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The relevance of multiple criteria decision-making/aiding is reinforced by the prominence of these methods in a wide range of applications. Whether by solving problems with a single decision-maker (DM) or a group of DMs, additive modelling, based on value or utility functions, is the most traditional. However, applying this kind of method raises a critical issue: the difficulty in eliciting DM's preferences and recommending a decision. Actually, it is a hard task for the DM to provide complete information regarding their preferences, because the DM may not be able to provide such information in the detailed way required, or even they may not be willing to do so. From this perspective, the emergence and growth of partial (incomplete or imprecise) information-based methods is indicative that these are a useful way of guiding decision-making as they require less cognitive input from a DM. Thus, this paper systematically reviews the literature on multicriteria decision methods that deal with partial information, focusing on the Multi-Attribute Value/Utility Theory context. Strategic research questions guide a bibliometric and content analysis of 105 peer-reviewed papers selected from the Web of Science (Main Collection). An integrated analysis of the results provides scholars, researchers and other professionals with a deeper comprehension of methodological advances and respective contributions, and of the main challenges and trends in this field of knowledge. Our analysis aims to show that when these methods are applied more reliable decision-making can be achieved. [ABSTRACT FROM AUTHOR]

The modelling of supply chain risk management (SCRM) has attracted increasing attention from researchers and professionals. However, a systematic network analysis of the literature to understand the development of research over time is lacking. Therefore, this study reviews SCRM modelling and its evolution as a scientific field. We collected 566 papers published in the Scopus database and shortlisted 120 for review. We have analysed the field's performance, mapped the most influential studies, as well as the generative and evolutionary research areas, and derived future research directions. Using bibliometric methods and tools for citation network analysis to understand the field's dynamic development, we find that five generative research areas provide the fundamental knowledge for four evolutionary research areas. The interpretation of gaps and trends in these areas provides an SCRM modelling timeline with 14 future research directions, which should consider adopting a holistic SCRM approach and developing prescriptive and normative risk models. The holistic approach enables more research on key factors—like process integration, design, information risk, visibility and risk coordination—that directly impact industry, decision-makers and sustainability needs. Risk models with evolved prescriptive and normative typology should respect both business model strategies and actual supply chain performance. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation (s), which would allow them to reach a given efficiency target. Since the early 2000s, inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability and supply chain management. In recent years, research has demonstrated the potential of inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in inverse DEA while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with inverse DEA models to address issues of infeasibility and nonlinearity. Moreover, applying inverse DEA to new sectors such as healthcare, agriculture and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach. [ABSTRACT FROM AUTHOR]

With outsourcing of work having become ubiquitous, and more importantly, given its potential to become controversial, the need for such outsourcing decisions to be drafted carefully, managed effectively and controlled accurately cannot be underscored. In this context, this paper has constructed a mathematical model of organizations with 'outsource' employees to study the problem of the monitoring and control of the extent of outsourcing, and the number and distribution of outsource manpower. Control policies for maintaining desired blends of internal and outsource manpower have been mathematically derived, thereby obviating the need for further statistical validation. The cost savings that could be expected to accrue due to outsourcing, as also the problem of optimal outsourcing have been investigated and illustrated with numerical examples. This paper thereby studies a problem of contemporary relevance and importance, and organizations could use the suggested models as a decision-making tool, to generate alternative trade-off scenarios between cost savings due to outsourcing on the one hand, and the need to restrict the extent of outsourcing on the other. [ABSTRACT FROM AUTHOR]

We highlight the state of the art in the eco-efficiency measurement using data envelopment analysis, including Malmquist–Luenberger productivity index. We also consider productivity change over time, provide directions for future studies in the field and gather the most recent policy suggestions for governments, organizations and sectors for reducing CO2 emissions. A structured literature search of the Web of Science academic database reveals 311 papers published between 1989 and 2022. We carry out network analysis of citations to show the evolution of the literature in this research topic. In doing so, we (a) examine the key-route main path of knowledge flows, (b) provide basic bibliometric information about the most active journals and authors, (c) conduct a qualitative in-depth analysis of the identified most important studies and (d) identify the research fronts and relate them to the emerging issues on the topic researched, focusing on the most recent period between 2000 and 2022. Based on the insights of the literature review, the second part of this paper critically analyses the papers on the key-route (main path) of this subject. This review can be used as guidance and a starting point for researchers and practitioners who want to further investigate optimal policies to reach NetZero. [ABSTRACT FROM AUTHOR]

Aspiring green agendas in conjunction with tremendous economic pressures are resulting in an increased attention to the environment and technological innovations for improving existing logistics systems. Last mile logistics, in particular, are becoming much more than a consumer convenience necessity and a transportation optimization exercise. Rather, this area presents a true opportunity to foster both financial and environmental sustainability. This paper investigates recent technological advancements and pending needs related to business and social innovations, emphasizing green logistics and city logistics concepts. We discuss various pertinent aspects, including drones, delivery robots, truck platooning, collection and pickup points, collaborative logistics, integrated transportation, decarbonization and advanced transport analytics. From a mathematical perspective, we focus on the basic features of the vehicle routing problem and some of its variants. We provide recommendations around strategies that may facilitate the adoption of new effective technologies and innovations. [ABSTRACT FROM AUTHOR]

Accepted by: Phil Scarf We model tactical changes in association football as a Markov game. The pitch is discretized into nine zones and the states of the Markov game are defined according to the zone in which the ball is located in-play, the team in possession and the score. We first model tactical changes in a Markov decision process framework, wherein one team maximizes their probability of winning. Then, we model tactical changes in a two-person zero-sum Markov game framework, wherein both teams maximize their probability of winning. Fundamental to our modelling is the notion that tactical changes impact upon transition rates. We verify the models using data from matches in a season of the Japan Professional Football League. We define a change in transition rates that can be realized by changes in tactics, and illustrate an example of optimal tactical changes when both teams can vary their tactics. The models we develop in the paper can support managers who are considering important decisions about substitutions and changes to formation, for example, when a match is in-play. [ABSTRACT FROM AUTHOR]

Accepted by: Aris Syntetos This paper examines the impact of temporary supply chain disruptions in a general equilibrium model with multiple stages of production for capital goods and a flexible time-to-build structure. Production disruptions at one or more stages result in declines in overall shipments and increases in the ratios of unfilled orders and work-in-process inventories to shipments. The model is calibrated to industries in durable goods manufacturing during the Covid-19 pandemic, and is shown to generate realistic dynamic responses to temporary production disruptions. Consistent with the data, an unanticipated 1-month disruption to upstream production results in a decline in shipments and an increase in the ratio of unfilled orders to shipments lasting more than 6 months. [ABSTRACT FROM AUTHOR]

Accepted by: Zied Babai Exponential smoothing in state space form (ETS) is a popular forecasting technique, widely used in research and practice. While the additive error ETS models have been well studied, the multiplicative error ones have received much less attention in forecasting literature. Still, these models can be useful in cases, when one deals with positive data, because they are supposed to work in such situations. Unfortunately, the classical assumption of normality for the error term might break this property and lead to non-positive forecasts on positive data. In order to address this issue, we propose using Log-Normal, Gamma and Inverse Gaussian distributions, which are defined for positive values only. We demonstrate what happens with ETS(M,*,*) models in this case, discuss conditional moments of ETS with these distribution and show that they are more natural for the models than the Normal one. We conduct the simulation experiments in order to study the bias introduced by point forecasts in these models and then compare the models with different distributions. We finish the paper with an example of application, showing how pure multiplicative ETS with a positive distribution works. [ABSTRACT FROM AUTHOR]

This paper derives an inspection policy for an economic production quantity (EPQ) model under the assumption that a process may produce non-conforming (NC) items. In various stages of a production process, a department receiving an order uses a single sampling inspection policy to detect NC items. Under such a policy, a lot is accepted if the number of NC items in the inspected sample is equal to or less than the acceptance number. The proposed model considers both EPQ- and quality-related costs. Moreover, economic production order quantity, sample size and acceptance number are considered decision variables. A numerical example is presented, and a set of sensitivity analysis are provided to highlight the effectiveness of the proposed model. The results reveal that when the inspection cost is high, the classical EPQ model achieves a lower expected total cost for the production system compared with the EPQ model with the inspection. In contrast, when the NC cost is high, the EPQ model with the inspection policy outperforms the classical EPQ model, which can significantly decrease the expected total cost. [ABSTRACT FROM AUTHOR]

This paper studies the situation in which a manufacturer acts as a dealer and buys a second-hand product from one customer and sells it with a two-dimensional warranty to another customer. The manufacturer plans to inspect the product during the warranty period to identify and remove hidden defects and hence prevent failure as well as their corresponding cost. We describe a two-dimensional delay time model in which the customer's usage rate affects both the time to defect and delay time. This concept is then used to derive a new class of failure models, namely, the two-dimensional and two-stage failure model. The number and time of inspections are determined to minimize the service cost during the warranty period under periodic and sequential inspection policies. Our approach is illustrated using an example from the automobile industry. A sensitivity analysis evaluates the effect of changes in the parameters of the model. Results indicate that the proposed model can aid the decision-making of a manufacturer, particularly in relation to the effect of customers' usage and the effect of inspection upon the service cost during the warranty period. [ABSTRACT FROM AUTHOR]

In higher education, student learning support centres are examples of walk-in services with nonstationary demand. For many centres, the major expenditure is tutor wages; thus, optimizing tutor numbers and ensuring value for money in this area are key. In University College Dublin, the mathematics support centre (MSC) has developed a software system, which electronically records the time each student enters the queue, their start time with a tutor and time spent with a tutor. In this paper, we show how data analysis of 25,702 student visits and tutor timetable data, spanning 6 years, is used to identify busy and quiet periods. Prediction modelling is then used to estimate the waiting time for future MSC visitors. Subsequently, we discuss how this is used for staffing optimization, i.e. to ensure there is sufficient coverage for busy times and no resource wastage during quieter periods. The analysis described resulted in the MSC reducing the number of queue abandonments and releasing funds from overstaffed hours to increase opening hours. The methods used are easily adapted for any busy walk-in service, and the code and data referenced are freely available: https://github.com/ehoward1/Math-Support-Centre-. [ABSTRACT FROM AUTHOR]

Quantitative benchmarking is widely used in the industry to compare relative performance across a sample of organizations. A key analytical challenge lies in obtaining accurate measures of intrinsic organizational performance net of contextual or exogenous influences. In this paper, we propose a model-based adjustment approach for comparative benchmarking that allows the analyst to recover targeted metrics for specific aspects of innate performance. We outline the statistical theory underpinning our method, provide simulations to demonstrate its properties and describe practical examples for computation. The managerial relevance of the method is demonstrated via two real-world transport industry applications: adjusting for economies of scale and density in benchmarking average costs of urban metros and for service characteristics in benchmarking metro journey times. [ABSTRACT FROM AUTHOR]

This note introduces the papers that follow in this special issue of the Journal, devoted to modelling pension scheme assets and liabilities, and arising from a second IMA conference on the future of pensions schemes, held in March 2003. It outlines the key public policy issues that have developed or arisen since the first such conference in February 1998, and summarizes in context the five papers. [ABSTRACT FROM AUTHOR]

Inverse data envelopment analysis (DEA) determines the optimal level of inputs and/or outputs of decision-making units (DMUs) to reach efficiency targets. This paper presents a new inverse DEA model for determining minimum inputs for working capital management. The proposed model is employed in the Indian textile industry to calculate working capital efficiency. Given the working capital efficiency, the decision maker's preferences will be estimating the change in inputs when outputs increase. Furthermore, unlike the standard inverse DEA model, this article discusses the inverse DEA model when negative ratio data exist. The DEA model requires additional attention when ratio data are present; therefore, a novel inverse DEA ratio model is proposed. The input targets obtained from the proposed model are less than the standard inverse DEA model. Also, the proposed model is a closer estimate of the production probability set for ratio data. [ABSTRACT FROM AUTHOR]

This paper presents a new application of Inverse data envelopment analysis (InvDEA) for strategic decision making: mergers & acquisitions (M&A) in the agricultural sector. Given a set of agricultural farms, the decision maker is interested in estimating the potential gains that are likely to result from the merger of two or more farms, as well as the redistribution of inputs among the merging farms, for an efficiency target set a priori for the post-merger farm. Using a sample of greenhouse (GH) farms from the Batinah region (Oman), an InvDEA approach is applied to investigate pairwise consolidations among GH farms and determine the level of inputs required for a merger to achieve full efficiency. Moreover, a DEA-based approach is introduced for selecting the best partners of a merger based on pertaining potential gains. The results highlight the importance of mergers as a strategic option for an efficient management of resources in the agricultural sector, especially scarce resources, like water and electricity. [ABSTRACT FROM AUTHOR]

In this paper, we formulate an inverse data envelopment analysis (DEA) model for a serially linked two-stage production process operating under constant returns to scale technology. The inverse DEA model determines a set of intermediate and output targets for an input augmented decision-making unit (DMU) to maintain its relative efficiency at a pre-specified level. We solve the inverse DEA model using the constraint method used in multi-objective optimization. The input augmented DMU with intermediate and output targets obtained in the inverse DEA model is a hypothetical DMU. Under our modelling framework, when such a hypothetical DMU established on an inefficient DMU is included in the observed DMU set, the frontier established with observed DMU set remains intact. This is important in practice as the intermediate and output targets of the hypothetical DMU would be feasible. When overall efficiency of the hypothetical DMU is decomposed, individual stages have the same efficiency level as that of the hypothetical DMU. This is important to DMU managers as sub-processes also maintain the desired overall efficiency level. We apply our inverse DEA model to a sample of Australian superannuation funds. We demonstrate that each unique Pareto optimal solution of the inverse DEA model obtained through the constraint method provides a specific set of intermediate and output targets and they may offer trade-off between intermediates and outputs. When fund managers anticipate expansion or growth in their funds, choice of targets allows comparison of different trade-off scenarios and makes informed decisions. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a mathematical programming technique for efficiency analysis. For dealing with the data in ratio form, the DEA model for ratio data known as DEA-R exists in the literature. However, some ratio data like financial risk may be negative naturally. In this paper, we contribute to the literature in two ways. In the first place, we deal with DEA-R models in the presence of negative ratio data by proposing an inverse DEA model for merger analysis. In the second place, we develop DEA-R models for merger analysis that can deal with negative data. We apply our models in a real-world application of efficiency and merger analysis of an Iranian bank with 66 branches. The proposed models maintain data confidentiality. This motivates managers to participate in the evaluation and merger process. Our models also provide a reasonable endogenous weight restriction framework without restricting weights exogenously. [ABSTRACT FROM AUTHOR]

We present a novel general inverse data envelopment analysis model for assessing the restructuring of decision-making units (DMUs) while observing predetermined value efficiency targets, under two distinct scenarios: merger and acquisitions and split. By considering the set of efficient DMUs as the Decision Maker's set of Most Preferred Solutions, virtual new units with desired targets for value efficiency scores and input and output levels can be computed. The innovations of this paper are threefold. First, a comparison between the proposed value efficiency and previous technical efficiency approaches reveals improvement in the estimation of restructured input/output levels. Second, the proposed approach is capable of handling the decision-maker's preferences during restructuring. Third, an algorithm to determine the worst case-scenario for restructuring is presented. An applied case study in banking operations is presented to illustrate the validity and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Advance selling activities are becoming more popular, especially in online retailing of new products. During the advance selling process, consumers may be loss averse. This influences the pricing strategy of the members of the supply chain. Using prospect theory and game theory, and considering consumers' loss aversion, this paper studies the pricing strategy of advance selling in a supply chain consisting of a manufacturer and an e-retailer under a resale contract or an agency contract. The study finds that as consumers' loss aversion increases, supply chain members set lower prices. Consumers' loss aversion has a positive impact on the member who directly prices to consumers, but it has a negative impact on the indirect member. Advance selling under an agency contract makes it easier to achieve a Pareto improvement than that under the resale contract. When the unit order fulfilment cost is low, the e-retailer prefers the agency contract. [ABSTRACT FROM AUTHOR]

We consider a hybrid preventive maintenance policy. As in the classic model of age-replacement, a system is replaced either on failure or on reaching a predetermined age T. Additionally, it can be replaced on reaching a predetermined level of deterioration at some intermediate time in [0,T). We use a transformed gamma process with dependent increments to model the deterioration. A failure of an item occurs when this process reaches a predetermined deterministic level. We show that the proposed policy outperforms the classical one, that is, it achieves a lower value of the long-run cost rate. A detailed numerical illustration is presented and sensitivity analysis for the main parameters of the model is performed. The work in the paper makes an important contribution to maintenance modelling because dependent increments can better represent deterioration of a real system. [ABSTRACT FROM AUTHOR]

In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston's method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain's method and Zhou and Viswanathan's method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective mathematical formulation for supplier-material selection in cardboard box manufacturing. The aim is to minimize three different scaled objectives: wastage of raw material, raw material cost and product surplus. To model industry reality more closely, the formulation is extended to an environment that includes uncertain costs and demands. In this sense, the model is reformulated as a robust optimization problem. To solve this problem, a weighted global criterion approach is developed and applied to find Pareto optimal solutions. A case study from a cardboard box manufacturer is used to test the efficiency of the proposed robust formulation and the proposed solution approach. The sensitivity of the Pareto optimal solutions to different levels of uncertainty in the parameters and different weights of the objective functions is also studied. [ABSTRACT FROM AUTHOR]

This paper studies alternative procurement modes for emergency supplies in the presence of a public sector and a private supplier. A key feature of such a supply chain is that the private supplier must consider commercial demand in addition to disaster demand. Three procurement modes are analysed: an option mode (OM), an order-before-disaster mode (OBDM) and a procurement-after-disaster mode (PADM). We provide the optimal decisions associated with these three modes. Theoretical results in the OM show that a large order is not always better for the private supplier. From theoretical and numerical comparative analyses, no mode is absolutely superior. For the public sector, there are two thresholds. When the disaster probability is less than the low threshold, the revenues of all procurement modes are the same; when the disaster probability is larger than the high threshold, the OBDM has the highest revenue; otherwise, the OM has the highest revenue. However, for the private supplier, the PADM always has the highest revenue. [ABSTRACT FROM AUTHOR]

Careful planning of an ambulance service is critical to reduce response times to emergency calls and make assistance more effective. However, the demand for emergency services is highly variable, and good prediction of the number of future emergency calls, and their spatial and temporal distribution, is challenging. In this work, we propose a Bayesian approach to predict the number of emergency calls in future time periods for each zone of the served territory. The number of calls is described by a generalized linear mixed effects model, and inference, in terms of posterior predictive distributions, is obtained through Markov chain Monte Carlo simulation. Our approach is applied in a large city in Canada. The paper demonstrates that using a model for areal data provides good results in terms of predictive accuracy and allows flexibility in accounting for the main features of the dataset. Moreover, it shows the computational efficiency of the approach despite the huge dataset. [ABSTRACT FROM AUTHOR]

The clustered and generalized vehicle routing problem (CGVRP) extends the well-known vehicle routing problem by grouping the demand points into multiple distinct zones, and within each zone, further separation is made by forming clusters. The objective of the CGVRP is to determine the optimal routes for a fleet of vehicles dispatched from a depot, visiting all zones within each cluster. This requires making two simultaneous optimization decisions. Firstly, each zone must be visited by exactly one node, and secondly, all zones within a cluster must be visited by the same vehicle. In this paper, we introduce two mixed-integer linear programming formulations for the CGVRP, aimed at solving a joint order batching and picker routing problem with alternative locations in a warehouse environment featuring mixed-shelves configuration. Both formulations are tested on three scenarios of randomly generated small- and medium-sized instances. Additionally, we propose a general rule approach for calculating a cost matrix in a rectangular environment. The results demonstrate the effectiveness of the proposed mathematical formulations in efficiently solving problems with up to 180 nodes. [ABSTRACT FROM AUTHOR]

Accepted by: Phil Scarf In practice, many engineering items have more than one failure mode, whereas most of the existing reliability acceptance sampling plans reported in the literature assume that they have only one basic failure mode. To fill the gap, in this paper, we propose a reliability sampling plan for items with an additional failure mode that is due to external shocks. Moreover, heterogeneous populations of items are considered when items' lifetime distributions differ from subpopulation to subpopulation. A new two-stage reliability sampling plan that takes into account these factors has been developed, where the lifetimes of items in a population are stochastically compared before and after the acceptance test. It is shown that the developed sampling plan improves the reliability characteristic of the population. [ABSTRACT FROM AUTHOR]

In this paper, we use a continuous time stochastic model to study a collective defined contribution pension plan when the interest rate is stochastic, and where the benefit levels are adjusted depending on the performance of the plan, and with risk sharing between different generations. The nominal interest rate is characterized by the Vasicek model, and the pension fund is invested in a financial market consisting of three assets: one risk-free asset, one bond and one risky asset. The participants of the pension plan are the risk bearers, and the plan seeks optimal investment and risk-sharing arrangements for plan sponsors and participants that maximize the expected accumulated discount utility of intermediate benefit adjustments and terminal wealth. Closed-form solutions are derived via the stochastic optimal control approach under constant relative risk aversion utility function. Numerical results show the effects of financial market parameters on the optimal investment strategy and how the optimal benefit changes with respect to different risk aversions and wage increase rates. [ABSTRACT FROM AUTHOR]

Organizations have successfully used dynamic pricing to optimize revenues for many years, where research and practice have mainly focused on applications with independent, discrete commodities; for example, an airline ticket. In this research we consider applications where the commodity is continuous and the value of the commodity available to sell depends on the combination of previously accepted demand. We focus on vehicle ferries, where the accepted vehicle bookings are packed in lanes in the ferry to leave a usable space for future bookings. Certain combinations of vehicles may result in areas of unusable space, which will affect future revenue. While this application is the focus of the paper, there are numerous industries that face similar challenges including freight and the sale of advertising on television and radio. In this paper, we simultaneously solve the pricing and resource utilization problem to optimality for a discrete set of product types and stochastic demand. Our approach combines a dynamic pricing model with a mixed-integer linear program to optimize the packing. We present results for real-world examples from the ferry industry and discuss extensions to the method to improve the selection of vehicle configurations. [ABSTRACT FROM AUTHOR]

This paper provides the first optimal portfolio analysis for a constant relative risk-averse and ambiguity-averse investor under the state-of-the-art 4/2 stochastic volatility model in a complete market setting. We determine the robust optimal strategy and the worst case measure by allowing separate levels of uncertainty for variance and stock drivers. Technical conditions for well-defined solutions are detailed together with a verification result. The robust optimal investment exposure displays a dependence on current volatility levels similar to the non-robust case further impacted by the ambiguity-aversion level. Using real-world parameters, the numerical analysis finds that wealth-equivalent losses (WELs) from ignoring uncertainty or market completeness are moderate. On the other hand, WELs for investors who follow simpler but popular strategies, such as Heston (1/2 model) and Merton (geometric Brownian motion [GBM] model), could be quite substantial, of up to 24 and 51%, respectively. This latest analysis comes from new non-affine representations for the suboptimal value function of the 1/2 and GBM strategies. [ABSTRACT FROM AUTHOR]

We study the problem of executing a large multi-asset portfolio in a short time period where the objective is to find an optimal trading strategy that minimizes both the trading cost and the trading risk measured by quadratic variation. We contribute to the existing literature by considering a multi-dimensional geometric Brownian motion model for asset prices and proposing an efficient Markov chain approximation (MCA) approach to obtain the optimal trading trajectory. The MCA approach allows us not only to numerically compute the optimal strategy but also to theoretically analyse the influence of factors such as price impact, risk aversion and initial asset price on the optimal strategy, providing both quantitative and qualitative guidance on the trading behaviour. Numerical results verify the theoretical conclusions in the paper. They further illustrate the effects of cross impact and correlations on the optimal execution strategy in a multi-asset liquidation problem. [ABSTRACT FROM AUTHOR]

Energy quanto options are risk management tools that have a payoff similar to the product of the payoffs of two options, each written on an energy-related underlying. These options, as opposed to standardized contracts that only account for price risk, are designed to manage both volumetric and price risk in energy markets. Since the use of such options enables actors in the energy market also to hedge against production volume risk, they are becoming very popular. This paper considers the valuation of such an option on futures when the underlying futures prices are governed by Markov-modulated additive processes, which have independent but non-stationary increments within each regime. We derive a valuation formula by using the Fast Fourier Transform (FFT) technique under the assumption that the joint characteristic function of the log-futures prices is known analytically. We study this approximation under different regime-switching models. Several numerical case studies illustrate that our FFT-based valuation has a high precision and is much faster than Monte Carlo estimates. [ABSTRACT FROM AUTHOR]

In the analysis of economic growth factors, researchers often use the Cobb–Douglas production function model to calculate the contribution rate of various influential factors upon economic growth. However, the traditional Cobb–Douglas production function model fails to consider the influence of policy factors on economic growth at different stages. Therefore, this paper establishes a modified model of the Cobb–Douglas production function. With regard to parameter estimation of the model, this paper proposes an improved simulated annealing algorithm; this method is characterized by high precision and rapid convergence. In terms of calculating the contribution rate of factors, the traditional method has a larger error. Therefore, by utilizing the modified Cobb–Douglas production function model, the paper proposes a new method to calculate the contribution rate of a factor accurately. Finally, the paper empirically analyses the contribution rate of various influential factors to economic growth in China. [ABSTRACT FROM AUTHOR]

In quantitative finance practice, model calibration is a key challenge. This is especially so when a local regime-switching model needs to be calibrated because designing an efficient and reliable algorithm to obtain local volatility values as a function of underlying price and time is important for the model to be successfully used in practice. Therefore, this paper proposes a new algorithm for calibrating local regime-switching models with observed option prices available for a particular market that is suitable for this type of model. The newly proposed algorithm is tested with calibrations performed on synthetic as well as real market data. Our empirical test results indicate that the algorithm has great potential to be used in financial risk management. [ABSTRACT FROM AUTHOR]

Accepted by: Phil Scarf This paper investigates optimal target locations for throw-ins in soccer. The investigation is facilitated by the use of tracking data which provide the positioning of players measured at frequent intervals (i.e. 10 times per second). The methods for the investigation are necessarily causal since there are confounding variables that impact both the throw-in location and the result of the throw-in. A simple causal analysis indicates that on average, backwards throw-ins are beneficial and lead to an extra two shots per 100 throw-ins. We also observe that there is a benefit to long throw-ins where on average, they result in roughly four more shots per 100 throw-ins. These results are corroborated by a more complex causal analysis that relies on the spatial structure of throw-ins. [ABSTRACT FROM AUTHOR]

Accepted by: M. Zied Babai It is natural to coordinate spare-parts inventory planning and maintenance. However, work in the former area often neglects part utilization, and work in the latter neglects the fact that effective execution of maintenance schedules is conditioned to the availability of the necessary spare parts. This paper is a call for further integration between the two areas, and to that end, we review the literature on mathematical modelling and analysis of inventory-maintenance-planning. We are not the first to address this issue (though we take a fresh perspective to the problem), but we are the first to complement such review with a discussion of simple stock keeping rules that may be used effectively in practice. We identify a growing gap between modelling and application, between theory and practice, which justifies the presentation of these simple stock keeping rules for the joint planning of inventory and maintenance. Thus, our work should be of interest not only to researchers who are looking for promising avenues for future research but also to practitioners who are seeking to improve inventory-maintenance operations. [ABSTRACT FROM AUTHOR]

This paper develops an optimization model for a sustainable closed-loop supply chain network with two conflicting objectives, namely, the minimization of the total logistic costs and the total amount of carbon emissions. The first objective relates to financial benefits, whereas the second represents the wider goal of guaranteeing cleaner air and hence a greener and healthier planet. The problem is first modelled as a mixed integer linear programming based-model. The aim is to determine the location of distribution centres and recycling centres, their respective numbers and the type of vehicles assigned to each facility. Vehicle type consideration, not commonly used in the literature, adds another dimension to this practical and challenging logistic problem. A matheuristic using compromise programming is put forward to tackle the problem. The proposed matheuristic is evaluated using a variety of newly generated datasets which produces compromise solutions that demonstrate the importance of an appropriate balance of both objective functions. The robustness analysis considering fluctuations in customer demand is assessed using Monte Carlo simulation. The results show that if the standard deviation of the demand falls within 10% of its average, the unsatisfied demand is insignificant, thus demonstrating the stability of supply chain configuration. This invaluable information is key towards helping senior management make relevant operational and strategic decisions that could impact on both the sustainability and the resilience of their supply chain networks. [ABSTRACT FROM AUTHOR]

In this paper, we present a routing heuristic for the Flying Sidekick Traveling Salesman Problem. This problem combines a single truck with a single drone. The drone may be launched from the truck from a customer location and can make a single package delivery before being recovered by the truck. While the drone is in flight, the truck can deliver packages independently. The problem is one of simultaneously assigning the customers to each vehicle and determining the synchronized routes for both vehicles to satisfy all customer demands in the shortest time possible. The problem is NP-hard. Our main contribution lies in developing a novel split algorithm that utilizes a shortest path approach for determining the optimal routing solution to a given order of customer locations. This split algorithm has polynomial complexity. We develop a heuristic solution to the problem by combining the split algorithm with local search techniques. This overall heuristic is shown to be accurate and computationally scalable to large, realistically sized problems applicable in last-mile logistics. [ABSTRACT FROM AUTHOR]

The interaction between nurses and their managers is a very important factor in nurses' error reporting behaviour, which is crucial to improving patient safety in healthcare organizations. However, little theoretical work has been undertaken to analyse this interaction. This paper uses a game-theoretic principal–agent framework with asymmetric information to study this interaction. We suppose that the principal (the nurse manager) asks the agent (the nurse) to perform a task with a certain patient. In case a mistake is made while treating the patient, the nurse has to decide whether to report it to the manager, who can observe whether the patient suffered an accident. We consider different manager's leadership styles and analyse their performance in obtaining error notification from nurses in this framework. [ABSTRACT FROM AUTHOR]

This paper models age replacement and block replacement when there is the possibility of defaulting on the planned maintenance. A default occurs when a planned preventive replacement is not executed, and we discuss how defaults can arise in practice. Our aim is to study the robustness of block replacement and age replacement, bearing in mind that (a) these policies are frequently used in practice, (b) in the standard scenario (no defaulting) age replacement has a lower economic cost rate than block-replacement and (c) block replacement is simple to manage because component age does not have to be monitored. We model defaults as independent Bernoulli trials. We prove that a cost-minimizing critical age for replacement in the age policy with defaulting exists if the time to failure distribution has an increasing failure rate. A numerical study of the policies indicates that: age replacement is effective if maintenance control is good, that is, when there is only a small chance of defaulting; block replacement is relatively robust to defaulting (postponement), but less so to lack of knowledge about component reliability. [ABSTRACT FROM AUTHOR]

Forecasting non-stationary time series, especially when the data generating processes contains a random walk component, is a difficult and sometimes impossible task. In this paper we suggest an intuitive, computationally fast and expedient way of forecasting time series of the above type using distance-based nearest neighbours (NN). We exploit to advantage the path and scale dependence present in a random walk model and so we provide a number of theoretical results (a) on the distances used for selecting the NN, (b) on a number of new forecasting models that use these distances and (c) on the properties of the resulting forecasts. We illustrate the efficacy of our method via a comprehensive empirical application on time series of exchange rates and commodities, where we present the resulting performance enhancements and discuss the importance of such results in a decision-making context, linking our forecasting approach with management mathematics and predictive analytics problems. [ABSTRACT FROM AUTHOR]

One of the main challenges when applying data envelopment analysis (DEA) is the selection of appropriate input and output variables. This paper addresses this important problem using a novel two-stage method. In the first stage, we use entropy theory to generate a comprehensive efficiency score (CES) of each decision-making unit. In the second stage, we select input and output variables using the Bayesian information criterion, when CES is treated as a dependent variable and the input and output variables are used as explanatory variables. We use stochastic data to demonstrate that our proposed method can improve the discrimination power of DEA and determine the important input and output variables. Finally, we compare the proposed method with principal component analysis using datasets on carbon emissions in China. This comparison demonstrates the practical value of our proposed method. [ABSTRACT FROM AUTHOR]

Traditional efficiency studies on network data envelopment analysis (DEA) consider decision-making units as black boxes that use a set of crisp inputs to produce a set of crisp outputs and that ignore intermediate measures and reverse flows. In real applications, however, we are faced with network systems with reverse flows in an uncertain environment. In this paper, therefore, a chance-constrained multistage DEA model is introduced to analyze the relative performances of supply chains and components in the presence of reverse flows and random factors. A real case in the sugar illustrates the proposed method. The results demonstrate the validity and applicability of the model. [ABSTRACT FROM AUTHOR]

The interest rate and the market price of risk may be stochastic in a real-world financial market. In this paper, the interest rate is assumed to be driven by a stochastic affine interest rate model and the market price of risk from the stock market is a mean-reverting process. In addition, the dynamics of the stock are simultaneously driven by random sources of interest rate and the stock market itself. In pension fund management, different fund managers may have different risk preferences. We suppose risk preference is described by the hyperbolic absolute risk aversion utility, which is a general utility function describing different risk preferences. Legendre transform-dual theory is presented to successfully obtain explicit expressions for optimal strategies. A numerical example illustrates the sensitivity of optimal strategies to market parameters. Theoretical results imply that the risks from stochastic interest rate and stochastic return may be completely hedged by adopting specific portfolios. [ABSTRACT FROM AUTHOR]

This paper provides some theoretical foundations for using moving average (MA) rules in the stock market. In particular, the paper analyzes the conditional probability of price increments and examines how this probability varies over time. We prove under certain assumptions that the probability of being in an uptrend is greater than the probability of being in a downtrend. This demonstration partially justifies the common use of MA rules in the stock market. Finally, we propose an ex-post empirical analysis to evaluate and compare the performance of some MA rules and other portfolio strategies in the US stock market. In this context, we also suggest a methodology that incorporates these trading rules as alarm rules to predict potential market failures. Our ex-post results confirm the advantages of using these trading rules to predict market trends and crises. [ABSTRACT FROM AUTHOR]

Introduces series of articles published in the July 2005 issue of the "IMA Journal of Management Mathematics." Construction of a two-timescale Markov chain model to capture hospital patient flows; Discussion of the robustness of length of stay as a performance indicator for costing and hospital management; Presentation of a non-linear optimization model for investigating reimbursement systems and the optimal allocation of budgets and in-patients across a region.

With the rise of e-commerce, an increasing number of retailers concentrate on the online channel to sell their products (strategy O), while some retailers select online and offline combined retailing. That is, the retailers introduce an offline experience store in addition to their online channels (strategy B). However, there is little literature on which strategies are better when a retailer's intrusion is considered. This paper constructs a competitive system in which the incumbent is a well-established pure online retailer and the new entrant will decide which strategy to select, strategy O or strategy B. The hotelling model is applied to represent the competitive market of both retailers through consumers' asymmetrical preference for products, and Nash games under two strategies are built. Through comparing equilibrium solutions, we show that when consumers prefer the incumbent's product, online and offline retailing is an effective strategy for the new entrant to compete with the incumbent. Meanwhile, the incumbent's advantage does not always benefit him. Consumers' asymmetrical preference for products may intensify the competition between them and hurt their profits, but the new entrant can benefit from providing offline retailing under most cases. [ABSTRACT FROM AUTHOR]