Finite Planning Horizon Research Articles

A Replenishment Policy for an Inventory Model with Price-Sensitive Demand with Linear and Quadratic Back Order in a Finite Planning Horizon

A Replenishment Policy for an Inventory Model with Price-Sensitive Demand with Linear and Quadratic Back Order in a Finite Planning Horizon

Optimal fiscal policy under finite planning horizons

AbstractWe propose a novel framework that revisits the seminal Chamley-Judd zero capital taxation result in light of bounded rationality stemming from a finite policy planning horizon and structural frictions in fiscal institutions. We show a mechanism that generates positive optimal capital taxation in the long run. Our numerical results indicate that the current tax system in the United States could be near-optimal in a constrained environment where policymakers exhibit limited policy planning horizons and imperfect altruism toward household welfare under subsequent governments.

Blockchain-Enhanced Inventory Management in Decentralized Supply Chains for Finite Planning Horizons

Blockchain-Enhanced Inventory Management in Decentralized Supply Chains for Finite Planning Horizons

Lot sizing model for multiple products over finite planning horizon under the effect of learning and time-value of money with cap-and-trade and carbon tax regulations: a fuzzy framework

Lot sizing model for multiple products over finite planning horizon under the effect of learning and time-value of money with cap-and-trade and carbon tax regulations: a fuzzy framework

Make-up strategies with finite planning horizons but infinitely forward-looking asset prices

How effective are forward-guidance and make-up strategies? Standard models find them extremely effective, but by assuming households’ inflation expectations respond much more strongly than in the data. Models where households discount the future find them much less effective and match the small reaction of inflation expectations, but not the actual large reaction of asset prices. We build a model that rationalizes both. Households cognitively discount the future, but more forward-looking financial market professionals incorporate their expectations of future policy into the long-term nominal rates faced by all. We find that make-up strategies have sizably better stabilization properties than inflation targeting.

The Effects of Fiscal Policy When Planning Horizons are Finite

AbstractWe study the importance of planning horizons for fiscal multipliers in a New‐Keynesian model with bounded rationality. We show that, when agents have shorter planning horizons, government spending multipliers are smaller, whereas labor tax cut multipliers are larger. Furthermore, Ricardian equivalence breaks down, and transfer shocks feature a negative multiplier. Results are driven by the cognitive limitations of finite planning horizons that lead agent's expectations to deviate from the fully rational benchmark. We find larger investment responses, which are more in line with empirical findings than those of models with longer planning horizons, rule‐of‐thumb households, or a Blanchard–Yaari structure.

Split demand and deliveries in an integrated three-level lot sizing and replenishment problem

We address a three-level lot sizing and replenishment problem (3LSRP), an extension of the production routing problem. We consider one production plant, with limited capacity, that produces items over a discrete and finite planning horizon. The items are sent from the plant to the warehouses using direct capacitated shipments, and routes are designed to deliver the goods from the warehouses to the retailers. The objective is to minimize the sum of all operational costs. We contribute to the literature on integrated problems by introducing the concept of demand splitting which allows the demand from a specific period to be satisfied by deliveries over multiple periods. Our second contribution lies in the development of the two heuristics that we propose: a top-down approach and a bottom-up approach. The production decisions and the transportation decisions between the warehouses and the retailers are the leading decisions in the top-down and the bottom-up approach, respectively. We compare them to a branch-and-cut algorithm that we also developed. We run computational experiments to assess the performance of each heuristic. We analyze the impact of allowing splitting possibilities. The results show that the top-down approach obtains better performance in terms of cost, except when we allow demand splitting only. The bottom-up approach leads to a greater use of delivery splitting. Results also show that we obtain large gains thanks to the splitting possibilities.

A Supply Chain Inventory Model for a Deteriorating Material under a Finite Planning Horizon with the Carbon Tax and Shortage in All Cycles

A Supply Chain Inventory Model for a Deteriorating Material under a Finite Planning Horizon with the Carbon Tax and Shortage in All Cycles

Selective Maintenance Strategy for a Finite Planning Horizon Considering Imperfect Maintenance

Most selective maintenance research strategies ignore the comprehensive evaluation of numerous following missions in favor of focusing just on the reliability of the next mission. In the same circumstance, however, assessing simply impending missions differs from considering the overall system’s maintenance planning outcomes. In this study, a decision‐making model for selective maintenance is developed by evaluating the total system reliability over a finite planning horizon. The purpose is to calculate the maintenance activities for each system component during each break and the best number of maintenance interruptions for the planning horizon to achieve maximum system reliability. Consequently, the selective maintenance problem is formulated as a max–min optimization model. Also, a hybrid imperfect maintenance model is used to formulate the component improvement after maintenance. Finally, simple case illustrations of maintaining the production system in coal transportation are given based on the assumed data.

Raising climate ambition in emissions trading systems: The case of the EU ETS and the 2021 review

We provide a quantitative assessment of policy options to inform the 2021 review of the EU Emissions Trading System (ETS) and raise climate ambition. We use a permit trading model in which firms utilize rolling finite planning horizons, which replicates historical price and banking developments well compared to an infinite horizon. When firms have bounded foresight, indirectly raising ambition through the Market Stability Reserve (MSR) is not equivalent to directly raising ambition through the emissions cap trajectory. Leveraging the MSR turns out to be efficiency improving as it compensates for firms’ bounded foresight by frontloading abatement efforts. We analyze the MSR interaction with the cap trajectory to exploit synergies and minimize the cost of raising ambition. We also provide a comparative assessment of a complete suite of changes in the MSR parameters. Whatever its parameters, MSR-induced resilience to demand shocks remains limited by design: the MSR acts more as an unconditional price support provider than as a responsive price stabilizer.

Sustainable operation of surface-groundwater conjunctive use systems in the agricultural sector

Several conjunctive use approaches can be distinguished. Drought cycling of groundwater (GW) usage and storage relies on more surface water (SW) during wetter years and delivers more water from GW during drought years. This method has the benefit of temporal changes in water availability. Additionally, it is usually desirable in areas with internal variability of SW where surface storage of wet-year surpluses is uneconomical, suffer excessive evaporative losses, or cause unacceptable environmental disruption. In previous studies, the purpose of operating the drought cycling was to reduce operating costs. In these studies, the objective function of the proposed model was to minimise the present value cost derived from the system design and operation to satisfy a predefined demand during a finite planning and operation horizon. However, it is important to consider other objectives in operating water resources systems, including minimising water shortages accurately. Hence, in this study, two scenarios were focused on: 1) mi-nimising water shortagages, 2) minimising operational costs. Pareto solutions are then presented with the objectives of minimising costs and water deficit. In this study, the weighting method has been used to extract Pareto options. The results show that reducing costs from 234 to 100 mln USD will increase water shortage from 9.3 to 11.3 mln m3.

Subgame Perfect Nash Equilibrium for Dynamic Pricing Competition with Finite Planning Horizon

Subgame Perfect Nash Equilibrium for Dynamic Pricing Competition with Finite Planning Horizon

Make-up Strategies with Finite Planning Horizons but Forward-Looking Asset Prices

Make-up Strategies with Finite Planning Horizons but Forward-Looking Asset Prices

Make-up Strategies with Finite Planning Horizons but Forward-Looking Asset Prices

Make-up Strategies with Finite Planning Horizons but Forward-Looking Asset Prices

Capacity expansion strategies for electric vehicle charging networks: Model, algorithms, and case study

AbstractGovernments in many jurisdictions are taking measures to promote the use of electric vehicles. As part of this goal, it is crucial to provide a sufficient number of charging stations to alleviate drivers' anxieties associated with the range of the vehicle. The goal of this research is to help governments develop vehicle charging networks for public use via the application of multistage stochastic integer programming model that determines both the locations and capacities of charging facilities over finite planning horizons. The logit choice model is used to estimate drivers' choices of nearby charging stations. Moreover, we characterize the charging demand as a function of the charging station quantity to reflect the range anxiety of consumers. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. An approximation algorithm, a heuristic algorithm, and a branch‐and‐price algorithm are designed to solve the model. We conduct numerical experiments to test the efficiency of these algorithms. Importantly, each algorithm has advantages over the CPLEX MIP solver. Finally, the City of Oakville in Ontario, Canada, is used to demonstrate the effectiveness of this model.

Value of structural health information in partially observable stochastic environments

Efficient integration of uncertain observations with decision-making optimization is key for prescribing informed intervention actions, able to preserve structural safety of deteriorating engineering systems. To this end, it is necessary that scheduling of inspection and monitoring strategies be objectively performed on the basis of their expected value-based gains that, among others, reflect quantitative metrics such as the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM). In this work, we introduce and study the theoretical and computational foundations of the above metrics within the context of Partially Observable Markov Decision Processes (POMDPs), thus alluding to a broad class of decision-making problems of partially observable stochastic deteriorating environments that can be modeled as POMDPs. Step-wise and life-cycle VoI and VoSHM definitions are devised and their bounds are analyzed as per the properties stemming from the Bellman equation and the resulting optimal value function. It is shown that a POMDP policy inherently leverages the notion of VoI to guide observational actions in an optimal way at every decision step, and that the permanent or intermittent information provided by SHM or inspection visits, respectively, can only improve the cost of this policy in the long-term, something that is not necessarily true under locally optimal policies, typically adopted in decision-making of structures and infrastructure. POMDP solutions are derived based on point-based value iteration methods, and the various definitions are quantified in stationary and non-stationary deteriorating environments, with both infinite and finite planning horizons, featuring single- or multi-component engineering systems.

Demand-driven condition-based maintenance planning under Markovian deterioration of machine condition

In this paper, single product single machine systems under Markovian deterioration of machine condition throughout a specified finite planning horizon are studied. It is assumed that the machine is subject to random failures and that any maintenance activities carried out in a period, reduces the system’s potential production capacity during that period. Furthermore, it is assumed that the machine is minimally repaired at failure, and PM is carried out, after inspection, to restore the machine to an ‘as-good-as-new’ status. The objective of the study is to find the optimal intervals for inspection and preventive maintenance (PM) activities in condition-based maintenance (CBM) planning with a discrete monitoring framework subject to minimize the sum of inspection, PM, minimal repair, and backlog costs. To achieve the goal, a stochastic dynamic programming model that enumerates demand is presented calling the demand-driven CBM model. The numerical results show that this model decreases the total cost significantly that depends on the demand and the unit backlog cost, which is an increasing and a concave function in the unit backlog cost regardless of the initial machine state.

Lot Sizing Model for multiple products over Finite Planning Horizon under the Effect of Learning and Time-Value of Money with cap-and-trade and carbon tax regulations: A Fuzzy Framework

Lot Sizing Model for multiple products over Finite Planning Horizon under the Effect of Learning and Time-Value of Money with cap-and-trade and carbon tax regulations: A Fuzzy Framework

Perceived uncertainty shocks, excess optimism-pessimism, and learning in the business cycle

What are the effects of beliefs, sentiment, and uncertainty, over the business cycle? To answer this question, we develop a behavioral New Keynesian macroeconomic model, in which we relax the assumption of rational expectations. Agents are, instead, boundedly rational: they have a finite-planning horizon, and they learn about the economy over time. Moreover, we allow agents to have a potentially asymmetric loss function in forecasting, which creates a direct channel for expected variances to affect the economy. In forming expectations, agents may be subject to shifts in optimism and pessimism (sentiment) and their beliefs may be influenced by their perceptions about future uncertainty. We estimate the behavioral model using Bayesian methods and exploit a large number of subjective expectation series (both point and density forecasts) at different horizons from the Survey of Professional Forecasters. We find that sentiment shocks are the key source of business cycle fluctuations. Shifts in perceived uncertainty can also affect real activity and inflation through a confidence channel, as they play an important role in belief formation. Overall, the results shed light on the importance of behavioral forces over the business cycles, and on the contribution and interaction of first-moment - sentiment - shocks versus second-moment - perceived uncertainty - shocks.

Benders decomposition for a stochastic three-level lot sizing and replenishment problem with a distribution structure

We address a stochastic three-level lot sizing and replenishment problem with a distribution structure in a two-stage decision process. We consider one production plant that produces one type of item over a discrete and finite planning horizon. The items produced are transported to warehouses and then to retailers using direct shipments. Each retailer is linked to a unique warehouse and there are no transfers between warehouses nor between retailers. The stochasticity comes from the uncertainty in the demand at the retailer level and is modelled through scenarios. The setup decisions are made in the first stage and the production, transportation and inventory decisions are made in the second stage, once the demands are revealed. The objective is to minimize the sum of the fixed production and replenishment costs, and of the expected variable inventory holding costs among all scenarios. We also study an extension where we allow for lost sales at the retailer level. We use a Benders decomposition approach and develop a Benders-based branch-and-cut algorithm to efficiently solve the problem. We take advantage of the substructures identified in the decomposition and design efficient procedures to solve the subproblems obtained. We also propose computational enhancements to speed up the solution process. Finally, we perform extensive computational experiments to assess the performance of our decomposition approach and analyze the impact of the enhancements. The Benders-based branch-and-cut algorithm we propose clearly outperforms CPLEX.