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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]

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: 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]

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: 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper reviews works that consider the mathematical modelling of mission-abort policies (MAPs). In a MAP, a valuable, and perhaps, vulnerable system performs a mission with two, sometimes conflicting objectives, mission success and system survival; and the purpose of modelling is to determine the conditions under which a mission should be aborted. Such problems are important in defence and are emerging in transportation and health management. We classify models by the nature of the mission and the system, the nature of the return or rescue, type of deterioration model and the decision objectives. We show that the majority of works consider a model of a one system, one target mission in which the mission is aborted once the hazard of failure reaches a critical level, and the operating environment is the same for the outbound and inbound parts of the mission. Typically, the hazard of failure depends on the number of shocks received so far. Our analysis indicates that there has been little modelling development for multiple systems that can multi-task and for dependent systems with common-cause failures, for example. We find no evidence that MAPs are used in practice and no works reviewed develop software demonstrators. We think there is considerable scope for modelling applications in transportation (e.g. dynamic train re-scheduling and last-mile logistics) and medical treatments, and MAPs may be more general than the literature that we have reviewed suggests. [ABSTRACT FROM AUTHOR]

Parisian options are a useful real-option tool in risk management, particularly for corporate bankruptcy protection. However, in the past, only one barrier with the Parisian feature was studied in the literature, possibly due to the additional complication that arises with the co-existence of two barriers. In this paper, we present an analytic solution for the valuation of European-style double-barrier Parisian options by casting the pricing problem into three inter-coupled partial differential equations. These are then solved using a dimension reduction procedure and the 'moving window' technique. Our solution is in an explicit and analytical form that is written in terms of multiple integrals. This represents an important advantage over the purely numerical approaches previously published in the literature. [ABSTRACT FROM AUTHOR]

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]

In this paper, we propose an integrated model for optimizing the process mean, the price of products and the production quantity. This optimization is conducted considering the impact of price differentiation in an imperfectly segmented market where demand leakages are common. The products that are produced by the production process are assigned to two market segments using sampling inspection. For optimization of the process mean, pricing and production quantities, a multi-objective model is formulated that maximizes (i) The expected total risk-adjusted profit across segments for a risk-tolerant firm, (ii) The satisficing level, that is, the probability of exceeding a target set for total risk-adjusted expected profit and (iii) The two conflicting objectives, expected total risk-adjusted profit and satisficing level. Depending on the complexity of models, either derivative-based closed form solutions or heuristic solution procedures are developed. The problem finds applications in process industries. A detailed numerical simulation is performed to corroborate the models, and it shows that a higher risk aversion results in a lower total expected risk-adjusted profit but a higher probability of achieving the target profit. [ABSTRACT FROM AUTHOR]

This paper considers an unobservable queue with adjustable service rate and strategic customers. A queueing-game-theoretic model is built to capture the interaction between the server's service rate and customers' joining decisions. Iterative and recursive methods are used to derive the steady-state distribution and the expected sojourn time in the queue. We obtain customers' equilibrium and socially optimal joining strategies under two information scenarios which are unobservable and partially unobservable queues, separately. It is found that there are four equilibrium joining strategies at most in the fully unobservable queue and two equilibrium joining strategies in the almost unobservable queue. Interestingly, the social optimal arrival rate is between the minimum and maximum stable equilibria. Thus, in most cases, managers need to charge a price to induce the social optimal customers' behaviour. However, if the minimum equilibrium is achieved, managers are required to provide a subsidy to maximize the social welfare. unobservable queue; |$(m, N)$| policy; equilibrium strategy; social welfare. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the effects of production disruptions (PDs) and physical distancing constraints due to the pandemic in a parallel-machine production environment. The machines are non-identical and are utilized for producing a finite set of jobs (parts) in a plastic injection moulding production. The production process is subjected to random production downtime disruptions. A mixed-integer linear programming (MILP) model is developed for optimizing the joint production plan and schedule, which maximizes the production's total benefit. The model is utilized to plan and schedule a set of 17 machines in a Canadian manufacturing company. To explore the effects of physical distancing and PDs on the production's total net profit, four different scenarios for normal operation and production during the pandemic, with and without production downtimes, are considered. A genetic algorithm is utilized to solve the model. The results show that considering machines' random breakdowns and physical distancing individually reduces the total profit of the production by 71.58 and 57.98%, respectively; while their joint effect results in a 88.54% reduction in the annual net profit. [ABSTRACT FROM AUTHOR]

In this paper, we consider the pricing of financial derivatives that involve dynamic hedging strategies and payments within the planning horizon. Equity-indexed annuities (EIAs), guaranteed investment certificates (GICs) and American and barrier options are typical examples of these products. Our exploration involves the use and comparison of alternative models that use risk measures. Although the hedging is done for each observation of the input stochastic process, the appropriate mix of risk measures and state dynamic equations helps the issuer to appropriately tailor the overall risk exercise. These different models are solved by a unified backward stochastic dynamic programming framework that we imbed with parametric techniques to shorten the running time and manage the curse of dimensionality in dynamic programming. To demonstrate the flexibility of this framework we present numerical examples featuring GICs and point-to-point EIAs. Finally, by using sampling techniques, optimal hedging strategies and specific indicators of the hedging performance, we make recommendations on how to fine tune the risk measure parameters. [ABSTRACT FROM AUTHOR]

In this paper, the pricing of foreign exchange options is considered under a modified Heston–Cox–Ingersoll–Ross hybrid model. This modified model reserves all the characteristics of the Heston–Cox–Ingersoll–Ross model and also additionally assumes regime switching in the key parameters of the volatility as well as the domestic and foreign interest rates. Even though complicated, we have derived a closed-form pricing formula for foreign exchange options after the affinity of this new model is verified. Various properties of the newly derived formula are also shown through numerical experiments. To show the performance of this newly proposed model, an empirical study is also conducted, the result of which suggests that our model is a good alternative to the Heston–Cox–Ingersoll–Ross model for practical purpose. [ABSTRACT FROM AUTHOR]

In dynamic decision problems, it is challenging to find the right balance between maximizing expected rewards and minimizing risks. In this paper, we consider NP-hard mean-variance (MV) optimization problems in Markov decision processes with a finite time horizon. We present a heuristic approach to solve MV problems, which is based on state-dependent risk aversion and efficient dynamic programming techniques. Our approach can also be applied to mean-semivariance (MSV) problems, which particularly focus on the downside risk. We demonstrate the applicability and the effectiveness of our heuristic for dynamic pricing applications. Using reproducible examples, we show that our approach outperforms existing state-of-the-art benchmark models for MV and MSV problems while also providing competitive runtimes. Further, compared to models based on constant risk levels, we find that state-dependent risk aversion allows to more effectively intervene in case sales processes deviate from their planned paths. Our concepts are domain independent, easy to implement and of low computational complexity. [ABSTRACT FROM AUTHOR]

This paper describes the optimization of preventive maintenance (PM) over a finite planning horizon in a semi-Markov framework. In this framework, the asset may be operating, and providing income for the asset owner, or not operating and undergoing PM, or not operating and undergoing corrective maintenance following failure. PM is triggered when the asset has been operating for τ time units. A number m of transitions specifies the finite horizon. This system is described with a set of recurrence relations, and their z-transform is used to determine the value of τ that maximizes the average accumulated reward over the horizon. We study under what conditions a solution can be found, and for those specific cases the solution τ * is calculated. Despite the complexity of the mathematical solution, the result obtained allows the analyst to provide a quick and easy-to-use tool for practical application in many real-world cases. To demonstrate this, the method has been implemented for a case study, and its accuracy and practical implementation were tested using Monte Carlo simulation and direct calculation. [ABSTRACT FROM AUTHOR]

This paper contributes to the research on multi-pillar pension systems with main focus on private pension funds (PFs). In this context, the specific objective of this study is to determine which second-pillar private fund is the best for participants in such systems on the basis of their risk profile. Based on the assumptions on utility functions of the participants in a pension scheme, four types of stochastic dominance (SD) relations are considered, specifically first order, second order, third order and SD generated by utility functions with decreasing absolute risk aversion. We conduct an analysis under two distributional assumptions: empirical and stable distribution of returns. Moreover, the investors for which non-dominated funds are the optimal choices are identified. Allowing for diversification, the efficiency of the PFs with respect to several types of SD is tested. Then, the observed behaviour of participants in the last quarter/year is compared to the results of SD analysis. Finally, the identified SD relations are stress-tested using data originating from a period of turmoil. Despite the focus on Lithuanian PFs, the methodology developed in this work can be employed by participants or PF managers in similar markets of other countries. [ABSTRACT FROM AUTHOR]

Models derived from distributions of order-statistics are useful for modelling ranked data. The well-known Bradley–Terry (BT) and Plackett–Luce (PL) models can be derived from the order statistics of the exponential distribution but cannot handle ties. However, ties often occur in sports, and the ability to accommodate them leads to more useful ranking models. In this paper, we use discrete distributions, principally the geometric distribution, to obtain modified BT and PL models and some others that allow tied ranks. Our methodology is introduced for some mathematically tractable and some less tractable distributions and is illustrated using test match cricket. [ABSTRACT FROM AUTHOR]

Inverse DEA models may also be improved by considering uncertainty in data, for example developing stochastic inverse DEA or fuzzy inverse DEA. Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by [4] and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. Current state of knowledge in inverse DEA Unlike the conventional DEA, inverse DEA seeks to estimate input and/or output targets for DMUs to achieve efficiency targets. The introduction of inverse DEA created a new direction in the literature of DEA and provided great opportunities to solve novel inverse optimization problems that were beyond the scope of the standard DEA. [Extracted from the article]

Uncertain demand information increases the difficulty of decision-making in supply chains, especially in the light of inventory costs. This article researches a two-echelon dual-channel fresh agricultural product (FAP) supply chain, and discusses optimal decision-making and the value of information sharing using the Stackelberg game. To eliminate the double marginalization effect between supply chain parties, a two-part linear tariff contract is introduced for coordination. The results show that information sharing is always profitable for the manufacturer, but not for the retailer. The inventory and shortage costs will reduce the manufacturer's optimal pricing and fresh-keeping effort level. When the manufacturer overestimates the market demand predicted by the retailer, asymmetric information will increase the manufacturer's inventory. The results provide important managerial implications for effective supply chain management. [ABSTRACT FROM AUTHOR]

The clustering problem has many applications in machine learning, operations research and statistics. We propose three algorithms to create starting solutions for improvement algorithms for the minimum sum of squares clustering problem. We test the algorithms on 72 instances that were investigated in the literature. We found five new best known solutions and matched the best known solution for 66 of the remaining 67 instances. Thus, we are able to demonstrate that good starting solutions combined with a simple local search get results comparable with, and sometimes even better than, more sophisticated algorithms used in the literature. [ABSTRACT FROM AUTHOR]

In most sport leagues, a schedule is announced before the start of the season. However, due to unexpected events (e.g. bad weather conditions), some games cannot be played on the announced date. To handle this, before the start of the season, empty so-called catch-up rounds are positioned in the schedule as a buffer. During the season, games can then be rescheduled to these catch-up rounds. We develop a two-stage stochastic programming approach to determine where to position the catch-up rounds in order to maintain the quality of the realized schedule. While our method is generally applicable, we demonstrate its use with soccer. Scenarios and their probabilities are deduced from historical data from 10 major European soccer leagues. We study the impact of the number of catch-up rounds and costs on the positions of catch-up rounds and compare our method with other proactive strategies from the literature. We conclude with a case study based on the English Premier League. In particular when many games cannot be played as planned and few catch-up rounds are available, our stochastic programming approach outperforms existing methods with respect to maintaining a fair ranking and avoiding cancelled games. [ABSTRACT FROM AUTHOR]