Near‐optimal heuristics and managerial insights for the storage constrained, inbound inventory routing problem

Abstract

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.

Tackling complexities of cyclic inventory routing under conditions of limited modelling and computing capacity

“…Often targeted in this pursuit is a more specific version of the problem referred to as the IRP, a problem Downloaded by [Northwestern University] at 19:57 05 February 2015 on which prominent works include Bramel (2003, 2004) and Bard et al (1998). The IRP arises in a wide range of real-world contexts involving items such as fuel (Popovic, Vidovic, and Radivojevic 2012), gasoline (Campbell and Savelsbergh 2004), automobile components (Natarajarathinam, Stacey, and Sox 2012), food (Custódio and Oliveira 2006), and blood products (Hemmelmayr et al 2009). As indicated in the previous section, this paper focuses on the IRP case of deterministic customer demands, the case tackled by, for example, Michel and Vanderbeck (2012) and Raa and Aghezzaf (2009).…”

A Unified Decomposition Matheuristic for Assembly, Production, and Inventory Routing

“…They also assume time windows at the suppliers. Stacey et al (2007) and Natarajarathinam et al (2012) offer new heuristics for the same problem. Ohlmann et al (2007) expand the work of Chuah and Yingling (2005) by assuming general visit frequencies.…”

Role of Optimal Production Plan at the Focal Firm in Optimization of the Supply Chain

“…Inventory management has always remained a challenge for managers. It plays a key role in making any supply chain competitive (Natarajarathinam, et al, 2012). Managers have to take decisions regarding appropriate levels of inventories.…”