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stochastic programming For Order Allocation And Production Planning

Computer Systems Science & Engineering 2022年第1期40卷 75-85页

作者： Phan Nguyen Ky Phuc International University-Vietnam National University Vietnam National UniversityHoChiMinh City70000Vietnam

stochastic demand is an important factor that heavily affects production *** influences activities such as purchasing,manufacturing,and selling,and quick adaption is *** production planning,for reasons such as reducing costs and obtaining supplier discounts,many decisions must be made in the initial stage when demand has not been *** effects of non-optimal decisions will propagate to later stages,which can lead to losses due to overstocks or *** find the optimal solutions for the initial and later stage regarding demand realization,this study proposes a stochastic two-stage linear program-ming model for a multi-supplier,multi-material,and multi-product purchasing and production planning *** objective function is the expected total cost after two stages,and the results include detailed plans for purchasing and production in each demand ***-scale problems are solved through a deterministic equivalent transformation *** solve the problems in the large scale,an algorithm combining metaheuristic and sample average approximation is *** algorithm can be implemented in parallel to utilize the power of the *** algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given,then the problems of the first and second stages can be decomposed.

关键词： Mixed integer programming two-stage stochastic programming production planning order allocation

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AI-enhanced robust method for integrated healthcare resource pre-positioning and patient scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第2期63卷 729-757页

作者： Liu, Yang Zhang, Jianghua Chan, Felix T. S. Peking Univ Guanghua Sch Management Beijing Peoples R China Shandong Univ Sch Management Jinan Shandong Peoples R China Macau Univ Sci & Technol Dept Decis Sci Taipa Macao Peoples R China Shandong Univ Sch Management 27 Shanda South Rd Jinan Shandong Peoples R China Macau Univ Sci & Technol Dept Decis Sci Ave Wai Long Taipa Macao Peoples R China

This paper aims to optimise healthcare resource pre-positioning, patient scheduling, and patient transferring under uncertain demands and stochastic resource consumption. We propose a two-stage stochastic programming model that formulates the patient scheduling problem as a Markov decision process. To address complexities and uncertainties, we use Artificial Intelligence (AI) techniques to improve both model formulation and algorithmic performance. To tackle the limited data challenge, we introduce a Wasserstein distance-based ambiguity set and propose a two-stage distributionally robust optimisation (DRO) model, which derives a deterministic equivalent using the Lagrangian dual of non-anticipativity constraints. The solution process is accelerated with a scenario decomposition approach and the K-means clustering method. Both theoretical and numerical results demonstrate the consistency of the two-stage DRO model with the sample average approximation (SAA) method. The potential of AI to improve the model's performance is evident through the significant reduction in computation time achieved with the K-means clustering approach without compromising solution quality. Compared to the SAA method, the DRO model exhibits a considerable reduction in the waiting penalty cost for out-of-sample cases, ranging from $ 43.06\% $ 43.06% to $ 81.23\% $ 81.23%. Numerical results show that the proposed algorithm outperforms SAA methods and several benchmark policies in terms of computational efficiency and solutions.

关键词： Healthcare resource pre-positioning patient scheduling two-stage stochastic programming Wasserstein distance K-means clustering

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Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

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ENERGY 2020年 205卷 117926-117926页

作者： Dadashi, Mojtaba Haghifam, Sara Zare, Kazem Haghifam, Mahmoud-Reza Abapour, Mehdi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

This article proposes a novel short-term decision-making model for electricity retailers within the electricity market and in the presence of Demand Response Aggregators (DRA). In this framework, retailers provide their required energy through the Day-ahead market, Real-time market and forward bilateral contracts. On the other hand, retailers participate in demand response (DR) programs based on the DRA's DR offers to maximize their benefits. In this study, to cope with the uncertainty, the two-stage stochastic programming scheme is utilized. In the first stage, the participation of retailers in the Day-ahead market, as well as power purchased from the forward contracts are decided. In the second stage, the power exchanged with the Real-time market, and the DRA are determined after the realization of stochastic parameters. On the contrary, the DRA enhances its profit through submitting offers to retailers for two DR options. For solving the problem, a strategy based on the Game Theory, the Bi-Level stochastic programming approach is exploited to maximize the profit of both players in a competitive environment. The model is turned into a Single-Level problem by substituting the lower-level with its Karush-Kuhn-Tucker conditions. Finally, merits of the presented method are illustrated in a typical case study. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electricity retailer Demand response aggregator Bi-level problem two-stage stochastic programming

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Integrated differentiated time slot pricing and order dispatching with uncertain customer demand in on-demand food delivery

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期323卷 471-489页

作者： Zhang, Bo Hassini, Elkafi Zhou, Yun Zhao, Meng Hu, Xiangpei Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China McMaster Univ DeGroote Sch Business Hamilton ON L8S 4L8 Canada

Differentiated time slot pricing (DTSP) is a promising approach to enhance the efficiency and cost-effectiveness of food delivery platforms by influencing customers' choices regarding delivery time slots. In this paper, we investigate the integrated problem of DTSP at the tactical level and order dispatching at the operational level, formulating it as a two-stage stochastic programming model. The first-stage model determines the delivery price for each time slot to maximize the system's expected profit. The second-stage model generates the optimal order dispatching plan to minimize the generalized system cost under each stochastic scenario. To efficiently estimate the order dispatching cost for each scenario, we develop an order consolidation dispatching algorithm (OCDA) to solve the second-stage order dispatching subproblem under each demand scenario. Building on OCDA, we propose a hybrid adaptive large neighborhood search (HALNS) heuristic to solve the integrated problem. Extensive case studies based on real-world data verify the effectiveness of the proposed approach and demonstrate the benefits of DTSP strategy. Our numerical analysis provides important managerial insights for operating food delivery platforms.

关键词： Logistics Differentiated time slot pricing On-demand order dispatching two-stage stochastic programming Adaptive large neighborhood search

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Resilient transportation network design with disruption uncertainty and lead times

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期322卷 827-840页

作者： Mullerklein, Daniel Fontaine, Pirmin Catholic Univ Eichstaett Ingolstadt Ingolstadt Sch Management Auf Schanz 49 D-85049 Ingolstadt Germany Catholic Univ Eichstaett Ingolstadt Math Inst Machine Learning & Data Sci Auf Schanz 49 D-85049 Ingolstadt Germany

Cost-efficient and reliable transports are needed to supply products competitively. Thus, particularly in increasingly complex and global supply chains, identifying the optimal transportation mode is a critical decision. Transportation modes, however, are prone to disruptions, such as hurricanes, low water levels, or port shutdowns, resulting in transportation stops and cost increases. To counteract these disruptions, different resilience strategies are studied to increase the capability of a network to withstand, adapt, and recover from disruptions. For a cost-optimal use, it is necessary to determine the optimal mix of strategic, tactical, and operational strategies. We provide a decision-support model that decides on the optimal mix of resilience strategies, such as multi- sourcing, inventory, or operational re-routing, fora supply chain with transportation disruption uncertainty to minimize total expected costs. The problem is formulated as a two-stage stochastic mixed-integer linear program that explicitly considers lead times. To handle large instances, we propose a Benders decomposition approach enhanced through lower-bound lifting and valid inequalities, branch-and-benders-cut, and a warm- start heuristic. Computational experiments show that large instances can be solved to near-optimality, whereas a commercial solver does not find feasible solutions. We present a case study fora company's inbound supply chain design with recurring transportation cost uncertainty. Considering disruption and lead time effects, a mix of resilience strategies from strategic to operational level leads to cost improvements of up to 50%. Furthermore, we show that the ability to predict disruptions can further reduce resilience-related costs by 10% if sufficient operational re-routing capacities are available.

关键词： Supply chain management Logistics Resilience two-stage stochastic programming Transportation disruption

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A two-stage stochastic model for an industrial symbiosis network under uncertain demand

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APPLIED MATHEMATICAL MODELLING 2024年 125卷 444-462页

作者： Das, Guelesin Sena Yesilkaya, Murat Birgoeren, Burak Kirikkale Univ Dept Ind Engn Fac Engn TR-71450 Kirikkale Turkiye Tokat Gaziosmanpasa Univ Niksar Vocat Sch TR-60600 Niksar Tokat Turkiye

Industrial Symbiosis (IS) networks are structures built by volunteer companies with the aim of exchanging unused or residual resources, benefiting all participating companies. The profitability of these volunteer companies is critical as it affects the sustainability of these networks. Fluctuations in a participating company's production level can potentially disrupt its network by altering the quantity and availability of wastes and by-products. In light of these considerations, we analyse the impact of fluctuations in demand for final products of the companies on company profitability, and waste and by-product usage. For this purpose, we formulated a two-stage stochastic programming model and solved it using the Sample Average Approximation (SAA) method. We tested our model on a theoretical IS network comprising companies in the forest products industry. The results demonstrate that companies in the network keep exchanging by-products and remain profitable despite uncertainties in demand. Consequently, we conclude that the established network exhibits resilience to demand fluctuations, which is an important aspect of its sustainability.

关键词： OR in environment and climate change Circular economy Industrial symbiosis Production planning two-stage stochastic programming

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Meal pickup and delivery problem with appointment time and uncertainty in order cancellation

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 193卷

作者： Xue, Guiqin Wang, Zheng Sheu, Jiuh-Biing Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116025 Peoples R China Natl Taiwan Univ Dept Business Adm 1 Sec 4Roosevelt Rd Taipei 10617 Taiwan

Online-ordered meal logistics services (OMLSs) that accept online bookings and make vehicle plans to deliver meals from restaurants to customers have recently emerged. Customers have the option to cancel orders that are not delivered by appointment times, leading to significant financial, reputational, and customer losses for the OMLS providers. This study aims to make an appropriate vehicle plan for OMLS providers to minimize the expected total cost under the uncertainty of order cancellations. The problem is formulated as a two-stage stochastic programming model, and sample average approximation equivalent problems are generated using Monte Carlo simulation. To solve the equivalent problems, a parallel adaptive large neighborhood search (pALNS) with statistical guarantees is developed. Experiment results show that the vehicle plan derived from the ALNS is much better than the solution found by Gurobi within 10,800 s, with an average improvement of 14.90%. Additionally, the pALNS provides better statistical bounds in a shorter time compared to both the ALNS and the unsynchronized pALNS. Analytical experiments reveal that earlier cancellations lead to more severe consequences, offering valuable insights for OMLS providers to implement proactive measures to retain "urgent" customers.

关键词： Order cancellation Sample average approximation two-stage stochastic programming Parallel adaptive large neighborhood search

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Optimal Configuration of Mobile-Stationary Hybrid Energy Storage Considering Seismic Hazards

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ENERGIES 2025年第8期18卷 2052-2052页

作者： Deng, Chengcheng Shen, Xiaodong Tang, Xisheng Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China

The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to provide resilience is not enough to justify having them installed economically. Therefore, this paper proposes a two-stage stochastic mixed-integer programming (SMIP) model for the configuration of stationary energy storage systems (SESSs) and mobile energy storage systems (MESSs) during earthquakes. The proposed model comprehensively considers both normal and disaster operation scenarios of DNs, maximizing the grid's economic efficiency and security. The first stage is to make decisions about the location and size of energy storage, using a hybrid configuration scheme of second-life batteries (SLBs) for SESSs and fresh batteries for MESSs. In the second stage, the operating costs of DNs are evaluated by minimizing normal operating costs and reducing load loss during seismic events. Additionally, this paper proposes a scenario reduction method based on hierarchical sampling and distance reduction to generate representative fault scenarios under varying earthquake magnitudes. Finally, the progressive hedging algorithm (PHA) is employed to solve the model. The case studies of the IEEE 33-bus and 12-node transportation network are conducted to validate the effectiveness of the proposed method.

关键词： seismic hazards mobile energy storage second-life batteries two-stage stochastic programming progressive hedging algorithm

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Aircraft maintenance scheduling under uncertain task processing time

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Villafranca, Matias Delgado, Felipe Klapp, Mathias Pontificia Univ Catolica Chile Dept Transport & Logist Vicun Mackenna 4860Casilla 306Correo 22 Santiago Chile

Unexpected delays while executing aircraft maintenance tasks can result in costly operational disruptions for airlines, including expensive flight delays and overtime. In this study, we address uncertainty in maintenance task processing times by designing a daily and cost-effective aircraft maintenance schedule using two-stage stochastic programming. In the first stage, we determine which daily maintenance tasks to outsource and which to complete with in-house technicians. Additionally, we schedule each task, outsourced or in-house, for its respective aircraft, specifying the start time and assigned maintenance base. In-house tasks are further assigned to a technician's work sequence. In the second stage, the start time of each task and the departure time of each flight are adjusted based on a specific realization of task processing times. We aim to minimize the expected costs incurred for outsourced maintenance tasks, overtime, and flight delays. To solve our model, we design an ad-hoc Adaptive Iterated Local Search heuristic that explores first-stage solutions via an efficient evaluation of the second-stage cost. We also present a proof of concept by testing our approach in a set of computationally simulated instances. Our proposed methodology yields 74% and 34% average cost savings compared to a deterministic approach assuming expected task processing times and to a conservative solution planning ahead with maximum possible processing times for each task, respectively. Moreover, we obtain 14% average cost savings compared to a benchmark solution, which plans maintenance tasks with an optimized time buffer parameter between consecutive task assignments. Furthermore, we study the cost impact of varying structural parameters, such as task granularity, processing time variability, workload, and cost structure.

关键词： Aircraft maintenance Heuristic algorithm two-stage stochastic programming Task scheduling

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Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期321卷 23-40页

作者： Almoghrabi, Mohammed Majthoub Sagnol, Guillaume Tech Univ Berlin Inst Math Str 17 Juni 136 D-10623 Berlin Germany

We study the elective surgery planning problem in a hospital with operating rooms shared by elective and emergency patients. This problem is split in two distinct phases. First, a subset of patients to be operated in the next planning period is selected and the selected patients are assigned to a block and a tentative starting time. Then, in the online phase of the problem, a policy decides how to insert the emergency patients in the schedule and may cancel planned surgeries. The overall goal is to minimize the expectation of a cost function representing the assignment of patient to blocks, case cancellations, overtime, waiting time and idle time. We model the offline problem by a two-stage stochastic program, and show that the optimal second-stage costs can be approximated by a convex piecewise linear surrogate model that can be computed in a preprocessing step. This results in a mixed integer program which can be solved very fast, even for large instances of the problem. We also describe a greedy policy for the online phase of the problem, and analyze the performance of our approach by comparing it to both heuristic methods or approaches relying on sampling average approximation (SAA) on a large set of benchmarking instances. Our simulations indicate that our approach can reduce the expected costs by as much as 30% compared to heuristic methods and it can solve problems with 1000 patients in about one minute, while SAA-approaches fail to obtain good solutions within 30 min on small instances.

关键词： OR in health services Surgery scheduling two-stage stochastic programming

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