Belief-Rule Inference Method And Its Applications In Inventory And Production Operations Management

With the rapid development of information and manufacturing technologies andgradually shortened product life cycle, inventory and production operations management inenterprise and supply chain management are facing more and more uncertainties, in whichthe biggest source is demand uncertianty. It has become a hot spot nowadays whichattracted much attention from researchers and practitioners to systematically analyze andsolve inventory and production operations management problems under uncertainties. Theanalytical method and simulation method have limitations with respects to computationalcomplexity, portability, and the expression ability of domain knowledge and uncertaininformation. Heuristic methods can effectively use expert domain knowledge and aresuitable for complex, changing and resource constrained environment, especially theartificial intelligence based heuristic methods have favorable capability to express andtransact uncertainty. Facing dynamically changing inventory and production operationalenvironments with frequent replanning and interconnected resource constraints, researcherand practitioner more and more tend to use AI based heuristic methods.As an AI based heuristic method, belief-rule-based inference (BRBI) method isproposed based on evidential reasoning theory and production rule based expert system fordealing with uncertain problems. This thesis studies deeply the BRBI method, and applies itto inventory and production operations management problems under uncertainty. The mainresearch works are as follows:This thesis introduces the foundation of BRBI theory, outlines the application modesand application mechanisms of BRBI method, and proposes a BRBI approach with intervaluncertain inputs. The application modes of the BRBI model are classified into systemapproximator and system controller. The application methanisms provide the key elimentsof the BRBI model, which has instruction and support functions to improvement andapplication of the BRBI method.For inventory control problem under nonstationary uncertain demand, abelief-rule-based inventory control (BRB-IC) method is proposed considering bothbackorder case and lost sales case. An optimal base stock policy under normal forecast erroris proved as a quantitative expert knowledge to initialize the belief-rule-base. A numericalexample and an auto4S store case study are provided to examine the BRB-IC method bycomparison with existing adaptive method, heuristic method, myopic method,certainty-equivalent-control, and robust optimization.For structure and parameter identifications of the belief-rule-base, a simultaneous identification approach and an asynchronous identification approach are developed andcompared. In the asynchronous approach, a belief K-means clustering algorithm is putforward for structure identification. For aggregate production planning (APP) underuncertain demand, a hierarchical BRBI method for APP is proposed including bothcontinuous and switching modes. The simultaneous identification approach andasynchronous identification approach are applied to identify the structure and parameter ofBRB. An automotive case study and the Pittsburgh paint factory example are provided. TheBRBI method is compared with nonlinear interval number programming, linear decisionrule and production switching heuristic. The sensitivity of BRBI method is analyzed underdifferent cost structures.For belief-rule-inference model under interval inputs, structure identification andparameter identification models in the asynchronous approach, corresponding genetic-conjugate gradient algorithms are proposed and applied into centralized supply chainmanagement problem under uncertain demand. The producer and distributor’s hierarchicalBRBI framework is constructed and their inference models and order policies are provided.In an automotive case study, the superiority of the genetic-conjugate gradient algorithmsover existing algorithms are provided, and the BRBI method is compared with the robustoptimization method.