Pub Date : 2025-03-22DOI: 10.1016/j.orp.2025.100335
Sona Babu, B.S. Girish
This paper studies the simultaneous minimization of total weighted earliness-tardiness (TWET) and makespan in a just-in-time single-machine scheduling problem (JIT-SMSP) with sequence-dependent setup times and distinct due windows, allowing idle times in the schedules. Multiple variants of variable neighbourhood descent (VND) based metaheuristic algorithms are proposed to generate Pareto-optimal solutions for this NP-hard problem. An optimal timing algorithm (OTA) is presented that generates a piecewise linear convex trade-off curve between the two objectives for a given sequence of jobs. The trade-off curves corresponding to the sequences of jobs generated in the proposed metaheuristics are trimmed and merged using a Pareto front generation procedure to generate the Pareto-optimal front comprising line segments and points. The computational performance of the proposed VND-based metaheuristic algorithms is compared with state-of-the-art metaheuristic algorithms from the literature on test instances of varying sizes using four performance metrics devised to compare Pareto fronts comprising line segments and points. The performance comparisons reveal that a proposed variant of backtrack-based iterated VND with multiple neighbourhood structures outperforms the other algorithms in most performance metrics.
{"title":"Neighbourhood search-based metaheuristics for the bi-objective Pareto optimization of total weighted earliness-tardiness and makespan in a JIT single machine scheduling problem","authors":"Sona Babu, B.S. Girish","doi":"10.1016/j.orp.2025.100335","DOIUrl":"10.1016/j.orp.2025.100335","url":null,"abstract":"<div><div>This paper studies the simultaneous minimization of total weighted earliness-tardiness (TWET) and makespan in a just-in-time single-machine scheduling problem (JIT-SMSP) with sequence-dependent setup times and distinct due windows, allowing idle times in the schedules. Multiple variants of variable neighbourhood descent (VND) based metaheuristic algorithms are proposed to generate Pareto-optimal solutions for this NP-hard problem. An optimal timing algorithm (OTA) is presented that generates a piecewise linear convex trade-off curve between the two objectives for a given sequence of jobs. The trade-off curves corresponding to the sequences of jobs generated in the proposed metaheuristics are trimmed and merged using a Pareto front generation procedure to generate the Pareto-optimal front comprising line segments and points. The computational performance of the proposed VND-based metaheuristic algorithms is compared with state-of-the-art metaheuristic algorithms from the literature on test instances of varying sizes using four performance metrics devised to compare Pareto fronts comprising line segments and points. The performance comparisons reveal that a proposed variant of backtrack-based iterated VND with multiple neighbourhood structures outperforms the other algorithms in most performance metrics.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100335"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739706","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":"2025-03-21","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}
We study a vehicle routing problem that originates from a Nordic distribution company and includes the essential decision-making components of the company’s logistics operations. The problem considers customer deliveries from a depot using heavy depot vehicles, swap bodies, optional switch points, and lighter local vehicles; a feature is that deliveries are made by both depot and local vehicles. The problem has earlier been solved by a fast metaheuristic, which does however not give any quality guarantee. To assess the solution quality, two strong formulations of the problem based on the column generation approach are developed. In both of these the computational complexity is mitigated through an enumeration of the switch point options. The formulations are evaluated with respect to the quality of the linear programming lower bounds in relation to the bounds obtained from a compact formulation. The strong lower bounding quality enables a significant reduction of the optimality gap compared to the compact formulation. Further, the bounds verify the high quality of the metaheuristic solutions, and for several problem instances the optimality gap is even closed.
{"title":"Evaluating metaheuristic solution quality for a hierarchical vehicle routing problem by strong lower bounding","authors":"Marduch Tadaros , Athanasios Migdalas , Nils-Hassan Quttineh , Torbjörn Larsson","doi":"10.1016/j.orp.2025.100332","DOIUrl":"10.1016/j.orp.2025.100332","url":null,"abstract":"<div><div>We study a vehicle routing problem that originates from a Nordic distribution company and includes the essential decision-making components of the company’s logistics operations. The problem considers customer deliveries from a depot using heavy depot vehicles, swap bodies, optional switch points, and lighter local vehicles; a feature is that deliveries are made by both depot and local vehicles. The problem has earlier been solved by a fast metaheuristic, which does however not give any quality guarantee. To assess the solution quality, two strong formulations of the problem based on the column generation approach are developed. In both of these the computational complexity is mitigated through an enumeration of the switch point options. The formulations are evaluated with respect to the quality of the linear programming lower bounds in relation to the bounds obtained from a compact formulation. The strong lower bounding quality enables a significant reduction of the optimality gap compared to the compact formulation. Further, the bounds verify the high quality of the metaheuristic solutions, and for several problem instances the optimality gap is even closed.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100332"},"PeriodicalIF":3.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681929","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 : 2025-02-27DOI: 10.1016/j.orp.2025.100328
Muhammad Tayyab , Hira Tahir , Muhammad Salman Habib
The electronic retailers face distinct challenges in information sharing compared to their purely offline counterparts, particularly in transparently communicating their environmental practices to increasingly eco-conscious consumers. This complexity increases in e-retailing due to the absence of direct interaction and makes it difficult for consumers to evaluate the sustainability efforts of retailing channel’s stakeholders. In response to it, manufacturers and e-retailers are leveraging social media and blockchain technology for personalized advertising to bridge this information transparency gap. This research presents a sustainable multi-item integrated model for manufacturer–retailer collaboration in e-retailing by incorporating multiple delivery policies and investments in technology aimed at information disclosure and environmental footprint reduction. The manufacturer adopts a smart production system and reuse returned goods in the manufacturing process while investing in Green Emissions Reduction Technology. Meanwhile, the e-retailer enhances product demand through Information Disclosure Technology on social media and blockchain by showcasing their environmental protection efforts. By employing a hybrid analytic-metaheuristic approach, we determine optimal production and delivery policies to improve green consumer service under varying budgetary and spatial constraints. The results demonstrate a 4.39% increase in online consumer demand through information sharing and a 3.86% improvement in profitability of the collaborative retailing system under single-setup multi-delivery policy that confirms robustness of the proposed model. Scenario analysis further provides decision-makers with actionable insights by showcasing 8.44% increment in the system profit by converting traditional production into smart production system. Moreover, the sensitivity of the proposed model to balancing the technology investments among emission control and information disclosure efforts suggests keeping track of the efficiency parameters of these investment options before making technology budget allocations.
{"title":"The impact of information disclosure and smart technology integration on e-retailing performance: A production delivery policy framework","authors":"Muhammad Tayyab , Hira Tahir , Muhammad Salman Habib","doi":"10.1016/j.orp.2025.100328","DOIUrl":"10.1016/j.orp.2025.100328","url":null,"abstract":"<div><div>The electronic retailers face distinct challenges in information sharing compared to their purely offline counterparts, particularly in transparently communicating their environmental practices to increasingly eco-conscious consumers. This complexity increases in e-retailing due to the absence of direct interaction and makes it difficult for consumers to evaluate the sustainability efforts of retailing channel’s stakeholders. In response to it, manufacturers and e-retailers are leveraging social media and blockchain technology for personalized advertising to bridge this information transparency gap. This research presents a sustainable multi-item integrated model for manufacturer–retailer collaboration in e-retailing by incorporating multiple delivery policies and investments in technology aimed at information disclosure and environmental footprint reduction. The manufacturer adopts a smart production system and reuse returned goods in the manufacturing process while investing in Green Emissions Reduction Technology. Meanwhile, the e-retailer enhances product demand through Information Disclosure Technology on social media and blockchain by showcasing their environmental protection efforts. By employing a hybrid analytic-metaheuristic approach, we determine optimal production and delivery policies to improve green consumer service under varying budgetary and spatial constraints. The results demonstrate a 4.39% increase in online consumer demand through information sharing and a 3.86% improvement in profitability of the collaborative retailing system under single-setup multi-delivery policy that confirms robustness of the proposed model. Scenario analysis further provides decision-makers with actionable insights by showcasing 8.44% increment in the system profit by converting traditional production into smart production system. Moreover, the sensitivity of the proposed model to balancing the technology investments among emission control and information disclosure efforts suggests keeping track of the efficiency parameters of these investment options before making technology budget allocations.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100328"},"PeriodicalIF":3.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520870","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 : 2025-02-19DOI: 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":"2025-02-19","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 : 2025-02-17DOI: 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":"2025-02-17","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 : 2025-02-16DOI: 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":"2025-02-16","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 : 2025-02-13DOI: 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":"2025-02-13","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 : 2025-02-12DOI: 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":"2025-02-12","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}
A cationic reagent is an essential raw material in printing paper production. The market environment of the cationic reagent industry is influenced by the printing paper industry. Owing to the COVID-19 pandemic, the global expansion of remote work and home education has decreased the demand for printing papers. Consequently, competition among market players (i.e., suppliers and buyers) in the cationic reagent industry is intensifying. This study focuses on cooperation between market players in the cationic reagent industry, representing a typical oligopolistic and mature industry. It proposes a supply chain optimization model that minimizes the costs of the entire supply chain, incorporating buyers’ risk hedge tendency to address market uncertainty. The model is empirically tested using accessible and reliable data to assess its business applicability. Numerical experiments are conducted to explore scenarios that can occur in real market environment, such as levels of risk hedging, trade disputes, decreases in demand, and changes in production capacity. The experimental results provide managerial implications. As buyers maximize the degree to which they diversify their purchase quantities across multiple suppliers to reduce risks, differential costs of the entire supply chain increase by 19%, which are costs that cannot be reduced by suppliers’ capabilities and inevitably arise due to differences between suppliers (e.g., geography, politics, and government policies). However, in unfavorable market conditions, such as trade disputes and decreases in demand, less competitive suppliers can survive. This study shows that when market demand in the cationic reagent industry decreases, two suppliers may potentially experience operational outages. In reality, these two suppliers deteriorated under the challenging market conditions during the COVID-19 pandemic.
{"title":"Cooperation and competition in an oligopolistic and mature industry: A case study on the cationic reagent industry based on an optimization model","authors":"Joohang Kang, Byoungil Choi, Chaehong Lim, Joonyup Eun","doi":"10.1016/j.orp.2025.100325","DOIUrl":"10.1016/j.orp.2025.100325","url":null,"abstract":"<div><div>A cationic reagent is an essential raw material in printing paper production. The market environment of the cationic reagent industry is influenced by the printing paper industry. Owing to the COVID-19 pandemic, the global expansion of remote work and home education has decreased the demand for printing papers. Consequently, competition among market players (<em>i.e</em>., suppliers and buyers) in the cationic reagent industry is intensifying. This study focuses on cooperation between market players in the cationic reagent industry, representing a typical oligopolistic and mature industry. It proposes a supply chain optimization model that minimizes the costs of the entire supply chain, incorporating buyers’ risk hedge tendency to address market uncertainty. The model is empirically tested using accessible and reliable data to assess its business applicability. Numerical experiments are conducted to explore scenarios that can occur in real market environment, such as levels of risk hedging, trade disputes, decreases in demand, and changes in production capacity. The experimental results provide managerial implications. As buyers maximize the degree to which they diversify their purchase quantities across multiple suppliers to reduce risks, differential costs of the entire supply chain increase by 19%, which are costs that cannot be reduced by suppliers’ capabilities and inevitably arise due to differences between suppliers (<em>e.g</em>., geography, politics, and government policies). However, in unfavorable market conditions, such as trade disputes and decreases in demand, less competitive suppliers can survive. This study shows that when market demand in the cationic reagent industry decreases, two suppliers may potentially experience operational outages. In reality, these two suppliers deteriorated under the challenging market conditions during the COVID-19 pandemic.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100325"},"PeriodicalIF":3.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102454","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}
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