Planning for demand failure: A dynamic lot size model for clinical trial supply chains

Fleischhacker, Adam; Zhao, Yue

doi:10.1016/j.ejor.2011.01.004

Cited by 27 publications

(11 citation statements)

References 38 publications

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“…To deal with uncertainties, different strategies were proposed in the literature (see Bookbinder and Tan (1988), Tempelmeier (2013) and Küçükyavuz (2011)). [45], [65], [69], [72], [87], [85], [86], [175], [209], [212], [233], [238], [239], [241], [243], [242], [250], [267], [266] [237], [141], [225] [271], [221], [130], [103] [22]…”

Section: Stochastic Modelsmentioning

confidence: 99%

Single-item dynamic lot-sizing problems: An updated survey

Brahimi

Absi

Dauzère‐Pérès

et al. 2017

European Journal of Operational Research

152

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Following our previous paper (Brahimi, N., Dauzère-Pérès, S., Najid, N. M., and Nordli, A. Single item lot sizing problems. European Journal of Operational Research, 168(1):1-16, 2006), we present an updated and extended survey of Single-Item Lot-Sizing Problems with focus on publications from 2004 to 2016. Exact and heuristic solution procedures are surveyed. A concise and comprehensive summary of different extensions of the problem is given. The classification of the extensions is based on different characteristics such as resource limitations, assumptions on demand and cost structure. The large number of surveyed papers shows the increased interest of researchers in lot-sizing problems in general and in single-item problems in particular. The survey and the proposed classification should help researchers to identify new research topics, to propose relevant problems and/or novel solution approaches.Since the publication of the seminal work of Wagner and Whitin (1958) in 1958, a lot of research has been conducted on the Single-Item Lot-Sizing Problem (SILSP) and its extensions. The SILSP is interesting in itself to model some tactical production and distribution planning problems, e.g. when planning the replenishment of one raw material with fixed and variable ordering and transportation costs. It is also important to efficiently solve the SILSP because it appears as a subproblem in the solution procedures of many complex lot-sizing problems such as capacitated multi-item problems.Extensions of the SILSP include production capacity, remanufacturing, backlogging, lost sales, demand and production time windows, bounded inventory, perishability, etc. The large number of publications on the SILSP and the large variety of its applications and extensions call for a literature review to put together all the relevant references and classify them. This will allow researchers in the field to more easily identify research trends and gaps to be filled in lot sizing in particular and in production ACCEPTED MANUSCRIPT and distribution planning in general. This survey is an extension and an update of a previous survey published by three of the authors in European Journal of Operational Research in 2006(Brahimi et al. (2006). Since this survey was published, there has been a growing interest in the study of the SILSP.There were at least 100 publications on the SILSP and its extensions in the past 8 years. The objective of this paper is to update the previous literature review and enrich it with the new research in the area. To avoid repetition and for space reasons, most of the references and details in (Brahimi et al. (2006)) are omitted.The SILSP can be defined as a planning problem in which there is time-varying demand for a single product over a planning horizon of T periods. The objective is to determine periods where production will take place and the quantities that have to be produced in these periods. The total production should satisfy the demands while minimizing the total costs. The basic costs are the unit producti...

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Section: Stochastic Modelsmentioning

confidence: 99%

Single-item dynamic lot-sizing problems: An updated survey

Brahimi

Absi

Dauzère‐Pérès

et al. 2017

European Journal of Operational Research

152

View full text Add to dashboard Cite

show abstract

“…Processed food, beverages and dairy industry 2 Ram et al (2006), Christou et al (2007) Pulp and paper industry 2 Rizk et al (2006), Almeder et al (2009) Sawmills, wood and furniture industry 1 Demirli and Yimer (2008) Pharmaceutical company 1 Lukac et al (2008) Pottery company 1 Petrovic and Akoez (2008) Fruit juice and soft drink industry 1 Ferreira et al (2009) Iron factory 1 Pan et al (2009) Semiconductor and electronic devices industry 1 Quadt and Kuhn (2009) Magazine publishing firm 1 Huh et al (2010) Refractory bricks manufacturer 1 Mateus et al (2010) X-ray film producer 1 Aghezzaf et al (2011) Clinical trial 1 Fleischhacker and Zhao (2011) Numerical examples inspired in:…”

Section: Number Of References Referencesmentioning

confidence: 99%

“…The capacity limitations pointed out by the authors are due to not considering production capacities (Aksen, 2007;Depuy et al, 2007;Ho et al, 2006;Kim et al, 2006;Li et al, 2006b;Liu et al, 2007;Merzifonluoglu and Geunes, 2006;Okhrin and Richter, 2011a;Palaniappan and Jawahar, 2011;Pitakaso et al, 2007;Rizk et al, 2006a;Tempelmeier, 2007;van den Heuvel and Wagelmans, 2006;van den Heuvel et al, 2007), storage capacities (Almada-Lobo and James, 2010;Bollapragada et al, 2011;Gonzalez-Ramirez et al, 2011;Gunnarsson and Ronnqvist, 2008;Stadtler and Sahling, 2013), outsourcing capacities (Erromdhani et al, 2012;Fleischhacker and Zhao, 2011;Gonzalez-Ramirez et al, 2011;Liu and Tu, 2008b;Ozturk and Ornek, 2010), overtime production capacities (Bilgen and Guenther, 2010;Gonzalez-Ramirez et al, 2011;Palaniappan and Jawahar, 2011) or transport capacities (Hwang, 2010;Sargut and Romeijn, 2007).…”

Section: Limitationsmentioning

confidence: 99%

A review of discrete-time optimization models for tactical production planning

Díaz‐Madroñero

Peidro

2014

International Journal of Production Research

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This paper presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, setup times, parallel machines, overtime capacities and network-type multi-site configuration stand out in terms of extensions; (4) the most widely used modeling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modeling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.

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“…His algorithm, similar to Wagner-Whitin's, gives us an optimal solution. In addition, Fleischhacker and Zhao (2011) extended the Wagner-Whitin algorithm to include the risk of failure in demands and therefore they could transform it into a stochastic production-inventory model.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Robust Wagner–Whitin algorithm with uncertain costs

Hanafizadeh

Shahin²,

Sajadifar

2018

J Ind Eng Int

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In real-world applications, costs for products are not deterministic: neither static nor dynamic. They actually tend to be non-stationary and cross-correlated. To overcome this drawback, there have been some efforts by researchers to extend the Wagner-Whitin algorithm to consider stochastic costs. However, they assume that the information of probability density function of random costs exists. This paper applied a robust approach in reformulating the uncertain lot-sizing problem and used the Wagner-Whitin algorithm to find an optimal solution of its robust counterpart. The solution of the proposed algorithm in an example from the literature is compared with the classical one.

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Planning for demand failure: A dynamic lot size model for clinical trial supply chains

Cited by 27 publications

References 38 publications

Single-item dynamic lot-sizing problems: An updated survey

Single-item dynamic lot-sizing problems: An updated survey

A review of discrete-time optimization models for tactical production planning

Robust Wagner–Whitin algorithm with uncertain costs

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