Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

Railroad industry has received tremendous challenges in the world in terms of handling cost and efficiency. For many years, the railroad business lost money in many countries such as Japan until many governments decided to privatize the industry in an attempt to reduce the cost components and to increase the efficiency of various units, significantly. In this paper, we propose a new goal programming technique to handle two objectives of operating cost and the number of passengers travel by train. We consider different types of trains for public transportation of passengers in order to make the proposed model of this paper more realistic. The implementation of the proposed model is demonstrated using some numerical examples to show the effectiveness of the method.

Decision making problem is the process of finding the best option from all of the feasible alternatives. One of the most important concepts in decision making process is to identify the weights of criteria. In real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not deterministic and can be treated in forms of fuzzy numbers. This paper investigates a method for deriving the weights of criteria from the pair-wise comparison matrix with fuzzy elements. Finding the weights of criteria has been one of the most important issues in the field of decision-making and the present method uses goal programming to solve the resulted model. In addition, using a ranking function we convert each obtained fuzzy weight to a crisp one, which makes it possible to compare the criteria. The proposed model of this paper is supported by several examples and a case study.

Many supply chain problems are involved with different parameters, which are under uncertainties. One of the primary concerns on supplier selection is to handle the uncertainty under different circumstances. The primary objective of this paper is to design a model to select suppliers and to determine the amount of purchase from any supplier in the supply chain system. For this purpose, we select the most important criteria using fuzzy questionnaires where the questionnaire uses experts’ opinions in terms of linguistic values. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to rank different suppliers and using a goal programming approach, we determine the amount of order product from each supplier. The implementation of the proposed model is demonstrated using a real-world case study.