ABSTRACT This paper introduces the Group Extended Entropy VIKOR (GE2-VIKOR) MCDM approach for assessing defense technology criticality in developing countries. GE2-VIKOR addresses uncertainty and complexity in this scenario by leveraging VIKOR’s ability to propose compromise solutions in discrete alternatives’ scenarios. GE2-VIKOR can be applied in both individual and group decision-making, incorporating weights based on decision-makers’ knowledge degree and criteria weights determined by the sponsor or Shannon’s entropy theory. The method aims to offer a compromise solution that maximizes group utility while minimizing individual regret under majority, consensus, or veto rules. A detailed demonstration illustrates GE2-VIKOR’s applicability and effectiveness in assessing defense technology criticality in developing countries. The results highlight the method’s strengths and limitations and suggest future research avenues, particularly exploring its applicability in developed countries, where technology criticality concepts differ. GE2VIKOR GEVIKOR GE2 GE (GE2-VIKOR countries VIKORs s alternatives scenarios decisionmaking, decisionmaking decision making, making decision-making decisionmakers makers decision-makers Shannons Shannon theory majority consensus rules GE2VIKORs GEVIKORs methods avenues differ

ABSTRACT This paper deals with the two-dimensional strip packing problem (2D-SPP) with the order/or multi-drop and vertical stability constraints. The existing exact algorithm that solves this problem is not able to provide optimal solutions on large instances in a reasonable amount of time. Hence, with a view to quickly obtain a physically stable packing of minimum height while satisfying the order constraint, the Biased Random-Key Genetic Algorithm (BRKGA) is combined with Bottom-Left-Fill (BLF) and Open Space (OS) heuristics. Both versions of the algorithm (BRKGA+BLF and BRKGA+OS) retrieved optimal solutions on many benchmark instances, consuming lesser computational time than the exact algorithm. A comparative study was also performed between the BRKGA, Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms on newly generated large instances. Moreover, the effectiveness of the BRKGA has also been checked on the classical 2D-SPP and two-dimensional orthogonal packing problem (2D-OPP) datasets. twodimensional two dimensional 2DSPP DSPP 2D SPP D (2D-SPP orderor or multidrop multi drop constraints Hence constraint RandomKey Random Key (BRKGA BottomLeftFill Bottom Left Fill BLF (BLF OS (OS heuristics BRKGA+BLF BRKGABLF BRKGA+OS BRKGAOS SA (SA PSO (PSO Moreover 2DOPP DOPP OPP (2D-OPP datasets

ABSTRACT Aggregate Production Planning (APP) aims at balancing the forecast demand for product families with the available production resources, thus allowing changes to be made in the system and the best action strategy to be found. APP decisions are strategic decisions for the organization, which take an intermediate time horizon which spans 12 months, in general. As different issues may influence business success, this kind of decision problem will take multiple objectives into account, when a strategic view is considered. Conflicts between objectives frequently increase the complexity of APP problems. In order to deal with this kind of problem, this paper proposes a multi-criteria decision model considering the five objectives related to manufacturing strategy, namely: quality, speed, dependability, flexibility, and cost. By applying a multi-objective optimizing model which considers the organization’s strategical objectives, and by using the NSGA-II algorithm, a subset of representative solutions of the Pareto Frontier is obtained, thereby enabling the application of the FITradeoff method. A multi-criteria method, such as the FITradeoff, assists Decision Makers (DMs) in choosing a production plan more suitable to their objectives. The results show that the FITradeoff method contributes to the evaluation of solutions by aiding the DM to consider a significant set of alternatives while at the same time reducing the cognitive effort that he/she needs to make. (APP resources found organization 1 months general success account considered problems multicriteria multi criteria namely quality speed dependability flexibility cost multiobjective objective organizations s NSGAII NSGA II algorithm obtained DMs (DMs heshe he she make

ABSTRACT The globally changing market has provided the customers with multiple choices for buying any product. During transportation, the unpredictable circumstances cause deterioration/damage to the goods. Also, there are factors like rising fuel prices and a need to reduce the carbon emissions which pose challenges in transportation of goods for industries. All these factors motivate the authors to propose Bilevel Fractional/Quadratic Green Transportation Problem with uncertain cost and multi-choice supply and demand parameters (BFQGMCTP). The innovation of this paper is that apart from delivering maximum quantity of goods, the intent is to minimize the cost of transportation, the deterioration cost during transit and the cost of carbon emission simultaneously. The present problem is solved using two different techniques and a comparative analysis for the two satisfactory solutions is exhibited. The methodology in this paper has been elaborated through a pragmatic example which is elucidated by a computing software. product deteriorationdamage damage Also industries FractionalQuadratic Fractional Quadratic multichoice multi choice BFQGMCTP. BFQGMCTP . (BFQGMCTP) simultaneously exhibited software (BFQGMCTP

ABSTRACT Uncertainty and disruption can significantly impact supply chains, and risk response is a means to diminish or circumvent these challenges. Therefore, this investigation aims to assess lead time variability in the supply chain of active ingredients. The research proposes a decision-making process to evaluate lead time variability in the active ingredient supply chain of the Brazilian chemical industry. It adopts Game Theory and linear programming in its solution. The two major contributions of this study are as follows: first, it presents a procedure to assess lead time variability; second, it proposes a supply chain risk response framework to address the lead time variability problem. Supply chain and lead time variability are emerging areas that offer significant opportunities for exploration, one of which is to evaluate lead time variability from the multiple perspectives presented in this study, thereby contributing to the current literature through a real application. chains challenges Therefore ingredients decisionmaking decision making industry solution follows first second problem exploration application