Logistics network design with inventory stocking for low-demand parts: Modeling and optimization

Jeet, Vishv; Kutanoğlu, Erhan; Partani, Amit

doi:10.1080/07408170802512602

Cited by 31 publications

(8 citation statements)

References 35 publications

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“…However, the current trend in the SPL industry is to consider the interdependency that exists between the network design and the inventory policies. In those lines, to our knowledge only two models have being developed; Candas (2007) and Jeet (2009) consider the effects of network design on inventory (and vice versa) in an integrated SPLS model, and Tovia (2004) provides an integrated solution to the technician coverage assignment and the inventory policies with time response service constraints by part criticality. The rest of the research in SPLS has being done by analyzing these policies separately.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and analysis of service parts logistics systems

Tovia

Brooks

Cassady

et al. 2011

IJOR

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Many organisations purchase equipment that is subject to unpredictable failures to carry out their services. When these breakdowns occur, maintenance technicians restore the equipment in a timely fashion either through repair or the installation of spare parts. Extensive research has been conducted on optimisation of the stock levels on spare parts, as well on the technician coverage assignment. However, in most of the cases the solutions are developed separately. In this research, the behaviour of the service parts logistics system (SPLS) is approximated with a modified M/G/1 HOL queuing model. Then, the costs structure of the SPLS is defined and approximated with a mathematical model that captures the interdependence of the inventory policy and the technician assignment. A heuristic "Assign Locations" is implemented based on the modified M/G/1 HOL queuing, the costs model and a genetic algorithm to search for the near-minimum cost technician assignment given a fixed technician's vehicle inventory policy.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling and analysis of service parts logistics systems

Tovia

Brooks

Cassady

et al. 2011

IJOR

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“…Chopra (2003) pointed out that studies related to the supply chain network considered several attributes, such as market position, market demand, inventory, regional culture, technology, labor, product property, or policy. In addition, many researchers studying the performance evaluation of supply chain networks have considered limited production capacity, transportation capacity, cost, inventory levels, or customer requirements (Ekşioglu et al, 2007;Sourirajan et al, 2007;Ozsen et al, 2008;Ho and Emrouznejad, 2009;Jeet et al, 2009;Mak and Shen, 2012;Kumar and Tiwari, 2013;Ramezani et al, 2013;Yu and Nagurney, 2013). However, the supply chain networks addressed in the aforementioned studies are deterministic.…”

Section: Introductionmentioning

confidence: 99%

A reliability indicator to measure a stochastic supply chain network with transportation damage and limited production capacity

2014

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This article proposes a reliability measurement for a supply chain network, in which a vertex denotes a supplier, a transfer center, or a customer, while a route connecting a pair of vertices denotes a carrier. Each carrier's available transportation capacity (e.g., number of containers) is not deterministic since the transportation capacity may be partially reserved by other customers. Thus, the supply chain network can be regarded as a Stochastic Supply Chain Network (SSCN). In an SSCN with multiple suppliers and markets, the goods may be damaged due to traffic accident, natural disaster, inclement weather, time, or collision during transportation such that the intact goods may not meet the customer's demands. In addition, the goods supplied by a specified supplier cannot exceed its production capacity, and the total transportation cost cannot exceed a budget. SSCN reliability is defined as the probability that the SSCN can successfully deliver goods to multiple customers subject to a specified level of damage, budget, and limited production capacity. An algorithm is proposed to evaluate the SSCN reliability based on minimal paths. A real case study of a pineapple supply chain network is utilized to demonstrate the utility of the proposed algorithm.

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“…Owing to the wide coverage of the research stream, some important study areas are inevitably omitted. For example, the design of spare parts inventory networks [5,6,9] is one of the active research areas not being covered in this article. The remainder of this article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Integrated Supply Chain Design Models

Mak

Shen

2011

Wiley Encyclopedia of Operations Research and Management Science

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Traditional facility location models tend to focus on the trade‐off between some measure of budget, often measured by fixed location costs, and some measure of convenience to customers, usually measured by the distance between customers and the respective nearest facilities. This simple setup, although proven to be effective in many other applications, fails to cover potential effects of tactical and operational issues on the strategic design decisions in supply chain settings. For example, inventory sharing and delivery vehicle consolidation lead to economies of scale in the operations of the facilities and thus affect the optimal distribution network configuration. Recently, researchers began to study integrated supply chain design models that take into account important tactical and operational issues when making strategic decisions on facility location and network design. In this article, we review some recent work that belongs to this new research stream. We discuss various modeling issues and the potential benefits of using integrated models over using traditional location models.

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Logistics network design with inventory stocking for low-demand parts: Modeling and optimization

Cited by 31 publications

References 35 publications

Modelling and analysis of service parts logistics systems

Modelling and analysis of service parts logistics systems

A reliability indicator to measure a stochastic supply chain network with transportation damage and limited production capacity

Integrated Supply Chain Design Models

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