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This paper presents a review of the current literature on the branch of multi-criteria decision modelling known as Goal Programming (GP). The result of our indepth investigations of the two main GP methods, lexicographic and weighted GP together with their distinct application areas is reported. Some guidelines to the scope of GP as an application tool are given and methods of determining which problem areas are best suited to the different GP approaches are proposed. The correlation between the method of assigning weights and priorities and the standard of the results is also ascertained. [ABSTRACT FROM AUTHOR]

Symbolic model checkers use BDDs to represent arithmetic constraints over bounded integer variables. The size of such BDDs can in the worst case be exponential in the number and size (in bits) of the integer variables. In this paper we show how to construct linear-sized BDDs for linear integer arithmetic constraints. We present basic constructions for atomic equality and inequality constraints and generalize our complexity results for arbitrary linear arithmetic formulas. We also present three alternative ways of handling out-of-bounds transitions and discuss heterogeneous bounds on integer variables. We experimentally compare our approach to other BDD-based symbolic model checkers and demonstrate that the algorithms presented in this paper can be used to improve their performance significantly. [ABSTRACT FROM AUTHOR]

In this paper we consider the weighted minimax (1-center) location problem in the plane when the weights are not given but rather drawn from independent uniform distributions. The problem is formulated and analyzed. For certain parameters of the uniform distributions the objective function is proven to be convex and thus can be easily solved by standard software such as the Solver in Excel. Computational experience is reported. [ABSTRACT FROM AUTHOR]

Economists have found a need to model agents who behave in ways that are not consistent with the traditional notions of rational behavior under uncertainty but that are oriented in some looser manner toward achieving ‘good’ outcomes. Adaptation over time in a myopic manner, rather that forward-looking optimization, has been proposed as one such model of behavior that displays bounded rationality. This paper investigates the relationship between adaptation as a model of behavior and as an algorithmic approach that has been used in computing solutions to optimization problems. It describes a specific adaptive model of behavior in discrete choice problems, one that is closely related to adaptive algorithms for optimization, and shows that this model can be fruitfully applied in studying several economic issues. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to efficiently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics. [ABSTRACT FROM AUTHOR]

Abstract  Design theory crosses the boundary between mathematics and statistics, and includes a wide range of disparate types of design. In this paper we present a classification scheme which aims to include as many important types as possible, based on a balance among concept, representation and use. [ABSTRACT FROM AUTHOR]

Abstract??This paper presents a homotopy interior point method for solving a semi-infinite programming (SIP) problem. For algorithmic purpose, based on bilevel strategy, first we illustrate appropriate necessary conditions for a solution in the framework of standard nonlinear programming (NLP), which can be solved by homotopy method. Under suitable assumptions, we can prove that the method determines a smooth interior path$$\gamma_{w^{(0)}}\subset (x^{0}\times\mathcal{y}^{0})\times\re_{}\times\re^{l}_{}\times(0,1]$$from a given interior point$$w^{(0)} \in (x^{0}\times\mathcal{y}^{0})\times\re_{}\times\re^{l}_{}$$to a pointw*, at which the necessary conditions are satisfied. Numerical tracing this path gives a globally convergent algorithm for the SIP. Lastly, several preliminary computational results illustrating the method are given. [ABSTRACT FROM AUTHOR]

This paper investigates a new variation in the continuous single facility location problem. Specifically, we address the problem of locating a new facility on a plane with different distance norms on different sides of a boundary line. Special cases and extensions of the problem, where there are more than two regions are also discussed. Finally, by investigating the properties of the models, efficient solution procedures are proposed. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for the solution of multiparametric mixed integer linear programming (mp-MILP) problems involving (i) 0–1 integer variables, and, (ii) more than one parameter, bounded between lower and upper bounds, present on the right hand side (RHS) of constraints. The solution is approached by decomposing the mp-MILP into two subproblems and then iterating between them. The first subproblem is obtained by fixing integer variables, resulting in a multiparametric linear programming (mp-LP) problem, whereas the second subproblem is formulated as a mixed integer linear programming (MILP) problem by relaxing the parameters as variables. [ABSTRACT FROM AUTHOR]

This paper considers the problem of sequencing n jobs in a two-machine re-entrant shop with the objective of minimizing the maximum completion time. The shop consists of two machines, M1 and M2, and each job has the processing route (M1, M2, M1). An O(n log n) time heuristic is presented which generates a schedule with length at most 4/3 times that of an optimal schedule, thereby improving the best previously available worst-case performance ratio of 3/2. [ABSTRACT FROM AUTHOR]

In this paper we develop a method for solving to optimality a general 0–1 formulation for uncapacitated location problems. This is a 3-stage method that solves large problems in reasonable computing times. The 3-stage method is composed of a primal-dual algorithm, a subgradient optimization to solve a Lagrangean dual and a branch-and-bound algorithm. It has a hierarchical structure, with a given stage being activated only if the optimal solution could not be identified in the preceding stage. The proposed method was used in the solution of three well-known uncapacitated location problems: the simple plant location problem, the p-median problem and the fixed-charge p-median problem. Computational results are given for problems of up to the size 200 customers ⊗ 200 potential facility sites. [ABSTRACT FROM AUTHOR]

We present the problem of locating vehicle depots in an intercity freight transportation system with the objective of satisfying client demand for empty vehicles, while minimizing depot opening and operating costs, bidirectional client-depot transportation costs, as well as the costs of the interdepot movements necessary to counter the unbalancing of demand. We propose a class of models which has the form of a two-echelon multimode multicommodity location formulation with interdepot balancing requirements. We analyse the models and their properties to determine their underlying structure and characteristics to be used m subsequent algorithmic developments. [ABSTRACT FROM AUTHOR]

The nonlinear convex programming problem of finding the minimum covering weighted ball of a given finite set of points in $${\mathbb{R}^n}$$ is solved by generating a finite sequence of subsets of the points and by finding the minimum covering weighted ball of each subset in the sequence until all points are covered. Each subset has at most n + 1 points and is affinely independent. The radii of the covering weighted balls are strictly increasing. The minimum covering weighted ball of each subset is found by using a directional search along either a ray or a circular arc, starting at the solution to the previous subset. The step size is computed explicitly at each iteration. [ABSTRACT FROM AUTHOR]

We examine a routing problem in which network arcs fail according to independent failure probabilities. The reliable h-path routing problem seeks to find a minimum-cost set of h ≥ 2 arc-independent paths from a common origin to a common destination, such that the probability that at least one path remains operational is sufficiently large. For the formulation in which variables are used to represent the amount of flow on each arc, the reliability constraint induces a nonconvex feasible region, even when the integer variable restrictions are relaxed. Prior arc-based models and algorithms tailored for the case in which h = 2 do not extend well to the general h-path problem. Thus, we propose two alternative integer programming formulations for the h-path problem in which the variables correspond to origin-destination paths. Accordingly, we develop two branch-and-price-and-cut algorithms for solving these new formulations, and provide computational results to demonstrate the efficiency of these algorithms. [ABSTRACT FROM AUTHOR]

We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach. [ABSTRACT FROM AUTHOR]

Abstract??Based on a self-contained account of the classical linear programming bounds for codes and orthogonal arrays we give a simplified description of the linear programming bounds for ordered codes, ordered orthogonal arrays (OOA) and tms-nets. The main result is a description in terms of a family of polynomials which generalize the Kravchouk polynomials of coding theory. The Plotkin bound and the sphere packing bound for ordered codes are consequences. We also derive a quadratic bound and illustrate by giving some improvements for bounds on the parameters of tms-nets. [ABSTRACT FROM AUTHOR]

It is shown that the problem of calculating the importance factors of objects on the basis of their interval paired comparisons, which is set up as a multicriteria problem of the minimization of equally important criteria that manifest themselves as multiplicative residuals of the system of inequalities for the required factors, comes to the sequence of a few problems of linear programming. [ABSTRACT FROM AUTHOR]

Constraint programming and local search are two well known optimization technologies. In recent years, methods for combining these two technologies have been put forward, one of which advocates the use of constraint programming for searching the local neighborhood of the current solution. We present a search technique which improves on the performance of this constraint programming-based local search method and perform experiments on a variety of both simple and more complex combinatorial problems. We also demonstrate the benefit of combining local and complete search methods. [ABSTRACT FROM AUTHOR]

A cutting plane method for linear programming is described. This method is an extension of Atkinson and Vaidya's algorithm, and uses the central trajectory. The logarithmic barrier function is used explicitly, motivated partly by the successful implementation of such algorithms. This makes it possible to maintain primal and dual iterates, thus allowing termination at will, instead of having to solve to completion. This algorithm has the same complexity (O(nL²) iterations) as Atkinson and Vaidya's algorithm, but improves upon it in that it is a ‘long-step’ version, while theirs is a ‘short-step’ one in some sense. For this reason, this algorithm is computationally much more promising as well. This algorithm can be of use in solving combinatorial optimization problems with large numbers of constraints, such as the Traveling Salesman Problem. [ABSTRACT FROM AUTHOR]

Cutting plane methods require the solution of a sequence of linear programs, where the solution to one provides a warm start to the next. A cutting plane algorithm for solving the linear ordering problem is described. This algorithm uses the primal-dual interior point method to solve the linear programming relaxations. A point which is a good warm start for a simplex-based cutting plane algorithm is generally not a good starting point for an interior point method. Techniques used to improve the warm start include attempting to identify cutting planes early and storing an old feasible point, which is used to help recenter when cutting planes are added. Computational results are described for some real-world problems; the algorithm appears to be competitive with a simplex-based cutting plane algorithm. [ABSTRACT FROM AUTHOR]

Spectral algorithms based on matrix representations of networks are often used to detect communities, but classic spectral methods based on the adjacency matrix and its variants fail in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about their community structure. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node, unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot, while performing comparably on benchmark stochastic block model networks and real world networks. We also show that the spectrum of the reluctant backtracking operator approximately optimises the standard modularity function. Interestingly, for this family of non- and reluctant-backtracking operators the main determinant of performance on real-world networks is whether or not they are normalised to conserve probability at each node. [ABSTRACT FROM AUTHOR]