Sparse, continuous policy representations for uniform online bin packing via regression of interpolants

Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation of policies. One of the main limitations of some previously-used policy representations is the trade-off between locality and granularity in the associated search space. In this article, we adopt an interpolation-based representation which has the jointly-desirable properties of being sparse and continuous (i.e. exhibits good genotype-to-phenotype locality). In contrast to previous approaches, the policy space is searchable via real-valued optimization methods. Packing policies using five different interpolation methods are comprehensively compared against a range of existing methods from the literature, and it is determined that the proposed method scales to larger instances than those in the literature

Drivers of Performance

This chapter uses the annual school census to analyze differences in primary school performances across regions. Our results, obtained from a stochastic frontier analysis, suggest that differences in efficiency explain only part of the observed variation, while resource availability is the most important driver of performance differences. In addition to this, we note that resources are distributed quite unevenly among regions and schools. By distributing more school inputs, or distributing existing inputs more equally to the benefit of underserved schools, performance can be expected to go up

Efficiency and Productivity of Norwegian Colleges

Regional colleges in Norway were reorganised in 1994 with the purpose of promoting efficiency and productivity. This is the first effort of checking what actually has happened afterwards with efficiency and productivity. DEA and Malmquist index approaches are used. Data for three years, 1994, 1995 and 1996 at a department level for about 100 units where collected by questionnaire and direct contacts. The three outputs where final exams distributed on two types; short- and long studies, and research publications. Inputs where number of academic and non-academic staff in full time equivalents, current expenses other than salaries, and building size in square metres. Typical cross section efficiency results show a large share of efficient departments, with a disproportionate number of efficient departments giving theoretical general education, and a large variation within the group of inefficient units. The difference between professional and arts and science departments may be explained by the nature of the teaching production function, but calculations for a sub-sample of professional departments (e.g. nurses, engineers, teachers) show almost the same variation within this group. The productivity change each year was mainly positive, with most departments experiencing a positive productivity effect from frontier shift, but a greater variation from positive to negative as regards the contribution from catching up. – Colleges ; efficiency ; DEA ; Malmquist productivity inde

The rank reversal problem in multi-criteria decision making : a literature review

Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field

Formulating and Solving Sustainable Stochastic Dynamic Facility Layout Problem: A Key to Sustainable Operations

Facility layout design, a NP Hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new Sustainable Stochastic Dynamic Facility Layout Problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated is then analyzed by Data Envelopment Analysis (DEA) to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi Attribute decision-making (MADM) Techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Interpretive Ranking Process (IRP) and Analytic hierarchy process (AHP), Borda-Kendall and Integer Linear Programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size N=12 for time period T=5 having Gaussian demand are considered