Distribution of goods is one of the main issues that directly affect the performance of the companies since efficient distribution of goods saves energy costs and also leads to reduced environmental impact. The multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles is encountered when dealing with this situation in many practical cases. This paper is motivated by the fuel delivery problem where the main objective of this research is to minimize the total driving distance using a minimum number of vehicles. Based on a case study of twenty petrol stations in northeastern Thailand, a novel two-phase heuristic, which is a variant of the Fisher and Jaikumar Algorithm (FJA), is proposed. The study first formulates an MCVRP model and then a mixed-integer linear programming (MILP) model is formulated for selecting the numbers and types of vehicles. A new clustering-based model is also developed in order to select the seed nodes and all customer nodes are considered as candidate seed nodes. The new Generalized Assignment Problem model (GAP model) is formulated to allocate the customers into each cluster. Finally, based on the traveling salesman problem (TSP), each cluster is solved in order to minimize the total driving distance. Numerical results show that the proposed heuristic is effective for solving the proposed model. The proposed algorithm can be used to minimize the total driving distance and the number of vehicles of the distribution network for fuel delivery.

Simultaneous products distribution and items retrieval in an integrated forward/reverse logistics network faces a complex freight-scheduling problem due to the constraints involved. In the high to intermediate network level, the problem usually exists in the form of single stop transportation. To reach a higher level of performances, there is a need to model and optimize the freight schedule. This research proposes a model to optimize a freight-scheduling problem. The proposed model of this paper based on Non-dominated sorting genetic algorithm-II is formulated to solve a conflicting bi-objective optimization and optimizes a real-world case study. A solution from the model demonstrates the solution interpretation in the form of delivery schedule, distribution as well as retrieval route, and vehicle assignment. Moreover, the solutions are also comparable to some current manual solution by its similarity. The results show that the model was capable of generating feasible solutions while satisfying all of its constraints.

The collaboration between the universities and industries is currently in the focus of attention globally. Governments, universities, and industries are interested in good and effective collaboration, which would be beneficial for all parties. To foster University-Industry Collaboration, and to help transfer the knowledge and technology between these two parties, academics, politicians and companies are paying attention to science and technology policies more than ever. In this study, the factors affecting the improvement of University-Industry Collaboration are identified and prioritized. In the first step, 20 factors are identified and 12 factors are selected using the Fuzzy Delphi method. Then, using the BWM method, prioritizing the extracted factors is determined for industry sponsorship of the university research. Finally, based on the results, the discussion is conducted and six major strategies are presented to improve this relationship.

There are huge amounts of emissions associated with construction industry during its different stages from cradle till building demolition. This study presents a methodology that integrates multi-objective optimization and multi-criteria decision making (MCDM) in order to enable construction decision-makers to select the most sustainable construction alternatives. Four objectives functions are investigated, which are: construction time, lifecycle cost, environmental impact and primary energy in order to construct the Pareto front. A novel hybrid MCDM is designed based on seven multi-criteria decision making techniques to select the best solution among the set of the Pareto optimal solutions. Sensitivity analysis is performed in order to determine the most sensitive attribute and construction stages that influence environmental emissions. The analysis illustrates that WSM, COPRAS and TOPSIS provided the best rankings of the alternatives, primary energy is the most sensitive attribute for different MCDM methods. Moreover, PROMETHEE II is the most robust MCDM method.

As archipelagic region, an effort to effectively enhance and accelerate the development of each sub-districts to boost the rapid development of Southeast Maluku district in Indonesia cannot happen as long as the local government fails to identify the real potentials and power in fisheries sector of each sub-districts. Identification of each sub-district fisheries top sub-sector has to be based on the human resources, natural resources, infrastructure, current and potential market, current policy of local and central government. A multi-criteria decision making (MCDM) is one of the powerful tools to provide a better result based on complicated factors involved. This paper proposed an integrated MCDM, to tackle the complicated factors in order to provide the best commodities on each sub-districts. Bottom-up concept was used to have a comprehensive result, by combining Fuzzy logic with Analytical Hierarchy Process (AHP) to measure the factors using fuzzy logic with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for determining the top sub-sector in fisheries. For comprehensive assessment of macro factors the study used experts ranging from government, scientists, practitioners to NGOs. On the other hand, for micro factors the survey used field instructor, field officer, fishers and farmers. The results provide a guideline for local and central government to form a better policy regarding the development of each sub-district including farmers, fishers and coastal communities in each sub-district to focus on commodities that benefited their regions’ resources and coastal community’s capabilities. By doing so, we hope to contribute on crafting an integrated and collective path on reaching the goal which is the welfare of coastal region.

This paper proposes a new method on ranking fuzzy numbers through the process of defuzzification by using maximizing and minimizing set on the triangular fuzzy numbers formed from generalized trapezoidal fuzzy numbers. In this method, a total utility value of each fuzzy number is defined by considering two left and two right utility values along with decision maker’s optimism which serves as a criterion for ranking fuzzy numbers and overcomes the limitations of Chen's (1985) [Chen, S. H. (1985). Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets and Systems, 17(2), 113-129] ranking method.

This article examines the nexus between Big data governance, dynamic capability and decision-making. Survey data was garnered from firms in Jordan and fuzzy sets (fsQCA) approach was applied. Fuzzy set configural modeling revealed two causal models for high decision-making effectiveness conditions. First, Big data governance, number of employees and firm age are necessary for higher levels of effective decision-making. Second, dynamic capability, number of employees and firm age are necessary for higher levels of effective decision-making. Insights for practitioners in an emerging economy and possible areas of future research are highlighted.

Effective transshipment network is currently recognized as an important success determinant for most manufacturing organizations, because the transshipment management has significant impact on cost and environmental impact. Due to the complexity of the multi-criteria transshipment problem for infectious waste management (IWM) for this case, forty hospitals and three candidate disposal municipalities in Northeastern Thailand, a novel holistic approach (combination of fuzzy AHP, transshipment model and DEA) was developed for solving this problem. We first utilized the fuzzy AHP technique to calculate the location weights of each candidate disposal municipalities. Secondly, a new cost-based transshipment model was formulated and solved in order to provide the set of feasible solutions. These solutions can be viewed as decision making units (DMUs), inputs and outputs. Finally, DEA-CCR model was applied to calculate the efficiency scores of candidate DMUs. The study results demonstrated that the proposed holistic approach can help decision makers (DMs) to choose a suitable transshipment network for IWM. The major advantage of the proposed holistic approach is that both costs and environmental impacts under constraints are focused on simultaneously. Future work will apply the developed approach with other real-world complex problems to enhance the validity of the research output further. For large-size transshipment problems in which an exact solution cannot be found, meta-heuristics must be applied.

In the era of digital manufacturing and highly competitive environment, it is desirable to deliver the right item, right quantity at right time at minimal cost. Under this volatile market environment, the inventory should be readily available at the manufacturing level at the lowest possible cost. Many industries have been conventionally employing traditional ABC analyses based on a single criterion of annual consumption cost for classification of inventory items in spite of other criteria such as unit cost, consumption rate, average inventory cost that may be important in inventory classification. To address such problems, incorporation of Multi-criteria decision making (MCDM) methods is considered an advantage. The present article focuses on a new approach to categorize inventory items using Modified similarity-based method. The proposed method is applied to the inventory data of raw materials from a renowned conveyor belt manufacturing company of West Bengal, India. By using Modified similarity-based method, the items are classified in A, B and C categories. Results obtained from the said method using R program are compared with those of well recognized TOPSIS and AHP methodologies to validate the application of this method for inventory classification.

In this paper, we propose a three-phase multi-attribute ranking approach having as outcomes of the modeling phase what we refer to as net superiority and inferiority indexes. These are defined as bounded differences between the classical superiority and inferiority indexes. The suggested approach herein named MANISRA (Multi-Attribute Net Inferiority and Superiority based Ranking Approach) employs in the aggregation phase a bi-parameterized family of compound averaging operators (CAOPs) referred to as generalized Heronian OWAWA (GHROWAWA) operators having the usual OWAWA operators as special instances. Note that the new defined operators are built by using a composition of an arbitrary bi-parameterized binary Heronian mean with the weighted average (WA) and the ordered weighted averaging (OWA) operators. Also, note that the current developed MANISRA method generalizes the superiority and inferiority ranking (SIR-SAW) method which is known to coincide with the quite popular PROMETHEE II method when the net flow rule is used. With net superiority and inferiority indexes and GHROWAWA operators, we are better equipped to rank rationally prespecified alternatives. The basic formulations, notations, phases and interlocking tasks related to the proposed approach are presented herein and its feasibility and effectiveness are shown in a real problem.