José Manuel Sánchez García

Production and delivery scheduling problem with time windows

This paper deals with the problem of selecting and scheduling the orders to be processed by a manufacturing plant for immediate delivery to the customer site. Among the constraints to be considered are the limited production capacity, the available number of vehicles and the time windows within which orders must be served. We first describe the problem as it occurs in practice in some industrial environments, and then present an integer programming model that maximizes the profit due to the customer orders to be processed. A tabu search-based solution procedure to solve this problem is developed and tested empirically with randomly generated problems. Comparisons with an exact procedure show that the method finds very good-quality solutions with small computation requirements.

Machine grouping using sequence-based similarity coefficients and neural networks

Abstract Most neural network approaches to the cell formation problem do not use information on the sequence of operations on part types. They only use as input the binary part-machine incidence matrix. In this paper we investigate two sequence-based neural network approaches for cell formation. The objective function considered is the minimization of transportation costs (including both intracellular and intercellular movements). Constraints on the minimum and maximum number of machines per cell can be imposed. The problem is formulated mathematically and shown to be equivalent to a quadratic programming integer program that uses symmetric, sequence-based similarity coefficients between each pair of machines. Of the two energy-based neural network approaches investigated, namely Hopfield model and Potts Mean Field Annealing, the latter seems to give better and faster solutions, although not as good as a Tabu Search algorithm used for benchmarking.

Coordinated scheduling of production and delivery from multiple plants and with time windows using genetic algorithms

This paper deals with the problem of selecting and scheduling a set of orders to be manufactured and immediately delivered to the customer site. We provide m plants for production and V vehicles for distribution. Furthermore, another constraints to be considered are the limited production capacity at plants and time windows within which orders must be served. A genetic algorithm to solve the problem is developed and tested empirically with randomly generated problems. In order to benchmark the GA, a graph-based exact method is proposed. However, such exact method is not efficient and, therefore, can only be used for small problems. Results attest that our GA produces good-quality solutions.

Production and vehicle scheduling for ready-mix operations

Abstract This paper deals with the problem of selecting and scheduling the orders to be processed by a ready-mix manufacturing plant for immediate delivery to the customer site. Orders have a fixed due date and must be prepared in a single plant with limited capacity. The objective is to maximize the total value of orders served. We first describe the problem as it occurs in practice in some industrial environments, and then present two scenarios to be considered: arbitrary and uniform profit margins for orders. The first scenario corresponds to the fixed job scheduling problem, which is solved by a minimum cost flow problem. A new scheme that reduces the number of nodes in the graph is proposed in this paper. The second scenario on the other hand requires more computationally expensive approaches as a second objective of minimizing the number of vehicles is to be considered. An exact graph-based method and a heuristic branch-and-bound based scheme are described. Computational experiences show that the optimal approach is not feasible for large problems and that the proposed heuristic approach gives high-quality solutions in short running times.

Genetic algorithm for planning cable telecommunication networks

Abstract Optical fiber networks over synchronous digital hierarchy operating on ATM mode appear as one of the best alternatives for the new high-speed telecommunications networks. The network management requires planning studies so that the designed networks allow efficient operation in real time. A quantitative model evaluating the global topological design of an optical fiber network over synchronous digital hierarchy is described. The model includes the economic aspects involved in the project, the civil works, the capacity evaluation costs and the introduction of reliability conditions. A genetic approach based on the exploitation of the Kuhn–Tucker optimality conditions is proposed to solve the model.

Decision support system for planning telecommunication networks: a case study applied to the Andalusian region

Network planning is essential to design real broadband integrated services digital networks (B-ISDN). This paper presents an operations research application to the design of an optic fibre network for the Andalusian region. The economical appraisal is the main consideration in order to take the appropriate decisions: hub location, region sizes and selection of the urban nodes that will receive telecommunication contents. A decision support system with a graphic interface that allows interactive analysis of different scenarios is presented. The system contains a set of mathematical programming models and it has the capability to dynamically construct and solve instances of those models. In addition, it provides data preparation and reports. The system is an integrated, user-friendly and powerful tool to make planning studies by firms developing cable network systems in the telecommunications market.

A Comparison of GRASP and an Exact Method for Solving a Production and Delivery Scheduling Problem

In this paper we consider the production and delivery scheduling problem, a problem that deals with the selection of orders to be processed by a manufacturing plant and immediately delivered to the customer site. Orders have a fixed due date and must be prepared in a single plant with limited capacity. A limited number of vehicles are available for delivery. A ready-mix concrete manufacturing case study has motivated this research. We describe an exact method to find optimal solutions based on the construction of a graph that collects all feasible solutions. We also describe a greedy randomized adaptive search (GRASP) for the problem that finds good, though not necessarily optimal, solutions.

Salt effects in reactions between ions of opposite charge

SummaryThe kinetics of reactions between ions of opposite charge have been studied in concentrated salt solutions of supporting electrolytes. An increase in rate constant is observed at the higher concentrations of supporting electrolytes. The results are discussed in connection with kinetic salt effects on reactions between similary charged ions. It is concluded that the positive salt effects observed in all cases can be explained either as a consequence of nonequilibrium solvation in the transition state or as a result of solvent reorganisation in the activation process.

Viral system algorithm: foundations and comparison between selective and massive infections

This paper presents a guided and deep introduction to viral systems (VS), a novel bio-inspired methodology based on a natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of VS, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches is presented. Finally, trends and new research opportunities are presented for this bio-inspired methodology.

A viral system massive infection algorithm to solve the Steiner tree problem in graphs with medium terminal density

The Steiner tree problem in graphs presents highest difficulties in case of graphs with a medium density of terminals. In fact, most of the efficient algorithms that have been developed to deal with the Steiner tree problem show their worse behaviour for a terminal density between 15 and 30%. Here we present a new bio-inspired approach based on a biological virus infection called viral system (VS) that emulates a massive infection in an organism (representing a massive exploration of the feasibility region). The approach is tested in a large-sized library of problems for which the optimal solution is known, and it is compared to other very efficient soft computing methodologies such as Tabu search and genetic algorithms that have been developed for this specific problem. The VS massive infection algorithm improves the results provided by such approaches.