Setting planned orders in master production scheduling under demand uncertainty

Feng, Keli; Rao, U. R. K.; Raturi, Amitabh S.

doi:10.1080/00207543.2010.495955

Cited by 17 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…inventory costs [27]. Similar conclusions along with practical considerations can be found in [28], where it is confirmed that the CV of demand has a significant performance impact. Depending on the manufacturing system's external context, the assumption of independent distributed observations may likewise not hold true.…”

Section: Background and Motivationsupporting

confidence: 80%

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Eriksen¹,

Nielsen²

2016

Management and Production Engineering Review

View full text Add to dashboard Cite

Accepted: 1 September 2016In this paper an investigation into the demand, faced by a company in the form of customer orders, is performed both from an explorative numerical and analytical perspective. The aim of the research is to establish the behavior of customer orders in first-come-first-serve (FCFS) systems and the impact of order quantity variation on the planning environment. A discussion of assumptions regarding demand from various planning and control perspectives underlines that most planning methods are based on the assumption that demand in the form of customer orders are independently identically distributed and stem from symmetrical distributions. To investigate and illustrate the need to aggregate demand to live up to these assumptions, a simple methodological framework to investigate the validity of the assumptions and for analyzing the behavior of orders is developed. The paper also presents an analytical approach to identify the aggregation horizon needed to achieve a stable demand. Furthermore, a case study application of the presented framework is presented and concluded on. KeywordsDemand distribution, Poisson processes, order quantities, Coefficient of Variation, Planning&Control. IntroductionTo achieve good customer service, companies aim at delivering products on time at competitive prices. One aspect in achieving competitive performances is companies' abilities to match customer expectations. In many situations resources are dedicated prior to knowing actual customer demand. This makes forecasting customers' ordering behavior of critical importance. Most research on this topic has been dedicated to either forecasting demand [1] or how to react in a quick robust manner to customer demand through e.g. better planning [2]. The research presented in this paper focuses on another aspect that has received limit research attention, namely the fact that demand is composed of individual customer orders. The fact that the demand a company faces is actually composed of a large number of presumed i.i.d. customer orders that can be aggregated is challenged in this research through an investigation of actual behavior and modelling of what to do when customer orders are in fact not i.i.d. This paper expands on the work reported in [3] through adding a framework, extending the complexity of the model, elaborating on the current state and adding more numerical examples. This paper focuses on customer order quantity distributions, analyses their behavior and investigates the consequence of transforming these into demand rates that are used for planning purposes. It also presents an analytical model to calculate the aggregation horizon necessary to ensure a stable distribution of demand for operational planning purposes. In this research main aim is to establish the specific need for aggregation in 39 Unauthenticated Download Date | 5/9/18 8:28 PM Management and Production Engineering Reviewthe form of the number of customer orders needed to aggregate to achieve a system-wide desired stability. In...

show abstract

Section: Background and Motivationsupporting

confidence: 80%

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Eriksen¹,

Nielsen²

2016

Management and Production Engineering Review

View full text Add to dashboard Cite

show abstract

“…Demand risk or demand uncertainty measured by coefficient of variation (CV, which is defined as σ(ε)/μ(ε), and higher demand uncertainty with high CV, see, e.g. Hua, Li, and Liang 2006;Feng, Rao, and Raturi 2011), and values of option contracts (w 0 , w 2 ) are changed to describe different scenarios for sensitivity analysis. The other parameters are kept constant as follows: A = -200, B = 800, w 1 = 10, s = 0.5 and g = 50.…”

Section: Numerical Examples and Sensitivity Analysismentioning

confidence: 99%

Optimal ordering policy for a price-setting newsvendor with option contracts under demand uncertainty

Wang

Chen

2015

International Journal of Production Research

View full text Add to dashboard Cite

In this article, we investigate the newsvendor problem in a joint ordering and pricing setting in the presence of option contracts under demand uncertainty. At the beginning of a single selling season, the newsvendor who faces additive stochastic demand can obtain goods through two ways: ordering from a firm or purchasing and exercising call options. Single ordering (ordering from a firm only or purchasing and exercising call options only) and mixed ordering (ordering from a firm and purchasing and exercising call options simultaneously) cases are investigated. We find that the newsvendor's optimal pricing and ordering strategies exist and are unique for both cases, respectively. In addition, when both cases are available, mixed ordering is the newsvendor's optimal ordering policy. If only single ordering is available, the newsvendor prefers ordering from a firm when demand risk is low, while enjoys purchasing and exercising call options when demand risk is high. We also find that with option contracts, the newsvendor's optimal order quantity and maximum expected profit are all decreasing in the option price and exercise price of product, while the optimal retail price in terms of option price and exercise price of product are intricate. Moreover, we show that, mixed ordering is more capable to deal with supply price volatility risk.

show abstract

“…Scenario-based [121], [120], [128], [133], [240], [289] [124], [122], [128], [292] [53] [151] On-line decisions [1], [270] Fuzzy logic [126], [156] [156] [274] [203] Interval arithmetic [153] Simulation [213], [101] • Static strategy: In this strategy, all production decisions (setup periods and produced quantities) are taken in advance and the whole production plan is executed independently of the actual realization of demands. This model is particularly useful when capacity is limited and a capacity-feasible production plan must be defined in advance.…”

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

155

View full text Add to dashboard Cite

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...

show abstract

Setting planned orders in master production scheduling under demand uncertainty

Cited by 17 publications

References 15 publications

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Optimal ordering policy for a price-setting newsvendor with option contracts under demand uncertainty

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

Contact Info

Product

Resources

About