On the Conversion of Program Specifications into Pseudo Code using Jackson Structured Programming

In this paper, we present a technique to automatically translate program specifications into pseudo code. This technique is developed in the context of the well-known programming method Jackson Structured Programming (JSP). The objective of our research is to investigate to what extent a programming method can be automated. Current CASE tools are only able to automate programming methods to a very limited extent, whereas our technique automates the entire programming cycle by creating pseudo code from program specifications. We show that the JSP programming method can be transformed into a set of formal rules when the scope of the technique is limited to a well-defined area of problems. The rules are implemented in a CASE tool, called JSPTool, which is currently operative, although still in a prototyping phase. We believe that the strength of the CASE tool lies in the fact that it is able to automate the programming process completely, although its scope possibly is still rather limited. In this paper, the technique is explained by solving an example programming problem. The source language that has been developed to enter program specifications is briefly explained. Also, the differences between other JSP CASE tools and JSPTool are dealt with. Some additional features of the method are discussed and suggestions for future research are given

Electronic design: a new field of investigation for large scale optimization

International audienceMobile phones, music players, personal computers, set-top boxes and countless other digital electronics items are part of our daily life. These nomad devices offer more and more functionnality. For example, recent mobile phones allow to communicate, to take pictures, to play video, to listen to the music, to watch TV, to access to the Internet... Thus, the integrated circuit (IC) achieving all these functions become more and more complex. Designers aim at developping these products taking into account two main axes: - shorten the delay to reach the market - reduce the size of these devices For a long time, designers are using a lot of optimization techniques such as Integer Linear Programming, heuristics and metaheuristics. A two year observation of those behaviors has led to the following conclusions, even if the electronic community know these techniques, there is a great need for more formal techniques, more appropriate models and efficient soving approaches especially designed for their specific problems. The talk, after a global introduction will introduce several problems where the help from the metaheuristic is needed and for which collaborations are necessary. This talk will be the starting point of the creation of a network for long term collaborations

Integrating partner objectives in horizontal logistics optimisation models

In this paper a general solution framework is presented for optimising decisions in a horizontal logistics cooperation. The framework distinguishes between the objective of the group and the objectives of the individual partners in the coalition. Although the importance of the individual partner interests is often acknowledged in the literature, the proposed solution framework is the first to include these objectives directly into the objective function of the optimisation model. The solution framework is applied to a collaborative variant of the clustered vehicle routing problem, for which we also create a set of benchmark instances. We find that by only considering a global coalition objective, the obtained solution is often suboptimal for some partners in the coalition. Providing a set of high quality alternative solutions that are Pareto efficient with respect to the partner objectives, gives additional insight in the sensitivity of a solution, which can support the decision making process. Our computational results therefore acknowledge the importance of including the individual partner objectives into the optimisation procedure

A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems

Many problem-specific heuristic frameworks have been developed to solve combinatorial optimization problems, but these frameworks do not generalize well to other problem domains. Metaheuristic frameworks aim to be more generalizable compared to traditional heuristics, however their performances suffer from poor selection of low-level heuristics (operators) during the search process. An example of heuristic selection in a metaheuristic framework is the adaptive layer of the popular framework of Adaptive Large Neighborhood Search (ALNS). Here, we propose a selection hyperheuristic framework that uses Deep Reinforcement Learning (Deep RL) as an alternative to the adaptive layer of ALNS. Unlike the adaptive layer which only considers heuristics’ past performance for future selection, a Deep RL agent is able to take into account additional information from the search process, e.g., the difference in objective value between iterations, to make better decisions. This is due to the representation power of Deep Learning methods and the decision making capability of the Deep RL agent which can learn to adapt to different problems and instance characteristics. In this paper, by integrating the Deep RL agent into the ALNS framework, we introduce Deep Reinforcement Learning Hyperheuristic (DRLH), a general framework for solving a wide variety of combinatorial optimization problems and show that our framework is better at selecting low-level heuristics at each step of the search process compared to ALNS and a Uniform Random Selection (URS). Our experiments also show that while ALNS can not properly handle a large pool of heuristics, DRLH is not negatively affected by increasing the number of heuristics.publishedVersio

An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization

The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions. Computational experiments show that our approach is very competitive as it is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions

Composing first species counterpoint with a variable neighbourhood search algorithm

In this article, a variable neighbourhood search (VNS) algorithm is developed that can generate musical fragments consisting of a melody for the cantus firmus and the first species counterpoint. The objective function of the algorithm is based on a quantification of existing rules for counterpoint. The VNS algorithm developed in this article is a local search algorithm that starts from a randomly generated melody and improves it by changing one or two notes at a time. A thorough parametric analysis of the VNS reveals the significance of the algorithm's parameters on the quality of the composed fragment, as well as their optimal settings. A comparison of the VNS algorithm with a developed genetic algorithm shows that the VNS is more efficient. The VNS algorithm has been implemented in a user-friendly software environment for composition, called Optimuse. Optimuse allows a user to specify a number of characteristics such as length, key and mode. Based on this information, Optimuse 'composes' both cantus firmus and first species counterpoint. Alternatively, the user may specify a cantus firmus, and let Optimuse compose the accompanying first species counterpoint. © 2012 Taylor & Francis

Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem

The traveling repairman problem is a customer-centric routing problem, in which the total waiting time of the customers is minimized, rather than the total travel time of a vehicle. To date, research on this problem has focused on exact algorithms and approximation methods. This paper presents the first metaheuristic approach for the traveling repairman problem