Transportation and assignment models are network flow problems that can be solved using linear programming techniques. Transportation models are used to determine optimal shipping quantities from sources to destinations to minimize costs, while assignment models optimally assign tasks to workers to minimize total time or costs. Both problems can have multiple optimal solutions if degenerate or unbalanced, and special algorithms like the northwest corner rule and Hungarian method are typically used to solve them more efficiently than general linear programming.
This chapter discusses transportation, assignment, and transshipment models as special cases of linear programming network flow problems. It provides learning objectives and an outline of topics to be covered, which include introducing the transportation problem using an example of distributing office desks from factories to warehouses, formulating it as a linear program, and solving it using the transportation algorithm. The chapter also discusses the assignment problem using an example of assigning workers to repair jobs and the transshipment problem using an example of shipping snow blowers through distribution centers. It describes developing initial feasible solutions using the northwest corner rule and improving solutions using the stepping stone method.
This document contains 36 multiple choice questions about queuing theory and waiting line models. It covers topics like the characteristics of queuing systems, different types of queuing models (M/M/1, M/D/1, etc.), assumptions of queuing models, and using queuing theory to analyze real world systems. Several questions also provide word problems to test the application of queuing concepts to calculate metrics like average queue length and server utilization. The questions assess understanding of key queuing theory terminology, assumptions, models, and calculations.
New Method for Finding an Optimal Solution of Generalized Fuzzy Transportatio...BRNSS Publication Hub
In this paper, a proposed method, namely, zero average method is used for solving fuzzy transportation problems by assuming that a decision-maker is uncertain about the precise values of the transportation costs, demand, and supply of the product. In the proposed method, transportation costs, demand, and supply are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed method, a numerical example is solved. The proposed method is easy to understand and apply to real-life transportation problems for the decision-makers.
Bba 3274 qm week 9 transportation and assignment modelsStephen Ong
This document provides an overview and introduction to transportation, assignment, and transshipment models in quantitative methods and linear programming. It discusses learning objectives, outlines the topics that will be covered, and provides examples to illustrate each type of model. Specifically, it presents examples of transportation problems involving distributing goods from suppliers to warehouses, an assignment problem assigning workers to repair jobs, and a transshipment problem shipping snow blowers through distribution centers. It also introduces the transportation and assignment algorithms for solving these types of linear programs.
This document contains information about simulation modeling from the textbook Quantitative Analysis for Management. It includes definitions, advantages and disadvantages of simulation, different types of simulation like Monte Carlo simulation, and examples of using simulation to model situations with probabilistic variables like customer demand, arrivals, service times and machine breakdowns. Random numbers are used with probability distributions to simulate outcomes over multiple runs and analyze the results.
This document discusses transportation and assignment models, specifically the transportation problem. It provides an example of a transportation problem involving Roxas Gravel Company scheduling shipments from plants to projects to minimize costs. The steps to solve this problem are outlined, which include setting up a transportation tableau, developing an initial solution, testing for improvements, and developing improved solutions iteratively until no further improvements can be made. The MODI method for finding alternative optimal solutions is also introduced.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
This document outlines the key concepts and methods for transportation and assignment models covered in Chapter 10. It introduces transportation and assignment problems, and describes specialized algorithms for finding optimal solutions, including the northwest corner rule, stepping-stone method, MODI method, and Vogel's approximation method for transportation problems. It also covers the Hungarian (matrix reduction) method for solving assignment problems optimally in a step-by-step manner. The chapter objectives are to solve both transportation and assignment problems, as well as address special cases like unbalanced supply/demand and degeneracy.
This chapter discusses transportation, assignment, and transshipment models as special cases of linear programming network flow problems. It provides learning objectives and an outline of topics to be covered, which include introducing the transportation problem using an example of distributing office desks from factories to warehouses, formulating it as a linear program, and solving it using the transportation algorithm. The chapter also discusses the assignment problem using an example of assigning workers to repair jobs and the transshipment problem using an example of shipping snow blowers through distribution centers. It describes developing initial feasible solutions using the northwest corner rule and improving solutions using the stepping stone method.
This document contains 36 multiple choice questions about queuing theory and waiting line models. It covers topics like the characteristics of queuing systems, different types of queuing models (M/M/1, M/D/1, etc.), assumptions of queuing models, and using queuing theory to analyze real world systems. Several questions also provide word problems to test the application of queuing concepts to calculate metrics like average queue length and server utilization. The questions assess understanding of key queuing theory terminology, assumptions, models, and calculations.
New Method for Finding an Optimal Solution of Generalized Fuzzy Transportatio...BRNSS Publication Hub
In this paper, a proposed method, namely, zero average method is used for solving fuzzy transportation problems by assuming that a decision-maker is uncertain about the precise values of the transportation costs, demand, and supply of the product. In the proposed method, transportation costs, demand, and supply are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed method, a numerical example is solved. The proposed method is easy to understand and apply to real-life transportation problems for the decision-makers.
Bba 3274 qm week 9 transportation and assignment modelsStephen Ong
This document provides an overview and introduction to transportation, assignment, and transshipment models in quantitative methods and linear programming. It discusses learning objectives, outlines the topics that will be covered, and provides examples to illustrate each type of model. Specifically, it presents examples of transportation problems involving distributing goods from suppliers to warehouses, an assignment problem assigning workers to repair jobs, and a transshipment problem shipping snow blowers through distribution centers. It also introduces the transportation and assignment algorithms for solving these types of linear programs.
This document contains information about simulation modeling from the textbook Quantitative Analysis for Management. It includes definitions, advantages and disadvantages of simulation, different types of simulation like Monte Carlo simulation, and examples of using simulation to model situations with probabilistic variables like customer demand, arrivals, service times and machine breakdowns. Random numbers are used with probability distributions to simulate outcomes over multiple runs and analyze the results.
This document discusses transportation and assignment models, specifically the transportation problem. It provides an example of a transportation problem involving Roxas Gravel Company scheduling shipments from plants to projects to minimize costs. The steps to solve this problem are outlined, which include setting up a transportation tableau, developing an initial solution, testing for improvements, and developing improved solutions iteratively until no further improvements can be made. The MODI method for finding alternative optimal solutions is also introduced.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
This document outlines the key concepts and methods for transportation and assignment models covered in Chapter 10. It introduces transportation and assignment problems, and describes specialized algorithms for finding optimal solutions, including the northwest corner rule, stepping-stone method, MODI method, and Vogel's approximation method for transportation problems. It also covers the Hungarian (matrix reduction) method for solving assignment problems optimally in a step-by-step manner. The chapter objectives are to solve both transportation and assignment problems, as well as address special cases like unbalanced supply/demand and degeneracy.
Pa 906.transportation problem and algorithms finalhsur2010
This document discusses transportation problems and their formulation as linear programs. It provides definitions and history on transportation problems, including how they were first proposed in 1941 and subsequent developments. It then gives an example of a transportation problem faced by Executive Furniture Corporation, formulating it as a linear program to minimize costs while meeting supply and demand constraints. It describes solving the problem using the Northwest Corner Rule and Stepping Stone Method transportation algorithms.
This document provides an overview of discrete choice analysis and nested logit models. It begins with a review of binary and multinomial logit models and the independence from irrelevant alternatives property. It then introduces nested logit models as a way to address IIA violations when alternatives are correlated or choices are multidimensional. The document provides an example of a nested logit specification and calculation of choice probabilities. It concludes with extensions like mixed logit models and an appendix on additional model specifications.
We all make choices between alternatives every day in many contexts - not just transport. There is theory to help planners forecast those decisions, but it is generally poorly understood. The aim of this presentation is to be of particular relevance to all PhD students and early career researchers - who should know something about DCM even if not planning to work in that area. No prior knowledge necessary.
Tony Fowkes first joined ITS in September 1976, coming from the University's School of Economic Studies, where he had been lecturing. Initially he worked on Car Ownership Forecasting, before working in a wide variety of areas of Transport Planning. In 1982 he joined the first UK Value of Time study, as well as a parallel project on Business Travel which led to pioneering work on Business Value of Time. On both those projects he helped to develop the new technique of Stated Preference estimation. In 1984 he began 4 years here as British Railways Senior Rail Research Fellow. He then moved to a mix of teaching and research, jointly with LUBS. He has published widely and contributed to many influential reports for government bodies. He retired in October 2016 as Reader in Transport Econometrics, and is now a Visiting Reader at ITS.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Iaetsd ones method for finding an optimalIaetsd Iaetsd
The document proposes a new method called Ones Method for finding an optimal solution to transportation problems directly. The method involves constructing a transportation table and allocating units to cells starting with the minimum demand/supply. Units are allocated to cells with the maximum number of ones until all demands are satisfied and supplies exhausted. The method is illustrated on sample problems and shown to find the same optimal solutions as existing methods but in a simpler way. It provides a systematic procedure that is easy to apply to transportation problems.
This paper presents an algorithm for efficiently updating the display on a video terminal to reflect changes made to text. The algorithm takes descriptions of the current and desired images as input. It uses an algorithm for the string-to-string correction problem to determine the minimum number of character insertions, deletions and transformations needed to transform the current image string into the desired image string. By analyzing the output trace from this algorithm, the display can be updated line-by-line using the basic operations available on most video terminals, such as inserting and deleting characters or lines. This allows the display to be updated optimally with minimal redrawn characters.
The document contains a 10 question general aptitude test with multiple choice answers. It then contains a 25 question mechanical engineering test in the same format. Some key details:
- The general aptitude test includes questions on topics like number of matches in a league, logical reasoning with statements, word usage, and idioms.
- The mechanical engineering test covers topics like fluid mechanics, materials properties, manufacturing processes, dynamics and vibrations.
- Both sections contain multiple choice questions testing concepts, with some including short explanations of the reasoning behind the answers.
5 45
4
+6 +4
20 10 20
42
40 0
040
30
30
vj
ui
6
0
-363024
Dummy
Plant 1
Plant 2
EastwoodWestwoodNorthwood
24
The document discusses transportation problems and provides an example to illustrate the transportation algorithm. The transportation algorithm involves a two phase process: 1) obtaining an initial feasible solution and 2) moving toward optimality. The example demonstrates applying the northwest corner rule and minimum cost method to obtain the initial feasible solution, then using the stepping stone method and MODI technique to iteratively improve the solution until reaching optimality.
The article presents different approaches to finding the optimal solution for a problem, which extends the classical traveling salesman problem. Takes into consideration the possibility of choosing a toll highway and the standard road between two cities. Describes the experimentation system. Provides mathematical model, results of the investigation, and a conclusion.
The document discusses various concepts related to assignment and transportation problems including:
1) The steps to solve an assignment problem using the Hungarian method.
2) Examples of assignment problems involving personnel assignment and swimmer selection.
3) The formulation of a transportation problem to minimize shipping costs involving plants, cities, supply, and demand.
4) Examples of transportation problems involving flight assignment and power plant shipping costs.
5) How to solve transshipment problems by converting them into transportation problems.
Dynamic programming is an algorithm design paradigm that can be applied to problems exhibiting optimal substructure and overlapping subproblems. It works by breaking down a problem into subproblems and storing the results of already solved subproblems, rather than recomputing them multiple times. This allows for an efficient bottom-up approach. Examples where dynamic programming can be applied include the matrix chain multiplication problem, the 0-1 knapsack problem, and finding the longest common subsequence between two strings.
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
The Modified Distribution Method or MODI is an efficient method of checking the optimality of the initial feasible solution. MODI provides a new means of finding the unused route with the largest negative improvement index. Once the largest index is identified, we are required to trace only one closed path. This path helps determine the maximum number of units that can be shipped via the best unused route.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
The transportation problem represents a particular type of linear programming problem used for allocating resources in an optimal way; it is a highly useful tool for managers and supply chain engineers for optimizing costs.
For clearly understand you can watch this video on my youtube channel
https://www.youtube.com/watch?v=5Ssnew58Yfc&t=2s
In mathematics and economics, transportation theory or transport theory is a name given to the study of optimal transportation and allocation of resources.A transportation matrix is a way of understanding the maximum possibilities the shipment can be done. It is also known as decision variables because these are the variables of interest that we will change to achieve the objective, that is, minimizing the cost function.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Mixed Integer Linear Programming Formulation for the Taxi Sharing Problemjfrchicanog
The document presents a mixed integer linear programming (MILP) formulation for solving the taxi sharing problem. The taxi sharing problem aims to optimize taxi routes by allowing passengers with similar pick-up and drop-off locations to share taxis. The formulation models the problem as sequences of passenger locations that represent taxi rides. Experiments on real-world taxi trip data show the MILP formulation finds lower cost solutions than a parallel evolutionary algorithm, especially on medium and large problem instances, demonstrating the benefits of the exact MILP approach.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
The document discusses linear programming and the simplex method for solving linear programming problems. It begins with definitions of linear programming and its history. It then provides an example production planning problem that can be formulated as a linear programming problem. The document goes on to describe the standard form of a linear programming problem and terminology used. It explains how the simplex method works through iterative improvements to find the optimal solution. This is illustrated both geometrically and through an algebraic example solved using the simplex method.
Pa 906.transportation problem and algorithms finalhsur2010
This document discusses transportation problems and their formulation as linear programs. It provides definitions and history on transportation problems, including how they were first proposed in 1941 and subsequent developments. It then gives an example of a transportation problem faced by Executive Furniture Corporation, formulating it as a linear program to minimize costs while meeting supply and demand constraints. It describes solving the problem using the Northwest Corner Rule and Stepping Stone Method transportation algorithms.
This document provides an overview of discrete choice analysis and nested logit models. It begins with a review of binary and multinomial logit models and the independence from irrelevant alternatives property. It then introduces nested logit models as a way to address IIA violations when alternatives are correlated or choices are multidimensional. The document provides an example of a nested logit specification and calculation of choice probabilities. It concludes with extensions like mixed logit models and an appendix on additional model specifications.
We all make choices between alternatives every day in many contexts - not just transport. There is theory to help planners forecast those decisions, but it is generally poorly understood. The aim of this presentation is to be of particular relevance to all PhD students and early career researchers - who should know something about DCM even if not planning to work in that area. No prior knowledge necessary.
Tony Fowkes first joined ITS in September 1976, coming from the University's School of Economic Studies, where he had been lecturing. Initially he worked on Car Ownership Forecasting, before working in a wide variety of areas of Transport Planning. In 1982 he joined the first UK Value of Time study, as well as a parallel project on Business Travel which led to pioneering work on Business Value of Time. On both those projects he helped to develop the new technique of Stated Preference estimation. In 1984 he began 4 years here as British Railways Senior Rail Research Fellow. He then moved to a mix of teaching and research, jointly with LUBS. He has published widely and contributed to many influential reports for government bodies. He retired in October 2016 as Reader in Transport Econometrics, and is now a Visiting Reader at ITS.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Iaetsd ones method for finding an optimalIaetsd Iaetsd
The document proposes a new method called Ones Method for finding an optimal solution to transportation problems directly. The method involves constructing a transportation table and allocating units to cells starting with the minimum demand/supply. Units are allocated to cells with the maximum number of ones until all demands are satisfied and supplies exhausted. The method is illustrated on sample problems and shown to find the same optimal solutions as existing methods but in a simpler way. It provides a systematic procedure that is easy to apply to transportation problems.
This paper presents an algorithm for efficiently updating the display on a video terminal to reflect changes made to text. The algorithm takes descriptions of the current and desired images as input. It uses an algorithm for the string-to-string correction problem to determine the minimum number of character insertions, deletions and transformations needed to transform the current image string into the desired image string. By analyzing the output trace from this algorithm, the display can be updated line-by-line using the basic operations available on most video terminals, such as inserting and deleting characters or lines. This allows the display to be updated optimally with minimal redrawn characters.
The document contains a 10 question general aptitude test with multiple choice answers. It then contains a 25 question mechanical engineering test in the same format. Some key details:
- The general aptitude test includes questions on topics like number of matches in a league, logical reasoning with statements, word usage, and idioms.
- The mechanical engineering test covers topics like fluid mechanics, materials properties, manufacturing processes, dynamics and vibrations.
- Both sections contain multiple choice questions testing concepts, with some including short explanations of the reasoning behind the answers.
5 45
4
+6 +4
20 10 20
42
40 0
040
30
30
vj
ui
6
0
-363024
Dummy
Plant 1
Plant 2
EastwoodWestwoodNorthwood
24
The document discusses transportation problems and provides an example to illustrate the transportation algorithm. The transportation algorithm involves a two phase process: 1) obtaining an initial feasible solution and 2) moving toward optimality. The example demonstrates applying the northwest corner rule and minimum cost method to obtain the initial feasible solution, then using the stepping stone method and MODI technique to iteratively improve the solution until reaching optimality.
The article presents different approaches to finding the optimal solution for a problem, which extends the classical traveling salesman problem. Takes into consideration the possibility of choosing a toll highway and the standard road between two cities. Describes the experimentation system. Provides mathematical model, results of the investigation, and a conclusion.
The document discusses various concepts related to assignment and transportation problems including:
1) The steps to solve an assignment problem using the Hungarian method.
2) Examples of assignment problems involving personnel assignment and swimmer selection.
3) The formulation of a transportation problem to minimize shipping costs involving plants, cities, supply, and demand.
4) Examples of transportation problems involving flight assignment and power plant shipping costs.
5) How to solve transshipment problems by converting them into transportation problems.
Dynamic programming is an algorithm design paradigm that can be applied to problems exhibiting optimal substructure and overlapping subproblems. It works by breaking down a problem into subproblems and storing the results of already solved subproblems, rather than recomputing them multiple times. This allows for an efficient bottom-up approach. Examples where dynamic programming can be applied include the matrix chain multiplication problem, the 0-1 knapsack problem, and finding the longest common subsequence between two strings.
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
The Modified Distribution Method or MODI is an efficient method of checking the optimality of the initial feasible solution. MODI provides a new means of finding the unused route with the largest negative improvement index. Once the largest index is identified, we are required to trace only one closed path. This path helps determine the maximum number of units that can be shipped via the best unused route.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
The transportation problem represents a particular type of linear programming problem used for allocating resources in an optimal way; it is a highly useful tool for managers and supply chain engineers for optimizing costs.
For clearly understand you can watch this video on my youtube channel
https://www.youtube.com/watch?v=5Ssnew58Yfc&t=2s
In mathematics and economics, transportation theory or transport theory is a name given to the study of optimal transportation and allocation of resources.A transportation matrix is a way of understanding the maximum possibilities the shipment can be done. It is also known as decision variables because these are the variables of interest that we will change to achieve the objective, that is, minimizing the cost function.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Mixed Integer Linear Programming Formulation for the Taxi Sharing Problemjfrchicanog
The document presents a mixed integer linear programming (MILP) formulation for solving the taxi sharing problem. The taxi sharing problem aims to optimize taxi routes by allowing passengers with similar pick-up and drop-off locations to share taxis. The formulation models the problem as sequences of passenger locations that represent taxi rides. Experiments on real-world taxi trip data show the MILP formulation finds lower cost solutions than a parallel evolutionary algorithm, especially on medium and large problem instances, demonstrating the benefits of the exact MILP approach.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
The document discusses linear programming and the simplex method for solving linear programming problems. It begins with definitions of linear programming and its history. It then provides an example production planning problem that can be formulated as a linear programming problem. The document goes on to describe the standard form of a linear programming problem and terminology used. It explains how the simplex method works through iterative improvements to find the optimal solution. This is illustrated both geometrically and through an algebraic example solved using the simplex method.
This document contains a chapter summary for a quantitative analysis textbook. It includes 54 multiple choice questions covering topics related to linear programming models, including graphical and computer solution methods. Key topics assessed include formulating linear programming problems, the requirements and assumptions of linear programs, graphical solutions, special cases like infeasibility and redundancy, and sensitivity analysis.
This document contains a chapter summary for a quantitative analysis textbook. It includes 54 multiple choice questions covering topics related to linear programming models, including graphical and computer solution methods. Key topics assessed include formulating linear programming problems, the requirements and assumptions of linear programs, graphical solutions, special cases like infeasibility and redundancy, and sensitivity analysis.
The document discusses several optimization techniques:
1. Linear programming is used to find optimal solutions when constraints are linear. It involves defining variables, constraints and an objective function to maximize or minimize.
2. Transportation problems involve optimizing distribution costs by assigning supplies from origins to destinations. The Hungarian method solves assignment problems by finding a minimum cost matching between rows and columns.
3. Fuzzy multi-criteria decision making allows evaluating alternatives according to multiple, sometimes conflicting criteria to determine optimal solutions under uncertainty.
In this paper an innovative method named MM method is proposed for finding an optimal solution directly. A new algorithm in MM method is discussed in this paper which gives optimal solution. Some example are provided to illustrate the proposed algorithm and result is compared to MOD I (modified distribution) method. The most attractive feature of this method is that it requires very simple arithmetical and logical calculation.
Optimal Allocation Policy for Fleet ManagementYogeshIJTSRD
The transportation problem is one of the biggest problem that face the fleet management, whereas the main objectives for the fleet management is reducing the operations cost besides improving the fleet efficiency. Therefore, this paper presents an application for a transportation technique on a company to distribute its products over a wide country by using its fleet. All the necessary data are collected from the company, analyzed and reformulated to become a suitable form to apply a transportation model to solve it. The results show that using this technique can be considered as powerful tool to improve the operation management for any fleet. Mohamed Khalil | Ibrahim Ahmed | Khaled Abdelwahed | Rania Ahmed | Elsayed Ellaimony "Optimal Allocation Policy for Fleet Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38673.pdf Paper URL: https://www.ijtsrd.com/management/operations-management/38673/optimal-allocation-policy-for-fleet-management/mohamed-khalil
This document describes a project report submitted by a group of MBA students at Bundelkhand Institute of Engineering and Technology Jhansi, Uttar Pradesh, India. The report analyzes a transportation problem and provides the mathematical formulation, methods to obtain an initial basic feasible solution, and the Modi method to find the optimal basic solution. It includes declarations by the students and their guide, as well as sample transportation problems solved using different methods.
This document contains a set of multiple choice questions related to k-Nearest Neighbors (k-NN) and Logistic Regression algorithms. Some key points covered include:
- k-NN is an algorithm used for both classification and regression tasks that assigns labels/values to new data based on similarity to nearby training examples. It involves choosing a value for k and computing distances between data points.
- Parameters like k, distance metrics, and handling of noise and high-dimensionality can impact k-NN performance and need to be optimized. Cross-validation is used to evaluate models and choose k.
- Logistic regression is a supervised learning algorithm used for classification. It fits data using maximum likelihood estimation and outputs probabilities between
This document provides an overview of transportation and assignment problems in operations research. It discusses the key characteristics and formulations of transportation models, including how to obtain initial basic feasible solutions using different methods like the Northwest Corner Rule and Vogel's Approximation Method. It also covers testing for optimality using the Modified Distribution method and how to handle unbalanced transportation problems. For assignment problems, the document outlines the Hungarian method for obtaining optimal solutions to assignment problems and how to deal with constrained variants like unbalanced or prohibitive assignment problems.
The document discusses transportation problems and their solutions. It begins by outlining the typical issues in operations research, such as formulating the problem, building a mathematical model with decision variables, objective functions and constraints, and optimizing the model. It then discusses how transportation problems can be formulated as linear programs and provides an example manufacturer situation. The document outlines the solution procedure for transportation problems, including finding an initial feasible solution using methods like the Northwest Corner, Least Cost and Vogel's Approximation methods. It also discusses finding the optimal solution using methods like the Stepping Stone and Modified Distribution methods. It concludes by mentioning some special cases in transportation problems.
Multi – Objective Two Stage Fuzzy Transportation Problem with Hexagonal Fuzzy...IJERA Editor
Fuzzy geometric programming approach is used to determine the optimal solution of a multi-objective two stage fuzzy transportation problem in which supplies, demands are hexagonal fuzzy numbers and fuzzy membership of the objective function is defined. This paper aims to find out the best compromise solution among the set of feasible solutions for the multi-objective two stage transportation problem. To illustrate the proposed method, example is used
The document summarizes transportation problems and assignment problems. It discusses how transportation problems seek to minimize shipping costs by determining optimal routes from sources to destinations. Assignment problems similarly aim to minimize costs by optimally assigning workers to jobs. The document outlines the linear programming formulations and network representations of these problems. It also describes specialized algorithms like the Hungarian method for solving assignment problems and the MODI method for obtaining reduced costs in transportation problems.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
This document discusses transportation problems and their solution. It begins by stating the aim is to find an optimal transportation schedule that minimizes transportation costs. It then provides an example transportation problem table and defines key terms. The remainder of the document explains assumptions of transportation models, their applications, and steps to solve them. It covers obtaining an initial basic feasible solution using methods like the Northwest Corner Rule, Least Cost Method, and Vogel's Approximation Method. It also discusses obtaining the optimal basic solution using the Stepping Stone Method.
Efficient Solution of Two-Stage Stochastic Linear Programs Using Interior Poi...SSA KPI
The document describes efficient solution methods for two-stage stochastic linear programs (SLPs) using interior point methods. Interior point methods require solving large, dense systems of linear equations at each iteration, which can be computationally difficult for SLPs due to their structure leading to dense matrices. The paper reviews methods for improving computational efficiency, including reformulating the problem, exploiting special structures like transpose products, and explicitly factorizing the matrices to solve smaller independent systems in parallel. Computational results show explicit factorizations generally require the least effort.
The document summarizes the transportation problem and assignment problem from operations research. The transportation problem seeks to minimize shipping costs from sources to destinations, and can be formulated as a linear program. The assignment problem assigns workers to jobs at minimum cost and is a special case of the transportation problem. Both problems can be solved using specialized algorithms like the MODI method, stepping stone method, and Hungarian method.
The document summarizes the transportation problem and assignment problem from operations research. The transportation problem seeks to minimize shipping costs from sources to destinations, and can be formulated as a linear program. The assignment problem assigns workers to jobs at minimum cost and is a special case of the transportation problem. Both problems can be solved using specialized algorithms like the MODI method, Hungarian method, or by modeling them as linear programs and using the simplex method.
The document discusses the transportation problem and how to solve it. The transportation problem aims to minimize the cost of transporting goods from multiple sources to multiple destinations, given supply and demand constraints. It describes the mathematical formulation and defines key terms like feasible and basic feasible solutions. It also outlines several methods to obtain the initial basic feasible solution, including the Northwest Corner Rule, Least Cost Method, and Vogel's Approximation Method. Finally, it discusses the Modi Method for obtaining the optimal basic solution through iterative testing of cell evaluations.
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....Lacey Max
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
Profiles of Iconic Fashion Personalities.pdfTTop Threads
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Ellen Burstyn: From Detroit Dreamer to Hollywood Legend | CIO Women MagazineCIOWomenMagazine
In this article, we will dive into the extraordinary life of Ellen Burstyn, where the curtains rise on a story that's far more attractive than any script.
The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Call8328958814 satta matka Kalyan result satta guessing➑➌➋➑➒➎➑➑➊➍
Satta Matka Kalyan Main Mumbai Fastest Results
Satta Matka ❋ Sattamatka ❋ New Mumbai Ratan Satta Matka ❋ Fast Matka ❋ Milan Market ❋ Kalyan Matka Results ❋ Satta Game ❋ Matka Game ❋ Satta Matka ❋ Kalyan Satta Matka ❋ Mumbai Main ❋ Online Matka Results ❋ Satta Matka Tips ❋ Milan Chart ❋ Satta Matka Boss❋ New Star Day ❋ Satta King ❋ Live Satta Matka Results ❋ Satta Matka Company ❋ Indian Matka ❋ Satta Matka 143❋ Kalyan Night Matka..
Digital Marketing with a Focus on Sustainabilitysssourabhsharma
Digital Marketing best practices including influencer marketing, content creators, and omnichannel marketing for Sustainable Brands at the Sustainable Cosmetics Summit 2024 in New York
SATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSS
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
Dive into this presentation and learn about the ways in which you can buy an engagement ring. This guide will help you choose the perfect engagement rings for women.
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
HR search is critical to a company's success because it ensures the correct people are in place. HR search integrates workforce capabilities with company goals by painstakingly identifying, screening, and employing qualified candidates, supporting innovation, productivity, and growth. Efficient talent acquisition improves teamwork while encouraging collaboration. Also, it reduces turnover, saves money, and ensures consistency. Furthermore, HR search discovers and develops leadership potential, resulting in a strong pipeline of future leaders. Finally, this strategic approach to recruitment enables businesses to respond to market changes, beat competitors, and achieve long-term success.