Solving integrated process planning and scheduling problem with constraint programming

Session - Scheduling and Sequencing 3: paper no. T3D3The APIEMS 2012 Conference proceedings' website is located at http://apiems.net/conf2012/Process planning and scheduling are two important manufacturing functions which are usually performed sequentially. However, due to the uncertainties and disturbances frequently occurring in the manufacturing environment, the separately conducted process plan and shopfloor schedule may lose their optimality, becoming ineffective or even infeasible. Researchers have considered the potential of integrated process planning and scheduling (IPPS) to conduct the two manufacturing planning activities concurrently instead of sequentially. That is, to integrate process planning with dynamic shopfloor scheduling to cope with the realtimeshopfloor status. The IPPS problem is very complex and it has been regarded as an NP-hard problem. Many researchers have attempted to solve the IPPS problem with intelligent approaches such as meta-heuristics and agent-based negotiation. In this paper, a constraint programming-based approach is proposed and implemented in the IPPS problem domain. Constraint programming (CP) features great modeling capabilities to reflect complex constraints of a problem, and there is a great potential for CP to be used to solve IPPS problems. The approach is implemented and tested on the IBM ILOG platform, and experimental results show that the CP can handle the IPPS problem efficiently and effectively.published_or_final_versio

Integration of process planning, scheduling, and mobile robot navigation based on triz and multi-agent methodology

U radu je predstavljena metodologija za razvoj softverske aplikacije za integraciju projektovanja tehnološkog procesa, terminiranja proizvodnje i navigacije mobilnog robota u tehnološkom okruženju. Predložena metodologija je bazirana na primeni teorije inventivnog rešavanja problema i multiagentske metodologije. Matrica kontradikcije i inventivni principi su se pokazali kao efektivan alat za otklanjanje kontradiktornosti u koncepcijskoj fazi razvoja softvera. Predložena multiagentska arhitektura sadrži šest agenata: agent za delove, agent za mašine, agent za optimizaciju, agent za planiranje putanje, agent za mašinsko učenje i agent mobilni robot. Svi agenti zajedno učestvuju u optimizaciji tehnološkog procesa, optimizaciji planova terminiranja, generisanju optimalnih putanja koje mobilni robot prati i klasifikaciji objekata u tehnološkom okruženju. Eksperimentalni rezultati pokazuju da se razvijeni softver može koristiti za predloženu integraciju, a sve u cilju poboljšanja performansi inteligentnih tehnoloških sistema.This paper presents methodology for development of software application for integration of process planning, scheduling, and the mobile robot navigation in manufacturing environment. Proposed methodology is based on the Russian Theory of Inventive Problem Solving (TRIZ) and multiagent system (MAS). Contradiction matrix and inventive principles are proved as effective TRIZ tool to solve contradictions during conceptual phase of software development. The proposed MAS architecture consists of six intelligent agents: job agent, machine agent, optimization agent, path planning agent, machine learning agent and mobile robot agent. All agents work together to perform process plans optimization, schedule plans optimization, optimal path that mobile robot follows and classification of objects in a manufacturing environment. Experimental results show that developed software can be used for proposed integration in order to improve performance of intelligent manufacturing systems

An automated optimisation framework for the development of re-configurable business processes: a web services approach

The practice of optimising business processes has, until recently, been undertaken mainly as a manual task. This article provides insights into an automated business process optimisation framework by using web services for the development of re-configurable business processes. The research presented here extends the optimisation framework by introducing additional web services as a mechanism for facilitating process interactions, identifying enhancements to support business processes and undertaking three case studies to evaluate the proposed enhancements. The featured case studies demonstrate that an increase in the amount of available web services gives rise to improvements in the business processes generated. This research highlights an increase in the efficiency of the algorithm and the quality of the business process designs that result from the enhancements. Future research directions are proposed for the further improvement of the framework

Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm

Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper conceptualizes a multi-agent methodology by considering four intelligent agents (job, machine, tool, and optimization agent) and presents developed modified particle swarm optimization (mPSO) algorithm to solve this combinatorial optimization problem effectively. In order to improve the search efficiency and increase ability to find global optimum, proposed mPSO algorithm has been enhanced with new crossover and mutation operators. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem

Solving Weighted Number of Operation Plus Processing Time Due-Date Assignment, Weighted Scheduling and Process Planning Integration Problem Using Genetic and Simulated Annealing Search Methods

Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Koncepcijsko projektiranje inteligentnog unutarnjeg transporta materijala korištenjem umjetne inteligencije

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matlab© software package is used for developing genetic algorithms, manufacturing process simulation, implementing search algorithms and neural network training. The obtained paths are tested by means of the Khepera II mobile robot system within a static laboratory model of manufacturing environment. The experiment results clearly show that an intelligent mobile robot can follow paths generated by using genetic algorithms as well as learn and predict optimal material transport flows thanks to using neural networks. The achieved positioning error of the mobile robot indicates that the conceptual design approach based on the axiomatic design theory can be used for designing the material transport and handling tasks in intelligent manufacturing systems.Pouzdan i efikasan transport materijala je jedan od ključnih zahtjeva koji utječe na povećanje produktivnosti u industriji. Iz tog razloga, u radu su predložena dva pristupa za inteligentan transport materijala korištenjem mobilnog robota. Prvi pristup se zasniva na primjeni genetskih algoritama za optimizaciju tehnoloških procesa. Optimalna putanja se dobiva korištenjem optimalnih tehnoloških procesa i genetskih algoritama za vremensko planiranje, uz minimalno vrijeme kao kriterij. Drugi pristup je temeljen na primjeni teorije grafova za generiranje putanja i neuronskih mreža za učenje generirane putanje. Matlab© softverski paket je korišten za razvoj genetskih algoritama, simulaciju tehnoloških procesa, implementaciju algoritama pretraživanja i obučavanje neuronskih mreža. Dobivene putanje su testirane pomoću Khepera II mobilnog robota u statičkom laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju kako inteligentni mobilni robot prati putanje generirane korištenjem genetskih algoritama, kao i da uči i predviđa optimalne tokove materijala zahvaljujući neuronskim mrežama. Ostvarena pogreška pozicioniranja mobilnog robota ukazuje da se koncepcijski pristup baziran na aksiomatskoj teoriji projektiranja može koristiti u projektiranju transporta i manipulacije u inteligentnom tehnološkom sustavu

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology