines for each period. The location problem incorporates the user decisions about mode and route. The network capacity expansion is a long-term planning problem because the network is built over several periods, in which the data (demand, resource price, etc.) are changing like the real problem changes. This complex problem cannot be solved by branch and bound, and for this reason, a heuristic approach has been defined in order to solve it. Both methods have been experimented in test networks.     

Short-term electricity procurement: A rolling horizon stochastic programming approach

This paper addresses the problem faced by a large electricity consumer in determining the optimal procurement plan over a short-term time horizon. The inherent complexity of the problem, due to its dynamic and stochastic nature, is dealt by means of the stochastic programming modeling framework. In particular, a two-stage problem is formulated with the aim of establishing the optimal amount of electricity to be purchased through bilateral contracts and in the Day-Ahead Electricity Market. Recourse actions are used to hedge against uncertainty related to future electricity prices and consumer’s needs. The optimal plan is defined so to minimize the overall cost and to control risk, which is measured in the form of violation of budget constraints. The stochastic model is dynamically solved in a rolling horizon fashion by iteratively considering more and more recent information and a planning horizon of decreasing length. Extensive numerical experiments have been carried out to assess the performance of the proposed dynamic decision approach. The results collected considering a real test case are very encouraging and provide evidence of the superiority of the approach also in comparison with other alternative procurement strategies.     

Pavement rehabilitation scheduling and toll pricing under different regulatory regimes

This paper addresses the highway pavement rehabilitation scheduling and toll pricing issues over a planning horizon. In the highway system concerned, two types of agents are considered, namely highway operator and road users. Two models, which account for different highway regulatory regimes (i.e. public and private), are proposed. In the public regulatory model, the government aims to maximize total discounted social welfare of the transportation system over the planning horizon by determining the optimal pavement rehabilitation schedule and toll level. In the private regulatory regime, a profit-driven private operator seeks to optimize the pavement rehabilitation schedule and toll level to maximize its own discounted net profit over the planning horizon. The proposed models treat the interactions between the highway operator and the road users in the system as a bi-level hierarchical problem in which the upper level is a multi-period pavement rehabilitation scheduling and toll pricing problem, while the lower level is a multi-period route choice equilibrium problem. A heuristic solution algorithm that combines a greedy approach and a sensitivity analysis based approach is developed to solve the proposed bi-level multi-period optimization models. An illustrative example is used to show the applications of the proposed models. The findings show that the highway regulatory regime, pavement deterioration parameter and the roughness-induced vehicle operating cost can significantly affect the pavement rehabilitation schedules and the toll level as well as the performance of transportation system in terms of total life-cycle travel demand, net profit and social welfare.