The power of information technology can be harnessed to help supply chain members establish partnerships for better supply chain system performance. Supply chain partnerships can mitigate deficiencies associated with decentralized control and reduce the “bullwhip effect”. This study illustrates the benefits of supply chain partnerships based on information sharing. For a decentralized supply chain comprising a manufacturer and a retailer, we derive the members’ optimal inventory policies under different information sharing scenarios. We show that increasing information sharing among the members in a decentralized supply chain will lead to Pareto improvement in the performance of the entire chain. Specifically, the supply chain members can reap benefits in terms of reductions in inventory levels and cost savings from forming partnerships with one another. A case study is provided for illustration.

Journal of the Operational Research Society (JORS) is the world's longest established OR journal and an official journal of The OR Society. It is the aim of the Journal to present papers which are relevant to practitioners, researchers, teachers, students and consumers of operational research, and which cover the theory, practice, history or methodology of OR.

To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This model seeks to enhance the precision of traffic flow prediction by integrating both historical and prospective data. Specifically, the model achieves prediction through two steps: encoding and decoding. In the encoding phase, convolutional neural networks are used to extract spatial correlations between weather and traffic flow in the input sequence, while the BiGRU model captures temporal correlations in the time series. In the decoding phase, an additive attention mechanism is introduced to weigh and fuse the encoded features. The experimental results demonstrate that the CNN-BiGRU model, coupled with the additive attention mechanism, is capable of dynamically capturing the temporal patterns of traffic flow, and the introduction of isolation forests can effectively handle data anomalies and missing values, improving prediction accuracy. Compared to benchmark models such as GRU, the CNN-BiGRU-AAM model shows significant improvement on the test set, with a 47.49 reduction in the Root Mean Square Error (RMSE), a 30.72 decrease in the Mean Absolute Error (MAE), and a 5.27% reduction in the Mean Absolute Percentage Error (MAPE). The coefficient of determination ($R^2$) reaches 0.97, indicating the high accuracy of the CNN-BiGRU-AAM model in traffic flow prediction. It provides a good solution for short-term traffic flow with spatio-temporal features, thereby enhancing the efficiency of traffic management and planning and promoting the sustainable development of transportation.

We investigate a firm's product line design and capacity investment problem for vertically differentiated products along design quality levels. Customers arrive according to a Poisson process and are heterogeneous in their marginal valuation of the quality level. Customers make product choices to maximize a linear utility function of price, quality level, and waiting cost. Resulting product demands are met through capacity investments in production processes, which are modeled as queuing systems. We consider two different types of production processes: product-focused, dedicated to the production of a single-product variant; and product-flexible, processing all product variants in the product line. Capacity investment and variable production costs are functions of the processed product's quality. We develop an integrated marketing-operations model that provides insights on the factors determining the right level of product variety to offer, the relative quality positioning of the products in the line, the resulting market coverage and segmentation, and the effects on production costs and congestion levels of the processes. We show that the statistical economies of scale resulting from the congestion phenomena in the production system impose limits on the optimal product variety. For product-focused processes the market size promotes a higher optimal product variety, whereas the per-unit capacity investment and customer waiting costs act as deterrents for higher product variety. For product-flexible processes optimal product variety also depends on the specific type of flexibility and the ratio of capacity investment to variable production costs.