Consolidation strategies for the delivery of perishable products

Nguyen, Christine; Dessouky, Maged; Toriello, Alejandro

doi:10.1016/j.tre.2014.05.018

Cited by 49 publications

(23 citation statements)

References 53 publications

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“…The share values α j can be defined according to different criteria. The simplest one is the "egalitarian" method (Dror, 1990;Berger and Bierwirth, 2010;Massol and Tchung-Ming, 2010;Lehoux et al, 2011), which assigns equal cost shares to all the players, that is, x j = C(N )/n ∀ j ∈ N. Some other frequent criteria to define the weights α j are the demand quantities (Engevall et al, 2004;Wong et al, 2007;Frisk et al, 2010;Massol and Tchung-Ming, 2010;Nguyen et al, 2014;Flisberg et al, 2015) and the stand alone costs (Fishburn and Pollak, 1983;Özener and Ergun, 2008;Liu et al, 2010;Audy et al, 2012a;Özener et al, 2013;Özener, 2014;Sun et al, 2015). Potters et al (1992), Derks and Kuipers (1997), Engevall et al (1998), Faigle et al (1998), Hamers et al (1999), Engevall et al (2004), Anily and Haviv (2007),Özener and Ergun (2008) 2015), Wang et al (2015), Zakharov and Shchegryaev (2015).…”

Section: Traditional Methodsmentioning

confidence: 99%

“…Vehicle routing Göthe-Lundgren et al (1996), Engevall et al (2004), Krajewska and Kopfer (2006), Krajewska et al (2008), Caprara and Letchford (2010), Cruijssen et al (2010), Liu et al (2010), Dahl and Derigs (2011),Özener (2014), Vanovermeire and Sörensen (2014b), Zakharov and Shchegryaev (2015). Joint distribution Hamers et al (1999),Özener and Ergun (2008), Agarwal and Ergun (2010), Audy et al (2011), Granot et al (2011), Dai and Chen (2012), Yilmaz and Savasaneril (2012), Lozano et al (2013), Nguyen et al (2014), Vanovermeire and Sörensen (2014a), Vanovermeire and Sörensen (2014b), Vanovermeire et al (2014), Dai and Chen (2015), Hezarkhani et al (2015), Yang et al (2015). Inventory Anupindi et al (2001), Anily and Haviv (2007), Wong et al (2007), Fiestras-Janeiro et al (2012, Cheng et al (2013), Fiestras-Janeiro et al (2013),Özener et al (2013, Fang and Cho (2014).…”

Section: Problem Articlesmentioning

confidence: 99%

“…Then, the companies need to decide how to share the total cost of transport procurement. Cost allocation in this type of problems is addressed inÖzener and Ergun (2008), Audy et al (2011), Yilmaz and Savasaneril (2012), Lozano et al (2013), Nguyen et al (2014), Sörensen (2014a, 2014b), and Vanovermeire et al (2014).…”

Section: Joint Distributionmentioning

confidence: 99%

“…Another protagonist in the literature is the natural resources sector. This sector is represented by articles in agriculture (Nguyen et al, 2014), forestry (Frisk et al, 2010;Lehoux et al, 2011; Potters et al (1992) A share of the stand-alone cost plus a share of the marginal cost. Derks and Kuipers (1997) Recursive definition for which a game theoretic interpretation is given.…”

Section: Sectormentioning

confidence: 99%

See 3 more Smart Citations

A review on cost allocation methods in collaborative transportation

Guajardo

Rönnqvist

2015

Int Trans Operational Res

217

113

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Collaboration in transportation between two or more agents is becoming an important approach to find efficient solutions or plans. Efficiency can be measured in, for example, lower cost or more flexibility. An important aspect of the collaboration is to decide on how to share the benefits-for example, cost, profit, or resources. There are many sharing mechanisms or cost allocations proposed in the literature. Some are based on simple proportional rules and others are based on theoretical concepts found in game theory. We provide a survey on cost allocation methods found in the literature on collaborative transportation, including problems on planning, vehicle routing, traveling salesman, distribution, and inventory. A total of 55 scientific articles compose the main part of the survey, most of them published between 2010 and 2015. We identify more than 40 cost allocation methods used in this stream of literature. We describe the theoretical basis for the main methods as well as the cases where they are used. We also report savings from the collaborations when they are based on industrial data. Some directions for future research are discussed.

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Section: Traditional Methodsmentioning

confidence: 99%

Section: Problem Articlesmentioning

confidence: 99%

Section: Joint Distributionmentioning

confidence: 99%

Section: Sectormentioning

confidence: 99%

See 2 more Smart Citations

A review on cost allocation methods in collaborative transportation

Guajardo

Rönnqvist

2015

Int Trans Operational Res

217

113

View full text Add to dashboard Cite

show abstract

“…Taniguchi et al [11] proposed a bilevel model to determine the optimal size and location of public logistics terminals and solved the model using queuing theory and nonlinear programming techniques. Nguyen et al [12] noted that logistics efficiency and cost were related not only to the structure of supply chains but also to the logistics network design and logistics infrastructure. Soysal et al [13] presented a network model of wagonload traffic that aimed to determine hub location and size considering the total cost and efficiency of the network system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Investment Timing and Size of a Logistics Park: A Real Options Perspective

Zhang

Jiang

et al. 2017

Complexity

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This paper uses a real options approach to address optimal timing and size of a logistics park investment with logistics demand volatility. Two important problems are examined: when should an investment be introduced, and what size should it be? A real option model is proposed to explicitly incorporate the effect of government subsidies on logistics park investment. Logistic demand that triggers the threshold for investment in a logistics park project is explored analytically. Comparative static analyses of logistics park investment are also carried out. Our analytical results show that (1) investors will select smaller sized logistics parks and prepone the investment if government subsidies are considered; (2) the real option will postpone the optimal investment timing of logistics parks compared with net present value approach; and (3) logistic demands can significantly affect the optimal investment size and timing of logistics park investment.

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Decomposition‐based approximation algorithms for the one‐warehouse multi‐retailer problem with concave batch order costs

Toriello

et al. 2020

Naval Research Logistics

Self Cite

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We study the one‐warehouse multi‐retailer problem under deterministic dynamic demand and concave batch order costs, where order batches have an identical capacity and the order cost function for each facility is concave within the batch. Under appropriate assumptions on holding cost structure, we obtain lower bounds via a decomposition that splits the two‐echelon problem into single‐facility subproblems, then propose approximation algorithms by judiciously recombining the subproblem solutions. For piecewise linear concave batch order costs with a constant number of slopes we obtain a constant‐factor approximation, while for general concave batch costs we propose an approximation within a logarithmic factor of optimality. We also extend some results to subadditive order and/or holding costs.

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Consolidation strategies for the delivery of perishable products

Cited by 49 publications

References 53 publications

A review on cost allocation methods in collaborative transportation

A review on cost allocation methods in collaborative transportation

Optimal Investment Timing and Size of a Logistics Park: A Real Options Perspective

Decomposition‐based approximation algorithms for the one‐warehouse multi‐retailer problem with concave batch order costs

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