# Multi-Agent Logistics Optimization

This is a simplified version of problem faced by logistics company.

Problem: Given there are # dispatchers and # of locations for delivery, optimize the paths for each dispatcher. \begin{aligned} & \min & & \sum_{i,j,k} W_{ij}X_{ijk} \\ & \text{where} & & W := \text{Euclidean distance matrix} \\ &&& X := \text{Adjacency matrix} \\ & \text{s.t.} & & i, j \in \{1, \dots, \#Locations \} \\ &&& k \in \{1, \dots, \#Dispatchers \} \\ \end{aligned}

Solution:

Linear Programming. Computing adjacency matrices for all dispatcher in a single run is very expensive and slow. One could adopt divide and conquer strategy for scalability. For example, during the first iteration, set number of dispatcher to 1 to get overall shortest path, then cut it into 2 and rerun the optimizer for each of the parts. More constraints can be introduced to set the minimum and maximum work to be carried out by each of the dispatcher.

from pulp import *
# X to be solved. X is the adjacency matrices. #num_dispatcher X adjacency matrix
range_num_loc,
range_num_loc),0,1,LpInteger)

prob = LpProblem("Logistics Problem", LpMinimize)

for i in range_num_loc:
for k in range_num_dispatcher:
# Ensuring no self-recursion.
prob += choices[k][i][i] == 0, ""

# Ensuring one route will only be taken up by one dispatcher.
for j in range_num_loc[int(i):]:
for k in range_num_dispatcher if i != j] +
for k in range_num_dispatcher if i != j]) <= 1, ""
if int(i) > 0:
# Ensuring every location is covered.
prob += lpSum([adj_mats[k][i][j] for j in range_num_loc
for k in range_num_dispatcher if i != j]) == 1, ""
prob += lpSum([adj_mats[k][j][i] for j in range_num_loc
for k in range_num_dispatcher if i != j]) == 1, ""

for k in range_num_dispatcher:
# Ensuring every dispatcher will go through the distribution centre.
prob += lpSum([adj_mats[k]["0"][j] for j in range_num_loc[1:]]) == 1, ""
prob += lpSum([adj_mats[k][i]["0"] for i in range_num_loc[1:]]) == 1, ""

for i in range_num_loc[1:]:
# Ensuring exit of location if entered.
prob += lpSum([adj_mats[k][i][j] for j in range_num_loc] + [adj_mats[k][j][i] for j in range_num_loc]) == lpSum([adj_mats[k][i][j] for j in range_num_loc]) * 2 , ""

# Objective function - Minimize travelling.
prob += lpSum([adj_mats[k][i][j] * euc_mat[int(i), int(j)] for k in range_num_dispatcher for i in range_num_loc for j in range_num_loc])

prob.solve()