The impact of geopolitical conflict and supply chain (SC) uncertainties in the global gas trading context is a burgeoning area of research. The strategic imperative of optimizing resource and technology utilization through cost optimization models within SC dynamics is realized. This study examines the effectiveness of linear programming techniques in mitigating the transportation challenges in the landscape of global gas trade, particularly amidst geopolitical disruptions in the SC. Computational tests underscore the substantial efficiency gains provided by this method, highlighting its capacity to generate significantly more efficient solutions to transportation problems. The findings indicate that the model shows promise for practical implementation, showcasing a notable reduction in transportation costs across the three primary markets for liquefied natural gas (LNG). Significantly, this reduction surpasses a quarter of the original expenses, indicating the potential for substantial cost savings in turbulent geopolitical environments and uncertain SCs. This study emphasizes the pivotal role of cost optimization models in navigating uncertainty and enhancing efficiency within the intricate and volatile landscape of global gas trading supply chains.

The pharmaceutical supply chain (PSC) plays a crucial role in ensuring the timely and reliable availability of essential drugs while maintaining high-quality standards. Balancing the triad of cost, time, and quality is paramount in optimizing the complexities of this supply chain. In this research, a multi-objective PSC optimization model is developed to maximize the key business factors. The dynamic nature of the PSC can significantly compromise the effectiveness of the decision making process. To deal with this challenge, a robust possibilistic flexible programming approach (RPFPA) solution methodology is proposed. This methodology provides a robust and flexible framework to tackle the uncertainties within the supply chain. To validate the proposed model and methodology, a computational analysis of a case study is conducted. The results of the analysis demonstrate the effectiveness of the model and methodology in addressing the uncertainties and complexities of the PSC. Specifically, the findings reveal that by accepting a 23.8% increase in costs, decision-makers can achieve a desirable level of robustness in their decisions. Moreover, the study identifies that the assignment of higher priority to cost objectives leads to more centralized decisions within the supply chain, while a greater emphasis on quality objectives results in a more decentralized approach. By employing the proposed approach, decision-makers can efficiently deal with the complexities and uncertainties inherent in the PSC, making well-informed choices that balance cost, time, and quality.

This paper deals with the total tardiness minimization problem in a parallel machines manufacturing environment where tool change operations have to be scheduled along with jobs. The mentioned issue belongs to the family of scheduling problems under deterministic machine availability restrictions. A new model that considers the effects of the tool wear on the quality characteristics of the worked product is proposed. Since no mathematical programming-based approach has been developed by literature so far, two distinct mixed integer linear programming models, able to schedule jobs as well as tool change activities along the provided production horizon, have been devised. The former is an adaptation of a well-known model presented by the relevant literature for the single machine scheduling problem with tool changes. The latter has been specifically developed for the issue at hand. After a theoretical analysis aimed at revealing the differences between the proposed mathematical models in terms of computational complexity, an extensive experimental campaign has been fulfilled to assess performances of the proposed methods under the CPU time viewpoint. Obtained results have been statistically analyzed through a properly arranged ANOVA analysis.

In many real-world applications, the quality of a process or a particular product can be characterized by a functional relationship called profile. A profile builds the relationships between a response quality characteristic and one or more explanatory variables. Monitoring the quality of a profile is implemented to understand and to verify the stability of this functional relationship over time. In some real applications, a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is fuzzy. The purpose of this paper is to develop an approach for monitoring process/product profiles in fuzzy environment. A model in fuzzy linear regression is developed to construct the quality profiles by using linear programming and then fuzzy individuals and moving-range (I-MR) control charts are developed to monitor both intercept and slope of fuzzy profiles to achieve an in-control process. A case study in customer satisfaction is presented to show the application of our approach and to express the sensitivity analysis of parameters for building a fuzzy profile.

In competitive electricity markets, power needed for the network’s reserve is purchased from the ancillary service market. In this market, producing units and buyers alike announce their offers. As will be seen, energy market and reserve market implementation is possible with simultaneous method and serial method by choosing each of the methods based on the type of market and other conditions. In this paper, the energy market and the active power reserve market are simulated in two formations as serial and simultaneous for a uniform pricing system. In each method, limitations of transferring power over the lines, based on available transfer capacity (ATC), is considered alongside the other constraints in the energy market and the active power reserve market. Then, during network overload, economic dispatch is accomplished between winner units in the reserve market by using a linear optimization problem, and needed power is provided from these units at a minimal cost. Finally, our proposed methods are implemented on an IEEE 39-bus test system and results are analyzed.

In this paper, we have modeled a decision making problem of a tea industry as a multi-objective optimization problem in interval environment. The goal of this problem is to maximize the overall profit as well as to minimize the total production cost subject to the given resource constraints depending on budget, storage space and allotted processing times in different machines. For this purpose, the problem has been formulated as a multi-objective integer linear programming problem with interval objectives. To solve the problem, we have proposed extended elitist non-dominated sorting genetic algorithm (ENSGA-II) for integer variables with interval fitness, crowded tournament selection, intermediate crossover, one neighborhood mutation and elitism. To develop this algorithm, we have proposed modified non-dominated sorting and crowding distance based on interval mathematics and interval order relations. Finally, to test the performance of the proposed algorithm, a numerical example has been solved.

During the past two decades, there have been tremendous efforts on developing efficient
business excellence models to improve the quality of organizations. A typical business
excellence model proposes several alternatives with different budget and the primary objective
is to use the best ones. In this paper, we present a robust LINMAP method for measuring the
relative importance of various alternatives in an EFQM self assessment. The presented robust
model is capable of handling uncertainty as part of the problem formulation. The proposed
model of this paper is implemented for a case-study of energy sector in Iran with various
alternatives. The orderings of the alternatives are measured using data from multiple experts
through applying the proposed model of this paper.