Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
An optimally designed of batch-storage network that uses a periodic square wave model provides analytical lot-sizing equations for a complex supply chain network characterized as a multisupplier, multiproduct, multi-stage, nonserial, multicustomer, cyclic system, with recycling or remanufacturing. The network structure includes multiple currency flows and material flows. The processes involve multiple feedstock and product materials with fixed compositions that are highly suitable for production processes. Transportation processes that carry multiple materials of unknown composition are included in this study, and the time frame is varied from a single infinite period to multiple finite periods to accommodate nonperiodic parameter variations. The objective function in the optimization is chosen to minimize the difference between the opportunity costs of currency/material inventories and stockholder benefits given in the numeraire currency. Expressions for the Kuhn-Tucker conditions for the optimization problem are reduced to a multiperiod subproblem describing the average flow rates and the analytical lot-sizing equations. The multiperiod lot-sizing equations are shown to differ from their single-period counterparts. The multiperiod subproblem yields a multiperiod planning model that has many advantages over existing planning models. For example, it contains terms that represent operation frequency dependent costs. Realistically sized numerical examples that deal with multinational corporations are formulated and tested. The effects of corporate income taxes, interest rates, and exchange rates are presented.
An optimally designed of batch-storage network that uses a periodic square wave model provides analytical lot-sizing equations for a complex supply chain network characterized as a multisupplier, multiproduct, multi-stage, nonserial, multicustomer, cyclic system, with recycling or remanufacturing. The network structure includes multiple currency flows and material flows. The processes involve multiple feedstock and product materials with fixed compositions that are highly suitable for production processes. Transportation processes that carry multiple materials of unknown composition are included in this study, and the time frame is varied from a single infinite period to multiple finite periods to accommodate nonperiodic parameter variations. The objective function in the optimization is chosen to minimize the difference between the opportunity costs of currency/material inventories and stockholder benefits given in the numeraire currency. Expressions for the Kuhn-Tucker conditions for the optimization problem are reduced to a multiperiod subproblem describing the average flow rates and the analytical lot-sizing equations. The multiperiod lot-sizing equations are shown to differ from their single-period counterparts. The multiperiod subproblem yields a multiperiod planning model that has many advantages over existing planning models. For example, it contains terms that represent operation frequency dependent costs. Realistically sized numerical examples that deal with multinational corporations are formulated and tested. The effects of corporate income taxes, interest rates, and exchange rates are presented.
The article contains sections titled: 1. Introduction 1.1. The Planning/Scheduling Problem 1.1.1. Enterprise‐Wide Long‐Term or Strategic Planning 1.1.2. Short‐Term Production Scheduling 1.2. Current State of Integrated Management of Process Operations 1.2.1. Corporate Finances and International Issues 1.2.2. Product Development 1.2.3. Environmental Management 1.2.4. Sales and Marketing 1.2.5. Decision‐Making under Uncertainty 1.2.5.1. Reactive Approaches 1.2.5.2. Preventive Approaches 2. Process Planning and Scheduling 2.1. Resource Planning 2.1.1. Structure of the Production Facility 2.1.2. Mode of Operation 2.1.3. Inventory Policy 2.1.4. Resources Availability 2.1.5. Structure of Demand 2.1.6. Planning Horizon 2.1.7. Performance Index 2.2. Planning of New Product Development 2.3. Planning Problem Solution Approaches 2.3.1. Hierarchical Decomposition 2.3.2. Rolling Horizon Solution Strategy 2.3.3. Enumeration Procedures 2.4. Production Planning for Parallel Multiproduct Plants 2.4.1. Solution Strategy 2.4.2. Optimization Procedure 2.4.3. Industrial Applications 2.4.3.1. The Pigment Factory 2.4.3.2. Textile Production 2.5. Single‐Site Production Scheduling 2.5.1. Scheduling Requirements for Industrial Problems 2.5.2. Mathematical Models 2.6. Operation Under Uncertainty 2.6.1. Generation of Robust Schedules 2.6.2. Preventive Maintenance 2.6.3. Simultaneous Production and Maintenance Tasks Scheduling 2.6.4. Flexible Schedules 2.6.4.1. Mathematical Formulation 2.6.4.2. Processing Unit Allocation Constraints 2.6.4.3. Flexible Recipe Model 2.6.4.4. Recipe Flexibility Region 2.6.4.5. Associated Cost of Deviations from Nominal Conditions 2.6.4.6. Lower Bound on the Start Time of the Tasks 2.6.4.7. Duration of Tasks 2.6.4.8. Duration of the First Tasks 2.6.4.9. Sequencing Constraints 2.6.4.10. Tardiness and Earliness 2.6.4.11. Problem Objective Function 2.6.4.12. Illustrative Example 2.7. Heuristic/Stochastic Approaches 2.8. Software Support Tools 2.8.1. Planning 2.8.2. Scheduling 2.8.2.1. gBBS 2.8.2.2. Virtecs 2.8.2.3. BOLD 2.9. Benefits and Challenges of Scheduling/Planning Applications 2.10. Nomenclature 2.10.1. Scheduling 2.10.2. Flexible Schedules 3. Supply Chain Management 3.1. Supply Chain Modeling 3.1.1. Organizational Structure 3.1.2. Model Elements 3.1.2.1. SC Drivers 3.1.2.2. SC Decisions 3.1.2.3. SC Constraints 3.2. Supply Chain Operations Strategic and Tactical Issues 3.2.1. Operations Model 3.2.1.1. Traditional Design‐Planning of Supply Chain Networks 3.2.1.2. Flexible Design‐Planning of Supply Chain Networks 3.2.2. Economic Performance Indicator 3.2.3. Mapping Environmental Impacts within SCM 3.3. Treatment of Uncertainty 3.4. Detailed Scheduling Considerations in SC Design 3.5. Illustrative Example 3.5.1. The Design Problem 3.5.2. Testing Solutions Using the MPC Framework 3.5.3. Consideration of Failures 3.6. Supporting Software 3.7. Nomenclature 3.7.1. Traditional Design Planning of Supply Chain Networks 3.7.2. Flexible Design and Planning of Supply Chain Networks 3.7.3. Mapping Environmental (Additional Nomenclature) 3.7.4. Treatment of Uncertainty 3.7.5. Scheduling Consideration in SC Design 4. Conclusions and Future Directions 5. Acknowledgments
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.