Shah / Mittal Optimization and Inventory Management

1. Auflage 2019
ISBN: 978-981-13-9698-4
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 470 Seiten

Reihe: Business and Management (R0)

This book discusses inventory models for determining optimal ordering policies using various optimization techniques, genetic algorithms, and data mining concepts. It also provides sensitivity analyses for the models’ robustness. It presents a collection of mathematical models that deal with real industry scenarios. All mathematical model solutions are provided with the help of various optimization techniques to determine optimal ordering policy. 

The book offers a range of perspectives on the implementation of optimization techniques, inflation, trade credit financing, fuzzy systems, human error, learning in production, inspection, green supply chains, closed supply chains, reworks, game theory approaches, genetic algorithms, and data mining, as well as research on big data applications for inventory management and control. Starting from deterministic inventory models, the book moves towards advanced inventory models. 

The content is divided into eight major sections: inventory control and management – inventory models with trade credit financing for imperfect quality items; environmental impact on ordering policies; impact of learning on the supply chain models; EOQ models considering warehousing; optimal ordering policies with data mining and PSO techniques; supply chain models in fuzzy environments; optimal production models for multi-items and multi-retailers; and a marketing model to understand buying behaviour. Given its scope, the book offers a valuable resource for practitioners, instructors, students and researchers alike. It also offers essential insights to help retailers/managers improve business functions and make more accurate and realistic decisions.

Autoren/Hrsg.

Shah, Nita H.

Mittal, Mandeep

Inhaltsverzeichnis

1;Contents;6
2;About the Editors;9
3;1 Economic Production Quantity (EPQ) Inventory Model for a Deteriorating Item with a Two-Level Trade Credit Policy and Allowable Shortages;10
3.1;1.1 Introduction;10
3.2;1.2 Suppositions and Notation;14
3.2.1;1.2.1 Suppositions;14
3.2.2;1.2.2 Notation;14
3.3;1.3 Inventory Model Formulation;15
3.4;1.4 Numerical Examples;23
3.5;1.5 Sensitivity Analysis;23
3.6;1.6 Conclusion;27
3.7;References;27
4;2 An Economic Order Quantity (EOQ) Inventory Model for a Deteriorating Item with Interval-Valued Inventory Costs, Price-Dependent Demand, Two-Level Credit Policy, and Shortages;29
4.1;2.1 Introduction;30
4.2;2.2 Suppositions and Notations;33
4.2.1;2.2.1 Suppositions;33
4.2.2;2.2.2 Notations;33
4.3;2.3 Mathematical Derivation of the Inventory Model;34
4.4;2.4 The Solution for Three Demand Functions;42
4.5;2.5 Numerical Examples;50
4.6;2.6 Sensitivity Analysis;50
4.7;2.7 Conclusion;59
4.8;References;60
5;3 Inventory Control Policies for Time-Dependent Deteriorating Item with Variable Demand and Two-Level Order Linked Trade Credit;62
5.1;3.1 Introduction;62
5.2;3.2 Notation and Assumptions;64
5.2.1;3.2.1 Notation;64
5.2.2;3.2.2 Assumptions;65
5.3;3.3 Mathematical Model;65
5.4;3.4 Numerical Examples with Sensitivity Analysis;69
5.4.1;3.4.1 Numerical Examples;69
5.4.2;3.4.2 Sensitivity Analysis;71
5.5;3.5 Conclusion;72
5.6;References;73
6;4 Inventory Modelling of Deteriorating Item and Preservation Technology with Advance Payment Scheme Under Quadratic Demand;75
6.1;4.1 Introduction;76
6.2;4.2 Notation and Assumptions;77
6.2.1;4.2.1 Notation;77
6.2.2;4.2.2 Assumptions;78
6.3;4.3 Mathematical Model;79
6.4;4.4 Numerical Example and Sensitivity Analysis;80
6.4.1;4.4.1 Numerical Example;80
6.4.2;4.4.2 Sensitivity Analysis for the Inventory Parameters;81
6.5;4.5 Conclusion;84
6.6;References;84
7;5 Dynamic Pricing, Advertisement Investment and Replenishment Model for Deteriorating Items;86
7.1;5.1 Introduction;87
7.2;5.2 Notation and Assumptions;88
7.2.1;5.2.1 Notation;88
7.2.2;5.2.2 Assumptions;88
7.3;5.3 Mathematical Model;89
7.4;5.4 Numerical Example and Sensitivity Analysis;92
7.5;5.5 Conclusion;95
7.6;References;96
8;6 A Production Reliable Model for Imperfect Items with Random Machine Breakdown Under Learning and Forgetting;98
8.1;6.1 Introduction;98
8.2;6.2 Assumptions and Notations;101
8.2.1;6.2.1 Assumptions;101
8.2.2;6.2.2 Notations;102
8.3;6.3 Mathematical Formulation of the Model;103
8.3.1;6.3.1 Crisp Model;103
8.3.2;6.3.2 Model Formulation with Learning and Forgetting in Setup Cost;109
8.3.3;6.3.3 Fuzzy Model Formulation;110
8.4;6.4 Optimal Solution Procedure;111
8.5;6.5 Numerical Examples;111
8.5.1;6.5.1 Crisp Model;112
8.5.2;6.5.2 Effect of Learning and Forgetting on Setup Cost;112
8.5.3;6.5.3 Fuzzy Model;112
8.6;6.6 Concavity of the Proposed Inventory System;115
8.7;6.7 Sensitivity Analysis;116
8.8;6.8 Effect of Inventory Parameters on Expected Average Profit (i = 1,2);116
8.8.1;6.8.1 Effect of Inventory Parameters on Time T11 and Time T21 (i = 1,2);117
8.8.2;6.8.2 Effect of Inventory Parameters on Time T12 and Time T22 (i = 1,2);120
8.8.3;6.8.3 Effect of Inventory Parameters on Demand D1 and D2 (i = 1,2);120
8.9;6.9 Conclusion;120
8.10;References;121
9;7 Inventory Policies with Development Cost for Imperfect Production and Price-Stock Reliability-Dependent Demand;123
9.1;7.1 Introduction;124
9.2;7.2 Notations and Assumptions;127
9.2.1;7.2.1 Notations;127
9.2.2;7.2.2 Assumptions;128
9.3;7.3 Mathematical Model Formulation;129
9.4;7.4 Numerical Example and Sensitivity Analysis;131
9.4.1;7.4.1 Numerical Example;131
9.4.2;7.4.2 Sensitivity Analysis of the Optimal Inventory Policy;132
9.5;7.5 Conclusion and Future Scope;137
9.6;References;138
10;8 Imperfect Quality Item Inventory Models Considering Carbon Emissions;141
10.1;8.1 Introduction and Related Literature;141
10.2;8.2 Low-Carbon EOQ Models for Imperfect Quality Items;143
10.2.1;8.2.1 Basic EOQ Model for Imperfect Quality Items Considering Carbon Emission;145
10.2.2;8.2.2 EOQ Model with Complete Backorder Considering Carbon Emission;147
10.2.3;8.2.3 Illustrative Examples;151
10.3;8.3 Low-Carbon Supply Chain Inventory Model for Imperfect Quality Items;152
10.3.1;8.3.1 Buyer’s Cost Function;153
10.3.2;8.3.2 Vendor’s Cost Function;155
10.3.3;8.3.3 Integrated Decision;158
10.3.4;8.3.4 Illustrative Example;159
10.4;8.4 Concluding Remarks;161
10.5;References;162
11;9 Non-instantaneous Deteriorating Model for Stock-Dependent Demand with Time-Varying Holding Cost and Random Decay Start Time;164
11.1;9.1 Introduction;164
11.2;9.2 Notations and Assumptions;166
11.2.1;9.2.1 Notations;166
11.2.2;9.2.2 Assumptions;167
11.3;9.3 Model Development;167
11.3.1;9.3.1 Different Costs for the Models;169
11.4;9.4 Total Cost of the Model and Solution Procedure;170
11.4.1;9.4.1 Case I: Fixed Holding Cost When t0 Is Known;170
11.4.2;9.4.2 Case II: Time-Varying Holding Cost and Known t0;171
11.4.3;9.4.3 Case III: Time-Dependent Holding Cost When t0 Is Random;173
11.5;9.5 Algorithm to Calculate Optimum Solution;175
11.6;9.6 Numerical Results;175
11.7;9.7 Sensitivity Analysis;177
11.7.1;9.7.1 Observation and Managerial Insights Based on Numerical Results and Sensitivity;178
11.8;9.8 Concluding Remarks;181
11.9;References;182
12;10 Stock-Dependent Inventory Model for Imperfect Items Under Permissible Delay in Payments;184
12.1;10.1 Introduction;185
12.2;10.2 Conclusion;196
12.3;References;196
13;11 Joint Effects of Carbon Emission, Deterioration, and Multi-stage Inspection Policy in an Integrated Inventory Model;198
13.1;11.1 Introduction;199
13.2;11.2 Problem Description;200
13.2.1;11.2.1 Problem Definition;200
13.2.2;11.2.2 Notation;201
13.2.3;11.2.3 Assumptions;201
13.3;11.3 Mathematical Model;202
13.3.1;11.3.1 Buyer’s Model;203
13.3.2;11.3.2 Vendor’s Model;204
13.3.3;11.3.3 Coordination Policy Between Vendor and Buyer;205
13.3.4;11.3.4 Solution Methodology;205
13.4;11.4 Numerical Experiment;207
13.5;11.5 Analysis Section;209
13.6;11.6 Sensitivity Analysis;209
13.7;11.7 Conclusions;210
13.8;References;210
14;12 A Note on “Inventory and Shelf-Space Optimization for Fresh Produce with Expiration Date Under Freshness-and-Stock-Dependent Demand Rate”;212
14.1;12.1 Introduction;212
14.2;12.2 Notations and Assumptions;214
14.2.1;12.2.1 Notations;214
14.2.2;12.2.2 Assumptions;215
14.3;12.3 Model Formulation;216
14.4;12.4 Numerical Examples;218
14.5;12.5 Sensitivity Analysis;219
14.6;12.6 Conclusion;220
14.7;References;220
15;13 EOQ Model Under Discounted Partial Advance—Partial Trade Credit Policy with Price-Dependent Demand;221
15.1;13.1 Introduction;221
15.2;13.2 Notations and Assumptions;223
15.3;13.3 The Model;224
15.3.1;13.3.1 Computation of Net;225
15.3.2;13.3.2 Analysis;227
15.4;13.4 Algorithm;229
15.4.1;13.4.1 Numerical Example;230
15.4.2;13.4.2 Sensitivity Analysis;231
15.4.3;13.4.3 Managerial Insights;233
15.5;13.5 Conclusion and Future Scope;235
15.6;Appendix 1 (Sufficiency Conditions);235
15.7;Appendix 2 (Determinant of the Hessian Matrix);236
15.8;References;238
16;14 Effects of Pre- and Post-Deterioration Price Discounts on Selling Price in Formulation of an Ordering Policy for an Inventory System: A Study;240
16.1;14.1 Introduction;240
16.2;14.2 Assumptions and Notations;241
16.3;14.3 Mathematical Formulation;242
16.4;14.4 Numerical Examples;248
16.5;14.5 Sensitivity Analysis;252
16.6;14.6 Conclusion;253
16.7;References;254
17;15 Efficient Supplier Selection: A Way to Better Inventory Control;256
17.1;15.1 Introduction;256
17.2;15.2 Supplier Selection: A Case Study;258
17.3;15.3 Sensitivity Analysis and Managerial Insights;272
17.4;15.4 Conclusion;278
17.5;References;279
18;16 Supply Chain Network Optimization Through Player Selection Using Multi-objective Genetic Algorithm;281
18.1;16.1 Introduction;281
18.2;16.2 Literature Review;282
18.3;16.3 Problem Description;283
18.4;16.4 Notations and Assumptions;284
18.4.1;16.4.1 Notations;284
18.4.2;16.4.2 Assumptions;285
18.5;16.5 Multi-echelon Inventory Model;286
18.6;16.6 Computational Algorithm;289
18.6.1;16.6.1 Multi-objective GA;289
18.6.2;16.6.2 3D-RadVis Visualization Technique;290
18.7;16.7 Numerical Example and Results;290
18.8;16.8 Conclusions;298
18.9;Appendix;298
18.10;References;315
19;17 Allocation of Order Amongst Available Suppliers Using Multi-objective Genetic Algorithm;316
19.1;17.1 Introduction;316
19.2;17.2 Notations and Assumptions;318
19.3;17.3 Mathematical Model;319
19.4;17.4 Algorithm;322
19.4.1;17.4.1 Multi-objective Genetic Algorithm;322
19.4.2;17.4.2 3D-RadVis Visualization Technique;323
19.5;17.5 Numerical Example;323
19.6;17.6 Conclusion;326
19.7;References;327
20;18 Some Studies on EPQ Model of Substitutable Products Under Imprecise Environment;329
20.1;18.1 Introduction;329
20.2;18.2 Mathematical Prerequisites;331
20.3;18.3 Assumptions and Notations;333
20.3.1;18.3.1 Assumptions;333
20.3.2;18.3.2 Notations;334
20.4;18.4 Model 1 : EPQ Model Having Substitution with the Constant Demand and Same Time Period;334
20.4.1;18.4.1 Model Formulation;334
20.5;18.5 Model 2 : EPQ Model Substitution Considering Shortage in One of the Items with Constant Demand and Same Time Period;337
20.5.1;18.5.1 Model Formulation;337
20.6;18.6 Different Types of Budget Constraints;341
20.7;18.7 Solution Methodology and Numerical Solution of Both the Models;343
20.8;18.8 Applications and Extensions of Proposed Model;344
20.9;18.9 Sensitivity Analysis and Discussion of Models;345
20.10;18.10 Conclusion and Future Work;356
20.11;References;356
21;19 An Effective MILP Model for Food Grain Inventory Transportation in India—A Heuristic Approach;359
21.1;19.1 Introduction;359
21.2;19.2 Literature Review;362
21.3;19.3 Problem Definition;363
21.4;19.4 Mathematical Model;364
21.5;19.5 Solution Methodology;365
21.6;19.6 Results and Discussion;367
21.7;19.7 Case Study;367
21.8;19.8 Conclusion;370
21.9;Appendix 1;370
21.10;Appendix 2;371
21.11;Appendix 3;372
21.12;References;373
22;20 Fuzzy Based Inventory Model with Credit Financing Under Learning Process;375
22.1;20.1 Introduction;376
22.2;20.2 Assumptions and Notations;377
22.2.1;20.2.1 Assumptions;378
22.2.2;20.2.2 Notations;378
22.3;20.3 Crisp Formulation Model;379
22.4;20.4 Fuzzy Methodology;382
22.4.1;20.4.1 Fuzzy Inventory Model;383
22.4.2;20.4.2 Derivation of widetilde?1 ( Tc ) and widetilde?2 ( Tc );383
22.4.3;20.4.3 Solution Procedure;384
22.4.4;20.4.4 Algorithm Procedure;385
22.5;20.5 Model Illustrated Examples;385
22.6;20.6 Sensitivity Analysis;385
22.6.1;20.6.1 Observations;385
22.7;20.7 Conclusion;387
22.8;References;388
23;21 A Fuzzy Two-Echelon Supply Chain Model for Deteriorating Items with Time Varying Holding Cost Involving Lead Time as a Decision Variable;389
23.1;21.1 Introduction;390
23.2;21.2 Assumptions and Notations;391
23.2.1;21.2.1 Notations;391
23.2.2;21.2.2 Assumptions;392
23.3;21.3 Mathematical Model;392
23.3.1;21.3.1 Retailer’s Model;392
23.3.2;21.3.2 Supplier’s Model;394
23.3.3;21.3.3 Fuzzy Model;397
23.4;21.4 Numerical Examples;398
23.5;21.5 Sensitivity Analysis;399
23.5.1;21.5.1 Observation;402
23.6;21.6 Conclusion;403
23.7;References;403
24;22 Transportation-Inventory Model for Electronic Markets Under Time Varying Demand, Retailer's Incentives and Product Exchange Scheme;405
24.1;22.1 Introduction;405
24.2;22.2 Literature Review;407
24.3;22.3 Mathematical Model;409
24.4;22.4 Model Formulation;410
24.5;22.5 Numerical Examples and Sensitivity Analysis;417
24.5.1;22.5.1 Numerical Problem;418
24.5.2;22.5.2 Sensitivity Analysis;419
24.6;22.6 Managerial Implications;420
24.7;22.7 Conclusion;421
24.8;References;421
25;23 Electronic Components’ Supply Chain Management of Electronic Industrial Development for Warehouse and Its Impact on the Environment Using Particle Swarm Optimization Algorithm;424
25.1;23.1 Introduction;425
25.2;23.2 Literature Review and Survey of Electronic Components’ Supply Chain Management;427
25.3;23.3 Related Works;429
25.3.1;23.3.1 Electronic Parts’ Supply Chain;429
25.3.2;23.3.2 Electronic Components Inventory Policy;430
25.3.3;23.3.3 Particle Swarm Optimization Algorithm;430
25.4;23.4 Model Design;431
25.4.1;23.4.1 Electronic Parts’ Supply Chain;431
25.4.2;23.4.2 Electronic Parts Model;432
25.4.3;23.4.3 Electronic Components Inventory Policy;433
25.5;23.5 Industrial Development and Its Impact on Environment;434
25.6;23.6 Simulation;435
25.6.1;23.6.1 Simulation Result;437
25.7;23.7 Conclusion;438
25.8;References;439
26;24 Interpretive Structural Modeling to Understand Factors Influencing Buying Behavior of Air Freshener;441
26.1;24.1 Introduction;442
26.2;24.2 Research Methodology;443
26.3;24.3 Case Study;445
26.4;24.4 Managerial Implications;451
26.5;24.5 Conclusion and Future Research Scope;453
26.6;References;453
27;25 Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains;455
27.1;25.1 Introduction;455
27.2;25.2 Background;457
27.3;25.3 Mathematical Model;459
27.4;25.4 Numerical Example;460
27.5;25.5 Conclusion and Future Scope;462
27.6;References;465