A three-level supply chain network design model with risk-pooling and lead times

This paper considers a single-sourcing network design problem for a three-level supply chain consisting of suppliers, distribution centers (DC’s) and retailers, where risk-pooling strategy and DC-to-supplier dependent lead times are considered. The objective is to determine the number and locations of suppliers and DC’s, the assignment of each location-fixed DC to a supplier and that of each retailer to a DC, which minimizes the system-wide location, transportation, and inventory costs. The problem is formulated as a nonlinear integer programming model, for which a two-phase heuristic solution algorithm is derived based on the Lagrangian relaxation approach. Numerical experiments show that the proposed heuristic is effective and also efficient.     

Why do warehouses decentralize more in certain metropolitan areas?

Over the last decade, warehousing and distribution centers have decentralized to the urban peripheries where land is cheaper and readily available. This change in location patterns has been driven by the demand to build more modernized and larger facilities to accommodate an ever-increasing influx of freight. Since efficient freight movement is essential for the smooth functioning of metropolitan areas, decentralization should occur everywhere. However, this is not necessarily true. It is hypothesized that depending on the volume of goods movement and the spatial distribution of land prices, the extent of decentralization varies across metropolitan areas. This hypothesis is tested using 48 US metropolitan areas. Results provide robust evidence that high land prices push large warehouses away from central locations. When freight demand and land prices are not as high, the effect becomes insignificant. Indeed, not only is decentralization linked with large metro areas but also with very large warehouses.     

Bi-objective bilevel optimization of distribution center locations considering user equilibria

We propose a bi-objective, bilevel optimization model for the location of relief distribution centers (DCs) in humanitarian logistics. The upper-level decision-maker (an aid-providing organization) selects locations for capacitated DCs. On the lower level, beneficiaries choose a DC according to distance and amount of supply to be expected. This effects a user equilibrium on the lower decision level. Upper level objectives are to minimize total opening cost for the DCs and total uncovered demand. We develop an exact algorithm for determining the Pareto frontier of the problem, integrating the adaptive epsilon-constraint method, a branch-and-bound procedure, and the Frank–Wolfe procedure.     

Locating manufacturing and distribution centers: An integrated supply chain-based spatial interaction approach

This study investigates the facility location problem of transnational personal computer (PC) manufacturing for the domestic market of China. An integrated supply chain-based spatial interaction model is formulated to determine facility locations of the transnational PC manufacturing centers and regional product distribution centers in China, with the goal of maximizing the potential rate of return on facility investment. The numerical results show that the Shanghai municipality is ranked highest for siting both the manufacturing and distribution centers for a transnational PC manufacturing enterprise.     

Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement

Internet retailing models support supply chains where consumer order locations are decoupled from inventory locations. In this setting, retailers dynamically consider inventory location speculation and postponement to fulfill their orders. Particularly, retailers can manage inventory to fulfill orders through two opposing strategies: in-stock inventory and drop-shipping. This paper extends the supply chain management literature by modeling Internet retailers' decisions to balance their offerings between these two strategies. The results show how retailers depend more on both of these strategies as their market share and product popularity increase. Thus, both inventory management strategies may be considered simultaneously to better manage Internet retailers' inventory.     

Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning

This paper proposes a deterministic mixed integer linear programming (MILP) model for downstream petroleum supply chain (PSC) network to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. A case study was considered with real data from the U.S. petroleum industry and transportation networks within Geographic Information System (GIS). A scenario analysis is also conducted to demonstrate the impact of key parameters on PSC decisions and total cost.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach

This paper uses a complex network approach to examine the network structure and nodal centrality of individual cities in the air transport network of China (ATNC). Measures for overall network structure include degree distribution, average path length and clustering coefficient. Centrality metrics for individual cities are degree, closeness and betweenness, representing a node’s location advantage as being directly connected to others, being accessible to others, and being the intermediary between others, respectively. Results indicate that the ATNC has a cumulative degree distribution captured by an exponential function, and displays some small-world (SW) network properties with an average path length of 2.23 and a clustering coefficient of 0.69. All three centrality indices are highly correlated with socio-economic indicators of cities such as air passenger volume, population, and gross regional domestic product (GRDP). This confirms that centrality captures a crucial aspect of location advantage in the ATNC and has important implications in shaping the spatial pattern of economic activities. Most small and low-degree airports are directly connected to the largest cities with the best centrality and bypass their regional centers, and therefore sub-networks in the ATNC are less developed except for Kunming in the southwest and Urumchi in the northwest because of their strategic locations for geographic and political reasons. The ANTC is relatively young, and not as efficient and well-developed as that of the US.     

Incorporating location,routing and inventory decisions in supply chain network design

This paper for the first time presents a novel model to simultaneously optimize location, allocation, capacity, inventory, and routing decisions in a stochastic supply chain system. Each customer’s demand is uncertain and follows a normal distribution, and each distribution center maintains a certain amount of safety stock. To solve the model, first we present an exact solution method by casting the problem as a mixed integer convex program, and then we establish a heuristic method based on a hybridization of Tabu Search and Simulated Annealing. The results show that the proposed heuristic is considerably efficient and effective for a broad range of problem sizes.     

Location factors for logistics facilities: Location choice modeling considering activity categories

While many past studies analyze the changes in the spatial distribution of logistics facilities and provide insights on the factors that drive the migration of logistics facilities, only a few studies analyze the factors of location choice at the individual facility level. Furthermore, the differences among various facility types, with respect to the key locational characteristics that attract logistics facility developments, are still not clear. This research attempts to fill the research gap by analyzing the location choices for logistics facility development. Using the data from the Paris Region, we characterize the locations and activities of logistics facilities and estimate a logistics facility location choice model for each activity category. The analysis reveals the key locational characteristics that influence logistics facility locations, such as zoning regulations, wholesale job accessibility, population density, and the accessibility to autoroute (controlled-access highway), as well as the heterogeneity in the effects of these characteristics by activity category. Zoning and traditional clusters play a significant role for the locations of newly developed logistics facilities, which underlines the importance of public policies for logistics land use in the Paris region. The effect of the accessibility to population is identified for the group of facilities which serve for retail shops and end-consumers only under the assumption that alternative locations are limited to specialized economic activity/logistics zones, indicating that population accessibility is a secondary factor to the designation as the specialized zone. Zoning changes to/from economic activity/logistics zones may cause significant impacts on the spatial distribution of logistics facilities, and thus, the distribution of goods vehicle traffic flow.     

Improving bus bridging responses via satellite bus reserve locations

This paper presents a new method for assessing satellite bus reserve location to best service unplanned rail service disruptions by optimising location in relation to travel time to rail replacement, the volume of rail replacement incidents and the scale and spatial distribution of passengers affected.When rail disruptions unexpectedly occur, re-establishing network connectivity is paramount and the provision of bus bridging (or bus replacement service) is common. Minimising response times are critical in reducing impacts to affected commuters. Currently, reserve buses for such purposes are usually sourced from existing bus depot locations, which are generally situated to suit regular day to day operations. Strategically locating satellite bus reserves according to criteria such as disruption likelihood provides the opportunity to better cater for disrupted demand.The method is presented and analysis highlights how ideal depot locations within the network changes as consideration is given to travel time to locations where bus bridging commences, likelihood of a disruption warranting bus bridging and commuter volumes affected. The paper discusses the implications of findings for future research and practice.     

Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand

In this paper, we propose a simultaneous approach to incorporate inventory control decisions––such as economic order quantity and safety stock decisions––into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon. We present a non-linear-mixed-integer model and a heuristic solution approach, based on Lagrangian relaxation and the sub-gradient method. In a numerical application, we found that the potential cost reduction, compared to the traditional approach, increases when the holding costs and/or the variability of demand are higher.     

A hedging policy for carriers’ selection under availability and demand uncertainty

We address a stochastic dynamic distribution problem where a family of products needs to be shipped from a warehouse to a distribution center (DC). Uncertainty is on carriers’ availability and demand at the DC. Internal, external and spot carriers must be optimally selected to minimize the expected discounted cost of transportation, inventories and shortages. We numerically prove that an optimal selection policy, SDMBSP, is based on three thresholds of the available inventory in the DC. A simulation model is proposed and proves the robustness of the SDMBSP and its outperformance over two other carrier selection policies.     

A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty

This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem.     

A stochastic programming approach for floods emergency logistics

This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events.     

Robust hub network design problem

This paper presents a robust formulation for the uncapacitated single and multiple allocation hub location problem where demand is uncertain and its distribution is not fully specified. The proposed robust model is formulated as a mixed integer nonlinear program and then transformed into a mixed integer conic quadratic program. An efficient linear relaxation strategy is proposed which is found to deliver the optimal solutions for all the cases considered in this paper. Numerical experiments suggest location of more number of hubs when accounting for demand uncertainty using robust optimization compared to the deterministic setting.     

Logistics facilities for intra and inter-regional shipping: Spatial distributions,location choice factors,and externality

Existing studies of urban logistics facility locations and their impacts on negative externality tend to focus on the movements of freight that start and end within a metropolitan area and overlook the role of the shipments with origin or destination outside of the urban area – external shipments – in characterizing logistics facilities. This research aims to fill such gap. Focusing on the Tokyo Metropolitan Area, we analyze locations and goods vehicle trips associated with four logistics facility groups that have different splits between intra and inter-regional trips for inbound and outbound shipments. The spatial density analysis and the location choice modeling highlight the differences in the distribution and location choice factors among those groups. The importance of trip distance minimization is more evident for the facilities that serve intra-regional shipments. Furthermore, we show that the traffic impacts of goods vehicle traffic associated with logistics facilities also differ in many ways among the above-mentioned groups. Finally, we discuss the policy insights to address their heterogeneity in the contribution to externality.     

On the design of closed-loop networks for product life cycle management: Economic,environmental and geography considerations

The management of the product life cycle needs industrial synergies along large-scale networks to collect, recycle, reuse, and recover the end-of-life products. This paper provides a tool to assess the enabling economic, environmental, and transport geography conditions to design sustainable closed-loop networks for the management of a generic product along its life-cycle. The proposed tool is built through a mixed-integer linear programming (MILP) model for the strategic design of a multi-echelon closed-loop network. The product life cycle is handled via a cascade through seven stages, including raw material suppliers, manufacturing plants, distribution centers, retailers, collection nodes for waste and by-products, recycling centers, and landfills.The model minimizes a cost-based and a carbon-based function to determine the optimal geographic location of the nodes of the network and the allocation of transport flows. The model is applied to a case study inspired by the furniture industry over the Italian geography and a multi-scenario analysis is illustrated. The resulting considerations on the economic, environmental performances of the network couple with the transport geography to provide guidelines for designer, logistics planners and regional geographers toward a circular economy scenario.     

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.