A Boolean satisfiability approach to the resource-constrained project scheduling problem

We formulate the resource-constrained project scheduling problem as a satisfiability problem and adapt a satisfiability solver for the specific domain of the problem. Our solver is lightweight and shows good performance both in finding feasible solutions and in proving lower bounds. Our numerical tests allowed us to close several benchmark instances of the RCPSP that have never been closed before by proving tighter lower bounds and by finding better feasible solutions. Using our method we solve optimally more instances of medium and large size from the benchmark library PSPLIB and do it faster compared to any other existing solver.     

Comparing constraint programming and mathematical programming approaches to discrete optimisation—the change problem

This paper is aimed at researchers and practitioners in Operational Research who are interested in the new field of Constraint Programming/Constraint Logic Programming. Due to differing terminology and problem representation they might have found it difficult to access the field. The paper focuses on discrete optimisation problems. The first part lists frequently used terms in Constraint Programming (CP), contrasting them with their counterparts in Mathematical Programming (MP). The second part explains some of the most important concepts and techniques in more detail by comparing the CP and MP implementations of a small example problem, the ‘Change Problem’. It includes an overview of the respective results. In conclusion a more generalised comparison of CP and MP techniques is given.     

A large neighbourhood search approach to the multi-activity shift scheduling problem

The challenge in shift scheduling lies in the construction of a set of work shifts, which are subject to specific regulations, in order to cover fluctuating staff demands. This problem becomes harder when multi-skill employees can perform many different activities during the same shift. In this paper, we show how formal languages (such as regular and context-free languages) can be enhanced and used to model the complex regulations of the shift construction problem. From these languages we can derive specialized graph structures that can be searched efficiently. The overall shift scheduling problem can then be solved using a Large Neighbourhood Search. These approaches are able to return near optimal solution on traditional single activity problems and they scale well on large instances containing up to 10 activities.     

A quadratic programming approach to the Randić index

Let G(k, n) be the set of connected graphs without multiple edges or loops which have n vertices and the minimum degree of vertices is k. The Randi? index χ = χ(G) of a graph G   is defined by χ(G)=_∑(uv)(_δuδv)^-1/2$\chi (G)={\sum }_{(\mathit{uv})}({\delta }_u{\delta }_v{)}^{-1/2}$, where δ_u is the degree of vertex u and the summation extends over all edges (uv) of G. Caporossi et al. [G. Caporossi, I. Gutman, P. Hansen, Variable neighborhood search for extremal graphs IV: Chemical trees with extremal connectivity index, Computers and Chemistry 23 (1999) 469–477] proposed the use of linear programming as one of the tools for finding the extremal graphs. In this paper we introduce a new approach based on quadratic programming for finding the extremal graphs in G(k, n) for this index. We found the extremal graphs or gave good bounds for this index when the number n_k of vertices of degree k is between n − k and n. We also tried to find the graphs for which the Randi? index attained its minimum value with given k (k ? n/2) and n. We have solved this problem partially, that is, we have showed that the extremal graphs must have the number n_k of vertices of degree k less or equal n − k and the number of vertices of degree n − 1 less or equal k.     

A bilinear approach to the pooling problem†

In this paper we present an algorithm for the pooling problem in refinery optimization based on a bilinear programming approach. The pooling problem occurs frequently in process optimization problems, especially refinery planning models. The main difficulty is that pooling causes an inherent nonlinearity in the otherwise linear models. We shall define the problem by formulating an aggregate mathematical model of a refinery, comment on solution methods for pooling problems that have been presented in the literature, and develop a new method based on convex approximations of the bilinear terms. The method is illustrated on numerical examples     

A note on “branch-and-price approach for the multi-skill project scheduling problem”

In a recent paper published in Optimization Letters, Montoya et al. (Optim Lett 8:1721–1734, 2014) proposed a branch-and-price approach for a multi-skill project scheduling problem. In that paper, an integer linear programming formulation was first introduced which, unfortunately, has a number of inconsistences. At the interest of mathematical rigor, in this note, we refine such formulation.     

A filter-and-fan approach to the 2D HP model of?the?protein folding problem

We examine a prominent and widely-studied model of the protein folding problem, the two-dimensional (2D) HP model, by means of a filter-and-fan (F&F) solution approach. Our method is designed to generate compound moves that explore the solution space in a dynamic and adaptive fashion. Computational results for standard sets of benchmark problems show that the F&F algorithm is highly competitive with the current leading algorithms, requiring only a single solution trial to obtain best known solutions to all problems tested, in contrast to a hundred or more trials required in the typical case to evaluate the performance of the best of the alternative methods.     

The multiparametric 0–1-integer linear programming problem: A unified approach

We designed an algorithm for the multiparametric 0–1-integer linear programming (ILP) problem with the perturbation of the constraint matrix, the objective function and the right-hand side vector simultaneously considered. Our algorithm works by choosing an appropriate finite sequence of non-parametric mixed integer linear programming (MILP) problems in order to obtain a complete multiparametrical analysis. The algorithm may be implemented by using any software capable of solving MILP problems.     

Optimization of chance constraint programming with sum-of-fractional objectives – An application to assembled printed circuit board problem

This paper presents a procedure to solve a chance constraint programming problem with sum-of-fractional objectives. The problem and the solution procedure are described with the help of a practical problem – assembled printed circuit boards (PCBs). Errors occurring during assembling PCBs are in general classified into three kinds, viz. machine errors, manual errors and other errors. These errors may lead to the rejection of the major portion of the production and hence result the manufacturer a huge loss. The problem is decomposed to have two objective functions; one is a sum-of-fractional objectives and the other is a non-linear objective with bounded constraints. The interest is to maximize the sum-of-fractional objectives and to minimize the non-linear objective, subject to stochastic and non-stochastic bounded environment. The first problem deals with the maximization of the profit (maximizing sum-of-fractional objectives) and the second deals with the minimization of the loss (errors), so as to obtain the maximum profit after removing the number of defective PCBs.     

Constructive and composite heuristic solutions to the P//∑Ci scheduling problem

In this paper, we study the permutation flowshop scheduling problem with the criterion of minimising the total flow time. We propose a new constructive heuristic procedure to solve the problem. This procedure is flexible in the computational effort required, as it can be adjusted to the requirements of the problem. We combine this procedure with local search methods, whose computational requirements can also be varied, to study the efficiency and effectiveness of different ways of forming composite solution methods. Computational experiments on standard benchmark problems are carried out. The results show that the new heuristic performs significantly better than previous ones and that combining constructive and search heuristics not only further improves the solution quality but also saves computation time. Discussions on the results are provided and future research is suggested.     

A new approach with new solutions to the Matkowski and Wesołowski problem

Aequationes mathematicae - Based on a result of de Rham, we give a family of functions solving the Matkowski and Weso?owski problem. This family consists of Hölder continuous...     

A note on the probabilistic approach to Turán's problem

The Turán number T(n, l, k) is the smallest possible number of edges in a k-graph on n vertices such that every l-set of vertices contains an edge. Given a k-graph H = (V(H), E(H)), we let X_s(S) equal the number of edges contained in S, for any s-set S?V(H). Turán's problem is equivalent to estimating the expectation E(X_l), given that min(X_l) ≥ 1. The following lower bound on the variance of X_s is proved:

$\text{Var(X}{}_{\mathrm{s}}\text{)?mm}\text{n?2k}\text{s?k}\text{n}\text{s}{}^{\mathrm{?1}}\text{n}\text{k}{}^1$

, where m = |E(H)| and $\text{m}\text{= (}{}_{\mathrm{k}}{}^{\mathrm{n}}\text{) ? m}$. This implies the following: putting t(k, l) = lim_n→∞T(n, l, k)(_kⁿ)^?1 then t(k, l) ≥ T(s, l, k)((_k^s) ? 1)^?1, whenever s ≥ l > k ≥ 2. A connection of these results with the existence of certain t-designs is mentioned.     

Using resource scarceness characteristics to solve the multi-mode resource-constrained project scheduling problem

In the past decades, resource parameters have been introduced in project scheduling literature to measure the scarceness of resources of a project instance. In this paper, we incorporate these resource scarceness parameters in the search process to solve the multi-mode resource constrained project scheduling problem, in which multiple execution modes are available for each activity in the project. Therefore, we propose a scatter search algorithm, which is executed with different improvement methods, each tailored to the specific characteristics of different renewable and nonrenewable resource scarceness values. Computational results prove the effectiveness of the improvement methods and reveal that the procedure is among the best performing competitive algorithms in the open literature.     

A goal programming approach for farm planning with?resources dimensionality

Crop production entails many decision making processes aimed at improving productivity and achieving the best yield from scarce resources, which are normally limited. Assuming that there is a certain technical path of tasks to be carried out within a period, and that each task can be done in different ways, the problem addressed in this paper consists of choosing how and when to carry out each one, in such a way that the tasks are scheduled in sequence at the lowest possible cost, taking account of any relations of precedence among them, and in such a way that each task is done within its time window and with the resources being assigned in a feasible way. Time windows are usually defined in a strict way, and thus, if adjusted, can be a cause of infeasibility for some of the problems, implying the need to acquire new resources. Nevertheless, in most cases small deviations from time windows could be acceptable. In this paper a mixed 0-1 programming model to attain the proposed objective is proposed, applying goal programming to model time windows in a more flexible way.     

A viscosity approach to the Dirichlet problem for complex Monge–Ampère equations

We present a viscosity approach to the Dirichlet problem for the complex Monge–Ampère equation ${\det u_{\bar{j} k} = f (x, u)}$ . Our approach differs from previous viscosity approaches to this equation in several ways: it is based on contact set techniques (the Alexandrov–Bakelman–Pucci estimate), on extensive applications of sup-inf convolutions, and on a relation between real and complex Hessians. More specifically, this paper includes a notion of viscosity solutions; a comparison principle and a solvability theorem; the equivalence between viscosity and pluripotential solutions; an estimate of the modulus of continuity of a solution in terms of that of a given subsolution and of the right-hand side f; and an Alexandrov–Bakelman–Pucci type of L ^∞-estimate.     

A DC programming approach for solving the symmetric Eigenvalue Complementarity Problem

In this paper, we investigate a DC (Difference of Convex functions) programming technique for solving large scale Eigenvalue Complementarity Problems (EiCP) with real symmetric matrices. Three equivalent formulations of EiCP are considered. We first reformulate them as DC programs and then use DCA (DC Algorithm) for their solution. Computational results show the robustness, efficiency, and high speed of the proposed algorithms.