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The paper aims to study the challenges and solutions of city logistics in the new retail era. The new retail, which is characterized by omni-channel, fragmented orders and decentralized 2C distribution, is becoming the mainstream of the retail industry worldwide. In order to achieve a comprehensive breakthrough in new retail, the change of order fulfillment mode is the most noteworthy issue. The aim of this paper is to design a city logistics operation model for new retail and verify its feasibility and efficiency.

A physical internet (PI) enabled two-tier city logistics solution is proposed by redefining the key facilities in city logistics with the PI concept. A “Container-as-a-Warehouse” operation mode is designed to provide a more flexible store and transfer solution. A mathematical model of the proposed solution is established. An adaptive large neighborhood search (ALNS) is designed based upon an iterative procedure, which ensures consistent and optimal results.

To quantitatively assess the feasibility of the proposed solution, a computational experiment is designed to compare the performance of the proposed model against the conventional two-tier city logistics operation. The effects of geographical location pattern, utilization of PI-hub as well as the fluctuation of customer orders are analyzed. The results show that the PI-enabled city logistics is more advantageous than the conventional solution.

This study does not consider the impact of new technologies in city logistics; for example, the replenishment problem of unmanned vending machines and the charging problem of electric vehicles.

The proposed PI-enabled solution and analysis results in this paper have positive guiding significance for future practical application.

Based on the concept of PI, this paper proposes an innovative and practical operation model to solve the city logistics challenges.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Underground logistics system (ULS) is recognized as sustainable alleviator to road-dominated urban logistics infrastructure with various social and environmental benefits. The purpose of this study is to propose effective modeling and optimization method for planning a hub-and-spoke ULS network in urban region.

Underground freight tunnels and the last-mile ground delivery were organized as a hierarchical network. A mixed-integer programming model (MIP) with minimum system cost was developed. Then a two-phase optimization schema combining Genetic-based fuzzy C-means algorithm (GA-FCM), Depth-first-search FCM (DFS-FCM) algorithm and Dijkstra algorithm (DA), etc. was designed to optimize the location-allocation of ULS facilities and customer clusters. Finally, a real-world simulation was conducted for validation.

The multistage strategy and hybrid algorithms could efficiently yield hub-and-spoke network configurations at the lowest objective cost. GA-FCM performed better than K-means in customer-node clustering. The combination of DFS-FCM and DA achieved superior network configuration than that of combining K-means and minimum spanning tree technique. The results also provided some management insights: (1) greater scale economies effect in underground freight movement could reduce system budget, (2) changes in transportation cost would not have obvious impact on ULS network layout and (3) over 90% of transportation process in ULS network took place underground, giving remarkable alleviation to road freight traffic.

Demand pairs among customers were not considered due to lacking data. Heterogeneity of facilities capacity parameters was omitted.

This study has used an innovative hybrid optimization technique to address the two-phase network planning of urban ULS. The novel design and solution approaches offer insights for urban ULS development and management.

The purpose of this study is to address an importance of an intermodal terminal regarding container drayage trips, which have a major concern for agricultural product exporters in the Upper Great Plains. Thus, this study aims to develop a geospatial model considering travel distance and total logistics costs for determining an alternative intermodal terminal location.

This paper develops a spatial model integrating integer linear programming to determine an intermodal facility location that minimizes total logistics costs. This research considers travel distance and total logistics costs including highway, rail and transshipment costs.

The results shows that a Dilworth, Minnesota, terminal reduces vehicle miles of travel on both the highways and rail networks and decreases system-wide total logistics costs compared to the do-nothing scenario while decreasing urban congestion costs in metropolitan areas.

The major contribution of the study is that it provides an integrated tool of spatial and economic analyses to support regional decision-making. The paper will be of interest to regional planners and to those in the private business sectors including farmers and manufacturers. The future study should address demand forecasting on the containerized freight in the region.

The novel approach of this paper is to use a link blocking constraint, considering the directions of the freight flow in a p-hub intermodal problem.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

Innovative startups have begun a trend using laser sintering (LS) technology patents expiration, namely, by introducing LS additive manufacturing (AM) machines that can overcome utilization barriers, such as the costliness of machines and productivity limitation. The recent rise of this trend has led the authors to investigate this new class of machines in novel settings, including hub configuration. There are various supply chain configurations to supply spare parts in industrial operations. This paper aims to explore the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it.

This study quantitatively examines the feasibility of different AM-enabled spare parts supply chain configurations. Using cost data extracted from a case study, three scenarios per AM machine technology are modeled and compared.

Results suggest that hub production configuration depending on the utilized AM machines can provide economic efficiency and effectiveness to reduce equipment downtime. While previous studies have suggested the need for AM machines with efficiency for single part production for a distributed supply chain, the findings in this research illustrate the positive relationship between multi-part production capability and the feasibility of a hub manufacturing configuration establishment.

This study explores the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it. Although the existing body of knowledge contains research on production decentralization, research on various levels of decentralization is lacking. Using a real-world case study, this study aims to compare the feasibility of different levels of decentralization for AM-enabled spare parts supply chains.

Transshipment points in container transport are a vital part of the functioning and competitiveness of global logistics systems. However, competition between different geographical locations is ongoing and therefore competitive landscape and leading actors are continuously changing. The purpose of this paper is to understand one of world's leading transshipment points, Singapore, and its competitiveness in the future as lower‐cost alternative locations try to challenge its position.

Observations are based and mostly drawn upon qualitative case study interviews among 11 key companies involved in Singapore's transshipment business. Along with this, second‐hand national statistics are used to support the findings.

As Singapore is the largest container sea port in the world, it has several advantages at its disposal to defend its position against the others. These are mainly due to connectivity around the world, and second, the high frequency of these connections. However, even though the customs procedures are generally argued to be smooth and state of the art, in some comments from real life actors, problematic areas and further room for improvement were inferred. Longitudinal statistics from national consensus also show, for example, that the electronics industry transshipment volumes are on the decline, and the main reason could possibly be due to the relocation of manufacturing sites to China or Vietnam, changing in turn the transshipment point in Asia. For future consideration, Singapore should enhance its capabilities in localization of value‐added services or manufacturing while goods are in‐transit, and also invest in technologies enabling higher container fill rates for airline shipments.

In most qualitative research, amount of observations is always limited and this work is not an exception with this regard. However, reliability caveat is tried to be covered with careful selection of interviewed organizations and using second‐hand national statistics to verify the interviewees' inputs.

This research has value for all countries with high‐cost transshipment points, in building their operations to sustain against new lower‐cost entrants. Also, the functioning of global supply chains with respect to cost, lead time and technical capability are better understood through this case study, as all the examples are drawn from real life.

The paper shows how global logistics networks have changed significantly in recent decades, and it is increasingly in line with case study findings after major sea ports such as Dubai and Shanghai have grown into leading position throughout the world.