Logistics network design with supplier consolidation hubs and multiple shipment options

Abstract

Abstract. An important service provided by third-party logistics (3PL) firms is to manage the inbound logistics of raw materials and components from multiple suppliers to several manufacturing plants. A key challenge for these 3PL firms is to determine how to coordinate and consolidate the transportation flow, so as to get the best overall logistics performance. One tactic is to establish consolidation hubs that collect shipments from several suppliers, consolidate these shipments, and direct the consolidated shipments to the appropriate manufacturing plant. We consider the network design problem to implement this tactic, namely deciding the number, location and operation of consolidation hubs so as to minimize the total logistics costs for the network. To solve this network design problem, we define candidate shipping options for each potential hub, for which we can pre-compute the shipping quantities required from each supplier, and the incurred shipping costs and inventory holding costs. We formulate the problem as an integer linear optimization model and illustrate how to solve large instances using Lagrangian relaxation and a subgradient optimization algorithm. Our results indicate that the bounds obtained are fairly tight and are superior to the bounds obtained from the solution of the LP relaxation.

Designing a hub location and pricing network in a competitive environment

“…In the model, they proposed a tactic to coordinate 3PL's transportations such that they established consolidation hubs, collect shipments from several suppliers, consolidate these shipments and direct the consolidated shipments to the appropriate manufacturers. Therefore, the number, location and operation of hubs are obtained based on minimizing the total logistics costs [8]. Ta et al presented partitioning-hub location-routing model and solved it by using DCA.…”

“…Firm 2s contract costs simply have been shown by λ = k βF k s k and η = k qs k ∆r k respectively. Equations (8) and (9) show the transportation costs and hub covering constrains, respectively. Equation (10) assigns the flows according to a logit model whose argument is the prices charged by firm 2.…”

Design and Freight Corridor-Fleet Size Choice in Collaborative Intermodal Transportation Network Considering Economies of Scale

“…The model is a non-linear integer programming problem. Cheong [32] introduced a Lagrangian relaxation, applying sub gradient optimization to the Lagrangian relaxation to find a good lower bound. However, we expect the computational difficulty of our model to increase rapidly as the number of binary decision variables increases.…”

Design of a single window system for e-government services: the chilean case

“…Thus, Constraint (6) alone, the one imposing the largest number of restrictions, translates into 21 × 1, 364 × 6 = 171,864 of them. Constraints (11) and (12) together contribute with 2 × 21 × 1, 364 = 57, 288 additional restrictions. 6.2.…”

“…This conception of the design problem can be compared to that arising with networks. This class of problems has been widely studied in many contexts including manufacturing and logistics systems [11,19,28,37,49], air freight [27], ship transport [1,2], land transport [50], evacuation planning [16], the transport and distribution of water, gas and petroleum [5,9,14,20], among other applications. More specifically, the design of communication networks with technology upgrading has been approached as a shortest path problem [18].…”