We present novel mathematical models for inventory management within a reverse logistics system. Technological advancements, sustainability initiatives, and evolving customer behaviours have significantly increased the demand for repaired products. Our models account for varying demand levels for newly produced and repaired items. To optimize overall costs with constrained scenarios, we formulated mixed integer programming problems. Solution procedures for the proposed problems are introduced, and the accuracy of these solutions has been validated through numerical experiments. Additionally, we address the cost of waste disposal as an environmental concern. This paper develops a multiobjective mathematical model and provides an algorithm for the Pareto solution. Various scalarization techniques are utilized to identify the Pareto front, and a comparison of these techniques is presented.

This paper is concerned with determining the shortest path for a pursuer aiming to intercept a moving target travelling at a constant speed. To address this challenge, we introduce an efficient mathematical model outlined as an optimal control problem. The proposed model is based on Dubin's path, where we concatenate two possible paths: a left-circular curve or a right-circular curve followed by a straight line. We develop and explore this model, providing a comprehensive geometric interpretation, and design an algorithm tailored to implement the proposed mathematical approach efficiently. Extensive numerical experiments involving diverse target positions highlight the strength of the model. The method exhibits a remarkably high convergence rate in finding solutions. We compare the proposed model and demonstrate its advantages through examples. For experiment purposes, we utilized the modelling software AMPL, employing a range of solvers to solve the problem. Subsequently, we simulated the obtained solutions using MATLAB, demonstrating the efficiency of the model in intercepting a moving target. The proposed model distinguishes itself by employing fewer parameters and making fewer assumptions, setting the model simplifies the complexities, and thus, makes it easier for experts to design optimal path plans.

Multi-objective integer or mixed-integer programming problems typically have disconnected feasible domains, making the task of constructing an approximation of the Pareto front challenging. The present article shows that certain algorithms that were originally devised for continuous problems can be successfully adapted to approximate the Pareto front for integer, and mixed-integer, multi-objective problems. Relationships amongst various scalarization techniques are established to motivate the choice of a particular scalarization in these algorithms. The proposed algorithms are tested by means of two-, three- and four-objective integer and mixed-integer problems, and comparisons are made. In particular, a new four-objective algorithm is used to solve a rocket injector design problem with a discrete variable, which is a challenging mixed-integer programming problem.

The path planning problem for unmanned aerial vehicles (UAVs) is important for scheduling the UAV missions. This paper presents an optimal path planning model for UAV to control its direction during target touring, where UAV and target are at the same altitude. Geometric interpretation of the given model is provided when the vehicles consider connecting an initial position to the destination position with specific target touring. We develop a nonlinear constrained model based on an arc parameterization approach to determine the UAV’s optimal path touring a target. The model is then extended to touring finite numbers of targets and optimizing the routes. The model is found reliable through several simulations. Numerical experiments are conducted and we have shown that the UAV’s generated path satisfies vehicle dynamics constraints, tours the targets, and arrives at its destination.

In this paper, we propose that the Lagrangian relaxation approach can be used to approximate the Pareto front of the multiobjective optimization problems. We introduce Lagrangian relaxation approach to solve scalarized subproblems. The scalarization is a technique employed to transform multiple objectives optimization problems into single-objective optimization problems so that existing optimization techniques are used to solve the problems. The relaxation approach exploits transformation and creates a Lagrangian problem in which some of the constraints are replaced from the original problem to make the problem easier to solve. The method is very effective when the problem is large scale and difficult to solve; this means if the problem has nonconvex and nonsmooth structure, then our proposed method efficiently solves the problem. We succeed in establishing proper Karush Kuhn-Tucker type necessary conditions for our proposed approach. We establish the relation between our proposed approach and the well-known existing approach weighted-sum scalarization methods. We conduct extensive numerical experiments and demonstrated the advantages of the proposed method of adopting a test problem. GANIT J. Bangladesh Math. Soc. 40.2 (2020) 126-133

We propose a new scalarization technique for nonconvex multiobjective optimization problems and establish its theoretical properties. By combining our new scalarization approach with existing grid generation techniques, we design new algorithms for constructing reliable approximations of the Pareto fronts of three-and four-objective optimization problems. Our algorithms can be extended for problems with more objective functions to minimize. Four three-objective algorithms are formed by pairing up the new scalarization technique and three existing scalarization techniques with a grid of weights generated over the convex hull of individual minima (the CHIM grid) due to Das and Dennis. Four more algorithms are obtained by using, instead of the CHIM grid, the grid in the successive boundary generation algorithm (the SBG grid) by Mueller-Gritschneder, Graeb, and Schlichtmann. The new algorithms seem to perform better than existing algorithms, in terms of computational time and accuracy in constructing the boundary and the interior of the Pareto front. The advantages are tested, in particular, through a problem whose Pareto front has a hole in it. A rocket injector design problem with four objective functions illustrates the effectiveness of our new approach further.

Proper Karush–Kuhn–Tucker (PKKT) conditions are said to hold when all the multipliers of the objective functions are positive. In 2012, Burachik and Rizvi introduced a new regularity condition under which PKKT conditions hold at every Geoffrion-properly efficient point. In general, the set of Borwein properly-efficient points is larger than the set of Geoffrion-properly efficient points. Our aim is to extend the PKKT conditions to the larger set of Borwein-properly efficient points.

We introduce and analyze a novel scalarization technique and an associated algorithm for generating an approximation of the Pareto front (i.e., the efficient set) of nonlinear multiobjective optimization problems. Our approach is applicable to nonconvex problems, in particular to those with disconnected Pareto fronts and disconnected domains (i.e., disconnected feasible sets). We establish the theoretical properties of our new scalarization technique and present an algorithm for its implementation. By means of test problems, we illustrate the strengths and advantages of our approach over existing scalarization techniques such as those derived from the Pascoletti–Serafini method, as well as the popular weighted-sum method.

To get positive Lagrange multipliers associated with each of the objective function, Maeda [Constraint qualification in multiobjective optimization problems: Differentiable case, J. Optimization Theory Appl., 80, 483-500 (1994)], gave some special sets and derived some generalized regularity conditions for first-order Karush-Kuhn-Tucker (KKT)-type necessary conditions of multiobjective optimization problems. Basing on Maeda's set, Bigi and Castellani [Second order optimality conditions for differentiable multiobjective problems, RAIRO, Op. Res., 34, 411-426 (2000)], tried to get the same result for second-order optimality conditions but their treatment was not convincing. In this paper, we have generalized these regularity conditions for second-order optimality conditions under different sets and obtained positive Lagrange multipliers for the objective function.

We consider a smooth multiobjective optimization problem with inequality constraints. Weak Kuhn–Tucker (WKT) optimality conditions are said to hold for such problems when not all the multipliers of the objective functions are zero, while strong Kuhn–Tucker (SKT) conditions are said to hold when all the multipliers of the objective functions are positive. We introduce a new regularity condition under which (WKT) hold. Moreover, we prove that for another new regularity condition (SKT) hold at every Geoffrion-properly efficient point. We show with an example that the assumption on proper efficiency cannot be relaxed. Finally, we prove that Geoffrion-proper efficiency is not needed when the constraint set is polyhedral and the objective functions are linear.

T. Maeda gave some constraint qualifications to get positive Lagrange multipliers associated with the vector-valued objective function and under these conditions, he derived Karush-Kuhn-Tucker (KKT) type necessary conditions for inequality constraints. In this paper, we have defined these Maeda-type constraint qualifications under different sets and have derived KKT type necessary conditions for both equality and inequality constraints. GANIT J. Bangladesh Math. Soc. (ISSN 1606-3694) 29 (2009) 99-105 DOI: http://dx.doi.org/10.3329/ganit.v29i0.8519