br Data The datasets of this article

Data The datasets of this article provide additional information to [1] and contains four categories of data (datasets). The first dataset contains the optimization input and output data used in the statistical analysis in section 4.1.4 of [1] to compare between two supplier ranking approaches in a context of varying number of suppliers. The two ranking approaches can be described as follows: The second dataset contains the quantities ordered from each supplier in the two previously mentioned cases (Case A and Case B) in each leukotriene receptor antagonist of the planning horizon based on the numerical example described in section 4.1.2 of [1]. The third dataset summarizes the quantities purchased from each supplier in all periods of the planning horizon for the example mentioned in section 4.2 of [1]. The last dataset provides the input data of the instances used in the time study presented in [1], the computation time data, as well as the MATLAB R201a code files. Moreover, it contains the input and output data of the additional time-study of Section 2.4 of this data article. The datasets of this article are used for the deterministic bi-objective optimization model which is described in [1]. For clarity and completeness purposes, we give here a brief summary of the model, which makes the use of the data even easier. This model described in [1] is an extension to the models presented in [2] and [3] that deal with the problem of green supplier selection and order allocation using fuzzy TOPSIS, AHP and bi-objective linear programming. This model provides a decision-making tool to select the optimal suppliers, the optimal quantities to be purchased and stored, and the optimal amount of shortage in each period. The model suggests a separation between the green aspects and the non-green aspects (traditional criteria) during the evaluation and ranking of suppliers. Originally, the non-linear programming model of [2] was converted into a multi-objective linear programming model as described in [3] with an extensive analysis of the separation approach and the model configuration (i.e. the use of bi-objective and multi-objective configurations). In addition, the extension of the models of [2] and [3] that is presented in [1], considers all-unit quantity discounts and allows the number of available suppliers to vary between the periods of the planning horizon due to capacity limitations, for instance, which was not allowed in the models presented in [2] and [3]. The model described in [1] has also been implemented in MATLAB R2014a and the corresponding codes are provided with this data article. To make the developed tool user-friendly, a graphical user interface (GUI) was designed. This GUI requires two MS Excel input files, one includes the quantity discount data (i.e. ranges and prices) and the other one contains the other input data (demand, inventory holding cost, shortage cost, green criteria evaluation, traditional criteria evaluation, etc.). The GUI launches then an optimization code that solves the bi-objective model and the optimization outputs are displayed on a screen, as shown in Fig. 1, and saved into an MS Excel file. Since the GUI was developed using academic MATLAB licenses, it cannot be made available as an open source file. Thus, we provide with this data article only our MATLAB R2014a codes that are used to solve the optimization models.
Experimental design, materials and methods
Data Table S1 presents the results of the accessibility criteria evaluation of the OCW UTPL case study (See Appendix A) describe on [3]. Specifically, the evaluation of the accessibility criteria is presented in terms of the principles, criteria and accessibility level (A, AA, AAA) conforming to the Web Content Accessibility Guidelines WCAG 2.0 [5]. In Table S1, for each criterion in which problems were found in the UTPL OCW evaluation, it specifies the identified problems and their improvements, as well as the accessibility level resulting from the evaluation being: