A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper describes an approach to manufacturing planning that seeks to integrate both process planning and scheduling. We show that separating these two related tasks, as is the common practice, can impose constraints that substantially reduce the quality of the final schedule. These constraints arise from premature decisions regarding operation sequence and allocation of manufacturing resources. Having formulated an integrated process planning and scheduling problem, we describe a solution technique based on simulated annealing. We compare this approach with others reported in the literature, considering both their generality and performance. In particular, we perform a detailed empirical comparison between simulated annealing and the popular technique of dispatching rules. Our results, achieved with two distinct sets of example problems, show that simulated annealing can produce solutions of significantly higher quality than those achieved through a published dispatching rule approach. <s> BIB001 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> The objective of this research is to develop and evaluate effective, computationally efficient procedures for scheduling jobs in a large-scale manufacturing system involving, for example, over 1000 jobs and over 100 machines. The main performance measure is maximum lateness; and a useful lower bound on maximum lateness is derived from a relaxed scheduling problem in which preemption of jobs is based on the latest finish time of each job at each machine. To construct a production schedule that minimizes maximum lateness, an iterative simulation-based scheduling algorithm operates as follows: (a) job queuing times observed at each machine in the previous simulation iteration are used to compute a refined estimate of the effective due date (slack) for each job at each machine; and (b) in the current simulation iteration, jobs are dispatched at each machine in order of increasing slack. Iterations of the scheduling algorithm terminate when the lower bound on maximum lateness is achieved or the iteration limit is reached. This scheduling algorithm is implemented in Virtual Factory, a Windows-based software package. The performance of Virtual Factory is demonstrated in a suite of randomly generated test problems as well as in a large furniture manufacturing facility. To further reduce maximum lateness, a second scheduling algorithm also incorporates a tabu search procedure that identifies process plans with alternative operations and routings for jobs. This enhancement yields improved schedules that minimize manufacturing costs while satisfying job due dates. An extensive experimental performance evaluation indicates that in a broad range of industrial settings, the second scheduling algorithm can rapidly identify optimal or nearly optimal schedules. <s> BIB002 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper describes a novel approach to improve manufacturing responsiveness, defined as the ability of a manufacturing system to make a rapid and balanced response to the predictable and unpredictable changes characterizing today's manufacturing environments. The approach aims at creating a dynamic environment that is capable of reacting to factory conditions and which has a significant impact on the reduction of cost, lead time and inventory. The concurrent engineering concept of information sharing to support all the product development phases is employed here. Techniques such as design for manufacture have been applied to the design-process planning interface. Efforts have been made towards eliminating the divisions between process and scheduling activities using the concept of resource elements, which are the exclusive and shared capability boundaries between processing elements in the manufacturing facility. A prototype integration system has been implemented consisting of a knowledge base with fac... <s> BIB003 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> In this paper, we propose an integrated process planning and scheduling (IPPS) model for the multi-plant supply chain (MSC), which behaves like a single company through strong coordination and cooperation toward mutual goals. The IPPS problem is one of the most important issues for supporting the global objectives, because the function takes part in the assignment of factory resources to production tasks. The problem is formulated as a mathematical model considering alternative machines and sequences, sequence-dependent setup, and distinct due dates. The objective of the model is to decide the schedules for minimizing total tardiness through analysis of the alternative machine selection and the operation sequences in MSC. In order to obtain good approximate solutions, genetic algorithm-based heuristic approach is developed. Numerical experiments are carried out to demonstrate the efficiency of the proposed approach. <s> BIB004 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper addresses the integrated problem of process planning and scheduling in job shop flexible manufacturing systems. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions of process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. For the performance improvement of the algorithm, it is important to enhance population diversity and search efficiency. We adopt the strategies of localized interactions, steady-state reproduction, and random symbiotic partner selection. Efficient genetic representations and operator schemes are also considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and an existing cooperative coevolutionary algorithm. The experimental results show that the proposed algorithm outperforms the compared algorithms. <s> BIB005 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Integration of process planning and scheduling is one of the most important functions to support flexible planning in a multi-plant. The planning and scheduling are actually interrelated and should be solved simultaneously. In this paper, we propose an advanced process planning and scheduling model for the multi-plant. The objective of the model is to decide the schedules for minimizing makespan and operation sequences with machine selections considering precedence constraints, flexible sequences, and alternative machines. The problem is formulated as a mathematical model, and an evolutionary algorithm is developed to solve the model. Numerous experiments are carried out to demonstrate the efficiency of the proposed approach. <s> BIB006 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper proposes an adaptive genetic algorithm for distributed scheduling problems in multi-factory and multi-product environment. Distributed production strategy enables factories to be more focused on their core product types, to achieve better quality, to reduce production cost, and to reduce management risk. However, when comparing with single-factory production, scheduling problems involved in multi-factory one are more complicated, since different jobs distributed to different factories will have different production scheduling, consequently affect the performance of the supply chain. Distributed scheduling problems deal with the assignment of jobs to suitable factories and determine their production scheduling accordingly. In this paper, a new crossover mechanism named dominated gene crossover will be introduced to enhance the performance of genetic search, and eliminate the problem of determining optimal crossover rate. A number of experiments have been carried out. For the comparison purpose, five multi-factory models have been solved by different well known optimization approaches. The results indicate that significant improvement could be obtained by the proposed algorithm. <s> BIB007 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing ::: alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be ::: chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling. <s> BIB008 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This study focused on the integration problem of process planning and scheduling in a job shop environment. In an effort to integrate process planning and scheduling by taking advantage of the flexibility that alternative process plans offer, we have designed a GA (Genetic Algorithm)-based scheduling method. The performance of this newly suggested GA-based method has been evaluated by comparing integrated scheduling with separated scheduling in a real company that has alternative process plans. Also, a couple of benchmark cases have been tested for performance evaluation. <s> BIB009 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This study focused on the integration problem of process planning and scheduling in a job shop environment. In an effort to integrate process planning and scheduling by taking advantage of the flexibility that alternative process plans offer, we have designed a GA (Genetic Algorithm)-based scheduling method. The performance of this newly suggested GA-based method has been evaluated by comparing integrated scheduling with separated scheduling in a real company that has alternative process plans. Also, a couple of benchmark cases have been tested for performance evaluation, thus proving that the integrated scheduling shown by this research can be effectively applied to the real case. <s> BIB010 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> New paradigms for manufacturing system control are required that provide manufacturers with the adaptability and responsiveness required to compete in today's market. In this paper, an integrated process planning and scheduling system, which is applicable to the holonic manufacturing system is presented. Basic architecture of the target holonic manufacturing system is discussed from the viewpoint of the process planning and the scheduling systems. Process planning are proposed to select suitable machining sequences of machining features and suitable sequences of machining equipment, taking into consideration of future schedules of machining equipment. A fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS is presented. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, an improved particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling <s> BIB011 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> The present market scenario demands an integration of process planning and scheduling to stay competitive with others. In the present work, an integrated process planning and scheduling model encapsulating the salient features of outsourcing strategy has been proposed. The paper emphasizes on the role of outsourcing strategy in optimizing the performance of enterprises in rapidly changing environment. In the present work authors have proposed an Artificial Immune System based AIS-FLC algorithm embedded with the fuzzy logic controller to solve the complex problem prevailing under such scenario, while simultaneously optimizing the performance. The authors have shown the efficacy of the proposed algorithm by comparing the results with other random search methods. <s> BIB012 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper proposes an ant colony optimisation-based software system for solving FMS scheduling in a job-shop environment with routing flexibility, sequence-dependent setup and transportation time. In particular, the optimisation problem for a real environment, including parallel machines and operation lag times, has been approached by means of an effective pheromone trail coding and tailored ant colony operators for improving solution quality. The method used to tune the system parameters is also described. The algorithm has been tested by using standard benchmarks and problems, properly designed for a typical FMS layout. The effectiveness of the proposed system has been verified in comparison with alternative approaches. <s> BIB013 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> A job shop needs to deal with a lot of make-to-order business, in which the orders are usually diverse in types but each one is small in volume. To increase the flexibility and responsiveness of the job shop in the more competitive market, process planning and scheduling modules have been actively developed and deployed. The functions of the two modules are usually complementary. It is ideal to integrate them more tightly to achieve the global optimization of product development and manufacturing. In this paper, a unified representation model and a simulated annealing-based approach have been developed to facilitate the integration and optimization process. In the approach, three strategies, including processing flexibility, operation sequencing flexibility and scheduling flexibility, have been used for exploring the search space to support the optimization process effectively. Performance criteria, such as makespan, the balanced level of machine utilization, job tardiness and manufacturing cost, have been systematically defined to make the algorithm adaptive to meet various practical requirements. Case studies under various working conditions and the comparisons of this approach with two modern evolutionary approaches are given. The merits and characteristics of the approach are thereby highlighted. <s> BIB014 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Optimality of a process plan and a production schedule frequently conflict. It is difficult to determine a proper plan that meets both objectives simultaneously. This paper proposes a new simultaneous process planning and scheduling method to solve dilemmas posed by such situations using evolutionary artificial neural networks based on emergent synthesis. The effectiveness of the proposed method is confirmed by solving a benchmark problem, thereby demonstrating high productivity resulting from role-sharing among machines. Results also show that the proposed method is applicable to more realistic problems including larger volumes of products and production demand fluctuations. <s> BIB015 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to deal with FMS scheduling problems with alternative production routing. In the traditional genetic algorithm (GA) approach, crossover and mutation rates should be pre-defined. However, different rates applied in different problems will directly influence the performance of genetic search. Determination of optimal rates in every run is time-consuming and not practical in reality due to the infinite number of possible combinations. In addition, this crossover rate governs the number of genes to be selected to undergo crossover, and this selection process is totally arbitrary. The selected genes may not represent the potential critical structure of the chromosome. To tackle this problem, GADG is proposed. This approach does not require a defined crossover rate, and the proposed similarity operator eliminates the determination of the mutation rate. This idea helps reduce the computational time remarkably and improv... <s> BIB016 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> The purpose of this research is to solve a general job shop problem with alternative machine routings. We consider four performance measures: mean flow time, makespan, maximum lateness, and total absolute deviation from the due dates. We first develop mixed-integer linear programming (MILP) formulations for the problems. The MILP formulations can be used either to compute optimal solutions for small-sized problems or to test the performance of existing heuristic algorithms. In addition, we have developed a genetic algorithm that can be used to generate relatively good solutions quickly. Further, computational experiments have been performed to compare the solution of the MILP formulations with that of existing algorithms. <s> BIB017 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Traditionally, process planning and scheduling for parts are carried out in a sequential way, where scheduling is done after process plans have been generated. Considering the two functions are usually complementary, there is a great potential to integrate them more tightly so that greater performance and higher productivity of a manufacturing system can be achieved. In this paper, a genetic algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the genetic algorithm-based approach, efficient genetic representations and operator schemes have been developed. Experiment studies have been conducted. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling. <s> BIB018 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> This paper reports a unique approach to integrate the process planning and scheduling functions. The proposed approach here is used to reduce the tardy jobs by exploring the process plan solution space of the selected jobs. The integrated system comprises a process planning module, a scheduling module and an integrator module. The integration is achieved in an iterative manner. The uniqueness of this approach is characterized by the flexibility of the process planning strategy, which makes full use of the plan solution space intuitively to achieve a satisfactory schedule. Two heuristic rules for modifying solution space are proposed and the simulation on the Web-based collaborative system shows that the proposed approach can achieve satisfactory schedule performance levels in a fair number of iterations. <s> BIB019 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling. <s> BIB020 </s> A review on integrated process planning and scheduling <s> Algorithm-based approaches of IPPS <s> Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhance... <s> BIB021
The basic steps of the algorithm-based approach are as follows. First, process-planning system is used to generate the alternative process plans for all jobs and select user-defined number optimal plans based on the simulation results. Then, the algorithm in the scheduling system is used to simulate scheduling plans based on the alternative process plans for all jobs. Finally, based on the simulation results, the process plan of each job and the scheduling plan are determined. This approach is workable. However, the biggest shortcoming is that the simulation time may be long and the approach cannot be used in the real manufacturing system. Therefore, one important future research trend is finding effective algorithm for IPPS and developing effective system. In this approach, the most researches focused on the evolutionary algorithm. Swarm intelligence, some other meta-heuristic methods, such as SA, TS and Artificial Immune System (AIS), and some hybrid algorithms were also used to solve IPPS. Morad and Zalzala (1999) described a GA-based algorithm that only considered the time aspect of the alternative machines, and then they extended this scope to include the processing capabilities of alternative machines, different tolerance limits and process costs.  presented the NLPP model, which was based on a GA. BIB004 proposed an IPPS model for the multi-plants supply chain, which behaved like a single company through strong coordination and cooperation towards mutual goals. And, then they developed a GA-based heuristic approach to solve this model. BIB005 used a symbiotic evolutionary algorithm for IPPS.  used a fuzzy inference system to select alternative machines for IPPS, and used a GA to balance the load of every machine. BIB006 proposed an advanced process planning and scheduling model for the multi-plant, and used an evolutionary algorithm to solve this model. BIB007 BIB016 proposed a GA with dominant genes to solve distributed scheduling problems. BIB009 designed a GA-based method to solve IPPS by taking advantage of the flexibility that alternative process plans offer. BIB010 designed a GA-based method to solve IPPS. BIB017 first developed mixed-integer linear programming formulations for IPPS based on NLPP model. And, then they used a GA to solve this model. BIB018 presented a GA-based approach to solve this problem. BIB020 proposed a modified GA-based approach to solve IPPS based on NLPP model. BIB013 proposed an ant-colony-optimisation-based software system for solving FMS scheduling in a job shop environment with routing flexibility, sequence-dependent set-up and transportation time.  developed the IPPS as a combinatorial optimisation model, and modified a Particle Swarm Optimisation (PSO) algorithm to solve IPPS. BIB008 and BIB011  ) used a fuzzy inference system to choose alternative machines for IPPS of a job shop manufacturing system, and used the hybrid PSO algorithms to balance the load of each machine. BIB001 proposed an SA-based approach to solve IPPS.  used an SA algorithm to solve IPPS in mass customisation. BIB014 used an SA-based approach for IPPS. BIB021 proposed an Enhanced Swift Converging Simulated Annealing (ESCSA) to solve IPPS. BIB002 presented a procedure for scheduling jobs with alternative processes in a general job shop, and then proposed a TS-based scheduling algorithm to solve it. BIB012 proposed an AIS-based AIS-FLC algorithm embedded with the Fuzzy Logic Controller (FLC) to solve the complex IPPS. Some other methods, such as heuristic dispatching rules BIB003 , neural network BIB015 , object-oriented integration approach   and web-based approach BIB019 , have also been proposed to do the research on IPPS.