Solving capacity problems as asymmetric

International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 2, March 2015
DOI : 10.5121/ijaia.2015.6204 53
SOLVING CAPACITY PROBLEMS AS ASYMMETRIC
TRAVELLING SALESMAN PROBLEMS
Tad Gonsalves and Takafumi Shiozaki
Department of Information and Communication Sciences,
Faculty of Science & Technology, Sophia University, Tokyo, Japan
ABSTRACT
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
KEYWORDS
Capacity Problems, Travelling Salesman Problem, Ant Colony Optimization, Swarm Intelligence, Soft
Computing.
1. INTRODUCTION
Rail capacity optimization is a prominent application problem in the transportation domain.
Railway domain experts define the railway capacity as the maximum number or pair of trains
with standard load passing by a fixed equipment in unit time on the basis of the given type of
locomotives and vehicles [1]. The optimal capacity usually depends on the condition of the fixed
equipment as well as the organization of the train operation. According to the European
Community directives, the provision, maintenance and marketing of the railway track capacities
should be separated from the operation of trains [2]. This would imply a separation of the
management of the railway infrastructure from the management of the railway operation. With
this in mind, we frame the aim of this study as: the optimization of rail capacity by managing the
train operation, given a fixed railway network and equipment infrastructure. In particular, we
focus on the capacity of a terminal station, i.e., the number of trains departing from the terminal
station in unit time. The problem basically boils down to constructing an optimal schedule of the
passenger trains so as to maximize the terminal station capacity. However, owing to the multiple
decision variables, the problem becomes a typical combinatorial optimization problem which
cannot be solved using the conventional optimization algorithms.

54
The Travelling Salesman Problem (TSP) is one of the most studied classical combinatorial
optimization techniques. We review the literature on TSP and its variants and on the various
computing techniques evolved by the operations research community to find the TSP solutions in
reasonable computational time. We model the rail capacity optimization problem as a form of an
asymmetric TSP and use some of the soft–computing techniques to find the optimal solution. The
TSP is known to be NP-hard problem. Apart from the exact methods [3], some of the
conventional heuristic techniques designed to solve the TSP include branch and cut [4], dynamic
programming [5], regression analysis [6], etc. Recently many meta-heuristic algorithms (i.e.
heuristics that do not depend on the domain knowledge of the problem) are successfully
employed to search for the optimal TSP solution. The Genetic Algorithm (GA) based on the
Darwinian theory of natural selection and its variants are reported to be successful in finding the
optimal solutions to the benchmark TSP problems in a reasonable amount of computing time [7-
10].
The most successful soft computing algorithm to obtain the optimal solution of the TSP is the Ant
Colony Optimization (ACO) algorithm. The development of the ACO algorithm has been
inspired by the foraging behaviour of some ant species. When foraging for food, the ants deposit
pheromone on the ground in order to mark some favourable path that should be followed by other
members of the colony. The ACO algorithm exploits a similar mechanism for solving
optimization problems [11-14]. Among the better performing ACO algorithms are variants such
as Ant Colony System (ACS) [15-16], MAX-MIN Ant System (MMAS) [17-19], rank-based Ant
System [20] and the cunning Ant System (cAS) [21].
In some studies, the ACO algorithm is combined with the Genetic Algorithm to improve the
optimization results of the TSP [22-25]. The Particle Swarm Optimization (PSO) and the Ant
Colony Optimization (ACO) are two well-known paradigms of Swarm Intelligence, which is a
rapidly developing branch of Computational Intelligence. PSO-ACO hybrid algorithms
performing better than each the individual algorithms in the solution of the TSP benchmark
problems are also reported in literature [26-28]. The artificial immune system algorithm based on
the immunological model of the vertebrates [29] is also hybridized with ACO to improve the
latter’s performance [30].
In this study, the railway capacity optimization problem is cast in the form of an asymmetric TSP.
The arrival/departure events in the schedule are treated as nodes which need to be ordered under
the given scheduling constraints so as to minimize the entire schedule time. Some of the other
constraints are imposed by the track-changing hardware equipment. The time between two events
is considered to be the distance between two TSP edges and the train operation schedule is
considered to be the tour length of the TSP. The standard ACO application to this problem yields
an optimal schedule, under the given infrastructure and operational constraints.
This paper is organized as follows: Section 2 reviews the literature on TSP and its variations.
Section 3 describes the TSP and the design of the ACO as its appropriate solution methodology.
Section 4 describes the formulation of the railway capacity optimization problem (RCP) as the
ATSP and its solution using the standard ACO algorithm. Section 5 presents the simulation
optimization results and section 6 concludes the paper.

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2. LITERATURE REVIEW
This section describes the different variations of the TSP and its applications to some real-life
problems.
2.1 TSP Variations
The Travelling Salesman Problem (TSP) can be stated as: Given N number of cities in a round-
tour, a salesman has to visit each city exactly once before returning to the starting city. What
should be the order of visiting the cities so that the length of the route becomes minimal? Simply
stated, the TSP deals with the determination of a permutation of N number of nodes so as to yield
a minimum cost of traversing through them. The TSP is a well-known classical combinatorial
optimization. It is proved to be NP-hard [31] and forms the corner stone as abenchmark problem
in operations research and combinatorial optimization studies. Variations of the TSP, as described
below, are also found in literature.
Asymmetric and symmetric TSP
The original TSP has two variants - the symmetric TSP (STSP) and the asymmetric TSP (ATSP)
[32]. In the case of the STSP, the distance between two cities is the same in each opposite
direction, so also the cost. In the ATSP, the distance between two cities is the different when
travelled in the opposite direction; the cost may also be different; at times only one-way traffic is
allowed on a path leading from one city to the next. Solving the TSP is comparatively easier than
the ATSP, since the symmetry reduces the number of feasible solutions to half.
The Generalized Traveling Salesman Problem (GTSP)
The Generalized Traveling Salesman Problem (GTSP) is a variant of the standard Traveling
Salesman Problem (TSP). As in the TSP, the graph considered consists of n nodes, and the cost
between any two nodes is known. The GTSP differs from the TSP in that the node set is
partitioned into m clusters. An optimal GTSP solution is a cycle of minimal cost that visits
exactly one node from each cluster [33].
Travelling Purchaser Problem (TPP)
The Travelling Purchaser Problem (TPP) deals with a set of m markets, and a set of p products
that the salesman must purchase. Each product is available, although with different quantities, in
a subset of markets. However, the price of each of the products depends on the market where it is
available. The demands for each product and the cost of traveling are the givens of the problem.
In TPP, the objective is to purchase the whole demand of products, departing and returning to a
depot, so as to minimize the sum of the costs of travelling and of the products purchased [34].
The applications of the TPP are mainly in the contexts of networking, routing and scheduling.
Heuristics and approximate algorithms to solve the TTP are found in [35-36], while TPP
applications and soft computing solutions techniques are found in [34], [37-38].

56
2.2 TSP Applications
Although a lot of research has been done in developing exact and approximate solution methods
of the TSP, most of these studies have been restricted to testing the performance of the solution
methods to the benchmark problems. In comparison, the applications of the TSP to real-life
problems appear to be not too many. The typical TSP applications reported in literature are
Vehicle Routing, Job Sequencing and Computer Wiring [39]. Other applications include
warehouse order-picking with multiple stock locations, airport selection/routing for courier
planes, and certain types of flexible manufacturing scheduling [40], clustering proteins from
protein interaction network [41], mobile testing [38] and chip insertion in electronic board
assembly [42].
The Railway Traveling Salesman Problem (RTSP) presented in [43-45] is an extension of the
Generalized Asymmetric Traveling Salesman Problem (GATSP). In the RTSP, a salesman using
the railway network wishes to visit a certain number of cities on business, starting and ending at
the same city, and having as goal to minimize the overall time of the journey. The RSP uses the
given railway network and train schedules and tries to minimize the overall journey time,
including the sojourn time. The RCP introduced in this study is very different from the RTSP.
Given a railway network, the RCP tries to determine the train schedule so as to maximize the
capacity of a given network of trains. It is not a TSP. However, it can be formulated as a TSP by
treating the train scheduling events as nodes of the TSP, as described in section 4.
3. TSP AND ACO
In this section, we introduce the Travelling Salesman Problem and the Ant Colony Optimization
algorithm. We show how the Ant Colony Optimization algorithm is designed to solve the
Travelling Salesman Problem.
3.1. Travelling Salesman Problem (TSP)
The Travelling Salesman Problem (TSP) is a classic problem in computer science which may be
stated as follows: Given a list of cities and their pairwise distances, the task is to find the
Figure 1. Three feasible routes of a 6-node TSP
shortest possible route that visits each city exactly once and then return to the original city. If n is
the number of cities to be visited, the total number of possible routes covering all cities, Sn is
given by:

57
A naive solution solves the problem in O(n!) time, simply by checking all possible routes, and
selecting the shortest one. A more efficient dynamic programming approach yields a solution in
O(n22n) time [3]. The TSP is proved to be NP-hard and various Operation Research (OR)
solution techniques have been proposed, yielding varying degrees of success [4-7]. The Ant
Colony Optimization, described in the following sub-section is a novel soft computing algorithm
developed to tackle combinatorial optimization problems like the TSP.
3.2. Ant Colony Optimization
The Ant Colony Optimization (ACO) which is based on the foraging behaviour of ants was first
proposed by Dorigo [11]. A generic ACO algorithm is shown in Figure. 2. In step 1, the
algorithm parameters are initialized and all the artificial ants (random solutions) are generated.
The steps inside the loop consist of evaluating the solutions, updating the pheromones and
constructing new solutions from the previous solutions. This loop is repeated until the termination
condition is met. The two main steps inside the loop are further described below.
Figure 2. The ACO algorithm

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Solution construction
Ant k on node i selects node j, based on the probability, pij, given by:
where denotes the set of candidate sub-solutions; τij and ηij denote, respectively, the
pheromone value and the heuristic value associated with eij.
Updating the pheromone
The pheromone update operator employed for updating the pheromone value of each edge eij is
defined as:
Where Lk
denotes the quality of the solution created by ant k; denotes the evaporation
rate.
4. CAPACITY PROBLEM AS TSP
This section describes in detail the railway capacity problem to be optimized. It explains the
optimization constraints, the framing of the railway capacity problem as a typical asymmetric
TSP and finally the solution process by using the standard ACO algorithm.
4.1. Capacity Problem
When dealing with the railway capacity problem, the railway management has to consider the
different types of capacities in the railway domain. Some of the relevant capacities, for instance,
are: (1) the capacity of the platform to hold passengers, (2) the capacity of the carriages to hold
passengers, (3) the rail network capacity to hold the number of trains at a given time, and (4) the
capacity of the railway station structure to schedule the maximum number of trains per unit time.
Dealing with all these types of capacities simultaneously is a complex problem. This study is
dedicated to the maximization of only the type 4 railway capacity, i.e., maximization of the
number of trains that can be scheduled at a railway station per unit time. The type 4 rail capacity
optimization in turn leads to optimization of the royalties and alleviation of the congestion
problem during rush hours.

59
4.2. Capacity problem as TSP
In the generalized form of the TSP, the cities are represented as nodes (Figure 3a). The task is
then finding the shortest route, starting from a given node and visiting each node in the network
exactly once before returning to the starting node. In the Railway Capacity Optimization (RCP)
problem, the arrival/departure events (Figure 3b) in the schedule are treated as nodes which need
to be ordered under the given scheduling constraints so as to minimize the entire schedule time.
Since the time interval between any two events ei and ej is not necessarily the same as the time
interval between ej, and ei the problem is clearly asymmetric.
Figure 3a. The TSP nodes (cities) Figure 3b. The RCP nodes (events)
4.3. Structure constraints
In this study, we consider a railway terminal station with four railroads, each with an attached
platform. The trains can arrive at the terminal and leave the terminal via any of these four
railroads. There are five train services, namely, S55, S5, L7, S2 and S1 and the railroad access
constraints are given in Table 1.
Figure 4. The platforms in the terminal station

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Operational Constraints
a) For a given train service, there cannot be a departure event before the arrival event.
b) After arriving on a platform, the train must stop there for a minimum period of 110 seconds.
c) All train services must be scheduled as exactly one train per hour, except for L7 which may be
scheduled at most two trains per hour.
d) At the beginning of the train schedule (at time t = 0) all trains are being stopped inside the
terminal station. This implies that the first event for each train is the departure event from the
terminal station.
e) There is a certain amount of “track recovery time” between two consecutive arrival-departure
events.
Table 1. Given parameters of the train capacity problem
5. OPTIMIZATION RESULTS
The aim of the simulation experiments using the ACO algorithm is to maximize the number of
trains leaving the terminal station in an hour. However, to reduce the calculation load, we divide
the hourly interval into 5 equal intervals, each being of 720 seconds (12 minutes) duration. The
assumption here is that the train schedule is periodic. The same period can then be repeated over
an hour’s interval. In Table 2, the final capacity of the terminal station is calculated by using the
following formula:
where, T is the total time for the entire schedule covering a period of 720 seconds.
The optimal schedule produced by the ACO algorithm is shown in Table 2. The column to the left
indicates the train service arriving at or departing from the platform shown in the adjoining
column. The time column indicates the time at which the arrival or the departure event takes
place. The minimum time for the entire schedule over a period of 720 seconds is found to be 555
seconds and correspondingly the maximum capacity is 38.9 trains/hour.

61
Table 2. The optimal schedule
Table 3. Varying the algorithm parameters
We conducted several experiments by varying the ,  and  parameters of the ACO algorithm.
Some of the optimal results obtained by these tuned parameters are shown in Table 3. Another
important parameter that needs an empirical tuning is the population size of the ant colony, N.
Table 4 shows the results obtained by varying this number. As expected, the larger the population
size, the better the results are, although this increases the computational overhead.

62
Table 4. Varying the population size of the ACO agents
6. CONCLUSIONS
The Ant Colony Optimization soft computing algorithm is apt for solving combinatorial
optimization problems like the classical NP-hard Travelling Salesman Problem. Basing the search
on the stigmery of the food foraging real-life ant colony, the algorithm explores the huge search
space of the NP-hard problems to find the optimal solution. In this study, the authors have applied
the ACO algorithm to optimize the capacity of a terminal railway station. The capacity
optimization problem is cast into the form of an asymmetric TSP-like problem, where the arrival
and departure events of the trains are considered to be the nodes and the schedule length as the
TSP total route. The standard ACO optimizes the schedule length subject to the infrastructure and
operational constraints. The simulation experiments validate the formulation of the railway
capacity problem as an asymmetric TSP. The optimal solutions obtained by the soft-computing
technique are superior to those produced by the domain experts.
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AUTHORS
Dr. Tad Gonsalves obtained the BS degree in theoretical Physics and the MS degree in Astrophysics from
the Pune University. He obtained the PhD in Systems Engineering from Sophia University, Tokyo, Japan.
Currently he is an Assistant Professor in the Department of Information and Communication Sciences,
Faculty of Science & Technology in the same university. His research interests include design of Expert
Systems, Evolutionary Algorithms, Machine Learning and Parallel Programming.
Takafumi Shiozaki is an under-graduate student is the Department of Information and Communication
Sciences, Faculty of Science & Technology, Sophia University, Tokyo, Japan. His research is on the
application of the Evolutionary Algorithms to diverse real-world problem

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