Review
Engineering, Industrial
Dusan Hrabec, Jiri Kucera, Pavel Martinek
Summary: This study examines a generalized version of the newsvendor problem with marketing efforts. A systematic review of existing formulations of marketing efforts was conducted, and the findings were applied to the newsvendor problem framework. The optimal marketing effort decision was found to be independent of uncertainty in the additive demand case, but dependent on uncertainty in the multiplicative form. The demand-effort response function and the cost of marketing effort were generalized and extended to an S-shaped demand-effort response function.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Engineering, Manufacturing
Rongchuan He, Ye Lu
Summary: The study addresses the price-setting newsvendor problem where retailers may have limited information on demand models, which creates a gap between academic research and practical applications. A robust optimization approach is proposed to minimize maximum regret, and extensive numerical studies demonstrate its superior performance compared to the regression method.
PRODUCTION AND OPERATIONS MANAGEMENT
(2021)
Article
Green & Sustainable Science & Technology
Milena Bieniek
Summary: Barter exchange is an effective model for maximizing asset utilization and unlocking the untapped value of resources. In the context of retailers dealing with uncertain demand and economic conditions, utilizing barter exchange can lead to optimal order quantities and prices.
Article
Management
Pol Boada-Collado, Sunil Chopra, Karen Smilowitz
Summary: This study examines the combined value of observing future demand realizations and flexible capacity when signing short-term capacity contracts as a hedging mechanism against demand uncertainty. The empirical results show that demand visibility can enhance its value when flexible capacity contracts are available.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2022)
Article
Economics
Xianhao Xu, Cheng Chen, Bipan Zou, Hongwei Wang, Zhiwen Li
Summary: This paper investigates the optimal shipping quantity and product pricing strategy of online retailers using the innovative logistics mode of shipping before order making. The study includes building a newsvendor model, exploring closed-form solutions, comparing optimal strategies under different risk attitudes, and analyzing the impact of key parameters. Numerical experiments were conducted to verify theoretical conclusions and analyze the effect of parameters on online retailers' optimal strategies and profits. The results show the importance of avoiding blind price wars and the significant influence of risk aversion and key parameters on optimal strategies.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Management
Hui Yu, Xiaoli Yan
Summary: This study discusses three selling strategies of a seller who produces and sells a seasonal product to a consumer under uncertain supply and demand, and designs a robust newsvendor model to solve the problem. The results show that implementing an advance selling strategy is always beneficial for the seller from the demand uncertainty perspective, but the seller should carefully choose the advance selling strategy from the supply uncertainty perspective.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Yini Zheng, Qi Fu, Juan Li, Lianmin Zhang
Summary: This paper explores the impact of judgement bias on demand forecast accuracy and profit, and identifies the driving force behind decision-makers' biases. The study finds that both accuracy-maximising forecasts and profit-maximising forecasts are biased, indicating that news vendors are motivated to be biased in order to achieve more accurate forecasts or higher profits. Moreover, the decision error under accuracy-maximising forecasts can be lower than that under unbiased demand forecasts, suggesting that biased accuracy-maximising forecasts perform well in both forecasts and decisions. The paper also extends the analysis to consider correlated and trended demand processes, demonstrating the robustness of the positive impact of judgement bias. Finally, the paper proposes a method to solve the pure data-driven newsvendor problem and evaluates its performance using empirical evidence.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Xi Xiang, Tao Fang, Changchun Liu, Zhi Pei
Summary: This study proposed an optimization method for a robust service network design problem, balancing objective value and penalty violation with penalty limit constraint and robustness index. A decomposition method was introduced to solve the problem, with numerical results demonstrating the efficiency of the algorithm. The robust optimization approach was validated using real data, resulting in a robust parcel delivery network design with satisfactory out-of-sample performances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Jing Shi
Summary: This paper proposes a prediction technology using a large amount of relevant information, which filters characteristic variables and establishes a prediction model to predict demand and make order decisions. The effectiveness and feasibility of this method are verified through a case study.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Chenyin Wang, Yaodong Ni, Xiangfeng Yang
Summary: This paper focuses on addressing uncertainty in the production routing problem (PRP) and formulates three uncertain programming models. Through experiments, the accuracy and usefulness of the proposed models are validated. In uncertain environments, increasing confidence levels lead to higher total costs, while increasing thresholds result in a greater probability that the optimal total cost is less than or equal to the threshold.
Article
Computer Science, Interdisciplinary Applications
Liming Guo, Jun Wang, Jianfeng Zheng
Summary: This study proposes a berth allocation problem considering the impact of weather conditions, develops a two-stage optimization method and a mixed-integer programming model to solve the problem, and designs an efficient particle swarm optimization algorithm with machine learning approach. Numerical experiments demonstrate the effectiveness of the proposed model and the efficiency of the algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
W. L. Cheung, R. Piplani, S. Alam, L. Bernard-Peyre
Summary: The proposed MIP model addresses peak airport traffic by optimizing flight slots with dynamic capacity estimation to minimize flight displacement and avoid excessive delays caused by high demand during the strategic planning phase.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Marine
A. M. P. Santos, K. Fagerholt, C. Guedes Soares
Summary: The paper proposes a two-stage stochastic programming algorithm to address the Supply Vessel Planning Problem (SVPP) with stochastic demands and uncertain weather conditions in offshore oil and gas logistics. The algorithm incorporates the cost of recourse actions in the objective function and uses a genetic algorithm with discrete event simulation to approximate the cost of each solution. The study shows that solving the stochastic program leads to average annual cost savings of approximately 12% compared to solving the deterministic version.
Article
Management
Chaolin Yang, Zhenyu Hu, Sean X. Zhou
Summary: The study examines a multilocation newsvendor model with a retailer and store managers who are risk averse, comparing centralized ordering decisions and inventory pooling strategies. The findings indicate that centralization benefits the retailer, especially when some store managers are more risk averse, while inventory pooling is less beneficial compared to centralization, particularly when store managers are highly risk averse or demand has heavy tails. Numerical experiments using data from an online retailer in China further support these conclusions.
MANAGEMENT SCIENCE
(2021)
Article
Engineering, Industrial
Xuemei Liu, Xiaolang Yang, Mingliang Lei
Summary: This study utilized uncertainty theory and complexity theory to consider uncertain demand in mixed-model assembly line balancing. By introducing scenario probability and triangular fuzzy number to describe uncertain demand, and measuring station complexity based on information entropy and fuzzy entropy, a new optimization model was established. An improved genetic algorithm was applied to solve the model, and the effectiveness of the model was verified on instances of mixed-model assembly line for automobile engines.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Engineering, Industrial
Hiroshi Matsuhisa, Nobuo Matsubayashi
Summary: This study investigates the formation of an alliance between competing manufacturers and a monopolistic platform retailer, and analyzes the impact of the degree of differentiation among manufacturers on the formation of the alliance and the profitability of the retailer.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Lingxuan Kong, Ge Zheng, Alexandra Brintrup
Summary: Supply Chain Financing is used to optimize cash flows in supply networks, but recent scandals have shown inefficiencies in risk evaluation. This paper proposes a Federated Learning framework to address order-level risk evaluation.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Jing Gu, Xinyu Shi, Junyao Wang, Xun Xu
Summary: The asymmetric market power between a firm and its partners negatively affects the firm's financial performance. Building relationships with suppliers or customers that have matched market power is the best approach. The strength of the buyer-supplier relationship amplifies the negative impact of asymmetric market power, while the level of relationship embeddedness reduces its negative effect. Moreover, firm-specific institutional, industry, and regional economic heterogeneities also influence the financial impact of asymmetric market power.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yu Du, Jun-qing Li
Summary: This study investigates the group scheduling of a distributed flexible job shop problem using the concrete precast process. The proposed solution utilizes three coordinated double deep Q-networks (DQN) as a learn-to-improve reinforcement learning approach. The algorithm shows superiority in minimizing costs and energy consumption.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Xiaoyu Yan, Weihua Liu, Ou Tang, Jiahe Hou
Summary: This study analyzes the market amplification effect and the impact of entrant's overconfidence on a two-sided platform. The results show that overconfident entrants can lead to price increases and benefit both the existing firms and themselves to a certain extent.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Illya Kaynov, Marijn van Knippenberg, Vlado Menkovski, Albert van Breemen, Willem van Jaarsveld
Summary: The One-Warehouse Multi-Retailer (OWMR) system is a typical distribution and inventory system. Previous research has focused on heuristic reordering and allocation strategies, which are time-consuming and problem-specific. This paper proposes a Deep Reinforcement Learning (DRL) algorithm for OWMR problems, which infers a multi-discrete action distribution and improves performance with a random rationing policy.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yimeng Sun, Ruozhen Qiu, Minghe Sun
Summary: This study considers a multi-period inventory management problem for a retailer offering limited-time discounts and having a joint service-level requirement under demand uncertainty. It proposes a double-layer iterative approach to solve the problem and maximize total profit while balancing the service level using a posteriori method and an affinely adjustable robust chance-constrained model.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Anas Neumann, Adnene Hajji, Monia Rekik, Robert Pellerin
Summary: This paper presents a new mathematical formulation for planning and scheduling activities of Engineer-To-Order (ETO) projects, along with a new ETO strategy to reduce the impacts of design uncertainty. The study proposes a hybrid Layered Genetic Algorithm combined with an adaptive Lamarckian learning process (LLGA) and compares it with the branch-and-cut procedure of CPLEX. The results show good performance of the proposed mathematical model for small and medium-sized instances.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Thilini Ranasinghe, Chanaka D. Senanayake, Eric H. Grosse
Summary: Production systems are undergoing transformative changes, necessitating adaptability from human workers. This study developed an analytical model to account for stochastic processing times and learning heterogeneity, revealing insights into system performance.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Sunil Tiwari, Pankaj Sharma, Ashish Kumar Jha
Summary: Black Swan events such as the COVID-19 pandemic and the Suez Canal blockage have a significant impact on firms' technology adoption decisions, especially in terms of disruptions and digitalization in the supply chains. This study investigates the influence of institutional forces and environmental contingencies on supply chain digitalization from an institutional and contingency theory perspective. The findings emphasize the importance of organizational readiness and people readiness, including top management involvement and employee training, in facilitating digitalization.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Fabio Neves-Moreira, Pedro Amorim
Summary: Omnichannel retailers are using stores as distribution centers to provide faster online order fulfillment services. However, in-store picking operations can impact the offline customer experience. To address this, we propose a Dynamic In-store Picker Routing Problem (diPRP) that minimizes customer encounters while fulfilling online orders. Our solution approach combines mathematical programming and reinforcement learning to find efficient picking policies that reduce customer encounters.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Richard Kraude, Ram Narasimhan
Summary: In this study, the relationship between Vertical Integration (VI) and Environmental Performance (EP) is examined, revealing that highly integrated firms produce less waste but engage in fewer environmental initiatives. These findings are crucial for understanding the impact of stakeholder exposure on organizational behavior.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Review
Engineering, Industrial
Korina Katsaliaki, Sameer Kumar, Vasilis Loulos
Summary: This research conducts a systematic literature review (SLR) and content analysis on Supply Chain Coopetition (SCC) through the PRISMA framework. It examines the theory of coopetition and organizational relationships in intra-firm and inter-firm supply chains, focusing on collaboration between rival manufacturers. The study identifies structures and mechanisms of coopetition, such as buyer-supplier coopetition, supply networks coopetition, and production and distribution/logistics coopetition. It provides a holistic approach to SCC management practices and serves as a guide for future research.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)