Month: September 2015

This might also be the full length Literature Review for a longer peer-reviewed paper or for a chapter you write for a book.

On the optimal container size in automated warehouses Y. Roll, M.J. Rosenblatt and D. Kadosh, Proceedings of the Ninth ICPR Automated storage and retrieval systems (AS/RS) are being introduced into the industry and warehousing at an increasing rate. Forecasts indicate that this trend will continue for the foreseeable future (see [1]). Research in the area of AS/RS has followed several avenues. Early work by Hausman, Schwarz and Graves [6,7] was concerned with storage assignment and interleaving policies, based on turnover rates of the various items. Elsayed [3] and Elsayed and Stern [4] compared algorithms for handling orders in AR/RS. Additional work by Karasawa et al. [9], Azadivar [2] and Parry et al. [11] deals with the design of an AS/RS and the determination of its throughput by simulation and optimization techniques.

Several researchers addressed the problem of the optimal handling unit (pallet or container) size, to be used in material handling and warehousing systems. Steudell [13], Tanchoco and Agee[14], Tanchoco et al. [15] and Grasso and Tanchoco [5] studied various aspects of this subject. The last two references incorporate the size of the pallet, or unit load, in evaluation of the optimal lot sizes for multi-inventory systems with limited storage space. In a report on a specific case, Normandin [10] has demonstrated that using the ‘best-size’ container can result in considerable savings. A simulation model combining container size and warehouse capacity considerations, in an AS/RS environment, was developed by Kadosh [8]. The general results, reflecting the stochastic nature of the flow of goods, are similar to those reported by Rosenblatt and Roll [12]. Nevertheless, container size was found to affect strongly overall warehousing costs.

In this paper, we present an analytical framework for approximating the optimal size of a warehouse container. The approximation is based on series of generalizations and specific assumptions. However, these are valid for a wide range of real life situations. The underlying assumptions of the model are presented in the following section.

Notice how the writers have:

• Grouped similar information: “Steudell [13], Tanchoco and Agee[14], Tanchoco et al. [15] and Grasso and Tanchoco [5] studied various aspects of this subject.”
• Shown the relationship between the work of different researchers, showing similarities/differences: “The general results, reflecting the stochastic nature of the flow of goods, are similar to those reported by Rosenblatt and Roll [12].

• Indicated the position of the work in the research area history: “Early work by Hausman, Schwarz and Graves [6, 7] . . . “

• Moved from a general discussion of the research in AS/RS to the more specific area (optimal container size) that they themselves are researching i.e. they relate previous work to their own to define it, justify it and explain it.

Adapted from: McMillan, Writing Papers in Biological Sciences.

Make critical arguments that account for why a well known  key text or theory is not pertinent to your research. Do not just omit a key area and hope your overall argument makes it obvious why you are not discussing X or Y seminal idea or text. By making a critical argument and referring to the text you let the reader know, by ‘showing’ not ‘telling’, that you understand that text, that you know it exists. If done well, this means the reader does not need to question further any apparent ‘omission’ of key thinkers later in the thesis.

This is part of good research writing practice for your PhD, what I often refer to as “relaxing the reader, the examiner”. In this case, by concisely explaining why an apparently key text is not central to your Literature Review, you stop the examiner making a note and wondering whether you have worrying gap in your knowledge. Do this in one or two sentences. These should acknowledge and summarise the role of that text or theory, very briefly. Other knowledge domains may be omitted because you “can’t cover everything” and they are not central enough to the domains you are covering. In such cases, again, reassure the reader that you know your stuff, that you DO know about that domain. Explain why it is not coming into your thesis, which again adds to your credibility as a scholar. End that couple of sentences with a statement such as, “it is therefore beyond the scope of this research to discuss X further.” This applies to all disciplines and the example below, while conversational in tone. illustrates why eliminating areas is good for you as a researcher.

“At CERN, there was a video where a particle physicist was asked “What if you don’t find the Higgs Boson? What if you’re wrong about this?” and he thought that would be brilliant, because then they’d know a whole area they could block out and go OK, not this, but how about this?”

James Bridle, http://booktwo.org/notebook/sxaesthetic/

As an examiner, if I have to read through the thesis and ‘guess’ why X or Y key thinker has been omitted I question the depth and breadth of the work being presented. If I am told clearly early on, with a convincing argument, I have one less question for you at the Oral Examination and this contributes to me believing that you know your stuff.