Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzyTOPSIS

PurposeSupplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.Design/methodology/approachThe concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.FindingsThis study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.Research limitations/implicationsThis research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.Practical implicationsThis work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.Originality/valueIn this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach

Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at the beginning of every term so that each student works with a different set of peers during every term, thus training his or her adaptation skills and expanding the peer network. Achieving diverse teams while avoiding–or minimizing—the repetition of student pairs is a complex and time-consuming task for MBA Directors. We introduce the Max-Diversity Orthogonal Regrouping (MDOR) problem to manage the challenge of splitting a group of people into teams several times, pursuing the goals of high diversity and few repetitions. We propose a hybrid Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Descent (GRASP/VND) heuristic combined with tabu search and path relinking for its resolution, as well as an Integer Linear Programming (ILP) formulation. We compare both approaches through a set of real MBA cohorts, and the results show that, in all cases, the heuristic approach significantly outperforms the ILP and manually formed teams in terms of both diversity and repetition levels.

Design optimization of multi energy systems for domestic hot water uses on the building sector

The burden that has come upon the environment, combined with the ever-declining fossil fuel reserves, has led to the need of reducing the conventional energy consumption in building sector and to the promotion of systems based on Renewable Energy Sources (RES). This paper deals with the optimization of multi energy systems in order to cover the needs of hot water in domestic use. In particular, integer linear programming models are formulated and the optimal solutions regarding the degree of participation of the multi energy systems are explored; economic, energy, and environmental criteria are assumed. The respective mathematical programming approaches include linear objective functions, multiple objective functions that either do or do not use weights, as well as goal programming-based ones. The modeling and solution of the problems is done with the General Algebraic Modeling System (GAMS). The case study refers to residential use; both conventional and RES systems are selected for the respective energy demand coverage. The time step of the analysis is 1 hour, in the context of annual operation. According to the results, in the case of the energy criterion, biomass predominates, or heat pumps, when biomass is not included, with an increase in participation of solar thermal collectors when the environmental criterion is introduced. The participation of solar thermal factor is also reinforced in the case of goal programming, because of the relaxation of the initial targets. The analysis demonstrated that the existing integer linear programming methodological tools can be used for investigating problems of multiple energy systems or comparing subsystems.