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In this paper, we consider a project-based organization that deals with an assignment problem in which a set of projects must be assigned to a group of project managers. This assignment is done based on the relative contributions of projects to the organizational mission and a matching score between each pair of a project and a project manager. We assume that some projects are deterministic, and the organization has signed their corresponding contracts while others are stochastic, i.e. the organization has submitted bids for these projects and may or may not win them in the future. Furthermore, we consider a finite planning horizon and presume a predetermined start time for each deterministic and stochastic project. We develop two models including a multi-stage stochastic integer programming model and a stochastic dynamic programming model to solve the problem. The latter shows better performance for small-size and less complex instances whereas the former gives better performance for more complex instances. We also developed a heuristic algorithm to solve large-size and more complex instances. Computational results indicate that the developed heuristic algorithm can reach near-optimal solutions in reasonable CPU run times and dominates the two other solution approaches particularly for large-size instances. [ABSTRACT FROM AUTHOR]

During an investigation using Forensic Investigative Genetic Genealogy, which is a novel approach for solving violent crimes and identifying human remains, reference testing—when law enforcement requests a DNA sample from a person in a partially constructed family tree—is sometimes used when an investigation has stalled. Because the people considered for a reference test have not opted in to allow law enforcement to use their DNA profile in this way, reference testing is viewed by many as an invasion of privacy and by some as unethical. We generalize an existing mathematical optimization model of the genealogy process by incorporating the option of reference testing. Using simulated versions of 17 DNA Doe Project cases, we find that reference testing can solve cases more quickly (although many reference tests are required to substantially hasten the investigative process), but only rarely (<1%) solves cases that cannot otherwise be solved. Through a mixture of mathematical and computational analysis, we find that the most desirable people to test are at the bottom of a path descending from an ancestral couple that is most likely to be related to the target. We also characterize the rare cases where reference testing is necessary for solving the case: when there is only one descending path from an ancestral couple, which precludes the possibility of identifying an intersection (e.g., marriage) between two descendants of two different ancestral couples. [ABSTRACT FROM AUTHOR]

We revisit a service provider's problem to match supply and demand via an online appointment system such as a doctor in the health care sector. We identify in a survey that an extensive set of available appointments leads to significantly less demand because customers infer a lower quality of the service, as part of an observational learning process. We capture the quality inference effect in a multinomial logit framework and present a Markov decision process for solving the problem of releasing available slots of the appointment system to optimality aiming at maximizing the expected profits. We further evaluate several simple decision rules and provide management insights on which rule to apply under different generic scenarios. Different from current literature, offering all available appointments may lead to suboptimal results when accounting for the quality inference effect. The profit-maximizing strategy then is to offer a subset of the available appointments. [ABSTRACT FROM AUTHOR]

Coconut plantations throughout the Asia–Pacific region are generally characterised by the presence of low-productivity senile palms over the age of 60, which have negative impacts on farming communities, coconut processors, and the wider economy. In Fiji, despite numerous senile coconut replacement programs, 60% of coconut palms are considered senile. The purpose of this study is to provide preliminary estimates of the financial viability of a market-based approach to senile coconut palm replacement in Fiji by utilising the palms as a feedstock, for the manufacture of rotary peeled veneer, along with plantation pine and mahogany. A mathematical model capable of supporting deterministic and stochastic dynamic optimisation was developed with an objective function to maximise the gross margin of marketable veneer manufacture per hour ( G M p z ) by procuring the optimal allocation of logs throughout the landscape. The majority of facility location and log processing scale scenarios evaluated found that utilising large volumes of senile coconut palms for the manufacture of veneer was optimal, whilst veneering mills situated near the coconut plantations in Vanua Levu were found to maximise G M p z . Overall, the results indicate that a coconut veneer and engineered wood product (EWP) value chain could present a financially viable opportunity to support large-scale senile coconut palm replacement in Fiji. [ABSTRACT FROM AUTHOR]

Some nonprofit organizations (NPOs) manage a complex workforce composed of a mix of volunteers, part-time workers, and full-time workers. We study the NPO's finite-horizon staffing problem to determine the optimal initial staff planning decisions and per period optimal hiring and assignment decisions given a budget, capacity constraints, and an uncertain supply of volunteers and part-time workers. Our main goal is to solve this problem in a way that is effective and easy to implement while obtaining interesting managerial insights. To this end, we first demonstrate that the optimal staffing policies are computationally challenging to identify in general. However, we demonstrate that a prioritization assignment policy and a hire-up-to policy for part-time workers can be conveniently applied and are close to optimal. These policies are, in fact, optimal under staff scarcity and staff sufficiency. In our numerical analysis, we study the value and impact of the general optimal solution that considers flexibility and turnover of part-time workers versus the prioritization assignment policy and a constant hire-up-to policy that omit flexibility and turnover behaviors. We further suggest two easy-to-implement heuristics and theoretically analyze them and run a numerical performance study. We observe that both heuristics have low relative optimality gaps. Finally, we extend our analysis by studying how the optimal policy varies under three different practical considerations: a concave social value objective, nonzero volunteer costs, and dynamic volunteer behaviors. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by the Comunidad de Madrid [Grant EPUC3M12], the Ministerio de Ciencia e Innovación [Grant PID2021-127657NA-I00], and the Ramon y Cajal Fellowship [RYC2020-029303-I]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4923. [ABSTRACT FROM AUTHOR]

Introduction: Considering the perishability and substitutability of products are among the most significant challenges in the design and optimization of decision-making in Vendor Managed Inventory (VMI) systems. This challenge becomes more pronounced when there is uncertainty in product demand. Therefore, the primary objective of this research is to present a stochastic dynamic programming approach for optimal control of decisions in VMI systems with dynamic demand uncertainty, optimizing the ordering levels and inventory of perishable products in a two-tier network including vendors and buyers. Methods: After defining the problem of interest in developing VMI systems, considering demand uncertainty and product perishability, the problem is formulated in a multi-period modeling framework, and a stochastic dynamic programming (SDP) approach is used for its formulation. In the proposed SDP model, the objective function is to maximize expected profit by taking into account various costs such as ordering and holding, where the holding cost is dependent on the remaining product life; meaning that as the product approaches its expiration date, the holding cost increases. The proposed SDP model is executed in a recursive manner, and MATLAB software is used for its implementation. Each step of the SDP model is a simpler linear optimization model that is efficiently solved using the CPLEX solver. Results and discussion (Findings): Numerical results demonstrate the computational effectiveness of the SDP method in solving this problem. Using this approach, it is possible to control different costs in a VMI system and make optimal decisions at different stages under any state, thereby improving profit at the end of the time periods. Product substitution in the event of a shortage ensures that, firstly, in the case of a shortage at one center and the supplier's inability to replenish, the center offers its excess inventory to prevent the shortage. Secondly, if the inventory in a distribution center approaches its expiration date, spoilage is prevented. Therefore, the substitution capability generally leads to a reduction in shortage costs and spoilage costs. Results related to product shelf life indicate that by considering the remaining shelf life of products in a VMI-based inventory control system, information between the distribution and supply layers can be used to reduce prices, transfer inventory to another center, and even change inventory control policies to not only prevent product spoilage but also reduce shortage and reordering costs. To model this feature, a full shelf life is initially defined for each newly supplied product, and then a set of periods is defined. For each planning period, if the product is not delivered to the end customer (remains in inventory), one period/day is deducted from the initially defined shelf life, and over time, it moves from a fresh state to the category of older products, which are subject to price reductions. Furthermore, comparing the proposed model with substitution capability in the case of a shortage to the case where a shortage is not considered, it is observed that there is a 10.5% improvement in profit. Conclusion: In the management of modern VMI systems, considering the dynamism in demand behavior and the associated uncertainty is very important and can significantly affect the profitability of enterprises. This importance is doubled when the system in question is managed for the inventory control of perishable products. The SDP approach, by considering potential scenarios of demand uncertainty, enabling substitution, and ultimately paying attention to product shelf life, provides optimal decisions in different situations and not only reduces the risk of decision-making but also leads to a noticeable improvement in final profit compared to nominal quantity models and classical optimization models in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we develop a framework to analyze stochastic dynamic optimization problems in discrete time. We obtain new results about the existence and uniqueness of solutions to the Bellman equation through a notion of Banach contractions that generalizes known results for Banach and local contractions. We apply the results obtained to an endogenous growth model and compare our approach with other well‐known methods, such as the weighted contraction method, countable local contractions, and the Q‐transform. [ABSTRACT FROM AUTHOR]

We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortest-path problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances. This paper was accepted by Chung Piaw Teo, optimization. Funding: R. Mogre acknowledges support from the U.S.-UK Fulbright Commission and the Lloyd's Tercentenary Research Foundation through the Fulbright-Lloyd's Scholar Award, which allowed him to spend an extended period of time at Massachusetts Institute of Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4876. [ABSTRACT FROM AUTHOR]

Introduction: Considering the perishability and substitutability of products are among the most significant challenges in the design and optimization of decision-making in Vendor Managed Inventory (VMI) systems. This challenge becomes more pronounced when there is uncertainty in product demand. Therefore, the primary objective of this research is to present a stochastic dynamic programming approach for optimal control of decisions in VMI systems with dynamic demand uncertainty, optimizing the ordering levels and inventory of perishable products in a two-tier network including vendors and buyers.Methods: After defining the problem of interest in developing VMI systems, considering demand uncertainty and product perishability, the problem is formulated in a multi-period modeling framework, and a stochastic dynamic programming (SDP) approach is used for its formulation. In the proposed SDP model, the objective function is to maximize expected profit by taking into account various costs such as ordering and holding, where the holding cost is dependent on the remaining product life; meaning that as the product approaches its expiration date, the holding cost increases. The proposed SDP model is executed in a recursive manner, and MATLAB software is used for its implementation. Each step of the SDP model is a simpler linear optimization model that is efficiently solved using the CPLEX solver.Results and discussion (Findings): Numerical results demonstrate the computational effectiveness of the SDP method in solving this problem. Using this approach, it is possible to control different costs in a VMI system and make optimal decisions at different stages under any state, thereby improving profit at the end of the time periods.Product substitution in the event of a shortage ensures that, firstly, in the case of a shortage at one center and the supplier's inability to replenish, the center offers its excess inventory to prevent the shortage. Secondly, if the inventory in a distribution center approaches its expiration date, spoilage is prevented. Therefore, the substitution capability generally leads to a reduction in shortage costs and spoilage costs. Results related to product shelf life indicate that by considering the remaining shelf life of products in a VMI-based inventory control system, information between the distribution and supply layers can be used to reduce prices, transfer inventory to another center, and even change inventory control policies to not only prevent product spoilage but also reduce shortage and reordering costs. To model this feature, a full shelf life is initially defined for each newly supplied product, and then a set of periods is defined. For each planning period, if the product is not delivered to the end customer (remains in inventory), one period/day is deducted from the initially defined shelf life, and over time, it moves from a fresh state to the category of older products, which are subject to price reductions. Furthermore, comparing the proposed model with substitution capability in the case of a shortage to the case where a shortage is not considered, it is observed that there is a 10.5% improvement in profit.Conclusion: In the management of modern VMI systems, considering the dynamism in demand behavior and the associated uncertainty is very important and can significantly affect the profitability of enterprises. This importance is doubled when the system in question is managed for the inventory control of perishable products. The SDP approach, by considering potential scenarios of demand uncertainty, enabling substitution, and ultimately paying attention to product shelf life, provides optimal decisions in different situations and not only reduces the risk of decision-making but also leads to a noticeable improvement in final profit compared to nominal quantity models and classical optimization models in the literature.

We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method exploited the convexity properties of the value function to sample the reservoir level space based on the local curvature of the value function, which is estimated by the difference between a lower and an upper bounds (error bound). Unlike the previous approach in which the state space is exhaustively partitioned into full dimensional simplices whose vertices formed a discrete grid over which the value function was approximated, here we propose instead a new randomized approach for selecting the grid points from a small number of randomly sampled simplices from which an error bound is estimated. Results of numerical experiments on three literature test problems and simulated midterm reservoir optimization problems illustrate the advantages of the randomized approach which can solve models of higher dimensions than with the exhaustive approach. [ABSTRACT FROM AUTHOR]

The pace and scale of environmental change represent major challenges to many organisms. Animals that move long distances, such as migratory birds, are especially vulnerable to change since they need chains of intact habitat along their migratory routes. Estimating the resilience of such species to environmental changes assists in targeting conservation efforts. We developed a migration modeling framework to predict past (1960s), present (2010s), and future (2060s) optimal migration strategies across five shorebird species (Scolopacidae) within the East Asian-Australasian Flyway, which has seen major habitat deterioration and loss over the last century, and compared these predictions to empirical tracks from the present. Our model captured the migration strategies of the five species and identified the changes in migrations needed to respond to habitat deterioration and climate change. Notably, the larger species, with single or few major stopover sites, need to establish new migration routes and strategies, while smaller species can buffer habitat loss by redistributing their stopover areas to novel or less-used sites. Comparing model predictions with empirical tracks also indicates that larger species with the stronger need for adaptations continue to migrate closer to the optimal routes of the past, before habitat deterioration accelerated. Our study not only quantifies the vulnerability of species in the face of global change but also explicitly reveals the extent of adaptations required to sustain their migrations. This modeling framework provides a tool for conservation planning that can accommodate the future needs of migratory species. [ABSTRACT FROM AUTHOR]

Introduction: Human-induced environmental change is driving a global redistribution of biodiversity, resulting in shifting prey and predation landscapes. These shifting landscapes can lead to changes in behavior, health, and vital rates, with potential implications for population dynamics. Methods: In the present study, a state-dependent life-history theory model was developed to investigate the individual- and population-level responses of Australian fur seals (Arctocephalus pusillus doriferus) to changes in prey availability and at-sea mortality risk. Results: Rates of pregnancy, pup nursing, and abortion were unaffected by prey availability in the simulated population. Likewise, on-land and at-sea durations were largely unaffected by prey availability, with more pronounced affects for nonreproductive and pregnant females than for lactating females. There was a strong influence of prey availability on the proportion of females that were concurrently pregnant and lactating, largely due to an increase in pup abandonments under low prey availability scenarios. This effect on pup abandonments also had flow on effects for pup recruitment. Increasing at-sea mortality risk resulted in greater offspring losses due to maternal death. The combined impact of prey availability and at-sea mortality risk on the number of simulated female offspring reaching sexual maturity was substantial. Discussion: Consequently, our results suggest high vulnerability of the Australian fur seal population to shifting prey and predation landscapes. These results indicate a need for continued monitoring of Australian fur seal pup production and population dynamics in the face of rapid environmental change. [ABSTRACT FROM AUTHOR]

Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, the application of optimality theory, has been comparatively neglected in collective animal behaviour. This article seeks to address this imbalance, by outlining an evolutionary theory framework for the discipline. The application of optimality theory to collective animal behaviour requires a number of questions to be addressed. First, what is the correct quantity to optimize? This can be achieved via a combination of considering the organisms' life history, alongside tools such as statistical decision theory and stochastic dynamic programming. Second, what mechanism is appropriate for optimal behaviour? This involves ensuring that models are self-consistent rather than assuming parameter values. Third, at what level of selection does optimization act? Selection acts on the individual except in very particular circumstances, yet collective animal behaviour phenomena are group level, thus introducing a risk of confusing at what level adaptive properties emerge. This article presents examples under each of the three questions, as well as discussing mismatches between theory and observation. In doing so, it is hoped that collective animal behaviour fully inherits the tools and philosophy of its parent discipline of behavioural ecology. • We undertake a review of collective animal behaviour using Tinbergen's four questions. • We thus identify a lack of optimality theory in collective animal behaviour. • We propose three new questions for optimality theory in collective animal behaviour. • Using these questions we review the literature for successes and missed opportunities. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach for solving optimal stopping problems and high-dimensional American-style options pricing problems. Through state-space partition, the method does not require recalculation of the structure of networks when the price of the asset changes, which makes tracking valuation more efficient. This paper also offers theoretical proof for the existence of a deep learning network that can determine the optimal stopping time via state-space partition. We present convergence proofs for the estimators and also test the method on Bermuda max-call options as examples. [ABSTRACT FROM AUTHOR]

This study delves into the realm of green credit policy, a recognized solution for harmonizing financial growth with environmental progress. It highlights the pivotal role of credit-granting orientation in determining the effectiveness of green credit. Introducing a dual-track mechanism that combines industry and enterprise activity orientation, the study provides novel insights into green credit granting. Based on theoretical modeling and simulation analysis, the study explores how different granting orientations in green credit impact enterprise economic and environmental performance using stochastic dynamic programming. The results underscore that industry-oriented granting constrains eco-innovation capital investment and credit fund inflow, while activity-oriented granting leads to reduced carbon emissions and enhanced enterprise value. A dual-track green credit approach demonstrates a superior balance between the two orientations, delivering higher carbon emission reduction effects than industry-oriented granting and more sustainable impacts on enterprise value than activity-oriented granting. Ultimately, the study advocates for differentiating green credit into two orientation types and leveraging eco-innovation activities as credit allocation criteria to enhance the efficiency of green credit operations. [Display omitted] [ABSTRACT FROM AUTHOR]

Robotic Mobile Fulfillment Systems (RMFSs) are a recent type of automated warehouse deployed in e-commerce. In this parts-to-picker system, a fleet of small robots is tasked with retrieving and storing shelves of items in the warehouse. Due to the nature of the e-commerce market, and the high flexibility of RMFSs, there are many opportunities to improve the productivity of the warehouse by optimising operational decisions. Online retailers promise extremely fast deliveries, which requires that new orders be included in the set of requests to fulfil as soon as they are revealed. For this reason, and because of the very dynamic nature of the robots' cycles, decision-making needs to be done in real time, in an uncertain environment. Because such a problem often lacks a formal description, we propose a mathematical framework that models the operational decisions taking place in an RMFS as a stochastic dynamic program. Our objective is to formalise optimisation opportunities, to allow researchers to develop more advanced methods in a well-defined environment. Embedded in a discrete event simulator, this model is illustrated by simulations to compare against standard storage decision rules. [ABSTRACT FROM AUTHOR]

This study covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and state chance constraints. The stochastic optimal control problem is reformulated in a dynamic programming fashion to obtain a closed-loop performance and is solved using the interior-point method combined with a Riccati-based approach. The proposed method eliminates active sets in conventional explicit model predictive control and does not suffer from the curse of dimensionality because it finds the value function and feedback policy only for a given initial state using the interior-point method. Moreover, the proposed method is proven to converge globally to the optimal solution Q-superlinearly. The numerical experiment shows that the proposed method achieves a less conservative performance with a low computational complexity compared to existing methods.

In this study, we consider the dynamic and stochastic resource-constrained multi-project scheduling problem where projects generate rewards at their completion, completions later than a due date cause tardiness costs, task duration is uncertain, and new projects arrive randomly during the ongoing project execution both of which disturb the existing project scheduling plan. We model this problem as a discrete-time Markov decision process and explore the performance and computational limitations of solving the problem by dynamic programming. We run and compare five different solution approaches, which are: a dynamic programming algorithm to determine a policy that maximises the time-average profit, a genetic algorithm and an optimal reactive baseline algorithm, both generate a schedule to maximise the total profit of ongoing projects, a rule-based algorithm which prioritises processing of tasks with the highest processing durations, and a worst decision algorithm to seek a non-idling policy that minimises the time-average profit. The performance of the optimal reactive baseline algorithm is the closest to the optimal policies of the dynamic programming algorithm, but its results are suboptimal, up to 37.6%. Alternative scheduling algorithms are close to optimal with low project arrival probability but quickly deteriorate their performance as the probability increases. [ABSTRACT FROM AUTHOR]

We use stochastic dynamic programming to design and solve an extended structural setting for which the illiquidity of the firm's assets under liquidation is interpreted as an intangible corporate security. This asset tends to reduce bond values, augment yield spreads, and, thus, partially explain the credit-spread puzzle. To assess our construction, we provide a sensitivity analysis of the values of corporate securities with respect to the illiquidity parameter. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an optimal investment strategy for defined-contribution (DC) pension plan under hybrid stochastic volatility (Heston--Hull--White) model, taking account of the inflation risk and the stochastic salary. The fund wealth is invested in financial market consisting of a risk-free asset, an inflation-indexed bond and a stock with hybrid Heston--Hull--White model. The goal of the pension fund manager is to maximize the expected utility of the terminal real wealth. We derive the Hamilton--Jacobi--Bellman (HJB) equation through the dynamic programming principle, under the constant relative risk aversion (CRRA) utility function, the optimal investment strategy is obtained. Finally, a numerical example is presented to characterize the impacts of financial parameters on the optimal investment strategy. [ABSTRACT FROM AUTHOR]

Weapon target assignment is the core link of command and control of naval warship air-defense and anti-missile operations. The quality of the assignment directly affects the final defense effect and survivability of surface ship. Due to the lack of consideration of the uncertainty of interception results and the requirements of multi-round interception, current algorithms is difficult to adapt to the rapidly changed battlefield. A decision-making algorithm for dynamic weapon target allocation based on stochastic dynamic programming is designed. Through base conversion encoding and calculation of the change of target state and weapon state, the sequential optimal assignment scheme is generated by using the backward iterative algorithm based on battlefield situation transition probability. Meanwhile, the decision-making problems “who will fight”“how much” and “when to fight” are also answered by the dynamic weapon target assignment algorithm. The example verification and numerical simulation show that compared with the static algorithm, using this algorithm can reduce the threat expectation of the incoming target by up to 72%.

Abstract Energy storage and renewable sources play a unique role in the future advances of smart grids. In this article, the optimal scheduling of the energy storage system in a hybrid microgrid is presented considering the uncertainties of the renewable generations and the load. The optimisation problem in this article is non‐linear and non‐convex, therefore conventional optimisation methods such as linear programming (LP) are unable to solve this problem. On the other hand, because of parameters uncertainty, special considerations are required to simulate these parameters. In this regard, a new optimisation algorithm that can solve the non‐linearity and non‐convexity of the objective function is proposed based on the Stochastic Quasi‐Gradient optimisation Method (SQGM). Moreover, the uncertainties of the wind, PV generation, and the load are modelled. Different optimisation algorithms: the conventional Stochastic Dynamic Programming (SDP), the Stochastic Dual Dynamic Programming (SDDP) and the proposed SQGM are compared. A 9‐bus benchmark system with distributed generation units is used to evaluate the optimisation strategies.

Coconut plantations throughout the Asia–Pacific region are generally characterised by the presence of low-productivity senile palms over the age of 60, which have negative impacts on farming communities, coconut processors, and the wider economy. In Fiji, despite numerous senile coconut replacement programs, 60% of coconut palms are considered senile. The purpose of this study is to provide preliminary estimates of the financial viability of a market-based approach to senile coconut palm replacement in Fiji by utilising the palms as a feedstock, for the manufacture of rotary peeled veneer, along with plantation pine and mahogany. A mathematical model capable of supporting deterministic and stochastic dynamic optimisation was developed with an objective function to maximise the gross margin of marketable veneer manufacture per hour (GMpz) by procuring the optimal allocation of logs throughout the landscape. The majority of facility location and log processing scale scenarios evaluated found that utilising large volumes of senile coconut palms for the manufacture of veneer was optimal, whilst veneering mills situated near the coconut plantations in Vanua Levu were found to maximise GMpz. Overall, the results indicate that a coconut veneer and engineered wood product (EWP) value chain could present a financially viable opportunity to support large-scale senile coconut palm replacement in Fiji.

One of the Principles of water resources management is the optimal use of the reservoirs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize reservoir inflow has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the reservoir storage. By the programming in the Python environment, the historical reservoir inflow in each season is classified to different clusters and obtained the best inflow cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan reservoir. In general, the change in inflow classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the reservoir inflow discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize reservoir inflow for reservoir operation by SDP.The k-means clustering algorithm was first used by James McQueen in 1967. K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. Actually, K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. In this algorithm, each cluster is introduced with one of the data close to the center. In this algorithm, according to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the reservoir storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the reservoir volume. The study area includes Jamishan reservoir sub-basin with an area of 527.07 km2 located in the southwest of Sanghar city near the Pirsalman hydrometric station. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively.Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90, -475.36 and -2.10, respectively. In order to investigate the effect of classification of flow in the results of the first order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters, and in this table, the specified numbers indicate the probability of moving each cluster for each season.In the following, using the backward recursive function, the calculations are continued until reaching the stationary state condition. Finally, the value of l* was obtained for all 4 periods and for different combinations of k and i.The results of steady state condition showed that l* happened mostly in spring up to 5 clusters of the reservoir storage and the least happened in summer with one cluster. Then, the calculations of the reservoir release probability in each period for each class of inflow and storage have been made. The highest value has occurred for reservoir storage class 4 in autumn, winter, and spring seasons but in summer season, due to less inflow and high water demand, it has happened in reservoir storage class 5.In this research, Stochastic Dynamic Programming(SDP) of Jamishan dam reservoir is discussed using K-means method in classifying the inflow discharge seasonally for the 41 years’ historical data. Moran's method is also used to classify the storage volume of the reservoir by 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The reservoir inflow in each season is classified from 2 to 20 classes by programming in the Python environment and especially with Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters. Changing in the number of inflow cluster leads to changes in the transition probability matrix and this process would change the results of reservoir operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam reservoir performance. In general, the use of new classification methods such as K-means method in the discretization of reservoir inflow for the reservoir stochastic dynamic programming can be very beneficial and effective

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Copyright of Journal of Industrial Engineering Research in Production Systems is the property of Bu-Ali Sina University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Patient‐reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment decisions. For example, athletes may underreport symptoms to expedite return‐to‐play (RTP) from sports‐related concussion (SRC). Thus, clinicians must implement treatment policies that mitigate the Price of Naïveté, that is, the reduction in health outcomes due to naïvely believing strategically reported symptoms. In this study, we analyze dynamic treatment cessation decisions with strategic patients. Specifically, we formulate the Behavior‐Aware Partially Observable Markov Decision Process (BA‐POMDP), which optimizes the timing of treatment cessation decisions while accounting for known symptom‐reporting behaviors. We then analytically characterize the BA‐POMDP's optimal policy, leading to several practical insights. Next, we formulate the Behavior‐Learning Partially Observable Markov Decision Process (BL‐POMDP), which extends the BA‐POMDP by learning a patient's symptom‐reporting behavior over time. We show that the BL‐POMDP is decomposable into several BA‐POMDPs, allowing us to leverage the BA‐POMDP's structural properties for solving the BL‐POMDP. Then, we apply the BL‐POMDP to RTP from SRC using data from 29 institutions across the United States. We estimate the Price of Naïveté by comparing the BL‐POMDP to naïve benchmark policies. Accordingly, the BL‐POMDP reduces premature RTP by over 44% and provides up to 3.63 additional health‐adjusted athletic exposures per athlete compared to current practice. Overall, changing the interpretation of reported symptoms can better reduce the Price of Naïveté over adjusting treatment cessation thresholds. Therefore, to improve patients' health outcomes, clinicians must understand how strategic behavior manifests in PROs. [ABSTRACT FROM AUTHOR]

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate Dynamic Programming and the Rolling-Horizon-based Approximate Dynamic Programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic Dynamic Programming. [ABSTRACT FROM AUTHOR]

Introduction One of the Principles of water resources management is the optimal use of the reservoirs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize reservoir inflow has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the reservoir storage. By the programming in the Python environment, the historical reservoir inflow in each season is classified to different clusters and obtained the best inflow cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan reservoir. In general, the change in inflow classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the reservoir inflow discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize reservoir inflow for reservoir operation by SDP. Methodology K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. Each cluster is introduced with one of the data close to the centers. According to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the reservoir storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the reservoir volume. The study area includes Jamishan reservoir sub-basin with an area of 527.07 km² located in the southwest of Sanghar city near the Pirsalman hydrometric station. This watershed is the part of Kermanshah province which is between 32-34° to 34-53° North latitude and 47-22° to 47-52° East longitude. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively. Results and discussion Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90, -475.36 and -2.10, respectively. The results of the First-order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters, and in this table, the specified numbers indicate the probability of moving each cluster for each season. The results of Steady-state condition showed that l* happened mostly in spring up to 5 clusters of the reservoir storage and the least happened in summer with one cluster. Then, the calculations of the reservoir release probability in each period for each class of inflow and storage have been made. The highest value has occurred for reservoir storage class 4 in the autumn, winter, and spring seasons but in the summer season, due to less inflow and high water demand, it has happened in reservoir storage class 5. Conclusions In this research, the Stochastic Dynamic Programming (SDP) of the Jamishan dam reservoir is discussed using the K-means method in classifying the inflow discharge seasonally for the 41 years of historical data. Moran's method is also used to classify the storage volume of the reservoir into 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The reservoir inflow in each season is classified from 2 to 20 classes by programming in the Python environment and especially with the Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters. Changing the number of inflow clusters leads to changes in the transition probability matrix and this process would change the results of reservoir operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam reservoir performance. In general, the use of new classification methods such as the K-means method in the discretization of reservoir inflow for the reservoir stochastic dynamic programming can be very beneficial and effective. [ABSTRACT FROM AUTHOR]

We consider the security maintenance of information systems where the extent of vulnerability is partially observable. However, the exact extent of the vulnerability can be observed by paying an inspection fee. In each period, the decision‐maker needs to take one of three decisions: (i) do nothing, (ii) inspect and implement (fix the vulnerability) if needed, and (iii) directly implement. We prove that the optimal policy follows a threshold structure. For each value of k (the known vulnerability), there are two thresholds for the partial information: the lower of the two thresholds dictates whether for this value of k, inspection is optimal before a possible implementation or whether direct implementation (i.e., without inspection) is optimal. If inspection is done, another threshold determines whether an implementation is done or not. If neither threshold applies, it is optimal to do nothing. We develop a numerical procedure to find the decision variables in the maintenance policy. We extend the main model to include variable implementation and inspection costs. The optimality of the threshold policy is shown to hold under more general settings. We apply the model to a real‐world problem and demonstrate its applicability and value in managing security systems. Here, we study the security maintenance policies for three different real‐world telecommunications operators and find that these operators can significantly reduce the cost of managing their security by adopting our proposed policy. Another finding is that inspection is more beneficial for medium‐sized to large‐sized operators. [ABSTRACT FROM AUTHOR]

We consider the assignment of servers to two phases of service in a two-stage tandem queueing system when customers can abandon from each stage of service. New jobs arrive at both stations. Jobs arriving at station 1 may go through both phases of service and jobs arriving at station 2 may go through only one phase of service. Stage-dependent holding and lump-sum abandonment costs are incurred. Continuous-time Markov decision process formulations are developed that minimize discounted expected and long-run average costs. Because uniformization is not possible, we use the continuous-time framework and sample path arguments to analyze control policies. Our main results are conditions under which priority rules are optimal for the single-server model. We then propose and evaluate threshold policies for allocating one or more servers between the two stages in a numerical study. These policies prioritize a phase of service before "switching" to the other phase when total congestion exceeds a certain number. Results provide insight into how to adjust the switching rule to significantly reduce costs for specific input parameters as well as more general multi-server situations when neither preemption or abandonments are allowed during service and service and abandonment times are not exponential. [ABSTRACT FROM AUTHOR]

We study direct load control contracts that utilities use to curtail customers' electricity consumption during peak-load periods. These contracts place limits on the number of calls and total number of hours of power reduction per customer per year as well as the duration of each call. The stochastic dynamic program that determines how many customers to call and the timing and duration of each call for each day is an extremely difficult (NP-hard) optimization problem. We design a scenario-based approximation method to generate probabilistic allocation polices in a reasonable amount of time. Our approach consists of three approximations: deterministic approximation of demand, discretization of the expected demand, and aggregation/disaggregation of the resources. We show the relative information error resulting from the deterministic approximation is O (1 / n) , the discretization error is O (1 / n) , and the aggregation/disaggregation error is O (1 / n) , where n represents the length of the horizon. Finally, we show the total relative error is O (1 / n). Our error analysis establishes that our approximation method is near optimal. In addition, our extensive numerical experiments verify the high quality of our approximation approach. The error, conservatively measured, is quite small and has an average and standard deviation of 8.6% and 1.4%, respectively. We apply our solution approach to the data provided by three major utility companies in California. Overall, our study shows our procedure improves the savings in energy-generation cost by 37.7% relative to current practices. This paper was accepted by Chung Piaw Teo, optimization. Funding: The authors thank the UCLA Ziman Center's Howard and Irene Levine Program in Housing and Social Responsibility and the Morrison Family Center for Marketing Studies and Data Analytics for generous funding. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4493. [ABSTRACT FROM AUTHOR]

We introduce a novel simulated certainty equivalent approximation (SCEQ) method for solving dynamic stochastic problems. Our examples show that SCEQ can quickly solve high‐dimensional finite‐ or infinite‐horizon, stationary or nonstationary dynamic stochastic problems with hundreds of state variables, a wide state space, and occasionally binding constraints. With the SCEQ method, a desktop computer will suffice for large problems, but it can also use parallel tools efficiently. The SCEQ method is simple, stable, and can utilize any solver, making it suitable for solving complex economic problems that cannot be solved by other algorithms. [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]

Finding a common currency for benefits and hazards is a major challenge in optimal foraging theory, often requiring complex computational methods. We present a new analytic approach that builds on the Marginal Value Theorem and giving‐up densities while incorporating the nonlinear effect of predation risk. We map the space of all possible environments into strategy regions, each corresponding to a discrete optimal strategy. This provides a generalised quantitative measure of the trade‐off between foraging rewards and hazards. This extends a classic optimal diet choice rule‐of‐thumb to incorporate the hazard of waiting for better resources to appear. We compare the dynamics of optimal decision‐making for three foraging life‐history strategies: One in which fitness accrues instantly, and two with delays before fitness benefit is accrued. Foragers with delayed‐benefit strategies are more sensitive to predation risk than resource quality, as they stand to lose more fitness from a predation event than instant‐accrual foragers. [ABSTRACT FROM AUTHOR]

Problem definition: On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The system operator needs to dispatch a set of drivers and specify their delivery routes facing random demand that arrives over a fixed number of periods. The resulting stochastic dynamic program is challenging to solve because of the curse of dimensionality. Methodology/results: We propose a novel structured approximation framework to approximate the value function via a parametrized dispatching and routing policy. We analyze the structural properties of the approximation framework and establish its performance guarantee under large-demand scenarios. We then develop efficient exact algorithms for the approximation problem based on Benders decomposition and column generation, which deliver verifiably optimal solutions within minutes. Managerial implications: The evaluation results on a real-world data set show that our framework outperforms the current policy of the company by 36.53% on average in terms of delivery time. We also perform several policy experiments to understand the value of dynamic dispatching and routing with varying fleet sizes and dispatch frequencies. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72222011 and 72171112], China Association for Science and Technology [Grant 2019QNRC001], and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1171. [ABSTRACT FROM AUTHOR]

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps via real-time bidding on ad exchanges. Each location is characterized by its win curve, that is, the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform's objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The lower bound on the regret under any policy is of the order of the square root of the number of seasons, and the regret under our policy matches this lower bound. We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4441. [ABSTRACT FROM AUTHOR]

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Hops are the flowers of a specialty crop that provide unique flavors to craft beer. We study a multi‐year crop planning problem for a farmer who seeks to add hops production to the current production of a conventional crop. The farmer must dynamically determine the number of acres to allocate to each crop and design the terms of the forward contract under which brewers purchase the hops, considering the uncertainty of weather conditions, hops yield, hops spot market price, and conventional crop price. We formulate a multi‐period stochastic dynamic programming framework that incorporates statistical learning methods that depend on exogenous factors. We also develop an easy‐to‐implement learning‐based marginal total profit heuristic which can potentially be used as a decision support tool. Our numerical analyses suggest that yield learning is particularly important for a farmer who is considering investing in a high margin, but potentially risky, new crop such as hops. We also characterize the conditions under which yield learning is most beneficial for farmers. This paper contributes to the literature on specialty crop planning by introducing a multi‐year planning framework that incorporates unique characteristics of specialty crop production. We fill a gap in the crop planning literature by considering how farmers can learn about crop yields based on realized yields and exogenous factors such as weather conditions. Our paper is also of practical importance for farmers who seek to diversify their crop portfolio to hedge against risks associated with trade tensions and potential price drops for conventional crops. [ABSTRACT FROM AUTHOR]

The construction and operation of linear infrastructure has major impacts on biodiversity through loss of habitat, increased mortality and loss of connectivity. In particular, minimising the impact of roads which pass through ecologically sensitive areas on surrounding species at the construction and operational phases is critical for conservation. However, potential impacts are rarely known perfectly at the construction phase and early in the operational phase. To address this problem, a company could build flexibility into road operation so that it can respond rapidly to future ecological impacts if necessary. In this paper we analyse the value of this flexibility using stochastic dynamic programming and use the results to guide a global search algorithm to find high value roads in the region. We consider flexibility in terms of the proportion of traffic volume routed along the road, with the remainder passing along an existing higher-cost, lower-impact road. We applied this to an example scenario where a road must be routed through a region with a vulnerable species present. By incorporating flexibility, the proposed model was able to find a road that met a desired ending population of animals and was more valuable than roads found under existing design alternatives. [ABSTRACT FROM AUTHOR]

Empirical studies have shown strong evidence that a seller with a better customer feedback score is able to charge a higher price for his/her product or service. We study the problem faced by the seller of setting the price with the objective of maximizing expected revenue over a finite number of periods. Modeling the problem requires building a system of processes that includes: (1) the customer arrival and formation of customer reservation price; (2) the customer feedback collection and aggregation into a Seller Service Rating (SSR); and (3) determining how much to charge customers. Because of the technical difficulty in finding analytical solutions that fully reflect the closed-loop interconnections between these processes, we develop a simulation–optimization approach to solve the problem. We present a computational study and report on results of numerical experiments providing interesting insights on how the retailer could startegically align the price he charges with his service quality performance. [ABSTRACT FROM AUTHOR]