job assignment problem using branch and bound in python

Branch and Bound Search with Examples and Implementation in Python

We’ll try to understand one of the heuristic search techniques in this article. The heuristic technique is a criterion for determining which among several alternatives will be the most effective in achieving a particular goal. Branch and bound search is also known as Uniform Cost Search.

What is the branch and bound search algorithm?

Branch and bound is a search algorithm used for combinatory, discrete, and general mathematical optimization problems. It is comparable to backtracking in that it similarly implements a state-space stream to represent the solution to the problem.

However, it is probably more suited to trying to address optimization problems and only minimization problems, not maximization problems. Statistically speaking, a branch and the bound algorithm find the best solution from the entire search space of possibilities for an NP-Hard problem.

How does the branch and bound search work?

In the branch and bound search strategy, a cost function (denoted by g(X)) is generated that, by using a sequence of operators, assigns a cumulative cost to the path from the start node to the current node X. A cheapest price path already discovered is extended at every step of the search space generation process until we reach the goal state.

Branch and bound search is also referred to as a uniform cost search since it expands the least-cost partial path. The actual distance traveled from the beginning to the current node X, for instance, may be represented as g(X) in the traveling salesman problem.

Steps for the algorithm

If g(X) = 1 for all operators, the branch and bound methodology degenerates into a straightforward breadth-first search. Artificial intelligence considers it to be just as detrimental as depth-first and breadth-first. If we add dynamic programming to it, we can make this better by eliminating redundant paths.

We note that the method typically necessitates creating a solution and evaluating its efficacy. Any technique can be used to develop the answer, and heuristics may be used in testing. The following is the basic structure of an algorithm for developing and testing strategies.

Brand and bound search algorithm in action

To understand the concept more clearly, let’s try to implement the 8 puzzle problem using the branch and bound algorithm. The problem description is given below.

A 3 x 3 board with 8 tiles (each tile has a number ranging from 1 to 8) and a single empty space is provided. The goal is to use the vacant space to arrange the numbers on the tiles so that they match the final arrangement. Four neighboring (left, right, above, and below) tiles can be slid into the available area.

For Example

To avoid searching in sub-trees that do not include an answer node, the search for an answer node can frequently be sped up using an approximation of the cost function. However, instead of using the backtracking method, it does a BFS-style search.

Basically, Branch and Bound involve three different kinds of nodes.

• A live node is a generated node whose children have not yet been produced.
• The children of the E-node, a live node, are now being examined. Or to put it another way, an E-node is a node that is currently expanding.
• A created node that is not to be developed or examined further is referred to as a dead node. A dead node has already extended all of its children.

Cost function: In the search tree, each node X has a corresponding cost. The next E-node can be found using the cost function. The E-node with the lowest cost is the next one. The definition of the cost function is

Implementing the Branch and Bound Search algorithm in Python

In this article, we have learned one of the most effective algorithms knowns as a branch and bound search. This search algorithm helps to solve many common problems like the N-Queen problem, 0-1 Knapsack Problem, Traveling salesman problem, etc. The algorithm is bit modified in each case according to the conditions provided in the problem but the basic idea of the searching method remains the same as explained earlier.

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Or check our popular categories..., branch and bound | set 4 (job assignment problem).

Let there be N workers and N jobs. Any worker can be assigned to perform any job, incurring some cost that may vary depending on the work-job assignment. It is required to perform all jobs by assigning exactly one worker to each job and exactly one job to each agent in such a way that the total cost of the assignment is minimized.

Let us explore all approaches for this problem.

Solution 1: Brute Force We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O(n!).

Solution 2: Hungarian Algorithm The optimal assignment can be found using the Hungarian algorithm. The Hungarian algorithm has worst case run-time complexity of O(n^3).

Solution 3: DFS/BFS on state space tree A state space tree is a N-ary tree with property that any path from root to leaf node holds one of many solutions to given problem. We can perform depth-first search on state space tree and but successive moves can take us away from the goal rather than bringing closer. The search of state space tree follows leftmost path from the root regardless of initial state. An answer node may never be found in this approach. We can also perform a Breadth-first search on state space tree. But no matter what the initial state is, the algorithm attempts the same sequence of moves like DFS.

Solution 4: Finding Optimal Solution using Branch and Bound The selection rule for the next node in BFS and DFS is “blind”. i.e. the selection rule does not give any preference to a node that has a very good chance of getting the search to an answer node quickly. The search for an optimal solution can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an optimal solution. It is similar to BFS-like search but with one major optimization. Instead of following FIFO order, we choose a live node with least cost. We may not get optimal solution by following node with least promising cost, but it will provide very good chance of getting the search to an answer node quickly.

There are two approaches to calculate the cost function:

• For each worker, we choose job with minimum cost from list of unassigned jobs (take minimum entry from each row).
• For each job, we choose a worker with lowest cost for that job from list of unassigned workers (take minimum entry from each column).

In this article, the first approach is followed.

Let’s take below example and try to calculate promising cost when Job 2 is assigned to worker A.

Since Job 2 is assigned to worker A (marked in green), cost becomes 2 and Job 2 and worker A becomes unavailable (marked in red).

Now we assign job 3 to worker B as it has minimum cost from list of unassigned jobs. Cost becomes 2 + 3 = 5 and Job 3 and worker B also becomes unavailable.

Finally, job 1 gets assigned to worker C as it has minimum cost among unassigned jobs and job 4 gets assigned to worker C as it is only Job left. Total cost becomes 2 + 3 + 5 + 4 = 14.

Below diagram shows complete search space diagram showing optimal solution path in green.

Complete Algorithm:

Below is its C++ implementation.

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• Branch and Bound Tutorial
• Backtracking Vs Branch-N-Bound
• 0/1 Knapsack
• 8 Puzzle Problem
• Job Assignment Problem
• N-Queen Problem
• Travelling Salesman Problem
• Solve Coding Problems
• Share Your Experience
• Branch and Bound Algorithm
• Introduction to Branch and Bound - Data Structures and Algorithms Tutorial
• 0/1 Knapsack using Branch and Bound
• Implementation of 0/1 Knapsack using Branch and Bound
• 8 puzzle Problem using Branch And Bound

Job Assignment Problem using Branch And Bound

• N Queen Problem using Branch And Bound
• Traveling Salesman Problem using Branch And Bound
• DSA to Development Course

Let there be N workers and N jobs. Any worker can be assigned to perform any job, incurring some cost that may vary depending on the work-job assignment. It is required to perform all jobs by assigning exactly one worker to each job and exactly one job to each agent in such a way that the total cost of the assignment is minimized.

Let us explore all approaches for this problem.

Solution 1: Brute Force   We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O(n!).

Solution 2: Hungarian Algorithm   The optimal assignment can be found using the Hungarian algorithm. The Hungarian algorithm has worst case run-time complexity of O(n^3).

Solution 3: DFS/BFS on state space tree   A state space tree is a N-ary tree with property that any path from root to leaf node holds one of many solutions to given problem. We can perform depth-first search on state space tree and but successive moves can take us away from the goal rather than bringing closer. The search of state space tree follows leftmost path from the root regardless of initial state. An answer node may never be found in this approach. We can also perform a Breadth-first search on state space tree. But no matter what the initial state is, the algorithm attempts the same sequence of moves like DFS.

Solution 4: Finding Optimal Solution using Branch and Bound   The selection rule for the next node in BFS and DFS is “blind”. i.e. the selection rule does not give any preference to a node that has a very good chance of getting the search to an answer node quickly. The search for an optimal solution can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an optimal solution. It is similar to BFS-like search but with one major optimization. Instead of following FIFO order, we choose a live node with least cost. We may not get optimal solution by following node with least promising cost, but it will provide very good chance of getting the search to an answer node quickly.

There are two approaches to calculate the cost function:  

• For each worker, we choose job with minimum cost from list of unassigned jobs (take minimum entry from each row).
• For each job, we choose a worker with lowest cost for that job from list of unassigned workers (take minimum entry from each column).

In this article, the first approach is followed.

Let’s take below example and try to calculate promising cost when Job 2 is assigned to worker A. 

Since Job 2 is assigned to worker A (marked in green), cost becomes 2 and Job 2 and worker A becomes unavailable (marked in red). 

Now we assign job 3 to worker B as it has minimum cost from list of unassigned jobs. Cost becomes 2 + 3 = 5 and Job 3 and worker B also becomes unavailable. 

Finally, job 1 gets assigned to worker C as it has minimum cost among unassigned jobs and job 4 gets assigned to worker C as it is only Job left. Total cost becomes 2 + 3 + 5 + 4 = 14. 

Below diagram shows complete search space diagram showing optimal solution path in green. 

Complete Algorithm:  

Below is its C++ implementation. 

Output : 

Reference :  www.cs.umsl.edu/~sanjiv/classes/cs5130/lectures/bb.pdf

This article is contributed by Aditya Goel . If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above  

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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

• Solving Blending Problem in Python using Gurobi
• Transshipment Problem in Python Using PuLP

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A Basic Branch and Bound Solver in Python using Cvxpy

Jun 13, 2019 • philzook58

Branch and bound is a useful problem solving technique. The idea is, if you have a minimization problem you want to solve, maybe there is a way to relax the constraints to an easier problem. If so, the solution of the easier problem is a lower bound on the possible solution of the hard problem. If the solution of the easier problem just so happens to also obey the more constrained hard problem, then it must also be the solution to the hard problem. You can also use the lower bound coming from a relaxed problem to prune your search tree for the hard problem. If even the relaxed problem doesn’t beat the current best found, don’t bother going down that branch.

A standard place this paradigm occurs is in mixed integer programming. The relaxation of a binary constraint (either 0 or 1) can be all the values in between (any number between 0 and 1). If this relaxed problem can be expressed in a form amenable to a solver like a linear programming solver, you can use that to power the branch and bound search, also using returned solutions for possible heuristics.

I built a basic version of this that uses cvxpy as the relaxed problem solver. Cvxpy already has much much faster mixed integer solvers baked in (which is useful to make sure mine is returning correct results), but it was an interesting exercise. The real reason I’m toying around is I kind of want the ability to add custom branching heuristics or inspect and maintain the branch and bound search tree, which you’d need to get into the more complicated guts of the solvers bound to cvxpy to get at. Julia might be a better choice.

A somewhat similar (and better) project is https://github.com/oxfordcontrol/miosqp which doesn’t use cvxpy explicitly, but does have the branch and bound control in the python layer of the solver. There are also other projects that can use fairly arbitrary solvers like Bonmin

As a toy problem I’m using a knapsack problem where we have objects of different sizes and different values. We want to maximize the value while keeping the total size under the capacity of the bag. This can be phrased linearly like so: $ \max v \cdot x$ s.t. $ \sum_i s_i x_i<= capacity $, $ x \in {0,1}$. The basic heuristic I’m using is to branch on variables that are either 0 or 1 in even the relaxed solution. The alternative branch hopefully gets pruned fast.

This is at least solving the problem fairly quickly. It needs better heuristics and to be sped up, which is possible in lots of ways. I was not trying to avoid all performance optimizations. It takes maybe 5 seconds, whereas the cvxpy solver is almost instantaneous.

Edit : I should investigate the Parameter functionality of cvxpy. That would make a make faster version than the one above based on deepcopy. If you made the upper and lower vectors on the binary variables parameters, you could restrict the interval to 0/1 without rebuilding the problem or copying everything.

Branch and Bound

I. introduction, ii. illustration on the job assignment problem, iii. the general branch and bound algorithm, iv. criteria for the choice of approximate cost functions, v. implementation of the b&b job assignment algorithm, the general branch and bound algorithm.

• Branch and Bound Tutorial
• Backtracking Vs Branch-N-Bound
• 0/1 Knapsack
• 8 Puzzle Problem
• Job Assignment Problem
• N-Queen Problem
• Travelling Salesman Problem
• Branch and Bound Algorithm
• Introduction to Branch and Bound - Data Structures and Algorithms Tutorial
• 0/1 Knapsack using Branch and Bound

Implementation of 0/1 Knapsack using Branch and Bound

• 8 puzzle Problem using Branch And Bound
• Job Assignment Problem using Branch And Bound
• N Queen Problem using Branch And Bound
• Traveling Salesman Problem using Branch And Bound

Given two arrays v[] and w[] that represent values and weights associated with n items respectively. Find out the maximum value subset(Maximum Profit) of v[] such that the sum of the weights of this subset is smaller than or equal to Knapsack capacity Cap(W) .

Note: The constraint here is we can either put an item completely into the bag or cannot put it at all. It is not possible to put a part of an item into the bag.

Input: N = 3, W = 4, v[] = {1, 2, 3}, w[] = {4, 5, 1} Output: 3 Explanation: There are two items which have weight less than or equal to 4. If we select the item with weight 4, the possible profit is 1. And if we select the item with weight 1, the possible profit is 3. So the maximum possible profit is 3. Note that we cannot put both the items with weight 4 and 1 together as the capacity of the bag is 4. Input: N = 3, W = 4, v[] = {2, 3.14, 1.98, 5, 3}, w[] = {40, 50, 100, 95, 30} Output: 235

Implementation of 0/1 Knapsack using Branch and Bound:

We strongly recommend to refer below post as a prerequisite for this. Branch and Bound | Set 1 (Introduction with 0/1 Knapsack) We discussed different approaches to solve above problem and saw that the Branch and Bound solution is the best suited method when item weights are not integers. In this post implementation of Branch and Bound method for 0/1 knapsack problem is discussed. 

How to find bound for every node for 0/1 Knapsack? 

The idea is to use the fact that the Greedy approach provides the best solution for Fractional Knapsack problem. To check if a particular node can give us a better solution or not, we compute the optimal solution (through the node) using Greedy approach. If the solution computed by Greedy approach itself is more than the best so far, then we can’t get a better solution through the node. 

• Sort all items in decreasing order of ratio of value per unit weight so that an upper bound can be computed using Greedy Approach.
• Initialize maximum profit, maxProfit = 0
• Create an empty queue, Q.
• Create a dummy node of decision tree and enqueue it to Q. Profit and weight of dummy node are 0.
• Extract an item from Q. Let the extracted item be u.
• Compute profit of next level node. If the profit is more than maxProfit, then update maxProfit.
• Compute bound of next level node. If bound is more than maxProfit, then add next level node to Q.
• Consider the case when next level node is not considered as part of solution and add a node to queue with level as next, but weight and profit without considering next level nodes.

Following is implementation of above idea. 

Time complexity: O(2 n ) because the while loop runs n times, and inside the while loop, there is another loop that also runs n times in the worst case. Auxiliary Space : O(n) because the queue stores the nodes, and in the worst case, all nodes are stored, so the size of the queue is proportional to the number of items, which is n.

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tteguayco/Assignment-Problem-Branch-and-Bound

Folders and files, repository files navigation, assignment problem, description.

The assignment problem is solved using a Branch and Bound design and the programming language Java.

Mode of use

Store the files which includes the specifications of the problem to be solved in the 'data' directory. The file containing the specification of the problem to solve must be passed as a first argument and, optionally, a second file name can be passed to export the solution of the algorithm.

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