PurposeThis paper aims to describe the storage constrained, inbound inventory routeing problem and presents bounds and heuristics for solutions to this problem. It also seeks to analyze various characteristics of this problem by comparing the solutions generated by the two proposed heuristics with each other and with the lower bound solutions.Design/methodology/approachThe proposed heuristics use a sequential decomposition strategy for generating solutions for this problem. These heuristics are evaluated on a set of problem instances which are based on an actual application in the automotive manufacturing industry.FindingsThe storage space clearly has a significant effect on both the routeing and inventory decisions, and there are complex and interesting interactions between the problem factors and performance measures.Practical implicationsFacility design decisions for the storage of inbound materials should carefully consider the impact of storage space on transportation and logistics costs.Originality/valueThis problem occurs in a number of different industrial applications while most of the existing literature addresses outbound distribution. Other papers that address similar problems do not consider all of the practical constraints in the problem or do not adequately benchmark and analyze their proposed solutions.

Purpose -The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities. Design/methodology/approach -Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions. Findings -Near optimal solutions can be obtained in a reasonable amount of time by utilizing information about the amount of storage space in the route design process. Practical implications -The traditional emphasis on high vehicle utilization in transportation management can lead to inefficient logistics operations by carrying excess inventory or by using longer, less efficient routes. Route formation and pickup quantities at the suppliers are simultaneously considered, as both are important from a logistics standpoint and are interrelated decisions. Originality/value -The two proposed heuristics dynamically define seed sets such that the solutions to the capacitated concentrator location problem (CCLP) are accurately estimated. This increased accuracy helps in generating near-optimal solutions in a practical amount of computing time.