Bi-objective optimization of a multihead weighing process

[EN] A multihead weighing process is a packaging technology that can be of strategic importance to a company, as it can be a key to competitive advantage in the modern food industry. The improvement in the process quality and sensory quality of food packaged in a multihead weighing process investigated in this paper is relevant to industrial engineering. A bi-objective ad hoc algorithm based on explicit enumeration for the packaging processes in multihead weighers with an unequal supply of the product to the weighing hoppers is developed. The algorithm uses an a priori strategy to generate Pareto-optimal solutions and select a subset of hoppers from the set of available ones in each packing operation. The relative importance of both aforementioned objectives is dynamically managed and adjusted. The numerical experiments are provided to illustrate the performance of the proposed algorithm and find the optimum operational conditions for the process.García-Díaz, JC.; Pulido-Rojano, A.; Giner-Bosch, V. (2017). Bi-objective optimization of a multihead weighing process. European J of Industrial Engineering. 11(3):403-423. doi:10.1504/EJIE.2017.084882S40342311

A tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints

This article addresses the well-known Capacitated Vehicle Routing Problem (CVRP), in the special case where the demand of a customer consists of a certain number of two-dimensional weighted items. The problem calls for the minimization of the cost of transportation needed for the delivery of the goods demanded by the customers, and carried out by a fleet of vehicles based at a central depot. In order to accommodate all items on the vehicles, a feasibility check of the two-dimensional packing (2L) must be executed on each vehicle. The overall problem, denoted as 2L-CVRP, is NP-hard and particularly difficult to solve in practice. We propose a Tabu Search algorithm, in which the loading component of the problem is solved through heuristics, lower bounds, and a truncated branch-and-bound procedure. The effectiveness of the algorithm is demonstrated through extensivecomputational experiments

Monitoring and control of the multihead weighing process through a modified control chart

Modified control charts are used to monitor and control manufacturing processes which are considered to be six-sigma processes. The use of these charts is based on the idea that the cost of identifying and correcting special causes is much higher than the cost of off-target products. Therefore, the process mean is essentially acceptable as long as it is anywhere within the specification limits. These concepts are applied to the packaging process in multihead weighers. The weight of the packed product, seen as the quality characteristic to be monitored, must be as close to a specified target weight as possible and comply with applicable regulations. The packaging process was previously optimized and improved using a packaging strategy, which was evaluated through a proposed packing algorithm. In this way, a set of numerical experiments were conducted to examine the solutions generated, which were subsequently monitored.Los gráficos de control modificados se utilizan para el seguimiento y control de procesos de fabricación que son considerados como procesos seis-sigma. El uso de estos gráficos se basa en la idea de que el costo de identificar y corregir causas especiales de variación es mucho más alto que el costo de productos alejados de su valor nominal. Por lo tanto, la media del proceso es esencialmente aceptable siempre que esté dentro de los límites de especificación. Estos conceptos han sido aplicados al proceso de envasado en pesadoras multicabezal. El peso del producto envasado, visto como la característica de calidad a ser monitoreada, debe ser lo más cercano posible a un peso objetivo y cumplir con la normativa. El proceso ha sido previamente optimizado y mejorado mediante una estrategia de envasado, la cual es evaluada a través de un algoritmo de envasado. De esta manera, un conjunto de experimentos numéricos fueron realizados para examinar las soluciones generadas, las cuales son posteriormente monitoreadas

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Cooperative Particle Swarm Optimization for Combinatorial Problems

A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms. In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution. Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure