Nowadays, energy conservation is one of the most talked about issues in our life, it is also increasingly important in dynamic flexible flow shop scheduling problems. Previous research did much on how to minimize total earliness and tardiness of all jobs, or to achieve just-in-time production, whereas little effort has been made on how to save energy. Therefore, to address such need, a dynamic machine switching method has been proposed in this paper. The main purpose is to minimize both energy consumption and just-in-time production objectives. Featured by switching machines dynamically, the proposed method has been tested on different environments, whose results show that it has good performance for energy conservation.

Job shop scheduling problem (JSP) is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. Teaching-learning-based optimization (TLBO) algorithm is a novel population oriented meta-heuristic algorithm. It has been proved that TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. In this paper, the traditional TLBO is improved to enhance diversification and intensification when exploring solutions for JSP. The improvements include changing the coding method, increasing number of teachers, introducing new learners and performing local search around potentially optimal solutions. To show effectiveness of the improved TLBO algorithm, the simulation results obtained by the improved TLBO for benchmark problems are compared with results obtained by the traditional TLBO and the best known lower bounds.

Competition and resultant complexity of today's production industry require that enterprises realize the importance of reducing total costs in production. Uncertainties of customer orders also make it difficult to determine the schedule for minimizing inventories, earliness and tardiness. In this paper, we propose a new production model for flexible flow shops, which aims to reduce the total inventories, earliness and tardiness. The model divides a flexible flow shop (FFS) into a make-to-stock (MTS) part and a make-to-order (MTO) part by applying a decoupling point. In the MTS part, jobs are manufactured into semi-finished products and then stored as inventories at the decoupling point. As soon as a customer order is received, the inventories are released into the system, starting undergoing processing in the MTO part. This shortens the lead time for manufacturing the products. Another advantage of the proposed model is that the less number of operations in the MTO part than that in a job makes it easier to avoid tardiness. In addition, we designed two types of models: dynamic and static, which depends on whether the decoupling point is dynamically adjusted to adapt to different arrival rates of customer orders. The reason why we design two types is to compare the performance of the proposed two models. Results show that the dynamic hybrid model outperforms pull, push and static hybrid models for reducing costs.

Flexible job-shop scheduling problem (FJSP) is an extended job-shop scheduling problem. FJSP allows an operation to be processed by several different machines. FJSP with overlapping in operations means that each operation is divided into several sublots. Sublots are processed and transferred separately without waiting for the entire operation to be processed. In previous research, a mathematical model was developed and a genetic algorithm proposed to solve this problem. In this study, we try to improve the procedure of previous research to achieve better results. The proposed improvements were tested on some benchmark problems and compared with the results obtained by previous research.

In this paper, we aim to solve the resource-constrained multiproject scheduling problem (rc-mPSP), in which more than one project are scheduled simultaneously, projects share global resources, and the average project delay and total project time are minimized as objectives. In order to solve this problem by a centralized scheduling method, we present a new genetic algorithm (GA) approach. In this procedure, we follow the GA described in Okada et al.. (2014) and improve its genetic operators, such as crossover and mutation, and local search so as to work better on rc-mPSP. Furthermore, in order to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing decentralized methods and centralized methods presented in the literature. We show that our procedure is one of the most competitive among such algorithms. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

In this paper, we aim to solve the resource-constrained multiproject scheduling problem (rc-mPSP), in which more than one project are scheduled simultaneously, projects share global resources, and the average project delay and total project time are minimized as objectives. In order to solve this problem by a centralized scheduling method, we present a new genetic algorithm (GA) approach. In this procedure, we follow the GA described in Okada et al.. (2014) and improve its genetic operators, such as crossover and mutation, and local search so as to work better on rc-mPSP. Furthermore, in order to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing decentralized methods and centralized methods presented in the literature. We show that our procedure is one of the most competitive among such algorithms. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.To reduce inventory costs and increase customer satisfaction in a real production environment, Just-in-Time (JIT) manufacturing should be combined with more flexibility. Flexibility in manufacturing system ensures improved product quality and mass customization. In this paper, we consider flexible job-shop problem (FJSP) to include several types of flexibility. Prior research on JIT scheduling problems paid little attention to flexible job shop systems. In this paper, an optimization method for FJSP for JIT manufacturing was proposed, genetic algorithm and time control are integrated to improve the JIT performance. Computational simulations and comparisons demonstrate that the proposed method shows more competitive performance than other evolutionary algorithms.

We consider the problem of estimating lead times of orders that arrive dynamically in a flexible flow shop. When an order arrives, we estimate the time between its arrival and its completion. Good estimation would help improve on-time delivery. Existing estimation methods are mostly designed based on an order's processing time and the shop's inventory status. We improve the accuracy of estimation by developing new methods that further take into account the characteristics of the shop system such as parallelization of queues. We also consider the bottleneck effect, which might largely dominate the waiting time in the system. Results of computational experiments show that the proposed methods outperform existing methods in terms of some accuracy measures.

We consider the problem of estimating flow times of jobs that arrive dynamically in a manufacturing system. A job's flow time refers to the time between the job's arrival and completion. Most existing methods use some predefined equations for such estimation, and most of the equations are designed for single machine manufacturing systems. To better estimate the flow time of a job in a more complex system in which there are multiple machines and multiple workstations, we propose a distributed learning approach that divides the manufacturing system into multiple small parts and collects real-time local information in each part to predict the waiting time for a job. We evaluate the proposed approach by comparing it with existing methods using a variety of problem instances. The results show that the proposed approach outperforms existing methods and accordingly might improve the level of customer service when being used for due date promising.

This article proposes a novel method to solve the manufacturing scheduling problems in multi-agent system (MAS) for dynamic environment. The study focuses on the machine selection work for jobs and proposes a remaining time prediction method, which will help a job choose machines based on estimated finishing time. In this way, we can keep the job finishing just-in-time, which aims to support a customer-oriented lead time policy, and can provide better manufacturing performance and increase customer service level. This article provides experimental results compared with another approach in previous research, through which we can find that in some case if we put the Just-in-time philosophy into consideration, the proposed method will deliver competitive performance.

Currently, expectations from Internet of Things (IoT) are extremely high worldwide. However, mechanical engineers working on product design processes do not always benefit. This paper utilizes the up-to-date IoT-enabled commercial product lifecycle management (PLM) system to examine the effectiveness of IoT value for mechanical engineers. Moreover, it discusses the maturity model for digital engineering for the new era of WT. Furthermore, this paper introduces significant initiatives such as a PLM functionality connected to everything, monitoring of field data for getting back to design process, and virtual 3D data and real field data superposition. This paper concludes that there is high potential to use IoT technology for mechanical engineers from the perspective of quality and reliability design innovations.

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. To reduce inventory costs and increase customer satisfaction in a real production environment, Just-in-Time (JIT) manufacturing should be combined with more flexibility. Flexibility in manufacturing system ensures improved product quality and mass customization. In this paper, we consider flexible job-shop problem (FJSP) to include several types of flexibility. Prior research on JIT scheduling problems paid little attention to flexible job shop systems. In this paper, an optimization method for FJSP for JIT manufacturing was proposed, genetic algorithm and time control are integrated to improve the JIT performance. Computational simulations and comparisons demonstrate that the proposed method shows more competitive performance than other evolutionary algorithms.

To reduce inventory costs and increase customer satisfaction in a real production environment, Just-in-Time (JIT) manufacturing should be combined with noire flexibility. Flexibility in manufacturing system ensures improved product quality and mass customization. In this paper, we consider flexible job-shop problem (FJSP) to include several types of flexibility. Prior research on JIT scheduling problems paid little attention to flexible job shop systems. En this paper, an optimization method for FJSP for JIT manufacturing was proposed, genetic algorithm and tinge control are integrated to improve the JIT performance. Computational simulations and comparisons demonstrate that the proposed method shows more competitive performance than other evolutionary algorithms. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This study proposes an efficient mixed integer linear programming (MILP) model for a generalized job shop production process. In the process, a workstation contains one or multiple machines and each job visits some of the workstations in a specific sequence. It is allowed that a job visits the same workstation more than once. Although some similar processes were modeled by MILP in previous studies, the models are unable to solve problems that involve more than ten jobs due to high computational complexity. Our proposed model outperforms the best model that is identified among 23 research papers with regard to computational complexity. Simulation results show that our model is able to solve a problem with a dozen of jobs, which is classified as a large-scale problem in the literature.

Although many production scheduling systems are developed, it is difficult to introduce for medium-sized enterprises, because of high cost of initial and maintenance investment. However functions provided by it, such as available-to-promise under the restrictions of the own process, resource leveling and so on, are required by all of enterprises to make their profits. To realize a scheduling system, which can be introduced more easily for the small and medium-sized enterprises, a novel spiral-evolutional scheduling system is proposed in this paper. This system is getting started with gathering of production information in factories, and through progress monitoring a scheduling function is installed and evolved in a spiral evolutional systemizing process. In this paper, the concept of the spiral-evolutional production scheduling system is described, and the implementation of this system and the merit of introduction of it are shown.

In this paper, we aim to solve the problem of resource-constrained project scheduling with multiple modes (rc-PSP/mM), in which multiple execution modes are available for each of the project's activity and with minimization of makespan as objective. We present a new genetic algorithm approach in order to solve this problem. In this procedure, we propose a new mutation operator that exploits a critical path and two new local search procedures, i.e. critical path improvement local search (cpiLS) and iterative forward/backward local search (ifbLS), using activity list characteristics. The cpiLS reduces the critical path and the ifbLS improves resource allocation of the schedule of rc-PSP/mM. Also, to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing heuristic procedures presented in the literature, and it has been revealed that our procedure is one of the most competitive among the algorithms. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The semiconductor final testing scheduling problem (SFTSP) is a variation of the complex scheduling problem, which deals with the arrangement of the job sequence for the final testing process. In this paper, we present an actual SFTSP case includes almost all the flow-shop factors as reentry characteristic, serial and batch processing stages, lot-clusters and parallel machines. Since the critical equipment needs to be utilized efficiently at a specific testing stage, the scheduling arrangement is then playing an important role in order to reduce both the makespan and penalty cost of all late products in total final testing progress. On account of the difficulty and long time it takes to solve this problem, we propose a multi-objective optimization approach, which uses a lot-merging procedure, a new job-based encoding method, and an adjustment to the non-dominated sorting genetic algorithm II (NSGA-II). Simulation results of the adjusted NSGA-II on this SFTSP problem are compared with its traditional algorithm and much better performance of the adjusted one is observed. © (2013) Trans Tech Publications, Switzerland.

In semiconductor manufacturing factories, the process of wafer fabrication is the most technologically complex and capital intensive stage. This process is configured as a reentrant flow shop process with many machines and processing steps. It needs an efficient and effective scheduling method for large size process in order to increase the competitiveness. The reentrant flow shop problem (RFSP) means that all jobs have the same route through the shop machines and the same shop machine is used several times to complete a job. This research provides an effective fuzzy-based multi-term genetic algorithm to solving RFSP with the objective of minimizing the total turn around time (TTAT). The proposed method focuses on the critical point in scheduled solutions. The middle position of longest TAT is defined as the critical point. According to the critical point and current generation, fuzzy logic chooses the focused term of chromosome, then the genetic algorithm effects on this term. In each evolution, only corresponded part of chromosome is evolved by crossover and mutation while other parts of chromosome remain unchanged. Through computational experiments, the effectiveness of the fuzzy-based multi-term genetic algorithm is evaluated.

In production processes, just-in-time (JIT) completion of jobs helps reduce both the inventory and late delivery of finished products. Previous research which aims to achieve JIT job completion mainly worked on static scheduling problems, in which all jobs are available from time zero or the available time of each job is known beforehand. In contrast, dynamic scheduling problems which involve continual arrival of new jobs are not much researched and dispatching rules remain the most frequently used method for such problems. However, dispatching rules are not high-performing for the JIT objective. This study proposes several routing strategies which can help dispatching rules realize JIT completion for jobs arriving dynamically in hybrid flow shops. The routing strategies are based on distributed computing which makes realtime forecast of completion times of unfinished jobs. The advantages include short computing time, quick response and robustness against disturbance. Computer simulations show that the performance of dispatching rules combined with the proposed routing strategies is significantly higher than that of dispatching rules only and that of dispatching rules combined with the previous routing methods. (c) 2012 Elsevier Ltd. All rights reserved.

In production processes, just-in-time (JIT) completion of jobs helps reduce both the inventory and late delivery of finished products. Previous research which aims to achieve JIT job completion mainly worked on static scheduling problems, in which all jobs are available from time zero or the available time of each job is known beforehand. In contrast, dynamic scheduling problems which involve continual arrival of new jobs are not much researched and dispatching rules remain the most frequently used method for such problems. However, dispatching rules are not high-performing for the JIT objective. This study proposes several routing strategies which can help dispatching rules realize JIT completion for jobs arriving dynamically in hybrid flow shops. The routing strategies are based on distributed computing which makes realtime forecast of completion times of unfinished jobs. The advantages include short computing time, quick response and robustness against disturbance. Computer simulations show that the performance of dispatching rules combined with the proposed routing strategies is significantly higher than that of dispatching rules only and that of dispatching rules combined with the previous routing methods. (c) 2012 Elsevier Ltd. All rights reserved.

For manufacturers, the integration of high performance manufacturing with customer-oriented practices plays an important role in improving the performance of their business system. The benefits from such integration can only be maximized when the two parts are designed to work cooperatively. Though previous research has contributed much to manufacturing control algorithms and customer service practices, there has been little consideration of the two parts as a whole; consequently, the methods proposed may not be well supported by the other practices adopted in the system. This study develops production control methods that support a customer-oriented lead time policy, and aims to increase the performance of both manufacturing and customer service. The control methods are proposed for hybrid flow shops handling orders arriving dynamically. Computer simulations are conducted on a large number of problem instances, and the results show that the designed distributed feedback and decision-making functions enable the proposed methods to significantly outperform existing methods in achieving just-in-time (JIT) job completion under customized product lead times. Even taking into account the possible tradeoff between JIT job completion and flow time length, the proposed methods still deliver competitive performance. (C) 2011 Elsevier B.V. All rights reserved.

Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm. (C) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper proposed a multi-objective local search procedure (MOLS). It is combined with NSGA-II for solving bi-criteria PFSP with the objectives of minimizing makespan and maximum tardiness. Utilizing the properties of active blocks for flow shop scheduling problem, neighborhood structures MOINS (multi-objective insertion) and MOEXC (multi-objective exchange) are designed in order to improve efficiency of perturbation. Any perturbation based on MOINS and MOEXC takes effect on different criteria simultaneously. The original idea of MOLS is systematic change neighborhoods in the local search procedure. The search direction of MOLS on an individual is naturally guided by interaction of MOINS and MOEXC. Moreover, there is no need to set parameters in MOLS. The MOLS combined with popular multi-objective evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is called as "NSGA-II- MOLS". To illustrate the efficacy of proposed approach, four different scaled problems are used to test performance of NSGA-II-MOLS. The numerous comparisons show efficacy of NSGA-II-MOLS is better than most of algorithms even with the same number of individual evaluations and parameters setting. © 2012 The Institute of Electrical Engineers of Japan.

Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics
however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm. © 2012 Institute of Electrical Engineers of Japan.

Multiobjective process planning and scheduling (PPS) is a most important practical but very intractable combinatorial optimization problem in manufacturing systems. Many researchers have used multiobjective evolutionary algorithms (moEAs) to solve such problems
however, these approaches could not achieve satisfactory results in both efficacy (quality, i.e., convergence and distribution) and efficiency (speed). As classical moEAs, nondominated sorting genetic algorithm II (NSGA-II) and SPEA2 can get good efficacy but need much CPU time. Vector evaluated genetic algorithm (VEGA) also cannot be applied owing to its poor efficacy. This paper proposes an improved VEGA with archive (iVEGA-A) to deal with multiobjective PPS problems, with consideration being given to the minimization of both makespan and machine workload variation. The proposed method tactfully combines the mechanism of VEGA with a preference for the edge region of the Pareto front and the characteristics of generalized Pareto-based scale-independent fitness function (gp-siff) with the tendency to converge toward the central area of the Pareto front. These two mechanisms not only preserve the convergence rate but also guarantee better distribution performance. Moreover, some problem-dependent crossover, mutation, and local search methods are used to improve the performance of the algorithm. Complete numerical comparisons show that the iVEGA-A is obviously better than VEGA in efficacy, and the convergence performance is also better than NSGA-II and SPEA2, while the distribution performance is comparable to and the efficiency is obviously better than theirs. © 2012 Institute of Electrical Engineers of Japan.

This paper proposes a parallel quantum evolutionary algorithm (PQEA) using Client-Server model for multi-objective optimization problems. Firstly, the PQEA uniformly decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems. All the sub-problems are classified into several groups according to their similarities. Each "Client" processes the evolution for a group of neighbor sub-problems in parallel. There is a quantum individual used to address the sub-problems of a group in a "Client". Since the quantum individual is a probabilistic representation, it can share evolutionary information of the neighbor sub-problems in one group, while the sub-problems are orderly solved using a same q-bit individual. The "Server" maintains non-dominated solutions that are generated by every "Client". The current best solution for each sub-problem can be found in the "Server", when the quantum individual updated its states for evolution. Experimental results have demonstrated that PQEA obviously outperforms the most famous multi-objective optimization algorithms MOEA/D on the bi-objectives. For the more objectives, the PQEA obtains the similar results with MOEA/D, even with the same evaluation times. Furthermore, in this paper, the scalability and sensitivity of PQEA have also been experimentally investigated.

The unsmooth job flow between the production shops along a manufacturing chain is a problem commonly seen in industries due to the inconsistency in processing speed and delivery between the chained production shops. With many factors involved in the coordination between production shops, the problem is complex and few solutions have been provided to date. This study presents an approach able to solve this problem by implementing time-based manufacturing that enables the speed and timing of each shop's job outflow to match those of its successor shop's job inflow. The proposed method is composed of offline schedule making and online job processing control. It aims to complete each job in a just-in-time (JIT) manner at the time the job is wanted by the next production shop. Designed upon a flexible job shop environment, which is easy to be transformed into other shops with similar characters, the proposed method is expected to be widely applicable to JIT scheduling problems. An industrial case study is made and results show that the proposed method has a strong ability in JIT job completion, tardy job prevention and makespan reduction.

This paper considers the dynamic flexible-flow shop scheduling problem in which the manufacturing environment consists of multiple stages with multiple machines at each stage. All jobs must go through all stages in sequence in order to become a product, and the processing time for each product on each machine is different. There exists a delivery time between machines of neighboring stages, and the release time of each job is unknown, which means that a new job may be released into the system at any time. The objective of this work is to minimize the total earliness and tardiness penalties of all jobs, or to achieve just-in-time production. Previous researches did much on the static scheduling of such problem with different objectives, whereas little effort has been made on dynamic scheduling for such problem, which is more difficult than the static problem but becomes more and more important under the increasingly competitive manufacturing industry. Therefore, to address such need, efforts are been made on dynamic scheduling and several distributed-intelligence approaches are proposed in this paper. Featured by concurrent computing, the distributed-intelligence approaches have been tested on two different manufacturing environments, whose result tells that the proposed approaches deliver competitive performance for the targeted problem. (C) 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

With the high speed innovation of information technology, many production scheduling systems have been developed. However, a lot of customization according to individual production environment is required, and then a large investment for development and maintenance is indispensable. Therefore now the direction to construct scheduling systems should be changed. The final objective of this research aims at developing a system which is built by it extracting the scheduling technique automatically through the daily production scheduling work, so that an investment will be reduced. This extraction mechanism should be applied for various production processes for interoperability. Using the master information extracted by the system, production scheduling operators can be supported to accelerate the production scheduling work easily and accurately without any restriction of scheduling operations. By installing this extraction mechanism, it is easy to introduce a scheduling system without a lot of expense for customization. In this paper, first a model for expressing a scheduling problem is proposed. Then the guideline to extract the scheduling information and use the extracted information is shown and some applied functions are also proposed based on it. (C) 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 93(1): 19-29, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10188

With the high speed innovation of information technology, many production scheduling systems have been developed. However, a lot of customization according to individual production environment is required, and then a large investment for development and maintenance is indispensable. Therefore now the direction to construct scheduling systems should be changed. The final objective of this research aims at developing a system which is built by it extracting the scheduling technique automatically through the daily production scheduling work, so that an investment will be reduced. This extraction mechanism should be applied for various production processes for interoperability. Using the master information extracted by the system, production scheduling operators can be supported to accelerate the production scheduling work easily and accurately without any restriction of scheduling operations. By installing this extraction mechanism, it is easy to introduce a scheduling system without a lot of expense for customization. In this paper, first a model for expressing a scheduling problem is proposed. Then the guideline to extract the scheduling information and use the extracted information is shown and some applied functions are also proposed based on it. (C) 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 93(1): 19-29, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10188

This paper proposed a new evolutionary algorithm based on concept and principles of quantum computing, called Multi-update Mode Quantum Evolution Algorithm (MMQEA), in which having two update modes beta-update and beta-update, and between their modes each update procedure provide its evolutionary information to other one, that to guide the other update-mode to maintain the population diversity and avoid premature. Meanwhile in MMQEA, proposed a new individual structure that composed by two Q-bit strings. Applying the multi-update mode to individual evolution improved the Q-gate updating efficiency in each generation of MMQEA. To demonstrate its effectiveness and applicability, the proposed algorithms were tested on a famous combinatorial optimization problem, the knapsack problem. The results show that MMQEA performs very well compared with Quantum Evolutionary Algorithm (QEA).

In the practice of scheduling of construction projects, there is a great variety of methods and procedures that need to be selected at each construction process during project. Accordingly, it is important to consider the different modes that may be selected for an activity in the scheduling of construction projects. In this study, first, we mathematically formulate the resource-constrained project scheduling problem with multiple modes while minimizing the total project time as the objective function. Following, we propose a new random key-based genetic algorithm approach which includes the mode reduction procedures to solve this NP-hard optimization problem. Finally, in order to evaluate the performance of our method, we are scheduled in the close future to implement the proposed approach on some standard project instances as the computational experiment and analyze these experimental results comparing with the bi-population-based genetic algorithm by Peteghem and Vanhoucke [1].

This paper presents some important improvements to a previously proposed intelligent production system dealing with a dynamic flexible flow shop scheduling problem under a multi-stage multi-machine factory environment. These improvements greatly help upgrade the overall system performance under stable demand situations as well as under fluctuated demand situations, build the system robust against demand increase, and raise the systems machine utilization rate. The research objective is to minimize the total earliness and tardiness penalties of all jobs during any given period of time. The system works on the basis of multi-agent feedbacks that are conducted by agents which collect realtime information, make decisions, and work interactively to give corresponding solutions to each job. Comparison between the previous system and the improved one has been carried out, and the experimental results demonstrate the effectiveness of the proposed improvements under various system situations. © 2010 IEEE.

The process planning and scheduling (PPS) is to determine a solution (schedule), which tells a production facility what to make, when, and on which equipment, to process a set of parts with operations effectively. Multiobjective PPS problems become more complex because the decision maker need to make a trade-off between two or more objectives while determining a set of optimal nondominated solutions effectively. The previous research works use evolutionary algorithms (EA) to solve such problems, however, the proposed approaches cannot get a good balance between efficacy and efficiency. This paper proposed an improved vector evaluated genetic algorithm with archive (iVEGA-A) mechanism to deal with PPS problem while considering the minimization of the makespan and minimization of the variation of workload of machine. The proposed algorithm has been compared with other approaches to verify and benchmark the optimization reliability on PPS problems. These comparisons indicate iVEGA-A is better than vector evaluated genetic algorithm (VEGA) did on efficacy and negligible difference on efficiency. The efficacy is not less than some famous approaches, such as, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficiency is obviously better than the latter. ©2010 IEEE.

Weighted linear scalar, which transfers a multi-objective problem to many single objective sub-problems, is a basic strategy in traditional multi-objective optimization. However, it is not well used in many multi-objective evolutionary algorithms because of most of them are lack of balancing between exploitation and exploration for all sub-problems. This paper proposes a novel multi-objective evolutionary algorithm called multi-objective quantum evolutionary algorithm (MOQEA). Quantum evolutionary algorithm is a recent developed heuristic algorithm, based on the concept of quantum computing. The most merit of QEA is that it has little q-bit individuals are evolved to obtain an acceptable result. MOQEA decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. Each sub-problem is optimized by one q-bit individual. The neighboring solutions that are defined as a set of nondominated solutions of sub-problem are generated from the corresponding q-bit individual. The experimental results have demonstrated that MOQEA outperforms or performs similarly to MOGLS and NSGA-II on discrete multi-objective problems.

The process planning and scheduling (PPS) is to determine a solution (schedule), which tells a production facility what to make, when, and on which equipment, to process a set of parts with operations effectively. Multiobjective PPS problems become more complex because the decision maker need to make a trade-off between two or more objectives while determining a set of optimal nondominated solutions effectively. The previous research works use evolutionary algorithms (EA) to solve such problems, however, the proposed approaches cannot get a good balance between efficacy and efficiency. This paper proposed an improved vector evaluated genetic algorithm with archive (iVEGA-A) mechanism to deal with PPS problem while considering the minimization of the makespan and minimization of the variation of workload of machine. The proposed algorithm has been compared with other approaches to verify and benchmark the optimization reliability on PPS problems. These comparisons indicate iVEGA-A is better than vector evaluated genetic algorithm (VEGA) did on efficacy and negligible difference on efficiency. The efficacy is not less than some famous approaches, such as, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficiency is obviously better than the latter. ©2010 IEEE.

Worker allocation problem of the manual labor factory consists in deciding who does what during the manual labor production. Manual labor factory, has concern for reducing the production duration, human cost and overwork. A novel scheduling model of worker allocation problem for manual labor is proposed in this paper. Given a set of jobs and a set of workers, worker allocation problem is to assign the workers into these jobs to reduce human cost, to shorten production duration and control production overwork. Worker allocation problem is a very important issue in the production scheduling, since total budget and human resource involved must be managed optimally. Worker allocation problem is NP-Hard problem to find optimal solution as increasing worker number and enlarging production scale. Genetic algorithm (GA) is considered a promising method so in this paper worker allocation problem is solved by using genetic algorithm. This paper shows it is very flexible and accurate for finding near-optimal solutions in the short time.

The flexible flow shop refers to such a manufacturing environment in which jobs are to be processed through serial stages, with one or multiple machines available at each stage. It is usually a complex task when specific objective is demanded such as minimum cost, minimum time, etc. Static scheduling of such problems has been much researched, however, little efforts have been made on real-time scheduling when the release time of each job is unknown. In this paper, some online scheduling methods are presented to deal with the tough problem of real-time Just-In-Time manufacturing. In addition to applicable dispatching rules, agent-based approaches are also proposed featuring feedback learning, and realtime prediction. The simulation result reveals that the presented distributed learning approach, especially when combined with realtime prediction, delivers a high performance.

The problem considered in this research is the just-in-time scheduling of a manufacturing environment that is able to produce several different products. New jobs come randomly into the system, expected to become one of the products. Each job must go through multiple stages before it can be finished as a product There are multiple machines at each stage, and the processing time of each product on each machine is different There exits delivery time between stages. Previous researches did much on the static scheduling of such problem by using mixed integer linear programming. However, little efforts have been made on realtime scheduling, which means the release time of jobs is unknown. But such scheduling is becoming more and more important under the increasingly competitive manufacturing market In this paper, two distributed feedback mechanisms are proposed to solve the realtime scheduling problem of minimizing earliness and tardiness penalties of all jobs. The simulation shows that the proposed distributed feedback mechanisms deliver quite competitive performance for the targeted problem.

Earliness and tardiness penalties are designed for Such scheduling problems where the popular Just-In-Time (JIT) concept is considered to be of significant importance. In this paper, a self evolution (SE) algorithm is proposed to solve the problem of single-machine total earliness and tardiness penalties with a common due date. Up to now, no specific attention has been paid to straddling V-shaped schedules of such problems, which may be better than pure V-shaped schedules for the early due date cases; and no specific discussions have been made on the start time setting of the first job in a schedule. Therefore, in this research, efforts have been made on digging out the straddling V-shaped schedules, improving the efficiency of setting the start time of a schedule, and reducing the execution time. In addition, a new RHRM approach is proposed to create the initial solution for evolution, which helps in achieving the fast contingency of the algorithm. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library, and the results show that the proposed SE algorithm delivers much higher efficiency in finding optimal or near-optimal solutions with both better results in total penalties and significant execution time reduction. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The Just-In-Time (JIT) concept is of great importance in many manufacturing processes. JIT scheduling problems affects the performance of the whole production procedure, because early in job completion causes inventory cost while delay in job completion raises penalties paid to customers. In this paper, a memorial self evolution algorithm is proposed to solve the problem of total earliness and tardiness penalties on a machine unit with a common due date. Up to now, researches on this problem have paid no specific attention to straddling V-shaped schedules, which may be better than pure V-shaped schedules for early due date cases; and no specific discussions have been made on the start time setting of the first job in a schedule. Thus, efforts have been made on searching good straddling V-shaped schedules, and optimizing start time setting of schedules. A GHRM approach is proposed to create the initial solution for memorial self evolution. Meanwhile a database which keeps the memories of the elite solutions is introduced to deliver better initial solutions for similar problems. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs. The results show that the proposed memorial self evolution algorithm delivers better results in in finding optimal or near-optimal solutions than previous researches.

The Just-In-Time (JIT) concept is of great importance in many manufacturing processes. JIT scheduling problems affects the performance of the whole production procedure, because early in job completion causes inventory cost while delay in job completion raises penalties paid to customers. In this paper, a memorial self evolution algorithm is proposed to solve the problem of total earliness and tardiness penalties on a machine unit with a common due date. Up to now, researches on this problem have paid no specific attention to straddling V-shaped schedules, which may be better than pure V-shaped schedules for early due date cases
and no specific discussions have been made on the start time setting of the first job in a schedule. Thus, efforts have been made on searching good straddling V-shaped schedules, and optimizing start time setting of schedules. A GHRM approach is proposed to create the initial solution for memorial self evolution. Meanwhile a database which keeps the memories of the elite solutions is introduced to deliver better initial solutions for similar problems. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs. The results show that the proposed memorial self evolution algorithm delivers better results in in finding optimal or near-optimal solutions than previous researches.©2008 IEEE.

Inventory cost and delay penalty are two kinds of annoying spendings in manufactory industry. Accordingly, earliness and tardiness penalties are proposed to simulate such scheduling problems where the popular just-in-time (JIT) concept is considered to be of significant importance. In this paper, a self evolution algorithm is proposed to solve the problem of single machine total earliness and tardiness penalties with a common due date. Up to now, such problem has been solved without specific consideration of straddling V-shaped schedules, which may be better than pure V-shaped schedules for early due date problems; without specific discussions on g improving, where g refers to the idle time before the start of the first job; and the many individuals in all so far proposed GA-like algorithms become the bottleneck of execution time reduction. Therefore, in this research, efforts have been made on digging out the straddling V-shaped schedules, improving the efficiency of g improving, and reducing the execution time. In addition, a new RHRM approach is proposed to create the initial solution for evolution, which helps achieve the fast contingency of the algorithm. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library, the results showing that the proposed self evolution algorithm delivers much higher efficiency in finding optimal or near-optimal solutions with both better results in total penalties and significant execution time reduction.

With the high speed innovation of information technology, many production scheduling systems (to support scheduling work) have been developed. However, a significant customization according to individual production environment is required, and then a large investment for development and maintenance is indispensable. Therefore now the direction to construct scheduling systems should be changed. The final objective of this research aims at developing a system which is built by itself extracting the scheduling technique automatically through the daily production scheduling work. By using the information extracted by the system, production scheduling operators can be supported to accelerate the production scheduling work easily and accurately without any restriction of scheduling works. In order to realize such a production scheduling system, the mechanism to extract scheduling information is needed. In this paper, using Gantt Chart Interface System that is developed to emulate the production scheduling work on papers through the analysis of operator's works, the method extracting and generalizing the relation information between operations from user manipulation is proposed. And the operator assistant function which uses the extracted information is proposed. Then by conducting experiment that uses the assistant function, the availability of the method of extracting and generalizing is shown.

Although decomposition procedure is a common approach to handle computational complexity which is caused by large scale production scheduling problems, in order to extend the applicability of the decomposition approach, the overall problem should be decomposed on which criterion is always a problem. Also how to improve decomposition approach's computational efficiency and resultant effectiveness is still a challenging task in this field.
With the purpose of improving decomposition approach's efficiency and decreasing computational complexity for job shop scheduling problems, a new dynamic local focusing approach which has a machine-based divided criterion focused on the longest active chain is proposed in this paper. Not liking the used decomposition procedure, the proposed one does not decompose a job shop into cells at the very beginning. It dynamically classifies the machines, which process operations on the identified longest active chain of the whole problem as one focused cell (decomposed sub-problem). The schedule is improved by redefining, adjusting and solving the focused cell schedule which is updated iteratively with the entire schedule's longest active chain in this dynamic procedure. The proposed approach is tested on make-span minimum benchmark job shop scheduling problems. Test results show that the algorithm is capable of efficiently generating good schedules.

This paper deals with the partial scheduling adjustment in the autonomous distributed scheduling method. The scheduling adjustment for the production system is required when uncertainty occurs. In that case, it does not need to adjust the whole schedule, but only the partial schedule which receives a direct influence by uncertainty according to the variable situation. From the viewpoint of the distributed system, to implement a function which adjusts such a partial schedule, each machine needs to manage schedule and exchange information. It has been studied by some researchers in recent years. However, the partial scheduling adjustment has not been controlled to change the processing sequence of operations in a machine, namely interchanging operations. Therefore, we propose realizing a function interchanging operations in a machine to diminish the influence occurred by a delay of a certain operation.

This paper deals with the partial scheduling adjustment in the autonomous distributed scheduling method. The scheduling adjustment for the production system is required when uncertainty occurs. In that case, it does not need to adjust the whole schedule, but only the partial schedule which receives a direct influence by uncertainty according to the variable situation. From the viewpoint of the distributed system, to implement a function which adjusts such a partial schedule, each machine needs to manage schedule and exchange information. It has been studied by some researchers in recent years. However, the partial scheduling adjustment has not been controlled to change the processing sequence of operations in a machine, namely interchanging operations. Therefore, we propose realizing a function interchanging operations in a machine to diminish the influence occurred by a delay of a certain operation.

At present, many production scheduling systems have been developed. However, a lot of customization according to individual production environment is required, and then a large investment for development and maintenance is indispensable. Therefore now the direction to construct scheduling systems should be changed. The final objective of this research aims at developing a system which is built by it extracting the scheduling technique automatically through the daily production scheduling work, so that an investment will be decreased. This paper shows a prototype of Gantt Chart Interface System that emulates the production scheduling work on papers. Through the analysis of operator's operations, this paper proposes how to extract the master information required by production scheduling. Using the information, the scheduling operators can be supported to accelerate the scheduling work easily and accurately without any restriction of scheduling operations. By installing this extraction mechanism, it is easy to introduce scheduling system without a lot of expense for customization. In this paper, the extraction mechanism of the master information and operator support functions are proposed through a model expressing a scheduling problem.

Ant Colony Optimization (ACO) is recently proposed as one of meta-heuristic methods for solving, hard combinatorial optimization problems.,,Job-shop Scheduling Problem (JSP) is to determine the, operation sequences on the machines for minimizing a makespan, and also has been confirmed as one of NP-hard problems.
In this paper, an ACO approach for solving JSP is presented. We propose hybrid ACO that is combined other heuristic method to find good solution. The traditional heuristic methods include shortest processing time (SPT), longest processing time (LPT), shortest remaining time (SRT), longest remaining time (LRT), longest remaining time excluding the operation under consideration (LRM) for assigning priorities to the unscheduled operations is defined. Some numerical examples are demonstrated to show the performance of JSP using hybrid ACO.

The paper describes attempts to provide a shell for building intelligent systems in consideration of a tight integration of the knowledge representation, the inference mechanism, the methodology of construction and the development environment. The Shell is called HyperAuk: Shell for building Intelligent Systems. It is a knowledge engineer's assistant tool that provides the graphical development environment to support the construction process, that includes planning, editing, debugging and testing phases.<br>HyperAuk enhances the object oriented knowledge representation auk: autonomic knowledge representation, we proposed. The methodology behind HyperAuk provides the concept of the top-down planning, the bottom-up construction and the phaseless development, and defines the sequence of its construction process. According to this concept, HyperAuk constructs the development environment to support the whole development process. This environment is also including the function of the graphical user interface construction. HyperAuk provides a uniform framework by generalization of top-down planning from the methodology view. So the environment for top-down planning is able to be used in any cases.<br>HyperAuk is now operational using Smalltalk-80, and its environment is constructed using the user interface management system Talkie, we also developed.

This paper describes user friendly construction methods for batch plant scheduling systems. Many kinds of flexibility handling many sorts, low quantity and high value products are indispensable for a plant operating scheduling system. Sorts of products are changed little by little in demand. For that, the plant may be reconfigured. In such case, the scheduling system must have an ability to customize easily by plant operators. We have already proposed a framework for an actual plant operating scheduling system. As the enhancement of this system, this paper describes a definition method for the automatic scheduling strategies based on the visual programming technology and a manual modification method for a plan that is scheduled by the automatic scheduling mechanism. This System is implemented on Objectworks_??_Smalltalk. It is applied on many actual plants, so in this paper an example is also described.

本稿では 知的システム構築用シェルAUKについて報告する.AUKは 効率のよい知的システムの開発を目的とし 知識ベースの構築 および開発されたシステムの利用において 優れた操作性を実現するという観点から設計を行ったシェルである.このシェルの開発にあたって まず オブジェクト指向知撤表現auk(autonomic knowledge unit:自律的知識単位)を提案した.この知識表現では 知識をオブジェクトとして扱い そのオブジェクトの外部インタフェースを定め 知識のモジュール性 再利用性 統合性を高めた.また 種々の表現に対応すべく 拡張が容易であるように設計した このような知識表現をベースに 知的システム構築用シェルAUKを Smalltalk-80上に開発した.AUKにおける知識ベースの構築は この知識表現の特徴を生かし グラフィカルな知識ベース開発環鏡内で インタラクティブに進めることができ 効率的な開発が可能である.