Search results for "Operations Research"
showing 10 items of 1297 documents
Minimizing weighted earliness-tardiness on parallel machines using hybrid metaheuristics
2015
We consider the problem of scheduling a set of jobs on a set of identical parallel machines where the objective is to minimize the total weighted earliness and tardiness penalties with respect to a common due date. We propose a hybrid heuristic algorithm for constructing good solutions, combining priority rules for assigning jobs to machines and a local search with exact procedures for solving the one-machine subproblems. These solutions are then used in two metaheuristic frameworks, Path Relinking and Scatter Search, to obtain high quality solutions for the problem. The algorithms are tested on a large number of test instances to assess the efficiency of the proposed strategies. The result…
ANOVA-MOP: ANOVA Decomposition for Multiobjective Optimization
2018
Real-world optimization problems may involve a number of computationally expensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitivity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables significantl…
NAUTILUS framework : towards trade-off-free interaction in multiobjective optimization
2016
In this paper, we present a framework of different interactive NAUTILUS methods for multiobjective optimization. In interactive methods, the decision maker iteratively sees solution alternatives and provides one’s preferences in order to find the most preferred solution. We question the widely used setting that the solutions shown to the decision maker should all be Pareto optimal which implies that improvement in any objective function necessitates allowing impairment in some others. Instead, in NAUTILUS we enable the decision maker to make a free search without having to trade-off by starting from an inferior solution and iteratively approaching the Pareto optimal set by allowing all obje…
Simultaneous optimization of harvest schedule and data quality
2015
In many recent studies, the value of forest inventory information in harvest scheduling has been examined. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. The imperfect information is obtained with a specific scenario tree fo…
Interactive Nonlinear Multiobjective Optimization Methods
2016
An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the role of the decision maker is very important in interactive methods, methods presented are classified according to the type of preference information that the decision maker is assumed to provide. peerReviewed
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
2018
We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…
Flexible Data Driven Inventory Management with Interactive Multiobjective Lot Size Optimization
2021
We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. We forecast demand with a Bayesian model, which is based on sales data. After identifying relevant objectives relying on the demand model, we formulate an optimisation problem to determine lot sizes for multiple future time periods. Our approach combines different interactive multi-objective optimisation methods for finding the best balance among the objectives. For that, a decision maker with substance knowledge directs the solution process with one’s preference inform…
New directions for the biopharma industry in Canada: modelling and empirical findings
2008
PurposeThe main purpose of this study is to identify features and trends shaping the business models currently prevailing in the Canadian biopharma industry, by disaggregating the business model analysis into four key areas: value creation, investment strategy, business strategy, success factorsDesign/methodology/approachResults arise from an empirical fieldwork of qualitative nature, undertaken by the end of 2004, involving deep interviews to a broad variety of key stakeholders of the biopharma industry in the Quebec region, including biopharma firms, large pharma firms, venture capital funds, research centers and recognized experts from consultancy firms and Universities.FindingsBiopharma…
Study of forming mechanics of magnetic field–assisted single point incremental forming
2022
This paper describes the forming characteristics and mechanics of magnetic field–assisted single point incremental forming (M-SPIF) in which an Nd-Fe-B magnet ball tool is placed on top of a piece of sheet metal and is driven by an Nd-Fe-B magnet placed below the workpiece. To gain an understanding of the force mechanics that power M-SPIF, the tool motion and forming force were experimentally analyzed. In M-SPIF, the forming force is applied multi-directionally, and the resultant force direction is nearly colinear with the polarity of the permanent magnet ball tool. This suggests that the forming characteristics in M-SPIF may be controllable by controlling the magnetic polarity of the tool.