Partner Selection and Job Shop  Scheduling for Virtual Enterprises  NIU SIHONG  (B.Eng., Xi’ an Jiaotong University, P.R. China) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF MECHANICAL ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2011 ACKNOWLEDGEMENTS  I would like to express my deep gratitude to my supervisors Prof. Andrew Nee Yeh Ching and A/Prof. Ong Soh Khim for their guidance and support throughout my four-year PhD study. It will not be possible to have this thesis without their unfailing encouragement, useful discussions, constant support, and practical help to this research project. I am extremely grateful to my supervisors for their invaluable suggestions during the preparation of this thesis. I would like to take this opportunity to express my gratitude to my parents and brothers for their support and encouragement during the course of this study. There are many challenges, ups and downs in the past four years of studies, no matter what happens, they are always on the standby and encourage me, comfort me, for only a call away. Finally, I would like to thank all who have helped and inspired me during my doctoral study. I especially want to thank the National University of Singapore for providing the Research Scholarship for my research, and I am really grateful. i TABLE OF CONTENTS  ACKNOWLEDGEMENTS i NOMENCLATURE ix LIST OF ABBREVIATIONS xxi LIST OF TABLES .xxiii LIST OF FIGURES . xxiv Chapter 1 Introduction . 1.1 Partnership Selection in Virtual Enterprises . 1.2 Scheduling . 1.2.1 Objectives and Criteria in Scheduling .7 1.2.2 The Complexity of Job Shop Scheduling 1.3 Research Motivations and Objectives . 10 1.4 Research Goals and Methodologies .11 1.5 Organization of the Thesis 13 Chapter 2 An  Enhanced  Ant  Colony  Optimizer  for  Multi­attribute  Partner  ii Selection in Virtual Enterprises 15 2.1 Introduction . 15 2.2 Literature Review 17 2.3 Partner Selection Formulation 20 2.4 Analysis of Weights of the Criteria and the Qualitative Variables 27 2.4.1 Representation of the Linguistic Terms using Positive Triangular Fuzzy Numbers 28 2.4.1.1 Representation of Main Criteria .28 2.4.1.2 Evaluation of an Enterprise Against Risk and Reputation .29 2.4.2 Synthetic Evaluation and Defuzzification 31 2.5 An Enhanced ACO Solution Methodology . 36 2.5.1 Ant Colony Optimization .36 2.5.2 Enhanced ACO for Partner Selection .38 2.5.3 The Need to Improve ACO 39 2.5.3.1 Fixed Moving Sequence .40 2.5.3.2 Strong Dependence on the Parameters .40 2.5.4 Improvements of ACO .41 2.5.4.1 Generating More Dispersed Solutions .41 2.5.4.2 Modified Scheme for Updating the Trail Intensity 42 2.6 Experiments 43 2.6.1 Parameter Selection 45 2.6.2 First Experiment .46 2.6.3 Second Experiment 55 iii 2.7 Summary . 56 Chapter 3 An  Algorithm  for  Enhanced  Achieving  IWD  Optimal  Single­objective JSSP Solutions 57 3.1. Introduction 57 3.2. JSSP Formulation and Representation . 60 3.3. Overview of the Original IWD Algorithm . 63 3.4. The Enhanced IWD Algorithm, EIWD for JSSP . 66 3.4.1 Overview of EIWD Algorithm .66 3.4.2 Schemes for Improving the Original IWD Algorithm .70 3.4.3 Applying the EIWD Algorithm to JSSP 75 3.5 Experimental Evaluation . 80 Chapter 4 An  Algorithm  for  Enhanced  Achieving  IWD  Optimal  Multi­objective JSSP Solutions . 85 4.1 Introduction . 85 4.2 Literature Review on the MOJSS Problem . 87 4.3 Problem Definition 90 4.4 IWD Algorithm based on Scoring Function . 91 iv 4.4.1 Disjunctive Graph for MOJSS .92 4.4.2 Overview of the MOJSS-IWD Algorithm .92 4.4.3 Details of the MOJSS-IWD Algorithm 96 4.4.4 Pareto Non-dominated Solution Set Generating Method 102 4.5 Experimental Evaluation and Discussion . 102 4.5.1 Experimental Evaluation 102 4.5.2 Discussion 115 4.6 Summary 115 Chapter 5 Integrated  A  Multi­agent  Total  Solution  based  (MITS)  Framework  for  the  Virtual  Enterprise  Environment .117 5.1 Overview of MITS .118 5.2 MITS Level 1: An Agent Service Management Platform for VE . 120 5.3 MITS Level 2: Agent-based Approaches for Scheduling . 122 5.3.1 Three Types of Multi-agent System Architectures .123 5.3.2 Multi-agent Based Dynamic Scheduling Methodology .124 5.4 MITS Level 3: Internet-based Manufacturing Resource Availability Monitoring 126 5.5 Discussion . 129 5.6 Conclusion 131 v Chapter 6 Conclusions  and  Recommendations 132 6.1 Research Summary . 132 6.2 Contributions . 133 6.2.1 A New Approach for Solving Partner Selection Problem in VEs133 6.2.2 Better Understanding and Handling of Partner Selection in VEs133 6.2.3 An Novel Approach for Single Objective JSSP .134 6.2.4 A New Methodology to Solve the Multi-objective JSSP .134 6.2.5 Proposal of a Multi-agent based Integrated Total Solution (MITS) Framework for Virtual Enterprise Environment 135 6.3 Recommendations . 136 6.3.1 Extension of the Enhanced ACO Algorithm to More Complex Partner Selection Problems 136 6.3.2 Study the Effect of Weights and Different Types of Criteria on the Partner Selection Results .136 6.3.3 Exploring Efficient and Effective Coding and Decoding Approaches for JSSP and MOJSSP .137 6.3.4 Implementation and Validation of MITS Concept .137 Publications from this Research .138 References .139 vi ABSTRACT  In today’s global market, it is crucial for enterprises, especially the Small and Medium-size Enterprises (SME), to form a Virtual Enterprise (VE) focusing their core competencies and respond better to business opportunities. Partner selection problem is the key issue related to the success of a VE. Besides, in order to succeed in the competitive global market, fully utilizing the machining resources in the enterprise alliance as well as inside the enterprise itself is also essential, especially as the manufacturing processes become more complex, dynamic and distributed. Thus, generating effective and efficient schedules definitely has great significance. Job shop scheduling problems (JSSPs) have been studied extensively and most instances of JSSP are NP-hard, which implies that there is no polynomial time algorithm to solve them. As a result, many approximation methods have been explored to find near-optimal solutions within reasonable computational efforts. The developments in optimization methodologies and the behavior of foraging ants and water drops have inspired the current studies to select the best group of candidate enterprises to form a VE, as well as generate schedules for both single optimization objective and multiple optimization objectives to better plan the resources. The optimization mechanism for solving the partner selection problem is realized through the enhancement of an algorithm titled Ant Colony Optimization (ACO). vii Experiments have been conducted to evaluate the enhanced ACO algorithm. The results show that the enhanced ACO algorithm can obtain better results with better search accuracy and computation time. The enhanced ACO optimization algorithm can be used as a black box, where the decision maker only needs to define his/her preference through specifying the search objectives, constraints and weights to confine the search, and the algorithm can be used to obtain the optimal set of partners. The methodology for solving the single objective JSSP and multi-objective JSSP is achieved through proposing five improvement schemes for a newly developed meta-heuristic called the Intelligent Water Drops Algorithm (IWD). Experiments were carried out to identify the effectiveness and efficiency of the modified algorithm named EIWD. The experimental results show that EIWD can outperform other approaches for both the single objective JSSP and multi-objective JSSP. viii NOMENCLATURE  List of Symbols for Chapter 1: n Number of jobs m Number of machines l Number of operations Ji The ith job which will be processed on a set of machines according to technological constraints and requirements. Oij Operation Oij refers to the jth task of job J i to be performed on a particular machine. P ij Processing time P ij of an operation Oij is the time period required to process the operation Oij . di Due date di of job J i is the time by which this job should be completed. Ci The completion time Ci of job J i is the time at which the last operation of the job J i is actually completed. F The average flow time of a schedule. Fi Flow time, it is the amount of time job J i spends in the shop floor ri The job release time Cmax Makespan Cmax the time interval between the time at which ix Chapter A Multi-agent based Integrated Total Solution (MITS) Framework for the Virtual Enterprise Environment Based on sensor data processing, the dynamic status of the machines can be obtained. Machine Resource Repository Machine Agent A Machine Agent B Static Dynamic Dynamic Static Information Information Information Information Camera View Camera View Internet Machine A Camera Machine B Machine resources Camera Figure 5.5 The machine resource repository Besides sensors, cameras are used in this research. In the shop-floor, a camera is installed on each machine. The camera serves two purposes. Firstly, the images from the camera can be transmitted to the corresponding machine agent through the Internet, to allow the planners to monitor the machines via the machine agents. Secondly, the status (operating or idling) of the machine can be tracked using the camera. To detect the status of the machine, the main moving part, such as the spindle can be tracked and analyzed from the images captured by the camera. When the main spindle stops, a message will be sent to the operator. Upon receiving the message, the machine operator will check the machine to determine whether the machine has completed an operation or broken down. If the machine has broken down, the operator will update the status of the machine with the estimated repair 128 Chapter A Multi-agent based Integrated Total Solution (MITS) Framework for the Virtual Enterprise Environment time, and the changing of the machine status will trigger the rescheduling process. 5.5 Discussion  The research reported in this chapter is devoted to designing a multi-agent based, efficient and effective, integrated, total solution framework for an enterprise to select the best set of partners to form a VE in order to respond quickly to the job opportunities, as well as to coordinate and plan the resources within the VE effectively. A three-level system design, namely, VE level, member enterprise level, and job shop level is proposed. A comprehensive scheduling system for a VE environment has been formulated, and real-time information on the resource availability will be updated in the scheduling process to make the scheduling more dynamic and reactive. The features of the proposed scheduling system are: (1) Reconfigurable The proposed MITS framework is logically reconfigurable: a) The partner selection methodology is logically reconfigurable and it can be used as a black box, where the decision maker only needs to define his/her preference through specifying the search objectives, constraints and weights to confine the search, and the algorithm can be used to obtain the best set of partners. b) The optimization model developed for the multi-objective scheduling is reconfigurable by tuning the scheduling criteria, e.g., minimum completion time, lowest cost, etc. (2) Distributed scheduling The proposed multi-agent system is capable of performing distributed scheduling. 129 Chapter A Multi-agent based Integrated Total Solution (MITS) Framework for the Virtual Enterprise Environment Scheduling agents are designed for the planning of machining resources. The resources will be modeled as agents and are Internet-enabled. They can be distributed in a number of factories or even in overseas factories. The users can choose workshops in different locations, e.g., near the raw material suppliers or the final destination of the products, to form a VE. Agent technology can be deployed to address the dynamic requirements in the scheduling process. (3) Real-time information Real-time machine information can be considered in the proposed MITS framework during scheduling. The information of a machine consists of two components, namely, the static and dynamic information. Sensors will be installed on the machines to obtain certain dynamic information of the machines, such as cutting forces, vibration, etc. Machine maintenance schedules, tool-life and cutting force monitoring will either be obtained directly from the sensors or modeled based on historical data. Such information will be very useful to allow real-time schedules to be planned and the allocation of the machining resources to machine the parts. Assuming all the specifications are met, which include the static and dynamic information of the machines, schedules can be generated according to different criteria, such as the minimum cost or shortest delivery time. When the planner generates a schedule, the dynamic information of a machine is taken into consideration at that time instant. This aspect aims to make the schedule highly robust and reactive since studying and monitoring the main dynamic events is more useful than increasing the complexity of the optimization algorithms. 130 Chapter A Multi-agent based Integrated Total Solution (MITS) Framework for the Virtual Enterprise Environment (4) Real-time re-scheduling The proposed MITS system can operate in real-time. It is capable of generating and re-generating schedules in real-time in order to respond to dynamic events and disturbances quickly. In this research, schedules are generated as and when they are needed according to changes in the manufacturing system. The scheduling system considers the machine status and the jobs status, and re-scheduling is evoked when disturbances occur. 5.6 Conclusion  This chapter presents a framework called MITS, where an agent service management platform for VE environment, a multi-agent based scheduling methodology for the scheduling processes in the VE, real time machine monitoring are integrated. MITS is proposed to be developed based on the Internet and agent technologies, consists of three levels, namely, the VE level, the member enterprise level and the job shop level. The agent service management platform allows the dominant enterprise to select potential partners, and check and coordinate the progress of each member enterprise. For each member enterprise, multi-agent based scheduling is carried out to generate feasible schedules for the jobs obtained after the bidding process. In each job shop, online monitoring is conducted to obtain the real-time status of the machines, and this machine information is provided to the scheduling system to generate the schedules. 131 Chapter Conclusions and Recommendations Chapter 6   Conclusions and Recommendations  This chapter first summarizes the research in this thesis in Section 6.1. Section 6.2 highlights the contributions and the conclusions made in the previous chapters. Finally, recommendations are outlined in Section 6.3 6.1 Research Summary  This thesis aims to solve three tightly related problems which need high attention especially as today’s global market requires high production and lower cost, etc. The thesis first presents a general background of VE, partner selection involved in the VE formation process, as well as job shop scheduling and the objectives and criteria in scheduling. The state-of-the-art of partner selection, single objective job shop scheduling and multiple objective scheduling are reviewed. Thereafter, methodologies to solve these issues are explained in detail. An enhanced ACO is proposed and applied to solve the partner selection in VE, and a new type of meta-heuristics, IWD, is improved to solve the single objective and multi-objective JSSP respectively. Extensive experiments have been carried out to validate the effectiveness and efficiency of the proposed methodologies in solving the partner selection problem, single objective job shop scheduling problem and multiple objective scheduling problems. A multi-agent based integrated total 132 Chapter Conclusions and Recommendations solution framework is proposed and discussed to encapsulate the partner selection, single and multiple objective job shop scheduling issues. 6.2 Contributions    A number of original contributions are achieved in this thesis. 6.2.1 A New Approach for Solving Partner Selection Problem  in VEs  A novel approach, namely, an enhanced ACO for multi-attribute partner selection in VE has been proposed in this research, and the details are presented in Chapter 2. ACO is a general optimizer and it can be applied to many optimization problems. In this research, it is applied to the partner selection problem through controlling the way each ant travels. The enhanced ACO takes advantage of its own strengths with special requirements. Experiments have been conducted to evaluate the enhanced ACO algorithm. The results show that the enhanced ACO algorithm can obtain better results with better search accuracy and computation time. 6.2.2 Better Understanding and Handling of Partner Selection  in VEs    Focusing on the VE creation phase, a general and flexible process to select partners for different partner selection scenarios is presented in this research. Five aspects are considered to evaluate the candidates, namely, time, cost, quality, 133 Chapter Conclusions and Recommendations reputation and risk. Both the qualitative objectives and quantitative objectives in multi-objective decision making process are considered. Fuzzy set theories are employed to obtain the weights of the criteria, and the linguistics and fuzzy numbers are used to transform the qualitative aspects into numerical numbers used in the optimization model. The decision maker can tune the criteria, objectives and constraints of the project to obtain a satisfactory solution according to his/her preference. 6.2.3 An Novel Approach for Single Objective JSSP    The IWD algorithm, which is a new meta-heuristics, is customized for solving JSSP. Five schemes are proposed to improve the original IWD algorithm and the improved algorithm is named the Enhanced IWD (EIWD) algorithm. The optimization objective is the makespan of the schedule. Experimental results show that the EIWD algorithm is able to find better solutions for the standard benchmark instances than the existing algorithms. This approach has made a contribution in two aspects: (1) To the best of the author’s knowledge, this research is the first to apply the IWD algorithm to JSSP. This work can inspire further studies of applying the IWD algorithm to other scheduling problems, such as open shop scheduling and flow shop scheduling; and (2) This research further improves the original IWD algorithm by employing five schemes to increase the diversity of the solution space as well as the solution quality. 6.2.4 A New Methodology to Solve the Multi­objective JSSP    The original IWD algorithm is also improved and customized to solve the 134 Chapter Conclusions and Recommendations multi-objective JSSP. The modified algorithm is called MOJSS-IWD in which a scoring function and a Pareto schedule checking process are embedded. The optimization objective is to find the best compromise solutions considering the makespan, the tardiness and mean flow time of the schedule. In MOJSS-IWD, a new approach of generating Pareto non-dominated set is proposed. Experimental results show that the MOJSS-IWD algorithm is able to identify the Pareto non-dominated solution set. Compared with PASA algorithm, MOJSS-IWD can find better results in general considering a more challenging issue is studied. The optimization model developed for the multi-objective scheduling in this thesis is reconfigurable by tuning the scheduling criteria, e.g., minimum completion time, lowest cost, etc. 6.2.5 Proposal  of  a  Multi­agent  based  Integrated  Total  Solution  (MITS)  Framework  for  Virtual  Enterprise  Environment    MITS is proposed and presented. It encapsulates the solution methodologies for partnership selection, single and multiple job shop scheduling problems to help an enterprise to select the best set of partners, and better plan the resources within the VE established through the auction and bidding process. An agent service management system, a comprehensive scheduling system for each member enterprise, and real-time resource availability in the scheduling process to make scheduling more dynamic and reactive are discussed. 135 Chapter Conclusions and Recommendations 6.3 Recommendations  A number of areas can be explored to improve the contributions made in this research. 6.3.1 Extension  of  the  Enhanced  ACO  Algorithm  to  More  Complex Partner Selection Problems  The life cycle of a VE is short, and rapid configuration is a feature of this kind of alliance. The research in this thesis focus on the VE creation phase in its life cycle and many complicated situations exist when the enterprises form an alliance. For instance, the candidate enterprises considered in this research are independently bidding for the sub-projects with only availability affects their bidding in the auction and bidding process, and the prices for each sub-project are fixed. However, in real situations, one candidate enterprise may bid for more than one sub-projects and offer different price packages for different combination of sub-projects. The proposed approach in Chapter can be further improved and employed to study these more challenging and complicated scenarios. 6.3.2 Study  the  Effect  of  Weights  and  Different  Types  of  Criteria on the Partner Selection Results  In the partnership selection process, weights are employed to indicate the relative importance of one criterion over another. For the same set of data evaluating the enterprises, changing of the weights does not influence the search efficiency; however it does affect the search results. When the initial data to evaluate the 136 Chapter Conclusions and Recommendations enterprise changes, the search result is expected to change, for example, if more qualitative data is involved, it needs more time to generate the finial results as additional time is required to process the initial data. More research work can be done to explore how the types of criteria influence the partner selection outcome, and the study along this direction can definitely provide an insight to this problem. 6.3.3 Exploring Efficient and Effective Coding and Decoding  Approaches for JSSP and MOJSSP  JSSP and MOJSSP are typical NP-hard optimization problems and they can be solved by customizing general optimizer IWD. Coding and decoding is extensively involved in each cycle wherever a solution is obtained, and they play an important role by affecting the performance of the optimization algorithm. Efficient and effective approaches to code and decode the JSSP and MOSSP problems can greatly improve the overall performance of the proposed solution methodology EIWD and MOJSSP-IWD. Further studies can be carried out to explore better ways of coding and decoding to be employed in the solution process. 6.3.4 Implementation and Validation of MITS Concept  MITS is a novel concept of generating an integrated total solution for enterprises, however it is a concept without implementation at this stage. Future studies can be carried out to implement this agent-based system, and find massive data to test this system. The data can either be real company data or generated using computers. The research on this issue is considered to be meaningful. 137 Publications from this Research Publications from this Research  1) Niu, S.H., Ong, S.K. & Nee, A.Y.C. 2009. A multi-agent based dynamic scheduling framework for virtual enterprises. 4th International Conference on Advanced Research in Virtual and Rapid Prototyping (VR@P). Polytechnic Institute of Leiria, Leiria: Taylor & Francis, 587-593. 2) Niu, S.H., Ong, S.K. & Nee, A.Y.C. 2011. A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-Attribute Partnership Selection in Virtual Enterprises, In Evolutionary Computing In Advanced Manufacturing, Ed. M K Tiwari and J Harding, Scrivener Publishing LLC., ISBN: 978-0-470-63924-5, 289-326. 3) Niu, S.H., Ong, S.K. & Nee, A.Y.C. May 2010. An enhanced ant colony optimizer for multi-attribute partner selection in virtual enterprises. International Journal of Production Research. 4) Niu, S.H., Ong, S.K. & Nee, A.Y.C. Feb 2011. An Improved Intelligent Water Drops Algorithm for Achieving Optimal Job Shop Scheduling Solutions, International Journal of Production Research. 5) Niu, S.H., Ong, S.K. & Nee, A.Y.C. An improved Intelligent Water Drops algorithm for achieving multi-objective job shop scheduling solutions (Under review). 6) Niu, S.H., Ong, S.K. & Nee, A.Y.C. Improved Intelligent Water Drops Optimization for Single and Multiple Objective Job Shop Scheduling (Under review). 138 References References  Adibi, M.A., Zandieh, M. & Amiri, M., 2010. Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Systems with Applications, 37 (1), 282-287. Aytug, H., Lawley, M.A., Mckay, K., Mohan, S. & Uzsoy, R., 2005. Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161 (1), 86-110. Balas, E., 1969. Machine sequencing via disjunctive graphs: An implicit enumeration algorithm. Operations Research, 17 (6), 941-957. Balas, E. & Vazacopoulos, A., 1998. Guided local search with shifting bottleneck for job shop scheduling. Management Science, 44 (2), 262-274. Beasley, J.E., 1990. Or-library: Distributing test problems by electronic mail. The Journal of the Operational Research Society, 41 (11), 1069-1072. Blum, C. & Sampels, M., 2004. An ant colony optimization algorithm for shop scheduling problems. Journal of Mathematical Modelling and Algorithms, 3, 285-308. Bu, Y., Zhou, W. & Yu, J., 2008. A discrete pso algorithm for partner selection of virtual enterprise. The 2nd International Symposium on Intelligent Information Technology Application. Shanghai, China: IEEE, 814-817. Buyukozkan, G., 2004. Multi-criteria decision making for e-marketplace selection. Internet Research: Electronic Networking Applications and Policy, 14 (2), 139-154. Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C.W. & Choy, K.L., 2008. Global supplier selection: A fuzzy-ahp approach. International Journal of Production Research, 46 (14), 3825-3857. Chang, D.-Y., 1996. Applications of the extent analysis method on fuzzy ahp. European Journal of Operational Research, 95 (3), 649-655. Che, Z.H., 2010. Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering weee/rohs directives. International Journal of Production Research, 48 (11), 3355-3381. Chen, S.-H., Lee, H.-T. & Wu, Y.-F., 2008. Applying ANP approach to partner selection for strategic alliance. Management Decision, 46 (3), 449-65. Cheng, T.C.E. & Gupta, M.C., 1989. Survey of scheduling research involving due date determination decisions. European Journal of Operational Research, 38 (2), 156-166. Choi, K.H., Kim, S.C. & Yook, S.H., 2000. Multi-agent hybrid shop floor controls system. International Journal of Production Research, 38 (17), 4193-4203. Chryssolouris, G., 2006. Manufacturing systems, theory and practice New York: Springer-Verlag. Colorni, A., Dorigo, M. & Maniezzo, V., 1992. Distributed optimization by ant colonies. In Varela, F.J. & Bourgine, P. eds. Proceedings of the 1st european conference on artificial life. Paris, France: MIT Press, 134-142. Crispim, J.A. & De Sousa, J.P., 2010. Partner selection in virtual enterprises. International Journal of Production Research, 48 (3), 683-707. 139 References Crispim, J.A. & Pinho De Sousa, J., 2009. Partner selection in virtual enterprises: A multi-criteria decision support approach. International Journal of Production Research, 47 (17), 4791 - 4812. De Giovanni, L. & Pezzella, F., 2010. An improved genetic algorithm for the distributed and flexible job-shop scheduling problem. European Journal of Operational Research, 200 (2), 395-408. Dorigo, M., Birattari, M. & Stutzle, T., 2006. Ant colony optimization: Artificial ants as computational intelligence technique. IEEE Computational Intelligence Magazine, (4), 28-39. Dorigo, M., Di Caro, G. & Gambardella, L.M., 1999. Ant algorithms for discrete optimization. Artificial Life, (2), 137-172. Dorigo, M., Maniezzo, V. & Colorni, A., 1996. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26 (1), 29-41. Drissen-Silva, M.V. & Rabelo, R.J., 2009. A collaborative decision support framework for managing the evolution of virtual enterprises. International Journal of Production Research, 47 (17), 4833 - 4854. Duan, H., Liu, S. & Lei, X., 2008. Air robot path planning based on intelligent water drops optimization. 2008 International Joint Conference on Neural Networks, IJCNN 2008. Hong Kong, China: Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ, United States, 1397-1401. Duan, H., Liu, S. & Wu, J., 2009. Novel intelligent water drops optimization approach to single ucav smooth trajectory planning. Aerospace Science and Technology, 13 (8), 442-449. Famuyiwa, O., Monplaisir, L. & Nepal, B., 2008. An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation. International Journal of Production Economics, 113 (2), 862-875. Fischer, M., Jahn, H. & Teich, T., 2004. Optimizing the selection of partners in production networks. Robotics and Computer-Integrated Manufacturing, 20 (6), 593-601. Fisher, H. & Thompson, G.L., 1963. Industrial scheduling Englewood Cliffs, NJ: Prentice-Hall. Garey, M.R., Johnson, D.S. & Sethi, R., 1976. The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, (2), 117-129. Ge, H.-W., Liang, S., Liang, Y.-C. & Qian, F., 2008. An effective pso and ais-based hybrid intelligent algorithm for job-shop scheduling. IEEE Transactions on Systems, Man and Cybernetics,Systems and Humans, Part A, 38 (2), 358-368. Giffler, B. & Thompson, L., 1960. Algorithms for solving production-scheduling problems. Operations Research, (4), 487-503. Gonçalves, J.F., Mendes, J.J.D.M. & Resende, M.G.C., 2005. A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167 (1), 77-95. Hajidimitriou, Y.A. & Georgiou, A.C., 2002. A goal programming model for partner selection decisions in international joint ventures. European Journal of Operational Research, 138 (3), 649-662. Hartmann, S. & Kolisch, R., 2000. Experimental evaluation of state-of-the-art heuristics for the 140 References resource-constrained project scheduling problem. European Journal of Operational Research, 127 (2), 394-407. Hong, Z., Waiman, C. & Leung, L.C., 2009. Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm. European Journal of Operational Research, 194 (3), 637-49. Ip, W.H., Huang, M., Yung, K.L. & Wang, D., 2003. Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise. Computers and Operations Research, 30 (2), 213-231. Ip, W.H., Yung, K.L. & Dingwei, W., 2004. A branch and bound algorithm for sub-contractor selection in agile manufacturing environment. International Journal of Production Economics, 87 (2), 195-205. Jarimo, T. & Salo, A., 2009. Multicriteria partner selection in virtual organizations with transportation costs and other network interdependencies. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 39 (1), 124-129. Khoo, L.P., Lee, S.G. & Yin, X.F., 2001. Agent-based multiple shop floor manufacturing scheduler. International Journal of Production Research, 39 (14), 3023-3040. Kis, T., 2003. Job-shop scheduling with processing alternatives. . European Journal of Operational Research, 151 (2), 307-332. Lawrence, S., 1984. Resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques. Thesis (PhD). Carnegie Mellon University. Leung, D.C.W., 2006. An agent-based approach for integrating process planning and scheduling. University of Hong Kong. Lin, T.-L., Horng, S.-J., Kao, T.-W., Chen, Y.-H., Run, R.-S., Chen, R.-J., Lai, J.-L. & Kuo, I.H., 2010. An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37 (3), 2629-2636. Nowicki, E. & Smutnicki, C., 1996. A fast taboo search algorithm for the job shop problem. Management Science, 42 (6), 797-813. Pan, J.C.H. & Han-Chiang, H., 2009. A hybrid genetic algorithm for no-wait job shop scheduling problems. Expert Systems with Applications, 36 (3), 5800-6. Percin, S., 2008. Use of fuzzy ahp for evaluating the benefits of information-sharing decisions in a supply chain. Journal of Enterprise Information Management, 21 (3), 263-284. Pezzella, F. & Merelli, E., 2000. A tabu search method guided by shifting bottleneck for the job shop scheduling problem. European Journal of Operational Research, 120 (2), 297-310. Reaidy, J., Diep, D. & Massotte, P., 2003. Management and control of complex production systems: Co-opetition through game theory principles and agents based information systems. IEEE International Conference on Industrial Informatics. Alberta, Canada, 217 - 223 Reza, H.S. & Saghafian, S., 2005. Flowshop-scheduling problems with makespan criterion: A review. international journal of production research, 43 (14), 2895-2929. Sari, B., Sen, T. & Kilic, S.E., 2008. Ahp model for the selection of partner 141 References companies in virtual enterprises. International Journal of Advanced Manufacturing Technology, 38 (3-4), 367-376. Seo, M. & Kim, D., 2010. Ant colony optimisation with parameterised search space for the job shop scheduling problem. International Journal of Production Research, 48 (4), 1143-1154. Sha, D.Y. & Lin, H.-H., 2010. A multi-objective pso for job-shop scheduling problems. Expert Systems with Applications, 37 (2), 1065-1070. Shah-Hosseini, H., 2007. Problem solving by intelligent water drops. IEEE Congress on Evolutionary Computation, CEC 2007. Singapore: Institute of Electrical and Electronics Computer Society, Piscataway, NJ, United States, 3226-3231. Shah-Hosseini, H., 2009a. The intelligent water drops algorithm: A nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation, (1-2), 71-79. Shah-Hosseini, H., 2009b. Optimization with the nature-inspired intelligent water drops algorithm. In Santos, W.P.D. ed. Evolutionary computation. Tech, Vienna, Austria, 297-320. Shen, W., 2002. Distributed manufacturing scheduling using intelligent agents. IEEE Intelligent Systems, 17 (1), 88-94. Shen, W., Hao, Q., Yoon, H.J. & D.H., N., 2006. Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, 20 (4), 415-431 Suresh, R.K. & Mohanasundaram, K.M., 2006. Pareto archived simulated annealing for job shop scheduling with multiple objectives. International Journal of Advanced Manufacturing Technology, 29 (1-2), 184-96. Timothy, J.R., 2010. Fuzzy logic with engineering applications, 3rd ed. Hoboken, New Jersey: John Wiley and Sons. Vilcot, G. & Billaut, J.C., 2008. A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. European Journal of Operational Research, 190 (2), 398-411. Wang, T.C. & Chen, Y.H., 2007. Applying consistent fuzzy preference relations to partnership selection. Omega, 35 (4), 384-8. Wang, Z.J., Xu, X.F. & Zhan, D.C., 2009. Genetic algorithm for collaboration cost optimization-oriented partner selection in virtual enterprises. International Journal of Production Research, 47 (4), 859-881. Wu, N. & Su, P., 2005. Selection of partners in virtual enterprise paradigm. Robotics and Computer-Integrated Manufacturing, 21 (2), 119-131. Yager, R.R., 1980. On a general class of fuzzy connectives. Fuzzy Sets and Systems, (3), 235-242. Yamada, T., 2003. Studies on metaheuristics for jobshop and flowshop scheduling problems. Kyoto university. Yao, Z., Pan, R. & Lai, F., 2009. Improvement of the fusing genetic algorithm and ant colony algorithm in virtual enterprise partner selection problem. In Burgin, M., Chowdhury, M.H., Ham, C.H., Ludwig, S., Su, W. & Yenduri, S. eds. 2009 World Congress on Computer Science and Information Engineering. Los Angeles, CA, United states: IEEE Computer Society, 242-246. Ye, F. & Li, Y.-N., 2009. Group multi-attribute decision model to partner selection 142 References in the formation of virtual enterprise under incomplete information. Expert Systems with Applications, 36 (5), 9350-9357. Zadeh, L.A., 1965. Fuzzy sets. Information and control, (3), 338-353. Zeng, Z.-B., Li, Y. & Zhu, W., 2006. Partner selection with a due date constraint in virtual enterprises. Applied Mathematics and Computation (New York), 175 (2), 1353-1365. Zhang, D.Z., Anosike, A. & Lim, M.K., 2007. Dynamically integrated manufacturing systems (dims) - a multi-agent approach. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 37 (5), 824-850. Zhang, G., Shao, X., Li, P. & Gao, L., 2009. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 56 (4), 1309-1318. Zhao, Q., Zhang, X. & Xiao, R., 2008. Particle swarm optimization algorithm for partner selection in virtual enterprise. Progress in Natural Science, 18 (11), 1445-1452. Zimmermann, H.-J., 1991. Fuzzy set theory and its applications 2ed. Boston Kluwer Academic Publishers. 143 [...]... when early jobs do not bring any rewards and penalties are incurred for the late jobs Number of tardy jobs ( nT ) This is the number of jobs that are not completed by their corresponding due dates 1.2.2 The Complexity of Job Shop Scheduling In job shops, the flow of raw materials and unfinished goods are random Job shop scheduling is often referred to as production scheduling It is difficult and time-consuming... according to the process they perform Figure 1.2 Schematics of a job shop (Chryssolouris 2006) There are many advantages of job shop processing and these advantages become more obvious when there is greater variety in the jobs and these jobs have different processing sequences This research focuses on job shop scheduling The advantages of job shop scheduling are as follows: (1) Each operation can be assigned... Number of Cycles List of abbreviations for Chapter 3 JSSP Job Shop Scheduling Problem SA Simulated Annealing SB Shifting Bottleneck TSP Travelling Salesman Problem BKS Best Known Solutions List of abbreviations for Chapter 4 MOJSSP Multi-Objective Job Shop Scheduling Problem List of abbreviations for Chapter 5 OMA Overall Monitoring Agent JSA Job Shop Agent JA Job Agent MA Machine Agent xxii LIST OF TABLES ... evolved from flexible job shop scheduling to multi-mode job shop scheduling and multi-resource shop scheduling (Kis 2003) When all these issues are considered in the scheduling problem, the solution space would become very large The scheduling problem is proven to be typically NP-hard; the computation time increases exponentially with the problem size It is time-consuming to search for an optimal solution... (10 ants, 130 candidates) 48 Figure 2.8 Performance of the enhanced ACO (10 ants, 130 candidates) 49 Figure 2.9 Performance of the enhanced ACO (10 ants, 130 candidates) 50 Figure 2.10 Performance of the original ACO (20 ants, 130 candidates) 51 Figure 2.11 Performance of the enhanced ACO (20 ants, 130 candidates) 52 Figure 2.12 Performance of the original ACO (30 ants, 130 candidates ) 53... systems (Cheng and Gupta 1989) Scheduling plays an important role in the manufacturing realm It can be used by high level production planning systems to check their capacity; it also provides visibility of future plans in the job shops for the suppliers and customers to adjust their actions; it can be -4- Chapter 1 Introduction used to evaluate the performance of job shop personnel and management;... mode One lot of jobs refers to a batch of jobs that are simultaneously released to a manufacturing shop floor and the lot size directly affects the inventory and the scheduling Normally, job shops are most suitable for small lot size production (Chryssolouris 2006) Raw material A A D D A A D D C C D D C C C C Ready part Machines/Resources are grouped according to the process they perform Figure 1.2... of each candidate enterprise ctij The cost indicator to choose candidate j for sub-project pi CO The operational cost of candidate enterprise CD The delivery cost of candidate enterprise CM The material cost of the candidate enterprise CL The logistic overhead of the candidate enterprise TE The time related criteria of the candidate enterprise teij The ‘time’ indicator to choose candidate j for sub-project... 2.13 Performance of enhanced ACO (30 ants, 130 candidates ) 54 Figure 2.14 Effect of number of ants on the convergence speed (130 candidates)54 Figure 2.15 Effect of number of candidates on the convergence speed (20 ants) 55 Figure 3.1 Disjunctive graph for the 3×3 job described in Table 3.1 and Table 3.2 63 Figure 3.2 Modified disjunctive graph for the 3×3 job in Table 3.1 and Table... graph for the 3×3 job in Table 3.1 and Table 3.2 (dash edges for O22 ) 77 Figure 3.4 Flow chart of EIWD algorithm for JSSP 79 Figure 4.1 Flowchart of the MOJSS-IWD algorithm 95 Figure 5.1 The three-level design of dynamic scheduling system for a VE 119 Figure 5.2 The service management platform for a VE 121 Figure 5.3 The events sequence of agent service management platform . 1.1 Partnership Selection in Virtual Enterprises 1 1.2 Scheduling 4 1.2.1 Objectives and Criteria in Scheduling 7 1.2.2 The Complexity of Job Shop Scheduling 9 1.3 Research Motivations and. Partner Selection and Job Shop Scheduling for Virtual Enterprises NIUSIHONG (B.Eng., Xi’ an Jiaotong University, P.R. China) A THESIS SUBMITTED FOR THE DEGREE. Conclusions and Recommendat ions132 6.1 Research Summary 132 6.2 Contributions 133 6.2.1 A New Approach for Solving Partner Selection Problem in VEs133 6.2.2 Better Understanding and Handling of Partner