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Glide paths for a retirement plan with deferred annuities

JOURNAL OF PENSION ECONOMICS & FINANCE 2022年第4期21卷 565-581页

作者： Jang, Chul Clare, Andrew Owadally, Iqbal City Univ London London England Hanyang Univ Ansan South Korea

We construct investment glide paths for a retirement plan using both traditional asset classes and deferred annuities (DAs). The glide paths are approximated by averaging the asset proportions of stochastic optimal investment solutions. The objective function consists of power utility in terms of secured retirement income from purchased DAs, as well as a bequest that can be withdrawn before retirement. Compared with conventional glide paths and investment strategies, our DA-enhanced glide paths provide the investor with higher welfare gains, more efficient investment portfolios and more responsive retirement income patterns and bequest levels to different fee structures and personal preferences.

关键词： Deferred annuity glide-path strategy multi-stage stochastic programming retirement planning

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An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Shiri, Mahdyeh Fattahi, Parviz Sogandi, Fatemeh Alzahra Univ Fac Engn Dept Ind Engn Tehran Iran Univ Torbat Heydarieh Dept Ind Engn Torbat Heydarieh Iran

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

关键词： multiple channels COVID-19 vaccine supply chain Fuzzy programming multi-stage stochastic programming Blockchain technology

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Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

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COMPUTERS & CHEMICAL ENGINEERING 2023年第1期178卷

作者： Abad, Amin Reza Kalantari Khalil Barzinpour, Farnaz Pishvaee, Mir Saman Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Food security is a fundamental prerequisite of human survival. This study focuses on providing a novel biobjective model for the design of a green closed-loop pomegranate supply chain based on the value chain. To maximize the benefits of circular economy, we propose a thermochemical conversion process and develop a novel hybrid risk-neutral and risk-averse multi-stage stochastic programming (RNRAMSSP) approach to cope with supply uncertainty. We employ the expected value of perfect information (EVPI) and value of stochastic solution (VSS) to validate the proposed approach. For the VSS metric, we define a novel measure to compare the worst-case cost of the stochastic and deterministic model solutions. To demonstrate the applicability of the proposed model, we provide a real case study in Iran. Based on the VSS metric, the risk-neutral and risk-averse stochastic model solutions save up to 1% and 1.76% in the worst-case cost, respectively, compared to the deterministic model.

关键词： Value chain Closed-loop supply chain Pyrolysis Biochar multi-stage stochastic programming

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Dealing with stochasticity in Wood Remanufacturing Operations Planning

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2021年第2期6卷 522-540页

作者： Rafiei, Rezvan Nourelfath, Mustapha De Santa-Eulalia, Luis Antonio Univ Sherbrooke Business Sch 2500 Blvd Univ Sherbrooke Sherbrooke PQ J1K 2R1 Canada Univ Laval Dept Mech Engn Local 3344Pavillon Adrien Pouliot Quebec City PQ G1V 0A6 Canada

The planning process of wood remanufacturing operations encompasses challenging characteristic, including divergent co-production (one log tree may produce several different products), alternative processes (different receipts exist to produce the same products), short order cycle, dynamic market behaviour (with highly varying demand) and imperfect raw materials (due to its biological nature, the yield vary considerably). To deal with this complexity, in this paper random demand is modeled as scenario tree and three new predictive multi-stage stochastic programming models are developed with multiple objective functions. After implementing them employing datasets from a wood remanufacturing partner in Canada, the proposed models are compared to a reactive re-planning approach. The obtained results indicate that the new models exhibit higher quality solutions in comparison with their corresponding deterministic two-stage models. We also determine the number of stages for which the multi-stage programs provide better planning than the re-planning approach.

关键词： multi-stage stochastic programming Production planning Demand uncertainty Co-production Wood remanufacturing sector

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A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem

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APPLIED ENERGY 2023年第1期338卷

作者： Abdoli, B. Hooshmand, F. MirHassani, S. A. Arak Univ Fac Sci Arak Iran Amirkabir Univ Technol Dept Math & Comp Sci Tehran Polytech Tehran Iran

Carbon-capture-and-storage (CCS) is one of the leading technologies to reduce CO2 emissions. A commercial way to deploy CCS on a large scale is to sequestrate CO2 in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this manner, not only the CO2 emission is reduced, but also the oil production increases. The collaborative CCS-EOR planning problem determines the proper allocation of available CO2 to depleted reservoirs and the scheduling of the EOR operations. This problem is of great importance, especially when there are multiple oil reservoirs. This paper presents a deterministic mixed-integer linear programming model as an improvement of an existing model in the literature. Then, it is extended to a multistage stochastic model with endogenous uncertainty in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the time of uncertainty realization is decision-dependent. Our deterministic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables to about one-third. Also, providing the possibility of selecting pipeline types among different options as well as incorporating uncertainty may lead to a significant cost-saving. The proposed models are examined over two case-studies taken from the literature. The results indicate that in comparison to the deterministic model, the cost-saving achieved by incorporating uncertainty is about 8.8%, on average.

关键词： Carbon-capture-and-storage Enhanced-oil-recovery multi-stage stochastic programming Decision-dependent uncertainty Non-anticipativity constraints Value of stochastic solution

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A flexible system design approach for multi-facility capacity expansion problems with risk aversion

A I I E Transactions

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A I I E Transactions 2023年第2期55卷 187-200页

作者： Sixiang Zhao William B. Haskell Michel-Alexandre Cardin a Sino-US Global Logistics Institute Shanghai Jiao Tong University Shanghai China b Krannert School of Management Purdue University West Lafayette USA c Dyson School of Design Engineering Imperial College London UK

This article studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the Conditional Value-at-Risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if–then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.

关键词： Capacity expansion problem system design real options risk aversion multi-stage stochastic programming decision rules

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The continuous delayed distribution problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 148卷

作者： Smirnov, Dina Herer, Yale T. Avrahami, Assaf Eindhoven Univ Technol Sch Ind Engn Eindhoven Netherlands Technion Israel Inst Technol Fac Ind Engn & Management Haifa Israel EyeOn BV Aarle Rixtel Netherlands

Nowadays, decision makers (DMs) at companies have access to extensive and accurate data, which means they have the opportunity to grow and improve if they use the latent potential effectively. We address the complex problem of optimizing decisions in a multi-period one-warehouse multi-retailer inventory system with stochastic continuous demand and an option of delayed distributions. At the beginning of each period, the DM determines each retailer's target stock levels, as well as the number of items to be held back at a central location for later distribution(s). Such a policy offers partial inventory pooling through the holdback quantity. The decisions in each period are data-driven, i.e., made based on sales data available through an information system up to that point in time. We model the problem as a multi-stage stochastic program with recourse. For the general case, we develop a new recursive solution algorithm, which is based on subgradient optimization and an analysis of system dynamics. For the special case of two identical retailers and two periods, we provide explicit optimality conditions based on the subgradients. Using a large numerical study, we evaluate the performance of our proposed policy and compare it to two benchmark policies. We also demonstrate the impact of various problem parameters on the optimal solution and objective value.

关键词： Data-driven decision making One-warehouse multi-retailer Delayed distribution multi-stage stochastic programming Subgradient optimization Infinitesimal perturbation analysis

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stochastic operation of energy constrained microgrids considering battery degradation

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期212卷

作者： Aaslid, Per Korpas, Magnus Belsnes, Michael M. Fosso, Olav B. SINTEF Energy Res Dept Energy Syst Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway

Power systems with high penetration of variable renewable generation are vulnerable to periods with low generation. An alternative to retain high dispatchable generation capacity is electric energy storage that enables utilization of surplus power, where the electric energy storage contributes to the security of supply. Such systems can be considered as energy-constrained, and the operation of the electric energy storage must balance the minimization of the current operating costs against the risk of not being able to meet the future demand. Safe and efficient operation requires stochastic methods with sufficient foresight. Operation dependent storage degradation is a complicating factor. This paper proposes a linear approximation of battery state-of-charge degradation and implements it in a stochastic dual dynamic programming based energy-management model in combination with cycling degradation. The long-term implications of degradation modeling in the daily operation are studied for a small Norwegian microgrid with variable renewable power generation and limited dispatchable generation capacity as well as battery and hydrogen storage to balance supply and demand. Our results show that the proposed strategy can prolong the expected battery lifetime by more than four years compared to the naive stochastic strategy but may cause increased degradation for other system resources.

关键词： Energy management Electric energy storage multi-stage stochastic programming Battery degradation

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stochastic single machine scheduling problem as a multi-stage dynamic random decision process

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COMPUTATIONAL MANAGEMENT SCIENCE 2021年第3期18卷 267-297页

作者： Roohnavazfar, Mina Manerba, Daniele Fotio Tiotsop, Lohic Pasandideh, Seyed Hamid Reza Tadei, Roberto Politecn Torino Dept Control & Comp Engn Turin Italy Kharazmi Univ Dept Ind Engn Tehran Iran Univ Brescia Dept Informat Engn Brescia Italy

In this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage stochastic programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time.

关键词： Single machine scheduling stochastic sequence-dependent setup times stochastic processing times Learning effect Deterministic approximation multi-stage stochastic programming

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Supplier selection decision making in disaster response

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 143卷 106412-106412页

作者： Olanrewaju, Oluwasegun Gabriel Dong, Zhijie Sasha Hu, Shaolong Texas State Univ Ingram Sch Engn Ind Engn Program San Marcos TX 78666 USA Southwest Jiatong Univ Sch Econ & Management Chengdu Peoples R China

In the United States, natural disasters have seriously affected the country's economy as well as people. They have also been challenging the country's emergency response capacity. In order to reduce the damage caused by disasters, there is a need for the proper planning and efficient management of relief supplies in place before the onset of a disaster. This paper proposes to integrate the decision making of supplier selection into the preparedness stage in responding to disasters for timely distribution of relief supplies. Agreement terms including the commitment quantity of the relief agency, the reserve capacity of the suppliers, and the quantity discount rate are considered. Compared to the traditional two-stage stochastic programming approach, which is commonly used in the field of humanitarian logistics, a multi-stage stochastic programming model is presented because of the stochastic nature of the proposed problem, and the need to make sequential decisions over time. In this research, a real-world setting which considers disasters such as earthquakes, floods and hurricanes in the mainland United States is used as a case study. The sensitivities of the model for variation of parameters are also studied. This paper provides the relief agencies insights on how the agreement terms affect the supplier selection decision, and how the total expected costs of having an agreement in place and procuring relief items from the suppliers can be minimized.

关键词： Disaster response Humanitarian logistics Supplier selection Procurement multi-stage stochastic programming

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