Integration Of Process Planning And Scheduling Research Articles

An Energy-efficient Multi Objectives MILP Model for Integrated Process Planing and Scheduling in RMS

The integration of process planning and scheduling(IPPS) is a crucial manufacturing strategy to optimize manufacturing resources utilization, which combines process planning and scheduling as an integrated function instead of two separated functions performing sequentially. Reconfigurable manufacturing system(RMS) is one of the latest manufacturing paradigms to meet unstable periodic market changes and environmental challenges. However, IPPS was seldom studied in context of RMS before. This paper proposed a multi objectives mixed integer linear programming model to solve IPPS problem in context of RMS, to optimize the makespan and total energy consumption in manufacturing. To be more realistic, the model considers transportation and reconfiguration factors, simultaneously meeting process planning flexibility.

A graph-based constraint programming approach for the integrated process planning and scheduling problem

Integration of process planning and scheduling (IPPS) is to carry out both functions simultaneously. This paper provides a graph-based constraint programming (GCP) approach to solve the type-2 IPPS problem that takes AND/OR graphs as input. The proposed GCP approach is implemented based on the IBM ILOG CP Optimizer. AND/OR graph is tailored to cope with IPPS instances. Directed arcs define both precedence and presence relationships. The or-link, a set of mutually-exclusive operations, is defined to represent alternative process routes. Interval variables and scheduling-oriented constraints are adopted to project the IPPS-specific AND/OR graph to a concise CP model with which minimizing makespan is incorporated as the objective. The GCP approach is tested on a set of benchmark problems. Experimental results show that the proposed approach outperforms compared algorithms on major IPPS instances.

Investigation of reconfiguration effect on makespan with social network method for flexible job shop scheduling problem

This paper presents a novel social network analysis based method (SNAM) to evaluate the reconfiguration effect i.e., identification of key machines and their influence on the system performance in the context of Flexible job shop scheduling problem (FJSSP). This research formulates a mathematical model along with the constraints by incorporating the total completion time of jobs as an objective function. The proposed SNAM has been applied to generate the collaboration networks by transforming the input data and presenting them in the form of an affiliation matrix to the network analysis software. Thereafter, to analyze the collaboration networks various SNA measures that have been calculated and different functional properties are evaluated. Finally, to investigate the reconfiguration effect on makespan integration of process planning and scheduling (IPPS) has been implemented with adopted effective game theory based hybrid deoxyribonucleic acid (DNA) algorithm. The validation of the proposed approach and its effectiveness is conducted through comparisons with benchmark instances and results confirm the efficiency of the proposed approach.

Telefacturing Based Distributed Manufacturing Environment for Optimal Manufacturing Service by Enhancing the Interoperability in the Hubs

Recent happenings are surrounding the manufacturing sector leading to intense progress towards the development of effective distributed collaborative manufacturing environments. This evolving collaborative manufacturing not only focuses on digitalisation of this environment but also necessitates service-dependent manufacturing system that offers an uninterrupted approach to a number of diverse, complicated, dynamic manufacturing operations management systems at a common work place (hub). This research presents a novel telefacturing based distributed manufacturing environment for recommending the manufacturing services based on the user preferences. The first step in this direction is to deploy the most advanced tools and techniques, that is, Ontology-based Protégé 5.0 software for transforming the huge stored knowledge/information into XML schema of Ontology Language (OWL) documents and Integration of Process Planning and Scheduling (IPPS) for multijobs in a collaborative manufacturing system. Thereafter, we also investigate the possibilities of allocation of skilled workers to the best feasible operations sequence. In this context, a mathematical model is formulated for the considered objectives, that is, minimization of makespan and total training cost of the workers. With an evolutionary algorithm and developed heuristic algorithm, the performance of the proposed manufacturing system has been improved. Finally, to manifest the capability of the proposed approach, an illustrative example from the real-time manufacturing industry is validated for optimal service recommendation.

Integration of process planning and scheduling using a hybrid GA/PSO algorithm

Process planning and scheduling are two of the most important activities in the manufacturing system. Integration of process planning and scheduling (IPPS) is treated as a crucial technology to realize computer-integrated manufacturing system (CIMS). This paper proposes a new IPPS method which contains two phases: static phase and dynamic phase. In the static phase, the process network of each job is generated according to the static shop floor status, and in the dynamic phase, the process plan of each job and the scheduling plan are generated according to the current shop floor status. With consideration of the optimization criteria of both process planning and scheduling, a hybrid algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) is presented for optimizing the IPPS problem. Furthermore, a new reactive scheduling method, in which the flexibility of the process plan is taken into account, is presented to improve the flexibility of rescheduling. Finally, the proposed IPPS method is compared with the three existing IPPS methods, which are nonlinear process planning (NLPP), closed-loop process planning (CLPP) and distributed process planning (DPP). Meanwhile, the reactive scheduling method is also compared with the right-shift rescheduling method and the route changing rescheduling strategy. The results show the effectiveness of the proposed IPPS method and the reactive scheduling method.

Distributed Genetic Algorithm for Integrated Process Planning and Scheduling Based on Multi Agent System

Process planning and scheduling are two crucial functions in manufacturing systems which are usually carried out sequentially. The scheduling is totally based on the outcomes of process planning, and the process planning may be restricted by manufacturing resources. Hence, conducting the process planning and scheduling separately is much likely to ruin the feasibility and optimality of both process planning and scheduling functions. The integration of process planning and scheduling (IPPS) is therefore fairly important for an efficient manufacturing system. In this paper, a distributed genetic algorithm (DGA) is suggested to cater for the IPPS problems domains, and the multi-agent system (MAS) is adopted to accommodate the algorithm. Here it is called the MAS-DGA system. Due to good properties of the MAS, a new agent-based architecture is proposed to accommodate subpopulations, support the traditional GA and provide channels for individuals' immigration. Furthermore, an negotiation mechanism is provided to support bilateral selections between individuals and subpopulations. Benchmark problems have been tested in the experiments, and the results are compared with a symbiotic evolutionary algorithm (SEA) and a cooperative co-evolutionary genetic algorithm (CCGA), which reveals that the proposed MAS-DGA system is feasible and efficient for the resolve of IPPS problems.

Integrated process planning and scheduling using an imperialist competitive algorithm

Effective performance of modern manufacturing systems requires integrating process planning and scheduling more tightly, which is consistently challenged by the intrinsic interrelation and intractability of these two problems. Traditionally, these two problems are treated sequentially or separately. Integration of process planning and scheduling (IPPS) provides a valuable approach to improve system performance. However, IPPS is more complex than job shop scheduling or process planning. IPPS is strongly NP-hard in that, compared to an NP-hard job shop scheduling problem with a determined process plan, the process plan for each job in IPPS is also to be optimised. So, an imperialist competitive algorithm (ICA) is proposed to address the IPPS problem with an objective of makespan minimisation. An extended operation-based representation scheme is presented to include information on various flexibilities of process planning with respect to determined job shop scheduling. The main steps of the proposed ICA, including empires construction, assimilation, imperialistic competition, revolution and elimination, are elaborated using an illustrative example. Performance of the proposed ICA was evaluated on four sets of experiments taken from the literature. Computational results of the ICA were compared with that of some existing algorithms developed for IPPS, which validates the efficiency and effectiveness of the ICA in solving the IPPS problem.

Integrated Process Planning and Scheduling Based on Genetic Algorithms

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

Integration of process planning and scheduling: a state-of-the-art review

Process planning and scheduling functions strongly influence profitability of manufacturing a product, resource utilisation and product delivery time. Several researchers have addressed the need for integration of process planning and scheduling (IPPS) functions to facilitate flexibility and for improving profitability of manufacturing a product, delivery time as well as creation of realistic process plans that can be executed readily on shop floor. This article presents a state-of-the-art review on IPPS. Three common integration approaches, non-linear approach, closed loop approach and distributed approach, are discussed with their relative advantages and disadvantages and reported research is classified accordingly. It also identifies several future research directions.

General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

A GRASP algorithm for the Integration of Process Planning and Scheduling in a flexible job-shop

The Integration of Process Planning and Scheduling (IPPS) is an important research issue in achieving optimum manufacturing processes. In IPPS, vast search spaces and complex technical constraints prove to be significant barriers to the effectiveness of the processes. This paper proposes a Greedy Randomised Adaptive Search Procedures (GRASP) algorithm for the integration of process planning with production scheduling in a flexible job-shop environment. The GRASP algorithm is a metaheuristic characterised by multiple initialisations. Basically, it comprises two phases: construction phase and local search phase. For this work, the construction phase is considered through computational experiments. The performance of the presented algorithm is evaluated and compared with benchmark problem and the results demonstrate that the proposed algorithm is an effective and practical approach for the flexible job-shop.

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.