The storage constrained, inbound inventory routing problem

Abstract

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.

Stochastic Inventory Routing for Perishable Products

“…As in a VMI system, the main decisions in an IRP are: (a) when to serve each customer, (b) how much to deliver to a customer when it is visited, and (c) which routes to use. The IRP has a wide range of applications including the distribution of gas Savelsbergh 2004a, Gronhaug et al 2010), fuel (Popović et al 2012), automobile components (Alegre et al 2007, Stacey et al 2007), perishable products Zipkin 1984, Federgruen et al 1986), groceries products (Gaur and Ficher 2004), cement (Christiansen et al 2011), and blood products (Hemmelmayr et al 2009). …”

Supply Chain Optimization Studies: A Literature Review and Classification

“…Table 4, classifies the works reviewed according to supply chain optimization topics. (Chandra, 1993), (Stacey et al,2007) x x (Martin et al,1993), (Fumero & Vercellis, 1999), (Timpe & x x x Kallrath, 2000), (Lei et al,2006), (Shiguemoto & Armentano, 2010), (Fahimnia et al,2015) (Fisher & Chandra, 1994), (Chen & Wang, 1997), (Sakawa et al,2001), (Ryu et al,2004), (Bertazzi et al,2005), (Oh & Karimi, 2006), (Roghanian et al,2007), (Park(a), 2007), (Dhaenens-Flipo & Finke, 2001), (Liang, 2007), (Boudia(a) et al, 2007), (Selim et al,2008), (Boudia (a) (Archetti et al, 2011), (Armentanoa et al,2011), (Chen et al,2009), (Amorim et al,2013), (Bilgen & Çelebi, 2013), (Nasiri et al,2014), (Adulyasak et al,2014) x x (Mcdonald & Karimi, 1997), (Gupta & Maranas, 2003), (Torabi & Hassini, 2008), (Khakdaman et al,2014), (Leung & Chan, 2009), (Mirzapour et al,2011, (Brahimia & Aouamb, 2015), (Shi et al,...…”

An Integrated Inventory-Transportation System with Periodic Pick-Ups and Leveled Replenishment

“…Scatter search is used to identify good solutions. A third work in the line of research is Stacey, Natarajarathinam, and Sox (2007). In contrast to Chuah and Yingling (2005), frequencies are not chosen from a discrete set of values but determined endogenously, based on a heuristic two-step approach.…”