NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, vol 206, issue 3, November 2010, DOI: 10.1016/j.ejor.2009.10.020This paper presents a literature review of the cell formation (CF) problem concentrating on formulations\ud proposed in the last decade. It refers to a number of solution approaches that have been employed for CF\ud such as mathematical programming, heuristic and metaheuristic methodologies and artificial intelligence\ud strategies. A comparison and evaluation of all methodologies is attempted and some shortcomings are\ud highlighted. Finally, suggestions for future research are proposed useful for CF researchers

This paper examines the effect of different types of collaboration on the level of Postharvest Food Losses (PHFL) and the proportion of low-quality peaches produced using a unique data-set of Greek peach producers. Quantile regression techniques are adopted to estimate the effects at different points of the conditional distribution of our variables of interest. The findings of this study suggest that high levels of collaboration between producers and cooperatives are associated with both low levels of PHFL and a low proportion of low-quality peaches. We also find that specific types of collaboration, such as ‘goal congruence’, can play a significant role in reducing PHFL and improving the quality of peach production at the extremes of the distribution. Important policy implications regarding collaborative practices and systems that can be implemented to reduce PHFL and boost a producer’s performance together with sustainability credentials are drawn from this study.

Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise: (a) cell formation, (b) machine-cell allocation, and (c) part-machine allocation. This paper will present extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously, and also a significant number of constraints together with an enhanced objective function are considered. The main study examines the integration of inter-cell movements of parts and machine set-up costs within the objective function, and also the combination of machine set-up costs associated with parts revisiting a cell when part machine operation sequence is taken into account. The latter feature incorporates a key set of constraints which identify the number of times a part travels back to a cell for a later machine operation. Due to two main drawbacks of IP modelling for cell formation, i.e. (a) only one objective function can be involved and (b) the decision maker is required to specify precisely goals and constraints, fuzzy elements like fuzzy constraints and fuzzy goals will be considered in the proposed model. Overall the paper will not only include an extended and enhanced integer programming model for assessing the performance of cell formation, but also perform a rigorous study of fuzzy integer programming and demonstrate the feasibility of achieving better and faster clustering results using fuzzy theory.

Free disposal hull (FDH) is a nonparametric model of production technology based on the single assumption of free disposability of all inputs and outputs. In this paper, we consider multicomponent production technologies in which every decision making unit (DMU) consists of several parallel component processes that can in principle operate independently of each other, provided they have sufficient resources. An example is universities viewed as DMUs, with their departments or groups of departments viewed as component processes. Each component process uses its own set of inputs and an unknown part of the shared inputs in order to produce its own set of outputs and an unknown part of the shared outputs. We allow combinations of component processes taken from different DMUs in order to construct new hypothetical DMUs, and refer to the resulting model of technology as the multicomponent FDH (MFDH) model. We further develop a larger, and mathematically nontrivial, variant of MFDH for the case in which we can specify certain bounds on the proportions in which shared inputs and outputs are allocated to component processes. We use an illustrative example in the context of universities to demonstrate the increasing discriminatory power of the new MFDH models over the standard FDH models in the multicomponent setting.