Pub Date : 2025-06-01Epub Date: 2025-02-16DOI: 10.1016/j.orp.2025.100329
Philipp Gabriel Mazur, Johannes Werner Melsbach, Detlef Schoder
Cargo stability is a crucial requirement for safe cargo loading and transport. Current state-of-the-art approaches simplify cargo loading to an idealized static problem and employ geometric- and force-based approaches. In this research, we model cargo loading stability as a dynamic problem and propose two approaches. We use (a) a physical simulation using a real-time physics engine fitted for cargo loading and (b) a physics-informed learning model trained on cargo loading data. Both approaches are capable of handling dynamic physical behavior, either explicitly through simulation, or implicitly through training a recurrent neural network on physically-biased sequential cargo loading data. Given our two objectives of maximal accuracy and minimal runtime, our benchmarking results show that our approaches can outperform current state-of-the-art static stability methods in terms of accuracy depending on the complexity scenario, but consume more runtime.
{"title":"Physical question, virtual answer: Optimized real-time physical simulations and physics-informed learning approaches for cargo loading stability","authors":"Philipp Gabriel Mazur, Johannes Werner Melsbach, Detlef Schoder","doi":"10.1016/j.orp.2025.100329","DOIUrl":"10.1016/j.orp.2025.100329","url":null,"abstract":"<div><div>Cargo stability is a crucial requirement for safe cargo loading and transport. Current state-of-the-art approaches simplify cargo loading to an idealized static problem and employ geometric- and force-based approaches. In this research, we model cargo loading stability as a dynamic problem and propose two approaches. We use (a) a physical simulation using a real-time physics engine fitted for cargo loading and (b) a physics-informed learning model trained on cargo loading data. Both approaches are capable of handling dynamic physical behavior, either explicitly through simulation, or implicitly through training a recurrent neural network on physically-biased sequential cargo loading data. Given our two objectives of maximal accuracy and minimal runtime, our benchmarking results show that our approaches can outperform current state-of-the-art static stability methods in terms of accuracy depending on the complexity scenario, but consume more runtime.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100329"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-17DOI: 10.1016/j.orp.2025.100326
Anindya Rachma Dwicahyani , I Nyoman Pujawan , Erwin Widodo
The increasing recognition of environmental concerns and the adoption of Extended Producer Responsibility (EPR) have contributed significantly to the development of sustainable industries. Reverse logistics (RL) and closed-loop supply chain (CLSC) are two concepts that involve effective management of product returns to minimise consumer waste. In this paper, the authors develop a mathematical model for inventory management in CLSC systems with multiple recovery options, including product, material and energy recoveries. The model was developed based on a supply chain structure that includes a supplier, a manufacturer, a retailer, and a material recovery facility (MRF). The proposed model helps to maximise the profit of the supply chain. A hybrid method of analytical and numerical approaches is used to determine the optimal inventory decisions, including order cycle time and number of shipments between parties. Solution procedures are proposed for decentralised (DDMS) and centralised decision-making structures (CDMS). Furthermore, a profit-sharing mechanism is also analysed in the model. A sensitivity analysis is carried out to investigate the model's behaviour concerning variations in crucial parameters, including demand, product returns, recycling cost, post-consumer recycled content, and energy recoverable item rate. The results of this study show that the CDMS, without profit-sharing, generates the highest profits for the system. On the other hand, implementing a profit-sharing mechanism provides a fairer profit enhancement to the parties involved. Applying the energy recovery at the supplier results in financial benefits for the system. Additional discussion is carried out to understand the impact of energy recovery on the model's optimal solution.
{"title":"Optimising a closed-loop supply chain inventory system with product, material, and energy recoveries under different coordination structures","authors":"Anindya Rachma Dwicahyani , I Nyoman Pujawan , Erwin Widodo","doi":"10.1016/j.orp.2025.100326","DOIUrl":"10.1016/j.orp.2025.100326","url":null,"abstract":"<div><div>The increasing recognition of environmental concerns and the adoption of Extended Producer Responsibility (EPR) have contributed significantly to the development of sustainable industries. Reverse logistics (RL) and closed-loop supply chain (CLSC) are two concepts that involve effective management of product returns to minimise consumer waste. In this paper, the authors develop a mathematical model for inventory management in CLSC systems with multiple recovery options, including product, material and energy recoveries. The model was developed based on a supply chain structure that includes a supplier, a manufacturer, a retailer, and a material recovery facility (MRF). The proposed model helps to maximise the profit of the supply chain. A hybrid method of analytical and numerical approaches is used to determine the optimal inventory decisions, including order cycle time and number of shipments between parties. Solution procedures are proposed for decentralised (DDMS) and centralised decision-making structures (CDMS). Furthermore, a profit-sharing mechanism is also analysed in the model. A sensitivity analysis is carried out to investigate the model's behaviour concerning variations in crucial parameters, including demand, product returns, recycling cost, post-consumer recycled content, and energy recoverable item rate. The results of this study show that the CDMS, without profit-sharing, generates the highest profits for the system. On the other hand, implementing a profit-sharing mechanism provides a fairer profit enhancement to the parties involved. Applying the energy recovery at the supplier results in financial benefits for the system. Additional discussion is carried out to understand the impact of energy recovery on the model's optimal solution.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100326"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-05DOI: 10.1016/j.orp.2025.100334
Filipe Rodrigues
Uncertainty is critical in bulk terminals because it is inherent to many operations. In particular, the berth allocation problem (BAP) is greatly affected by the uncertain arrival times of the vessels. In this paper, we propose the first distributionally robust optimization (DRO) model for the BAP in bulk terminals, where the probability distribution of the arrival times is assumed to be unknown but belongs to an ambiguity set. To solve the model, we use an exact decomposition algorithm (DA) in which the probability distribution information is iteratively included in the master problem through optimal dual cuts. The DA is then enhanced with two improvement strategies to reduce the associated computational time; however, with these strategies, the DA may no longer be exact and is still inefficient for solving large-scale instances. To overcome these issues, we propose a modified exact DA where the dual cuts used in the original DA are replaced by powerful primal cuts that drastically reduce the time required to solve the DRO model, making it possible to handle large-scale instances. The reported computational experiments also show clear benefits of using DRO to tackle uncertainty compared to stochastic programming and robust optimization.
{"title":"The berth allocation problem in bulk terminals under uncertainty","authors":"Filipe Rodrigues","doi":"10.1016/j.orp.2025.100334","DOIUrl":"10.1016/j.orp.2025.100334","url":null,"abstract":"<div><div>Uncertainty is critical in bulk terminals because it is inherent to many operations. In particular, the berth allocation problem (BAP) is greatly affected by the uncertain arrival times of the vessels. In this paper, we propose the first distributionally robust optimization (DRO) model for the BAP in bulk terminals, where the probability distribution of the arrival times is assumed to be unknown but belongs to an ambiguity set. To solve the model, we use an exact decomposition algorithm (DA) in which the probability distribution information is iteratively included in the master problem through optimal dual cuts. The DA is then enhanced with two improvement strategies to reduce the associated computational time; however, with these strategies, the DA may no longer be exact and is still inefficient for solving large-scale instances. To overcome these issues, we propose a modified exact DA where the dual cuts used in the original DA are replaced by powerful primal cuts that drastically reduce the time required to solve the DRO model, making it possible to handle large-scale instances. The reported computational experiments also show clear benefits of using DRO to tackle uncertainty compared to stochastic programming and robust optimization.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100334"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-31DOI: 10.1016/j.orp.2025.100342
Daniël Roelink , Giovanni Campuzano , Martijn Mes , Eduardo Lalla-Ruiz
A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (i) profit maximization including CO2 costs, (ii) soft time windows, and (iii) soft time windows including CO2 costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO2 costs shows a slight sensitivity of the routing schedules to the CO2 emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible.
{"title":"The selective multiple depot pickup and delivery problem with multiple time windows and paired demand","authors":"Daniël Roelink , Giovanni Campuzano , Martijn Mes , Eduardo Lalla-Ruiz","doi":"10.1016/j.orp.2025.100342","DOIUrl":"10.1016/j.orp.2025.100342","url":null,"abstract":"<div><div>A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (<em>i</em>) profit maximization including CO<sub>2</sub> costs, (<em>ii</em>) soft time windows, and (<em>iii</em>) soft time windows including CO<sub>2</sub> costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO<sub>2</sub> costs shows a slight sensitivity of the routing schedules to the CO<sub>2</sub> emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100342"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-12DOI: 10.1016/j.orp.2025.100330
Xingjian Zhou , Yan Feng , Hongming Chen , Lihua Cai , Vladimir Bashkarev
Extended warranty services (EWS) offers avenues for new profit sources and growth opportunities. In a time-sensitive market, the response time has an important impact on the pricing of EWS and satisfying consumer utility. Applying Stakelberg Game theory, a two-echelon product-service supply chain consisting of a manufacturer and two retailers (Self-owned, Franchised) is construct. Considering the EWS response time and price to characterize the consumer utility function, the EWS pricing strategies in different market stages are studied based on the scenarios of identical response time (IRT) and different response time (DRT). The research shows that: (1) under IRT scenario, the optimal EWS pricing and cost of the self-owned and franchised retailers are negatively related to the response time, therefore, both retailers should consider a trade-off strategy between the EWS price and the response time; (2) under DRT scenario, an EWS response time threshold exists, based on which the self-owned and franchised retailers should develop the optimal EWS pricing strategies; (3) under DRT scenario, the retailers’ optimal EWS prices have a negative relationship with consumers’ price sensitivity coefficient, and a positive relationship with consumers’ time sensitivity coefficient. The manufacturer and the self-owned retailer can significantly reduce EWS response time with a limited increase in the prices. While the franchised retailer need to follow the self-owned retailer in developing its pricing strategy. The study construct a time-sensitive consumer utility function by integrating response time and pricing, more accurately portraying the expected value of EWS. Based on the market characteristics of EWS growth and maturity periods, the EWS pricing strategies are expanded regarding response time differentiation in multiple cycles. It helps companies better understand consumer demand for EWS, and assists them in formulating pricing strategies for different stages of EWS market development,and improving EWS supply chain management.
{"title":"Pricing strategy of supply chain considering response time of extended warranty service","authors":"Xingjian Zhou , Yan Feng , Hongming Chen , Lihua Cai , Vladimir Bashkarev","doi":"10.1016/j.orp.2025.100330","DOIUrl":"10.1016/j.orp.2025.100330","url":null,"abstract":"<div><div>Extended warranty services (EWS) offers avenues for new profit sources and growth opportunities. In a time-sensitive market, the response time has an important impact on the pricing of EWS and satisfying consumer utility. Applying Stakelberg Game theory, a two-echelon product-service supply chain consisting of a manufacturer and two retailers (Self-owned, Franchised) is construct. Considering the EWS response time and price to characterize the consumer utility function, the EWS pricing strategies in different market stages are studied based on the scenarios of identical response time (IRT) and different response time (DRT). The research shows that: (1) under IRT scenario, the optimal EWS pricing and cost of the self-owned and franchised retailers are negatively related to the response time, therefore, both retailers should consider a trade-off strategy between the EWS price and the response time; (2) under DRT scenario, an EWS response time threshold exists, based on which the self-owned and franchised retailers should develop the optimal EWS pricing strategies; (3) under DRT scenario, the retailers’ optimal EWS prices have a negative relationship with consumers’ price sensitivity coefficient, and a positive relationship with consumers’ time sensitivity coefficient. The manufacturer and the self-owned retailer can significantly reduce EWS response time with a limited increase in the prices. While the franchised retailer need to follow the self-owned retailer in developing its pricing strategy. The study construct a time-sensitive consumer utility function by integrating response time and pricing, more accurately portraying the expected value of EWS. Based on the market characteristics of EWS growth and maturity periods, the EWS pricing strategies are expanded regarding response time differentiation in multiple cycles. It helps companies better understand consumer demand for EWS, and assists them in formulating pricing strategies for different stages of EWS market development,and improving EWS supply chain management.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100330"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-13DOI: 10.1016/j.orp.2025.100327
Yali Lv, Jian Yang, Xiaoning Sun, Huafei Wu
The technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substantial technological advancements. However, their extensive deployment has amplified concerns regarding data privacy risks, which are attributed not only to technological vulnerabilities but also to the intricate conflicts of interest among model providers, application service providers, and privacy regulators. To tackle this challenge, this research develops a tripartite evolutionary game model that examines the strategic interactions and dynamic relationships among large language model providers, application service providers, and privacy regulatory agencies. By employing replicator dynamic equations and Jacobian matrices, the research investigates the stability of strategic equilibria and simulates optimal adjustment paths across diverse policy scenarios. Drawing on the research findings, this paper offers practical recommendations to strengthen data privacy protection in large language models, delivering a solid theoretical foundation for policymakers and industry practitioners.
{"title":"Evolutionary game analysis of stakeholder privacy management in the AIGC model","authors":"Yali Lv, Jian Yang, Xiaoning Sun, Huafei Wu","doi":"10.1016/j.orp.2025.100327","DOIUrl":"10.1016/j.orp.2025.100327","url":null,"abstract":"<div><div>The technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substantial technological advancements. However, their extensive deployment has amplified concerns regarding data privacy risks, which are attributed not only to technological vulnerabilities but also to the intricate conflicts of interest among model providers, application service providers, and privacy regulators. To tackle this challenge, this research develops a tripartite evolutionary game model that examines the strategic interactions and dynamic relationships among large language model providers, application service providers, and privacy regulatory agencies. By employing replicator dynamic equations and Jacobian matrices, the research investigates the stability of strategic equilibria and simulates optimal adjustment paths across diverse policy scenarios. Drawing on the research findings, this paper offers practical recommendations to strengthen data privacy protection in large language models, delivering a solid theoretical foundation for policymakers and industry practitioners.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100327"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-01-02DOI: 10.1016/j.orp.2024.100324
Johannes Zischg, Immanuel Bomze
Copositivity is a property of symmetric matrices which is NP-hard to check. Nevertheless, it plays a crucial role in tight bounds for conic approaches of several hard optimization problems. In this paper, we present novel promising shortcut strategies to exploit favorable instances in a systematic way, using decomposition strategies based upon the idea to allow for overlapping, smaller blocks, profiting from a beneficial sign structure of the entries of the given matrix. The working hypothesis of this approach is the common empirical observation in the community that for detection of copositivity, a negative certificate is easier to obtain than a positive one. First empirical results on carefully orchestrated randomly generated instances seem to corroborate our approach.
{"title":"Novel shortcut strategies in copositivity detection: Decomposition for quicker positive certificates","authors":"Johannes Zischg, Immanuel Bomze","doi":"10.1016/j.orp.2024.100324","DOIUrl":"10.1016/j.orp.2024.100324","url":null,"abstract":"<div><div>Copositivity is a property of symmetric matrices which is NP-hard to check. Nevertheless, it plays a crucial role in tight bounds for conic approaches of several hard optimization problems. In this paper, we present novel promising shortcut strategies to exploit favorable instances in a systematic way, using decomposition strategies based upon the idea to allow for overlapping, smaller blocks, profiting from a beneficial sign structure of the entries of the given matrix. The working hypothesis of this approach is the common empirical observation in the community that for detection of copositivity, a negative certificate is easier to obtain than a positive one. First empirical results on carefully orchestrated randomly generated instances seem to corroborate our approach.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100324"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Disruptive events like the COVID-19 pandemic have exposed supply chain vulnerabilities. This study focuses on dual sourcing as a resilient strategy and examines a stochastic, single-item, multi-echelon, multi-period, dual sourcing inventory system under backorders. In each echelon, the decision-maker faces a dual-sourcing situation wherein the item can be replenished from a slow regular supplier or a more expensive and faster emergency supplier. We compare two inventory management policies: the Dual-Index Policy (DIP) and the Tailored Base-Surge (TBS) Policy, while also investigating how various factors influence policy effectiveness and the role of demand disruptions. Our findings indicate that the TBS policy generally relies more on upstream suppliers than the DIP. However, in scenarios of high demand uncertainty, upstream suppliers are seldom used. DIP is more effective for short networks facing sudden demand drops, whereas TBS excels when experiencing demand spikes.
{"title":"Simplicity or flexibility? Dual sourcing in multi-echelon systems under disruption","authors":"Sadeque Hamdan , Youssef Boulaksil , Kilani Ghoudi , Younes Hamdouch","doi":"10.1016/j.orp.2025.100333","DOIUrl":"10.1016/j.orp.2025.100333","url":null,"abstract":"<div><div>Disruptive events like the COVID-19 pandemic have exposed supply chain vulnerabilities. This study focuses on dual sourcing as a resilient strategy and examines a stochastic, single-item, multi-echelon, multi-period, dual sourcing inventory system under backorders. In each echelon, the decision-maker faces a dual-sourcing situation wherein the item can be replenished from a slow regular supplier or a more expensive and faster emergency supplier. We compare two inventory management policies: the Dual-Index Policy (DIP) and the Tailored Base-Surge (TBS) Policy, while also investigating how various factors influence policy effectiveness and the role of demand disruptions. Our findings indicate that the TBS policy generally relies more on upstream suppliers than the DIP. However, in scenarios of high demand uncertainty, upstream suppliers are seldom used. DIP is more effective for short networks facing sudden demand drops, whereas TBS excels when experiencing demand spikes.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100333"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-19DOI: 10.1016/j.orp.2025.100331
Lu Yang, Zhouwang Yang
As production scales up, transportation networks increasingly involve nonlinear costs, leading to the concave cost network flow problem (CCNFP), which is notably challenging due to its nonlinearity. Existing nonlinear programming methods addressing the CCNFP often suffer from low efficiency and high computational cost, limiting their practical application. To overcome these limitations, this paper proposes the Successive Derivative Shortest Path (SDSP) algorithm, an efficient approach that combines a sequential linear approximation framework with regional first-order information of the objective function. By integrating regional first-order information and employing an interval reduction mechanism, the SDSP algorithm effectively avoids premature convergence to suboptimal solutions, thereby achieving higher-quality solutions. Numerical experiments, including parameter selection, validation, and comparative analysis, demonstrate that the SDSP algorithm outperforms existing methods in terms of both solution quality and convergence speed. This research offers a robust and efficient solution for the CCNFP, with potential applications in various fields, including logistics and supply chain networks, where concave cost network flow issues are common.
{"title":"An advanced Successive Derivative Shortest Path algorithm for concave cost network flow problems","authors":"Lu Yang, Zhouwang Yang","doi":"10.1016/j.orp.2025.100331","DOIUrl":"10.1016/j.orp.2025.100331","url":null,"abstract":"<div><div>As production scales up, transportation networks increasingly involve nonlinear costs, leading to the concave cost network flow problem (CCNFP), which is notably challenging due to its nonlinearity. Existing nonlinear programming methods addressing the CCNFP often suffer from low efficiency and high computational cost, limiting their practical application. To overcome these limitations, this paper proposes the Successive Derivative Shortest Path (SDSP) algorithm, an efficient approach that combines a sequential linear approximation framework with regional first-order information of the objective function. By integrating regional first-order information and employing an interval reduction mechanism, the SDSP algorithm effectively avoids premature convergence to suboptimal solutions, thereby achieving higher-quality solutions. Numerical experiments, including parameter selection, validation, and comparative analysis, demonstrate that the SDSP algorithm outperforms existing methods in terms of both solution quality and convergence speed. This research offers a robust and efficient solution for the CCNFP, with potential applications in various fields, including logistics and supply chain networks, where concave cost network flow issues are common.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100331"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-11DOI: 10.1016/j.orp.2025.100337
Fabian Lange , Rainer Schlosser
Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers’ and competitors’ behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems with incomplete knowledge. RL algorithms have shown to outperform rule-based competitor heuristics if the underlying Markov decision process is kept simple and customers are myopic. However, the myopic assumption is becoming increasingly unrealistic since technology like price trackers allows customers to act more strategically. To counteract unknown strategic behavior is difficult as pricing policies and consumers buying patterns influence each other and hence, approaches to iteratively update both sides sequentially are time consuming and convergence is unclear. In this work, we show how to use RL algorithms to optimize prices in the presence of different types of strategic customers that may wait and time their buying decisions. We consider strategic customers that (i) compare current prices against past prices and that (ii) anticipate future price developments. To avoid frequently updating pricing policies and consumer price forecasts, we endogenize the impact of current price decisions on the associated changes in forecast-based consumer behaviors. Besides monopoly markets, we further investigate how the interaction with strategic consumers is affected by additional competing vendors in duopoly markets and present managerial insights for all market setups and customer types.
{"title":"Dynamic pricing with waiting and price-anticipating customers","authors":"Fabian Lange , Rainer Schlosser","doi":"10.1016/j.orp.2025.100337","DOIUrl":"10.1016/j.orp.2025.100337","url":null,"abstract":"<div><div>Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers’ and competitors’ behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems with incomplete knowledge. RL algorithms have shown to outperform rule-based competitor heuristics if the underlying Markov decision process is kept simple and customers are myopic. However, the myopic assumption is becoming increasingly unrealistic since technology like price trackers allows customers to act more strategically. To counteract unknown strategic behavior is difficult as pricing policies and consumers buying patterns influence each other and hence, approaches to iteratively update both sides sequentially are time consuming and convergence is unclear. In this work, we show how to use RL algorithms to optimize prices in the presence of different types of strategic customers that may wait and time their buying decisions. We consider strategic customers that (i) compare current prices against past prices and that (ii) anticipate future price developments. To avoid frequently updating pricing policies and consumer price forecasts, we endogenize the impact of current price decisions on the associated changes in forecast-based consumer behaviors. Besides monopoly markets, we further investigate how the interaction with strategic consumers is affected by additional competing vendors in duopoly markets and present managerial insights for all market setups and customer types.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100337"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号