Designing difficult office space allocation problem instances with mathematical programming

Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters

The stochastic generalised assignment problem with Bernoulli demands

Generalized Assignment Problem, Stochastic Optimization, Heuristics, 90C15, 90C10, 90C27,

A Multidimensional Multiple-Choice Knapsack Model for Resource Allocation in a Construction Equipment Manufacturer Setting Using an Evolutionary Algorithm

Part 2: Knowledge Discovery and SharingInternational audienceThis paper presents an approach to production resource allocation. The approach is applied to a real-world problem within the construction equipment manufacturing industry. A multidimensional knapsack problem formulated; was the proposed model being based on an evolutionary algorithm using a three-dimensional binary-coded chromosome. Various tests were carried out to show the appropriateness of the solution. The experiment results suggest to be satisfactory from the manufacturing company perspective

On solving the Lagrangian dual of integer programs via an incremental approach

Incremental algorithms, Convex minimization, Finite min-max, Lagrangian relaxation, Nonsmooth optimization, Bundle methods,

Stochastic binary problems with simple penalties for capacity constraints violations

This paper studies stochastic programs with first-stage binary variables and capacity constraints, using simple penalties for capacities violations. In particular, we take a closer look at the knapsack problem with weights and capacity following independent random variables and prove that the problem is weakly NP -hard in general. We provide pseudo-polynomial algorithms for three special cases of the problem: constant weights and capacity uniformly distributed, subset sum with Gaussian weights and strictly positively distributed random capacity, and subset sum with constant weights and arbitrary random capacity. We then turn to a branch-and-cut algorithm based on the outer approximation of the objective function. We provide computational results for the stochastic knapsack problem (i) with Gaussian weights and constant capacity and (ii) with constant weights and capacity uniformly distributed, on randomly generated instances inspired by computational results for the knapsack problem.SCOPUS: ar.jinfo:eu-repo/semantics/publishe