Course Name

Algorithmic Problem Solving

Course Code

23ECSE309

Name

Ravishankar S Bevinal

University

KLE Technological University, Hubballi-31

Project Description

An optimized supply chain is critical in today’s highly competitive and dynamic business environment. Efficient supply chain management not only reduces operational costs but also enhances customer satisfaction and increases overall business profitability. This project highlights the application of data structures and algorithms in optimizing various aspects of supply chain management, including inventory management, logistics planning, and route optimization. By employing the data structures and algorithms I have learned so far, this project has also introduced me to new ones, further improving my understanding. The project aims to solve complex supply chain problems with innovative and efficient solutions.

The features focused on in this project encompass route optimization for last-mile delivery, inventory optimization systems, supplier performance tracking systems, and predictive maintenance systems. Each feature is designed to address specific challenges within the supply chain, providing significant market benefits and improving overall efficiency. Through these solutions, the project demonstrates the transformative potential of combining DSA, ML and supply chain management to drive business success and operational efficiency.

Contents

1. Goals
2. Algorithms and Data Structures used
3. Business Use Cases
4. References

Goals

Skills Development

• Data Structures and Algorithms (DSA) Proficiency: -Apply efficient data structures (e.g., graphs, queues, trees) and algorithms (e.g., sorting, searching, shortest path) to solve complex supply chain problems.

• Develop research curiosity:
  • Develop innovative solutions using appropriate DSA knowledge, and research better ways to optimize them.
• Algorithmic Optimization:
  • Showcase an understanding of algorithm complexity and the ability to tailor them for specific supply chain requirements.

Career Advancement

• Recognise and Understand the domain of my interest: -Research different fields that seem aligned with my self-reflection. Read about careers, watch videos, and talk to seniors/professionals in these areas of SCM.

• Enhanced Engineering Capabilities:
  • Build robust and efficient supply chain systems using DSA, elevating my engineering skillset.
  • Demonstrate proficiency in applying computer science concepts to tackle real-world supply chain problems.
• Portfolio Impact:
  • Create a strong portfolio showcasing innovative projects that address real-world supply chain problems using DSA.
  • Impress potential employers with my ability to design and implement effective solutions, making you a strong candidate for roles in supply chain management and software development.

Business Use Cases

1. Route Optimization for Last-Mile Delivery
2. Inventory Optimization System
3. Supplier Performance Tracking System
4. Warehouse Layout Optimization
5. Demand Forecasting System
6. Supply Chain Risk Management System
7. Multi-Echelon Inventory Optimization
8. Predictive Maintenance System
9. Collaborative Demand and Supply Planning
10. Smart Contract Management System
11. Network Design Optimization
12. Reverse Logistics Optimization
13. Dynamic Pricing Optimization
14. Sustainable Supply Chain Management
15. Supply Chain Finance Optimization

The list of Algorithms and Data Structures used

The project is focused on using the data structure knowledge, algorithms I have learnt and the ML models that I consider necessary to optimise the business cases identified.

Note:

• The Ideation and sudo code were written then formatted and commented using the AI tools
• The diagrams were generated by prompting the models with all the necessary instructions.
• Codes implemented have only the used algorithms and not the whole code.

Algorithms:

• A* Search Algorithm
• Ford-Fulkerson Algorithm
• Dynamic Programming
• Monte Carlo Simulation
• Depth-First Search (DFS)
• Consensus Algorithm
• Boruvka’s Algorithm
• Reinforcement Learning

Data Structures:

• Fenwick Tree (Binary Indexed Tree)
• Red-Black Trees
• Heap
• KD Tree
• Segment Trees
• Trie
• B-Tree
• Merkle Trees
• AVL Trees
• Decision Trees
• K-Means Clustering
• Radix Tree
• Skip List

Route Optimization for Last-Mile Delivery

A system that calculates the most efficient routes for delivery vehicles, considering factors like traffic, delivery windows, and vehicle capacity.

Description

• Challenges: Real-time route adjustments, handling dynamic traffic conditions, balancing multiple objectives (time, cost, customer satisfaction).
• Market Benefits: Reduced fuel costs, improved on-time deliveries, increased delivery capacity.
• Design Techniques and Algorithms:
  • A* Search Algorithm: For finding optimal routes.
    • Time Complexity: O(b^d), where b is the branching factor and d is the depth of the goal.
    • Space Complexity: O(b^d), for storing the open and closed lists.
  • Ford-Fulkerson Algorithm: For maximizing the flow of deliveries through the network.
    • Time Complexity: O(E * max_flow), where E is the number of edges in the graph.
    • Space Complexity: O(V), where V is the number of vertices in the graph.

Code Implementation

GitHub Link to Code

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Inventory Optimization System

An AI-driven system that predicts demand, optimizes stock levels, and automates reordering processes across multiple warehouses.

Description

• Challenges: Handling variable lead times, accounting for seasonal fluctuations, and aintegrating data from multiple sources.
• Market Benefits: Reduced carrying costs, minimized stockouts, improved cash flow.
• Design Techniques and Algorithms:
  • Fenwick Tree (Binary Indexed Tree): For efficient range sum queries on historical sales data.
    • Time Complexity: O(log n) for update and query operations, where n is the number of data points.
    • Space Complexity: O(n), for storing the tree.
  • Exponential Smoothing: For time series forecasting.
    • Time Complexity: O(n), where n is the number of historical data points.
    • Space Complexity: O(1), as it only needs to store a few parameters.

Code Implementation

GitHub Link to Code

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Supplier Performance Tracking System

A system that monitors, evaluates, and ranks supplier performance based on multiple criteria such as quality, on-time delivery, and cost. Description

• Challenges: Integrating diverse data sources, maintaining data accuracy, and providing real-time insights.
• Market Benefits: Improved supplier relationships, reduced supply chain risks, better negotiation position.
• Design Techniques and Algorithms:
  • Red-Black Trees: For efficient insertion, deletion, and retrieval of supplier performance data.
    • Time Complexity: O(log n) for insert, delete, and search operations, where n is the number of suppliers.
    • Space Complexity: O(n), for storing the tree.
  • Heap: For maintaining a priority queue of suppliers based on performance scores.
    • Time Complexity: O(log n) for insert and delete-max operations, where n is the number of suppliers.
    • Space Complexity: O(n), for storing the heap.

Code Implementation

GitHub Link to Code

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Warehouse Layout Optimization

A system that optimizes the placement of goods within a warehouse to minimize picking time and improve overall efficiency.

Description

• Challenges: Handling frequently changing inventory, accommodating various item sizes and storage requirements, and optimizing for multiple objectives (e.g., picking efficiency, space utilization).
• Market Benefits: Increased warehouse productivity, reduced operational costs, improved order fulfilment speed.
• Design Techniques and Algorithms:
  • KD Tree: For efficient spatial partitioning and nearest neighbour searches.
    • Time Complexity: O(log n) for search operations on average, where n is the number of items.
    • Space Complexity: O(n), for storing the tree.
  • Genetic Algorithm: For evolving optimal layout solutions.
    • Time Complexity: O(g * p * f), where g is the number of generations, p is the population size, and f is the cost of fitness evaluation.
    • Space Complexity: O(p), for storing the population.

Code Implementation

GitHub Link to Code

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Demand Forecasting System

Description

An advanced analytics system that predicts future demand for products based on historical data, market trends, and external factors.

• Challenges: Handling seasonality, incorporating external factors (e.g., economic indicators, weather), and dealing with new product introductions.
• Market Benefits: Improved inventory management, better production planning, reduced stockouts and overstocks.
• Design Techniques and Algorithms:
  • Segment Trees: For efficient range queries on historical sales data.
    • Time Complexity: O(log n) for range query and update operations, where n is the number of data points.
    • Space Complexity: O(n), for storing the tree.
  • MO’s Algorithm: For efficiently answering multiple range queries offline.
    • Time Complexity: O((n+q) * √n), where n is the number of data points and q is the number of queries.
    • Space Complexity: O(n), for storing the input array and query results.

Code Implementation

GitHub Link to Code

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Supply Chain Risk Management System

Description

A system that identifies, assesses, and mitigates risks across the supply chain, including supplier failures, geopolitical events, and natural disasters.

• Challenges: Integrating diverse risk factors, quantifying risk impacts, providing actionable mitigation strategies.
• Market Benefits: Improved supply chain resilience, reduced disruptions, better strategic decision-making.
• Design Techniques and Algorithms:
  • DFS (Depth-First Search): For analyzing supply chain dependency graphs.
    • Time Complexity: O(V + E), where V is the number of vertices and E is the number of edges in the graph.
    • Space Complexity: O(V), for the recursion stack.
  • Monte Carlo Simulation: For risk assessment and scenario analysis.
    • Time Complexity: O(n * s), where n is the number of risk factors and s is the number of simulations.
    • Space Complexity: O(n), for storing simulation results.

Code Implementation

GitHub Link to Code

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Multi-Echelon Inventory Optimization

Description

A system that optimizes inventory levels across multiple tiers of the supply chain, from raw materials to finished goods.

• Challenges: Coordinating inventory decisions across multiple parties, handling varying lead times and demand patterns, balancing service levels and costs.
• Market Benefits: Reduced overall inventory costs, improved service levels, enhanced supply chain visibility.
• Design Techniques and Algorithms:
  • Dynamic Programming: For optimizing inventory decisions across time periods and echelons.
    • Time Complexity: O(n * m * k), where n is the number of time periods, m is the number of echelons, and k is the number of possible inventory levels.
    • Space Complexity: O(n * m * k), for storing the dynamic programming table.
  • Square Root Decomposition: For efficiently handling range queries and updates on inventory data.
    • Time Complexity: O(√n) for query and update operations, where n is the number of inventory items.
    • Space Complexity: O(n), for storing the decomposed array.

Code Implementation

GitHub Link to Code

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Predictive Maintenance System

Description

An IoT-based system that monitors equipment health in real-time and predicts maintenance needs to prevent breakdowns and optimize maintenance schedules.

• Challenges: Integrating data from diverse sensors, developing accurate predictive models, balancing maintenance costs with downtime risks.
• Market Benefits: Reduced equipment downtime, lower maintenance costs, extended equipment lifespan.
• Design Techniques and Algorithms:
  • Random Forest: For predictive modeling of equipment failures.
    • Time Complexity: O(n * log(n) * m * k), where n is the number of samples, m is the number of features, and k is the number of trees.
    • Space Complexity: O(m * k), for storing the forest.
  • Trie: For efficient storage and retrieval of equipment maintenance histories.
    • Time Complexity: O(k) for insert and search operations, where k is the length of the key.
    • Space Complexity: O(n * k), where n is the number of keys and k is the average key length.

Code Implementation

GitHub Link to Code

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Collaborative Demand and Supply Planning

Description

A platform that enables real-time collaboration between suppliers, manufacturers, and retailers to align demand forecasts with supply plans.

• Challenges: Ensuring data consistency across parties, handling conflicting objectives, providing real-time visibility and decision support.
• Market Benefits: Reduced bullwhip effect, improved forecast accuracy, enhanced supply chain agility.
• Design Techniques and Algorithms:
  • Consensus Algorithm: For reaching agreement on shared forecasts.
    • Time Complexity: O(n * r), where n is the number of participants and r is the number of rounds.
    • Space Complexity: O(n), for storing participant states.
  • B-Tree: For efficient storage and retrieval of large volumes of planning data.
    • Time Complexity: O(log n) for search, insert, and delete operations, where n is the number of keys.
    • Space Complexity: O(n), for storing the tree.

Code Implementation

GitHub Link to Code

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Smart Contract Management System

Description

A blockchain-based system that automates contract execution, payments, and compliance monitoring across the supply chain.

• Challenges: Ensuring contract security and privacy, integrating with legacy systems, handling complex contract logic.
• Market Benefits: Reduced transaction costs, improved contract compliance, increased trust among supply chain partners.
• Design Techniques and Algorithms:
  • Merkle Trees: For efficient verification of blockchain transactions.
    • Time Complexity: O(log n) for proof generation and verification, where n is the number of transactions.
    • Space Complexity: O(n), for storing the tree.
  • State Machine: For modeling and executing smart contract logic.
    • Time Complexity: O(1) for state transitions.
    • Space Complexity: O(s), where s is the number of possible states.

Code Implementation

GitHub Link to Code

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Network Design Optimization

Description

A system that optimizes the design of the supply chain network, including the location and capacity of facilities, transportation links, and inventory placement.

• Challenges: Handling complex constraints, balancing multiple objectives (cost, service level, risk), incorporating future scenarios.
• Market Benefits: Reduced overall supply chain costs, improved service levels, enhanced strategic decision-making.
• Design Techniques and Algorithms:
  • Boruvka’s Algorithm: For finding the minimum spanning tree in the supply chain network.
    • Time Complexity: O(E log V), where E is the number of edges and V is the number of vertices.
    • Space Complexity: O(E + V), for storing the graph and MST.
  • Integer Programming: For solving complex network design optimization problems.
    • Time Complexity: Exponential in the worst case, but often practical for real-world problems with modern solvers.
    • Space Complexity: O(n * m), where n is the number of variables and m is the number of constraints.

Code Implementation

Here is sample code:

GitHub Link to Code

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Reverse Logistics Optimization

Description

A system that optimizes the collection, processing, and disposition of returned goods, minimizing costs and maximizing value recovery.

• Challenges: Handling unpredictable return volumes and conditions, optimizing disposition decisions, integrating with forward logistics.
• Market Benefits: Reduced reverse logistics costs, improved customer satisfaction, increased value recovery from returns.
• Design Techniques and Algorithms:
  • K-Means Clustering: For grouping return locations and optimizing collection routes.
    • Time Complexity: O(n * k * i), where n is the number of points, k is the number of clusters, and i is the number of iterations.
    • Space Complexity: O(n + k), for storing point assignments and centroids.
  • Decision Trees: For automating disposition decisions based on product condition and market factors.
    • Time Complexity: O(n * log(n)) for training, where n is the number of samples.
    • Space Complexity: O(m), where m is the number of nodes in the tree.

Code Implementation

Here is sample code:

GitHub Link to Code

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Dynamic Pricing Optimization

Description

A system that adjusts product prices in real-time based on demand, inventory levels, competitor pricing, and other market factors.

• Challenges: Handling real-time data streams, balancing multiple pricing objectives, ensuring pricing consistency across channels.
• Market Benefits: Increased revenue and profit margins, improved inventory turnover, enhanced competitive positioning.
• Design Techniques and Algorithms:
  • Reinforcement Learning: For optimizing pricing decisions over time.
    • Time Complexity: O(s * a * e), where s is the number of states, a is the number of actions, and e is the number of episodes.
    • Space Complexity: O(s * a), for storing the Q-table.
  • AVL Trees: For maintaining sorted price lists with efficient updates.
    • Time Complexity: O(log n) for insert, delete, and search operations, where n is the number of prices.
    • Space Complexity: O(n), for storing the tree.

Code Implementation

Here is sample code:

GitHub Link to Code

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Sustainable Supply Chain Management

Description

A system that monitors, reports, and optimizes the environmental and social impact of supply chain operations, including carbon emissions, water usage, and fair labor practices.

• Challenges: Collecting accurate sustainability data, quantifying and optimizing multiple sustainability metrics, ensuring compliance with diverse regulations.
• Market Benefits: Improved brand reputation, compliance with regulations, reduced environmental impact and associated costs.
• Design Techniques and Algorithms:
  • Multi-Objective Optimization: For balancing economic, environmental, and social objectives.
    • Time Complexity: O(n * m * g), where n is the population size, m is the number of objectives, and g is the number of generations.
    • Space Complexity: O(n * m), for storing the population and Pareto front.
  • Radix Tree: For efficient storage and retrieval of hierarchical sustainability data.
    • Time Complexity: O(k) for insert and search operations, where k is the length of the key.
    • Space Complexity: O(n * k), where n is the number of keys and k is the average key length.

Code Implementation

Here is sample code:

GitHub Link to Code

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Supply Chain Finance Optimization

Description

A system that optimizes working capital across the supply chain through techniques such as dynamic discounting, reverse factoring, and inventory financing.

• Challenges: Assessing creditworthiness in real-time, optimizing payment terms across multiple parties, managing financial risks.
• Market Benefits: Improved cash flow for all parties, reduced financing costs, strengthened supplier relationships.
• Design Techniques and Algorithms:
  • Network Flow Algorithms: For optimizing cash flows across the supply chain network.
    • Time Complexity: O(V * E^2) for Edmonds-Karp algorithm, where V is the number of vertices and E is the number of edges.
    • Space Complexity: O(V + E), for storing the residual graph.
  • Skip List: For maintaining sorted lists of invoices with efficient insertion and removal.
    • Time Complexity: O(log n) expected time for search, insert, and delete operations, where n is the number of elements.
    • Space Complexity: O(n), for storing the skip list.

Code Implementation

Here is sample code:

GitHub Link to Code

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References

1. Bandekar, Shraddha. (2019). Optimization Algorithm in Supply Chain Management. International Journal of Innovative Technology and Exploring Engineering. 8. 5072-5079. 10.35940/ijitee.L2724.1081219. LINK

2. Supply chain optimization using machine learning methods. A manufacturing case study. Manasas Vasileios SID: 33081170010 LINK

3. Supply Chain Design and Optimization: Challenges and Opportunities Daniel J. Garcia, Fengqi LINK