Stopover and hub-and-spoke shipment strategies in less-than-truckload carriers

This paper presents a methodology to identify when freight consolidation strategies are cost-efficient in the less-than-truckload carriers operations. Shipments are assigned based on proximity and cost criteria to build an initial long-haul shipment solution. This initial solution is later improved by the implementation of Tabu Search algorithm. The proximity criterion takes into account the spatial distribution of shipments loads among centers. The results show that the proposed methodology may reduce the transportation cost by 20% compared to the solution of those heuristics only considering cost criterion.     

Optimizing shipment,ordering and pricing policies in a two-stage supply chain with price-sensitive demand

In this paper we focus on a two-stage supply chain consisting of one vendor and one buyer. We develop an integrated production–inventory–marketing model to determine the relevant profit-maximizing decision variable values. The model proposed is based on the joint total profit of both the vendor and the buyer, and it finds out the optimal ordering, shipment and pricing policies. We are able to ascertain the optimal decision variable values employing an analytical solution procedure. The numerical evidence suggests that it is more beneficial for the buyer and the vendor to cooperate with each other when the demand is more price sensitive.     

A study on pricing and delivery strategy for e-retailing systems

This study investigates the dynamics between price and lead time for an e-retailing system in which one of its commodities is offered by two duopolistic suppliers. A Stackelberg game is formulated by considering the two suppliers as the leaders and the e-retailer as the follower. The proposed model assists the channel members in getting an equilibrium relationship in a competitive environment. The results suggest that when a supplier chooses a shorter lead time as the competitive strategy, the other supplier should choose a lower price for counteraction, and channel members should understand the characteristics of demand before promoting their commodities.     

Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads

This paper addresses the routing problem with unpaired pickup and delivery with split loads. An interesting factor of our problem is that the quantity and place for pickup and delivery are decision variables in the network. We develop an easy-to-implement heuristic in order to gain an efficient and feasible solution quickly. Then, a local search algorithm based on the variable neighborhood search (VNS) method is developed to improve the performance of the heuristic. Computational results show that the proposed VNS method is able to obtain an optimal or near optimal solution in reasonable time for the formulated problem.     

Carrying capacity procurement of rail and shipping services for automobile delivery with uncertain demand

The determination of the optimal carrying capacity procurement of rail and shipping services in the automobile intermodal network with unique characteristics is essential to save automobile delivery cost. In this research we develop a two-stage stochastic programming model for the tactical-level decision problem arising in the special automobile intermodal network. Furthermore, we improve the sample average approximation algorithmic procedure to solve the model. We apply the model and solution method to a case study associated with the Shanghai Automobile Industry Corporation. We believe that this study deals with an emerging new research topic with practical significance for the automobile industry.     

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.