A fuzzy goal programming approach to solving decentralized bi-level multi-objective linear fractional programming problems

This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method

Presentation and Solving Non-Linear Quad-Level Programming Problem Utilizing a Heuristic Approach Based on Taylor Theorem

The multi-level programming problems are attractive for many researchers because of their application in several areas such as economic, traffic, finance, management, transportation, information technology, engineering and so on. It has been proven that even the general bi-level programming problem is an NP-hard problem, so the multi-level problems are practical and complicated problems therefore solving these problems would be significant. The literature shows several algorithms to solve different forms of the bi-level programming problems (BLPP).Not only there is no any algorithm for solving quad-level programming problem, but also it has not been studied by any researcher.  The most important part of this paper is presentation and studying of a new model of non-linear multi-level problems.Then we attempt to develop an effective approach based on Taylor theorem for solving the non-linear quad-level programming problem. In this approach, by using aproposedsmoothing method the quad-level programming problem is converted to a linear single problem. Finally, the single level problem is solved using the algorithm based on Taylor algorithm. The presented approach achieves an efficient and feasible solution in an appropriate time which has been evaluated by solving test problems

Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy Parameters: A FGP Approach

The motivation behind this paper is to present multi-level multi-objective quadratic fractional programming (ML-MOQFP) problem with fuzzy parameters in the constraints. ML-MOQFP problem is an important class of non-linear fractional programming problem. These type of problems arise in many fields such as production planning, financial and corporative planning, health care and hospital planning. Firstly, the concept of the -cut and fuzzy partial order relation are applied to transform the set of fuzzy constraints into a common crisp set. Then, the quadratic fractional objective functions in each level are transformed into non-linear objective functions based on a proposed transformation. Secondly, in the proposed model, separate non-linear membership functions for each objective function of the ML-MOQFP problem are defined. Then, the fuzzy goal programming (FGP) approach is utilized to obtain a compromise solution for the ML-MOQFP problem by minimizing the sum of the negative deviational variables. Finally, an illustrative numerical example is given to demonstrate the applicability and performance of the proposed approach

Direct solution of multi-objective optimal control problems applied to spaceplane mission design

This paper presents a novel approach to the solution of multi-phase multi-objective optimal control problems. The proposed solution strategy is based on the transcription of the optimal control problem with Finite Elements in Time and the solution of the resulting Multi-Objective Non-Linear Programming (MONLP) problem with a memetic strategy that extends the Multi Agent Collaborative Search algorithm. The MONLP problem is reformulated as two non-linear programming problems: a bi-level and a single level problem. The bi-level formulation is used to globally explore the search space and generate a well spread set of non-dominated decision vectors while the single level formulation is used to locally converge to Pareto efficient solutions. Within the bi-level formulation, the outer level selects trial decision vectors that satisfy an improvement condition based on Chebyshev weighted norm, while the inner level restores the feasibility of the trial vectors generated by the outer level. The single level refinement implements a Pascoletti-Serafini scalarisation of the MONLP problem to optimise the objectives while satisfying the constraints. The approach is applied to the solution of three test cases of increasing complexity: an atmospheric re-entry problem, an ascent and abort trajectory scenario and a three-objective system and trajectory optimisation problem for spaceplanes

TOPSIS Approach for Solving Bi-Level Non-Linear Fractional MODM Problems

TOPSIS (technique for order preference similarity to ideal solution) is considered one of the known classical multiple criteria decision making (MCDM) methods to solve bi-level non-linear fractional multi-objective decision making (BL-NFMODM) problems, and in which the objective function at each level is considered nonlinear and maximization type fractional functions. The proposed approach presents the basic terminology of TOPSIS approach and the construction of membership function for the upper level decision variable vectors, the membership functions of the distance functions from the positive ideal solution (PIS) and of the distance functions from the negative ideal solution (NIS). Thereafter a fuzzy goal programming model is adopted to obtain compromise optimal solution of BL-NFMODM problems. The proposed approach avoids the decision deadlock situations in decision making process and possibility of rejecting the solution again and again by lower level decision makers. The presented TOPSIS technique for BL-NFMODM problems is a new fuzzy extension form of TOPSIS approach suggested by Baky and Abo-Sinna (2013) (Applied Mathematical Modelling, 37, 1004-1015, 2013) which dealt with bi -level multi-objective decision making (BL-MODM) problems. Also, an algorithm is presented of the new fuzzy TOPSIS approach for solving BL-NFMODM problems. Finally, an illustrative numerical example is given to demonstrate the approach

Building Pareto Frontiers for Ecosystem Services Tradeoff Analysis in Forest Management Planning Integer Programs

Decision making in modern forest management planning is challenged by the need to recognize multiple ecosystem services and to address the preferences and goals of stakeholders. This research presents an innovative a posteriori preference modeling and multi-objective integer optimization (MOIP) approach encompassing integer programming models and a new technique for generation and interactive visualization of the Pareto frontier. Due to the complexity and size of our management problems, a decomposition approach was used to build the Pareto frontier of the general problem using the Pareto frontiers of its sub-problems. The emphasis was on the approximation of convex Edgeworth–Pareto hulls (EPHs) for the sub-problems by systems of linear inequalities; the generation of Edgeworth–Pareto hulls by the convex approximation of the Pareto frontier evinced a very small discrepancy from the real integer programming solutions. The results thus highlight the possibility of generating the Pareto frontiers of large multi-objective integer problems using our approach. This research innovated the generation of Pareto frontier methods using integer programming in order to address multiple objectives, locational specificity requirements and product even-flow constraints in landscape-level management planning problems. This may contribute to enhancing the analysis of tradeoffs between ecosystem services in large-scale problems and help forest managers address effectively the demand for forest products while sustaining the provision of services in participatory management planning processe

Integrated production and inventory routing planning of oxygen supply chains

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework

Line Search and Genetic Approaches for Solving Linear Tri-level Programming Problem

In the recent years, the multi-level programming problems specially the bi-level and tri-level programming problems (TLPP) are interested by many researchers and these problems, particularly TLPP, are known as an appropriate tool to solve the real problems in several areas of optimization such as economic, traffic, finance, management, transportation, computer science and so on. Also, it has been proven that the general bi-level and TLPP are NP-hard problems. The literature shows it has been proposed a few attempts for solving using TLPP. In this paper, we attempt to propose a new function for smoothing the tri-level programming problem after using Karush-Kuhn-Tucker condition, also we develop two effective approaches, one based on Genetic algorithm, which it is an approximate approach, and the other based on the hybrid algorithm by combining the proposed method based on penalty function and the line search algorithm for solving the linear TLPP. In both of these approaches, by using the Karush-Kuhn-Tucker condition the TLPP is converted to a non-smooth single problem, and then it is smoothed by proposed functions. Finally, the smoothed problem is solved using both of the proposed approaches. The presented approaches achieve an efficient and feasible solution in an appropriate time which has been evaluated by comparing to references and test problems

Advances in multi-parametric mixed-integer programming and its applications

At many stages of process engineering we are confronted with data that have not yet revealed their true values. Uncertainty in the underlying mathematical model of real processes is common and poses an additional challenge on its solution. Multi-parametric programming is a powerful tool to account for the presence of uncertainty in mathematical models. It provides a complete map of the optimal solution of the perturbed problem in the parameter space. Mixed integer linear programming has widespread application in process engineering such as process design, planning and scheduling, and the control of hybrid systems. A particular difficulty arises, significantly increasing the complexity and computational effort in retrieving the optimal solution of the problem, when uncertainty is simultaneously present in the coefficients of the objective function and the constraints, yielding a general multi-parametric (mp)-MILP problem. In this thesis, we present novel solution strategies for this class of problems. A global optimization procedure for mp-MILP problems, which adapts techniques from the deterministic case to the multi-parametric framework, has been developed. One of the challenges in multi-parametric global optimization is that parametric profiles, and not scalar values as in the deterministic case, need to be compared. To overcome the computational burden to derive a globally optimal solution, two-stage methods for the approximate solution of mp-MILP problems are proposed. The first approach combines robust optimization and multi-parametric programming; whereas in the second approach suitable relaxations of bilinear terms are employed to linearize the constraints during the approximation stage. The choice of approximation techniques used in the two-stage method has impact on the conservatism of the solution estimate that is generated. Lastly, multi-parametric programming based two-stage methods are applied in pro-active short-term scheduling of batch processes when faced with varied sources of uncertainty, such of price, demand and operational level uncertainty.Open Acces