Implementing Pairwise Interchange Heuristics

I have 3 different python scripts: lap.py, lap1.py, and lap2.py. They are as follows:

lap.py

import sys
import copy

sys.path.append('c:\\svns\\code\\python')
from parseCSV import parseCSV


def initialize(fname, show):
    costs = parseCSV(fname)
    people = []
    tasks = []
    for k in range(len(costs)):
        people.append(-1)
        tasks.append(-1)
    if show:
        print '{} people, {} tasks, {}x{} costs, lb = {:.2f}'.format(
            len(people), 
            len(tasks), 
            len(costs), 
            len(costs),
            simple_lb(costs))
    return costs, people, tasks

def show_solution(costs, people, tasks):
    for k in range(len(people)):
        if people[k] > -1:
            task = 'T{}'.format(people[k])
            cost = costs[k][people[k]]
        else:
            task = 'n/a'
            cost = 0.0
        print '\tP{}, {}, {:.2f} '.format(k, task, cost)
    print '\nTotal cost: {:.2f} (lower bound: {:.2f})'.format(
        calc_cost(costs, people)[0],
        simple_lb(costs)
        )

def calc_cost(costs, people):
    total_cost = 0
    num_assigned = 0
    for k in range(len(people)):
        if people[k] != -1:
            total_cost += costs[k][people[k]]
            num_assigned += 1
    return total_cost, num_assigned

def low_cost_task(costs, person, tasks):
    min_cost = 1e308 
    min_idx = -1
    for k in range(len(tasks)):
        if tasks[k] == -1:
            if costs[person][k] < min_cost:
                min_cost = costs[person][k]
                min_idx = k
    return min_idx, min_cost

def simple_lb(costs):
    total_cost1 = 0;
    for k in range(len(costs)) :
        total_cost1 += min(costs[k])
    total_cost2 = 0;
    for k in range(len(costs)):
        total_cost2 += min([c[k] for c in costs])
    return max(total_cost1, total_cost2)

def clear_solution(people, tasks):
    for k in range(len(people)):
        people[k] = -1
    for k in range(len(tasks)):
        tasks[k] = -1

def store_solution(people, tasks, cost, seq):
    solution = {}
    solution['people'] = copy.copy(people)
    solution['tasks'] = copy.copy(tasks)
    solution['obj_val'] = cost
    solution['seq'] = copy.copy(seq)
    return solution

def main():
    fname = 'r.csv'
    if len(sys.argv) > 1:
        fname = sys.argv[1]
    costs, people, tasks = initialize(fname, 1)
    for k in range(len(people)):
        people[k] = k;
        tasks[k] = k;
    print '\nSolution:'
    show_solution(costs, people, tasks)

if __name__ == '__main__' : main()

lap1.py

import sys
import lap

show_intermediate = 1
fname = 'r.csv'
if len(sys.argv) > 1:
    fname = sys.argv[1]
    if len(sys.argv) > 2:
        show_intermediate = int(sys.argv[2])
costs, people, tasks = lap.initialize(fname, 1)
for k in range(len(people)):
    task, min_cost = lap.low_cost_task(costs, k, tasks)
    people[k] = task
    tasks[task] = k
    if show_intermediate:
        if people[k] != -1:
            print 'Assigned P{} task T{} at cost {:.2f}'.format(
                k,
                people[k], 
                min_cost
                )
print '\nFinal solution:'
lap.show_solution(costs, people, tasks)

lap2.py

import sys
from random import sample
import lap

def solve(costs, people, tasks, seq):
    total_cost = 0
    for k in seq:
        task, min_cost = lap.low_cost_task(costs, k, tasks)
        people[k] = task
        tasks[task] = k
        total_cost += min_cost
    return total_cost

def main():
    nreps = 100
    fname = 'r.csv'
    if len(sys.argv) > 1:
        fname = sys.argv[1]
        if len(sys.argv) > 2:
            try:
                nreps = int(sys.argv[2])
            except:
                print 'Invalid parameters.  Syntax: lab_h2.py fname nreps'
                exit()

    costs, people, tasks = lap.initialize(fname, 1)
    cost = solve(costs, people, tasks, range(len(people)))
    solution = lap.store_solution(people, tasks, cost, range(len(people)))
    print 'Iteration {:3d}  Cost: {:.2f}'.format(-1, solution['obj_val'])
    for k in range(nreps):
        lap.clear_solution(people, tasks)
        seq = sample(range(len(people)), len(people))
        cost = solve(costs, people, tasks, seq)
        print 'Iteration {:3d}  Cost: {:.2f}'.format(k, cost)
        if cost < solution['obj_val'] :
            solution = lap.store_solution(people, tasks, cost, seq)
    print '\nFinal solution:'
    print 'Sequence: {}'.format(solution['seq'])
    lap.show_solution(costs, solution['people'], solution['tasks'])

if __name__ == '__main__' : main()

I would like to implement the random pairs pairwise interchange heuristic for the LAP, using 100,000 random pairs, but I do not know how to continue after developing the three scripts. Any help would be appreciated