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A Simulated Annealing Approach for the Train Design Optimization Problem.

1. Introduction

An ample range of investigations have been undertaken to develop optimization algorithms for various problems encountered in the realm of rail systems, like routing [1], scheduling [2-4], crew assignment [5, 6], and blocking [7, 8], among others [9-12]. However, to our knowledge, the Train Design Optimization Problem as defined below is the only attempt to simultaneously deal with block-to-train assignment, train routing, and crew assignment [13], arising as one of the most fundamental and difficult combinatorial optimization problems formulated in the railroad industry [13-17], with a huge potential to benefit from the application of operations research techniques.

In rail systems, a railroad car, railcar, or train car (car, for short), is a vehicle used for the carrying of cargo. Such cars, when coupled together and hauled by one or more locomotives, form a train. A car block (block, for short) is a semipermanently arranged formation of cars. Trains are then built of one or more blocks coupled together as needed. Also, in the operation of rail systems a block swap can occur, namely, when a locomotive delivers a block at a station distinct from the block's destination, and another locomotive picks up the block afterwards. Each time a train stops en route at a station to pick up and/or to deliver blocks, a work-event takes place.

In this paper we address the Train Design Optimization Problem (TDOP) arising in the operation of railroad freight transport as part of the logistics chain. It consists in determining, at minimal total cost, and subject to capacity and operational constraints, the number of locomotives and crews, together with the logistics related to the movement of locomotives, blocks, and crews through a railway network, so as to transport goods from a set of shippers to a set of destinations.

The total cost depends on the number of assigned locomotives, the distance traveled by locomotives and cars, the distance traveled by crews to return to their departure stations, the number of block swaps and work-events, the number of blocks not arriving to destination, and the difference between the number of locomotives arriving to and departing from stations. The constraints include maximum number of blocks per locomotive, maximum number of block swaps, maximum number of work-events per locomotive, maximum length, weight, and number of trains passing through a railroad segment, and crew limitations as to the route to follow.

Only a handful of approaches have been developed for the solution of the TDOP. In [18] a mixed integer programming model is proposed; block routes are first generated together with train routes to cover them, then a matching is sought to minimize the objective function, and finally several greedy and local search rules are iteratively invoked to update the block-paths and train routes so as to improve the solution quality. In [19, 20] the problem is formulated as one of integer programming where the number of variables and constraints is exponential, proposing for its solution a column-row generation heuristic followed by clever tabu search methods. An iterative procedure is suggested in [21] to solve two subproblems of the TDOP: train design and block-to-train assignment, where the former consists in determining train routes to be operated, and the latter deals with the routing decision for blocks; both subproblems are solved by integer programming techniques. In [22, 23] a column-generation approach is designed: first, a set of promising train routes is generated based on the crew segments; then an integer linear programming model is developed for the subsequent decisions including train route selection and block-to-train assignment.

We propose hereafter a method to heuristically solve the TDOP in two steps. By means of an ad hoc procedure the first step aims to produce an initial feasible solution, namely, a solution satisfying all the constraints. The second step uses the simulated annealing method to improve the initial solution, followed by simple, specialized procedures that attempt to reduce the number of needed trains without increasing the overall cost. To test our proposal we solved the only three published instances to date, finding it competitive with other approaches in the literature. Also, we randomly generated 20 synthetic instances as well as (improvable) lower bounds for their corresponding optimal solutions; our heuristic came up with results averaging an error close to 25% on these bounds.

The rest of the paper is organized as follows: Section 2 provides the necessary terminology and makes a detailed description of constraints and objective function of the TDOP; a toy example is also furnished to help understand the problem. In Section 3 the TDOP is formulated in mathematical programming terms. Our solution approach--basically combining an ad hoc algorithm to find an initial feasible solution followed by the metaheuristic known as simulated annealing--is explained in Section 4. Further, Section 5 is devoted to the computational experiments that we conducted to test our procedures; for this experimentation we used the three available known instances as well as a new set of 20 randomly generated ones. Finally, Section 6 presents our conclusions and a proposal for future work.

2. Problem Description

This section is aimed to describe the TDOP, borrowing some terminology from [20]. An alternative description can be found in [13]. The TDOP terminology, constraints, and objective function are provided in Section 2.1. To help in grasping our description, an instance of the TDOP is presented in Example 1 by means of a toy example, followed by one of its feasible solutions in Section 2.2.

2.1. Terminology, Constraints, and Objective Function Terminology

(i) A block is a formation of cars sharing origin and destination. Trains are built of one or more blocks coupled together as needed.

(ii) A train is a locomotive carrying or not carrying blocks.

(iii) A block swap occurs when a block is moved from one train to another.

(iv) A block-path is a sequence of railroad segments through which a block can be feasibly routed from its origin to its destination.

(v) A work-event occurs each time a train stops en route at a station to pick up and/or deliver blocks. The train stop at its destination (or origin) station is not considered as a work-event.

(vi) A crew segment is a minimal length route between two stations, called end points, on which crews operate trains in either direction.

(vii) The crew imbalance on a crew segment, defined by say end points x and y, is the absolute difference between the number of crews going from x to y, and the number of crews going from y to x.

(viii) The train imbalance on a station, say x, is the absolute difference between the number of trains originating in x and the number of trains terminating in x.

(ix) A car is missed if it is not transported from its origin to its destination.


(i) Blocks per train: trains are constrained by an upper bound on the number of blocks they carry.

(ii) Swaps per block: each block is constrained by an upper bound on the number of times it can be swapped.

(iii) End points: crews must start and end traveling at end points.

(iv) Crew-to-train assignment: every train has to be assigned to a crew on each crew segment, and it must originate and terminate at the end points of a crew segment, even if the train has to move part of the way along a crew segment not carrying any blocks (the entire train routes should be decomposed in crew segments).

(v) Upper bounds on segments: railroad segments are constrained by upper bounds on the number, length, and weight of trains traversing them in either direction.

The objective of the TDOP is to minimize the sum of eight components:

(1) Locomotive cost: product of the number of scheduled locomotives and the unit locomotive cost [C.sub.1].

(2) Locomotive travel cost: product of the total traveled miles by all scheduled locomotives, and the per mile locomotive travel cost [C.sub.2].

(3) Work-event cost: product of the total number of work-events of all scheduled trains, and the unit work-event cost [C.sub.3].

(4) Car travel cost: product of the total traveled miles by all cars, and the per mile car travel cost [C.sub.4].

(5) Block swap cost: let s(v) be the unit block swap cost in station V. For a given block b, denote [beta](b) the set of stations where block b is swapped; then its (total) block swap cost is [[summation].sub.v[member of][beta](b)] s(v).

(6) Crew imbalance cost: product of the number of all crew imbalances and the unit crew imbalance cost [C.sub.5].

(7) Train imbalance cost: product of the number of all train imbalances and the unit train imbalance cost [C.sub.6].

(8) Missed car cost: product of the total number of missed cars and the cost per missed car [C.sub.7].

Example 1. Consider a railroad network with five stations A, B, C, D, and E and six railroad segments as schematically depicted in Figure 1, where distances (in miles) are assumed symmetrical and all segments are bidirectional. Seven blocks must be delivered. Table 1 furnishes the relevant data.

The only nonadjacent end points defining a crew segment are B and D. The corresponding crew segment is BCD because its length is minimal among all possible paths connecting B and D. Clearly, the other crew segments are trivially found. Thus, if the route of some train is, say, D [right arrow] C [right arrow] B [right arrow] A, then at D a crew from crew segment BCD could be assigned to this train in the subroute D [right arrow] C [right arrow] B, and subsequently at B a crew from crew segment BA could be assigned to the train in the subroute B [right arrow] A. When a train crosses over from one crew segment to another, the onboard crew gets off the train and a new crew gets onboard. Further, crew segments are bidirectional. Hence, crews in crew segment BA can take a train from A to B or vice versa. It is assumed that crews always travel along the shortest path between any pair of stations.

2.2. A Feasible Solution of Example 1. Consider the solution shown in Table 2. Two trains, [t.sub.1] and [t.sub.2], are scheduled to transport blocks. Note that train [t.sub.1] picks up 63 cars at station D and then goes to C, where it delivers 63 cars and picks up 5 cars. From station C train [t.sub.1] travels to B to pick up 42 cars; then it goes to station A to deliver 47 cars and to pick up 13 cars. Finally, it travels to station B to deliver 13 cars.

The distance traversed by both [t.sub.1] and [t.sub.2] is 1,273 miles. Train [t.sub.1] stops to pick up or drop blocks in stations C, B, and A, while train [t.sub.2] stops to pick up or drop blocks in stations D, B, and C. Hence, the total number of work-events produced by both trains is six.

Table 3 indicates the train on which each block travels between stations. For instance, block [b.sub.3] travels first on train [t.sub.1] from A to B, then on train [t.sub.2] from B to D, yielding one block swap (with cost 60).

The column labeled "Car miles" is computed as the product of the distance traveled by a block on a segment, and the number of cars in the block. Note that the block-to-train assignment satisfies the constraints on the maximum number of block swaps, and on the maximum number of blocks per train (see Tables 1(a) and 1(b)).

In Table 4 the crew-to-train assignment information is provided. It is shown, for instance, that a crew travels from D to B on train [t.sub.1]. Then another crew travels from B to A on train [t.sub.1]. Finally, this crew travels from A to B on the same train.

Note that there is one train imbalance in station B because it is the origin of no train, and one train terminates there. Similarly, there is one train imbalance in station E. Hence, this solution yields two train imbalances.

The objective function value of this solution is as much as 47,603, computed in Table 5 (costs in $).

3. The Model

We consider the railroad network as a directed graph G = (V, E) whose set of nodes V corresponds to the set of stations, and the set of arcs E is derived from the railroad segments between stations, associating a couple of arcs with opposite directions to each rail segment. The length of arc e = (u, v) [member of] E is equal to the distance between stations u, v [member of] V. Also, for e [member of] E, let [bar.L](e), [bar.W](e), and [bar.T](e) denote, respectively, the maximum train length, the maximum weight, and the maximum number of trains allowed to traverse arc (or segment) e in either direction.

Denote C the set of crew segments, where each crew segment c [member of] C is a path through a set of stations [chi](c)--including end points--and let H := [[union].sub.c[member of]C] [chi](c). We assume crews are always traveling on crew segments.

Let B be the set of blocks to be delivered. For block b [member of] B, its origin, destination, number of cars, length, and weight are denoted o(b), d(b), r(b), l(b), and w(b), respectively.

Let [GAMMA] be the set of all possible trains, and let P(x) be a generic sequence of arcs of E, with [L.sub.P(x)] denoting its length. Thus, the arc sequence followed by train t [member of] [GAMMA] is P(t), and that of block b is P(b); namely, P(b) is a block-path of b. In case train t [member of] [GAMMA] is used, let CR(t) and WE(t) be, respectively, the set of crews traveling on train t and the number of work-events of train t. Our mathematical formulation below closely follows the one proposed in [20].

Let ct(t) denote the cost of train t [member of] [GAMMA], which consists of the unit locomotive cost [C.sub.1], plus the train travel cost, and plus the cost of work-events. Hence,

ct(t) = [C.sub.1] + ([C.sub.2] x [L.sub.P(t)]) + ([C.sub.3] x WE (t)). (1)

Each train t [member of] [GAMMA] can be seen, in fact, as a sequence of crew segments, forming a path P(t). The subset of trains passing through arc e[member of]E is [T.sub.e] [member of] [GAMMA], and [K.sup.e.sub.t] is the number of times train t passes through e. From set [GAMMA] and graph G, one can obtain a multigraph MG = (V, A), where A is obtained by replacing each arc [mathematical expression not reproducible] copies of it. If a train passes several times through arc e, then each passage induces a different copy.

The binary variable [[lambda].sub.t] states whether train t [member of] [GAMMA] is used or not in the solution. Let [[DELTA].sub.v](t) be equal to -1 if train t starts its route in node v, and equal to +1 if train t ends its route in v. Thus, [[DELTA].sub.v] = [absolute value of ([[summation].sub.t[member of][GAMMA]] [[DELTA].sub.v](t)[[lambda].sub.t])] is the train imbalance in node v.

Let u and v be the end points of a crew segment c [member of] C. Then [[DELTA].sub.c](t) denotes the difference between the number of times train t goes through c, from u to v, and the number of times it goes through c from V to u. So, [[DELTA].sub.c] = [absolute value of ([[summation].sub.t[member of][GAMMA]] [[DELTA].sub.c](t)[[lambda].sub.t])] is the crew imbalance of crew segment c.

Let [[OMEGA].sub.b] denote the set of all block-paths P(b) associated with block b [member of] B, which includes a dummy block-path of one arc from o(b) to d(b) with cost [C.sub.7] x r(b). Further, let [[OMEGA].sup.t.sub.b] [subset or equal to] [[OMEGA].sub.b] be the subset of paths in [[OMEGA].sub.b] that use train t, and let [OMEGA] = [[union].sub.b[member of]B] [[OMEGA].sub.b] be the whole set of block-paths.

For b [member of] B, let [c.sub.b](b) denote the block-path cost for block b, namely, in case of a dummy block-path; then cb(b) = [C.sub.7] x r(b); otherwise

cb (b) = ([C.sub.4] x r (b) x [L.sub.P(b)]) + [summation over (v[member of][beta](b))] s(v), (2)

whose two terms correspond to block travel cost and block swap cost, respectively. Recall that r(b) is the number of cars of block b, each block swap at node v [member of] V costs s(v), and [beta](b) is the set of stations where b is block swapped.

Let [[OMEGA].sub.e] be the set of block-paths that use arc e [member of] A of multigraph MG. Note that there is one train corresponding to [[OMEGA].sub.e]. Binary variable [x.sub.P(b)] states whether block-path P(b) [member of] [OMEGA] is used or not in the solution.

Therefore, our mathematical model of the TDOP is

min z = [summation over (t[member of][GAMMA])] ct(t) [[lambda].sub.t] + [summation over (P(b)[member of][OMEGA])] cb (b) [x.sub.P(b)] + [C.sub.5][summation over (c[member of]C)] [[DELTA].sub.c] + [C.sub.6][summation over (v[member of]V)] [[DELTA].sub.v] (3)

[mathematical expression not reproducible] (4)

[mathematical expression not reproducible] (5)

[mathematical expression not reproducible] (6)

[mathematical expression not reproducible] (7)

[mathematical expression not reproducible] (8)

[mathematical expression not reproducible] (9)

WE (t) [less than or equal to] [M.sub.W], [for all]t [member of] [GAMMA] (10)

[x.sub.P(b)], [[lambda].sub.t] [member of] {0, 1}, [for all]P (b) [member of] [OMEGA], t [member of] [GAMMA]. (11)

The terms in the objective function (3) correspond to the costs of locomotives, blocks, crew imbalances, and train imbalances, in that order. Constraint (4) imposes the existence of a block-path--real or dummy--for each block b [member of] B. Constraint (5) forces selecting train t if any of the selected block-paths uses it. Constraints (6)-(10) stand for upper bounds on number of blocks that a train can transport, length and weight of trains passing through arcs, number of trains passing through arcs, and number of work-events per train, respectively. We emphasize that every sequence of train t [member of] [GAMMA] must be decomposed in a sequence of crew segments.

4. Algorithm

This section presents the algorithm developed by us to tackle the TDOP. It consists of two main steps.

Step 1 (initial feasible solution). Composed by substeps (1.1)-(1.3), this step is aimed to produce from scratch a feasible solution of the TDOP, namely, where constraints (6)-(11) are satisfied.

(1.1) Crew Selection. For every block b [member of] B determine a minimum length path [[xi].sub.b] from o(b) to d(b), where each arc can be operated by at least one crew. The set of crews [pi](b) to operate along path [[xi].sub.b] is chosen following substeps (1.1.1)-(1.1.5) below. For every [pi](b) a minimum set of trains is constructed, so as only one train corresponds to every pair of crews such that one ends where the other starts. Note that with this rule the corresponding trains to carry b can be easily deduced. Let Q(b) := {Q | Q be a set of crew segments covering the arcs of [[xi].sub.b]}.

In substep (1.1.4) every possible effort is made to avoid the overlapping of crews in Q(b); therefore, even though Q(b) contained overlapping crews, this is not an issue since the probability of this happening is truly negligible. In the sequel Q(b) will be simply written Q. Although Q is theoretically of exponential size, in all our experiments we were able to enumerate it completely.

(1.1.1) For Q [member of] Q, let [gamma](Q) be the number of swaps of block b when transported through crew segments Q. Determine [[gamma].sup.*] := min{[gamma](Q) : Q [member of] Q} and reduce Q by eliminating from it every Q such that [gamma](Q) > [[gamma].sub.*]. If Q has a unique element, say, [??], make [pi](b) := [??]; otherwise go to (1.1.2).

(1.1.2) If in Q there is a crew set starting in o(b), then reduce Q by eliminating from it every Q not starting in o(b). If Q has a unique element, say, [??], make [pi](b) := [??]; otherwise go to (1.1.3).

(1.1.3) If in Q there is a crew set ending in d(b), then reduce Q by eliminating from it every Q not ending in d(b). If Q has a unique element, say, [??], make [pi](b) := [??]; otherwise go to (1.1.4).

(1.1.4) Let [rho](Q) be the total distance traveled by the trains corresponding to the crew segments in Q [member of] Q, and let [[rho].sup.*] := min{[rho](Q) : Q [member of] Q}. Reduce Q by eliminating from it every Q such that [rho](Q) > [[rho].sup.*]. If Q has a unique element, say, [bar.Q], then make [pi](b) := [bar.Q]; otherwise go to (1.1.5). For example, assume we have a block with origin A and destination D, together with crews A-B-C, B-C-D, and A-B. One solution is to use crews A-B-C and B-C-D, while another uses crews B-C-D and A-B; with this criterion the second solution is chosen.

(1.1.5) Let [q.sup.*] = min{[absolute value of (Q)] : Q [member of] Q}. Reduce Q by eliminating from it every Q such that [absolute value of (Q)] > [q.sup.*]. If Q has a unique element, say, [??], make [pi](b) := [??]; otherwise make [pi](b) equal to an arbitrarily selected Q [member of] Q.

(1.2) Block Ordering. An order [bar.B] on the set of blocks B is constructed as follows. Let L([[xi].sub.b]) be the length of path [[xi].sub.b]. If L([[xi].sub.b']) > L([[xi].sub.b"]) then block b' precedes block b" in [bar.B]. However, if L([[xi].sub.b']) = L([[xi].sub.b"]), then b' precedes b" in [bar.B] whenever [nu](b') > [nu](b"), where [nu](x) denotes the number of blocks with origin o(x) and destination d(x). In case L([[xi].sub.b']) = L([[xi].sub.b"]) and [nu](b') = [nu](b") hold together, block b' precedes b" whenever r(b') > r(b"). Finally, if L([[xi].sub.b']) = L([[xi].sub.b"]), [nu](b') = [nu](b"), and r(b') = r(b"), this tie between b' and b" is arbitrarily broken to produce [bar.B].

(1.3) Train Assignment. Blocks are considered one after the other, according to order [bar.B] = ([b.sub.1], ..., [b.sub.m]). First, assign block [b.sub.1] to the crews selected in Step (1.1), together with the required train(s). For j = 2, ..., m, if path [xi]bj is contained in path [mathematical expression not reproducible] for some i [member of] [1, j - 1], then assign block [b.sub.j] to path [mathematical expression not reproducible] whenever upper bounds on feet, tons, and max blocks per train allow. Otherwise, assign block [b.sub.j] to the crews selected in Step (1.1), together with the required train(s). This step produces a feasible solution S.

Step 2 (simulated annealing). Proposed more than three decades ago [24, 25] Simulated Annealing (SA, for short) is one of the most successful metaheuristics to find good solutions of many combinatorial optimization problems (see [26] and the references therein), including the railroad freight transportation design problem [27]. In its theoretical formulation, SA converges with probability 1 to a global minimum under certain assumptions on the control parameters. In practice, these assumptions are impossible to be implemented, but adequate cooling schemes increase the likelihood of obtaining a near optimal solution [28].

Central to SA is the neighborhood concept. For our proposal we hand tailored it as follows. Let [PHI](J) denote the set of feasible solutions of an instance J of the TDOP. The neighborhood of a solution S [member of] [PHI](J) is

N (S) = [N.sub.1] (S) [union] [N.sub.2] (S) [union] [N.sub.3] (S), (12)

where (i) [N.sub.1](S) [subset or equal to] [PHI](J) contains every solution obtainable from S by adding one or more trains to deliver one block alone, say b, through [[xi].sub.b]. (ii) [N.sub.2](S) [subset or equal to] [PHI](J) contains the solutions obtainable from S by removing one block, say b, from the set [PSI] of trains carrying it and delivering it through a minimal length route from o(b) to d(b) without using any train of [PSI], using another trains in S. (iii) [N.sub.3](S) [subset or equal to] [PHI](J) contains every solution obtainable from S by the removal of one block, say b, from the set [PSI] of trains carrying it, and delivering it through a minimal length route from o(b)to d(b) without using any train of [PSI], with at least one added train, and using at least one existing train.

We implemented a simulated annealing procedure with geometrical cooling scheme as shown below, where the variable [tau] stands for the system temperature, and the parameters [T.sub.o], [T.sub.f], [alpha], [theta], denote, respectively, the initial and the final temperature, the cooling factor, and the internal cycle length.

Procedure Neighbor(S) randomly produces (with uniform distribution) a feasible solution in N(S), and Rand delivers a uniformly distributed random number in the interval (0, 1). SA starts with the solution S delivered by Step 1 above (see Algorithm 1).

Throughout the process, the variable [S.sup.*] holds the best solution so far, and the counter k helps to control the number of iterations of the internal cycle. Thus, the system temperature [tau] drops if and only if [theta] iterations occur without any improvement on [S.sup.*].

Added at the end of the external cycle, procedure Fusion attempts to improve solution [S.sup.*] by successively applying the following four operations in the order shown. Before passing to the next operation, for each operation all possibilities are considered, repeating it exhaustively as long as the cost is lowered and feasibility is preserved.

(1) When two trains have identical routes, one locomotive is eliminated once its blocks are passed to the other.

(2) If there are stations u, V, such that [[summation].sub.t[member of][GAMMA]] [[DELTA].sub.u](t)[[lambda].sub.t] < 0 and [[summation].sub.t[member of][GAMMA]] [[DELTA].sub.v](t)[[lambda].sub.t] > 0, then a train with no blocks is added starting in V and ending in u.

(3) If a train starts its route in a station where another train terminates its travel, one locomotive is eliminated once its blocks are passed to the other.

(4) If there are trains t', t", such that the t" route is contained in the t' route, then the t" locomotive is eliminated once its blocks are passed to t'.

Example 2. For clarity sake, consider a railroad network as schematically depicted in Figure 2. Assume a block b with o(b) = A and d(b) = D, and let the crew segments be AF, AD, AI, AP, FD, IL, LD, QR, and DP. Also, let S be a feasible solution with six trains in correspondence to routes: A [right arrow] E [right arrow] F, F [right arrow] G [right arrow] D, A [right arrow] H [right arrow] I, I [right arrow] J [right arrow] F [right arrow] K [right arrow] L, A [right arrow] N [right arrow] O [right arrow] P, and L [right arrow] M [right arrow] D, where block b is delivered by the trains assigned to routes A [right arrow] E [right arrow] F and F [right arrow] G [right arrow] D; hence 63 is their total traveled distance. Thus, solution S' [member of] [N.sub.1](S) is identical to S with the exception that block b is delivered by a new train through route A [right arrow] B [right arrow] C [right arrow] D, with length 42.

Solution S" [member of] [N.sub.2](S) is identical to S with the exception that block b is delivered by the trains with routes A [right arrow] H [right arrow] I, I [right arrow] J [right arrow] F [right arrow] K [right arrow] L, and L [right arrow] M [right arrow] D, with total length 91, and without adding a new train.

Solution S"' [member of] [N.sub.3](S) is identical to S with the exception that block b is delivered through route A [right arrow] N [right arrow] O [right arrow] P, adding a new train for segment PD; the total length of this route is 47.

In regard to simulated annealing parameters, we tested diverse settings, modifying one parameter at a time. Among the possible combinations of the cooling factor [alpha] = 0.70, 0.75, 0.80, 0.85, 090, 0.95, and 0.99, and the internal cycle lengths [theta] = 500, 1,000, 1,500, 2,000, 4,000, 6,000, 8,000, and 10,000, for every pair {[alpha], [theta]} we run ten times the algorithm on a PC with Windows 8.

Table 6 displays the average results obtained for instance Data Set 2, where computer times are shown in italics. Thus, we chose [alpha] = 0.90 and [theta] = 1,000 since together they yielded a good trade-off between computer time and solution quality. As initial temperature we chose [T.sub.o] = 30,000, because on average it yielded an acceptance rate of around 95%. For the final temperature [T.sub.f] = 1 was selected, yielding an acceptance rate of around 1% on average.

5. Numerical Results

Our simulated annealing approach, as described in Section 4 to deal with the TDOP, was implemented on a computer with Xeon E5-2643 v2 3.5 GHz processor (we run in a single processor), 64 GB RAM, and g++ compiler. Two experiments were conducted to investigate its efficiency.

In the first experiment--see Section 5.1--we tested SA on three instances available to us: Example (a toy, synthetic instance), Data Set 1 (real), and Data Set 2 (real), from [13].

The second experiment was designed to evaluate the performance of SA from the point of view of the quality of the results and the required computer time. We did extensive testing on randomly generated instances of various sizes; they are dealt with in Section 5.2.

5.1. Testing SA on Specific Instances. Employing parameters [theta] = 1000 and [alpha] = 0.9, we compared SA results on instances Example, Data Set 1, and Data Set 2, as can be seen in Table 7, where computation times are also shown to give an idea of the performance of the implementation, although the platforms used were not the same. So far [18] had provided--using a mixed integer programming model--the best results for the two real instances but at the expense of very high computing time. On the other hand, the network-oriented formulation in [21] yielded the fastest algorithm to date.

Other approaches include column-generation [23], with better results than those found in [22], and column-generation combined with tabu search [20] improving on [19]. In the case of instance Example the only published results come from [23]. The best results found with SA were 1,999,315 with [theta] = 8000 and [alpha] = 0.99, in 19,819 seconds for instance Data Set 1, and 3,155,267 with [theta] = 10,000 and [alpha] = 0.95 in 41,233 seconds for instance Data Set 2, with the more relevant results found in the literature; our results yield lower cost for Data Set 1 (-0.95%), same cost for Example, and higher cost for Data Set 2 (+0.10%).

5.2. Testing SA on Random Instances. A set of 20 random instances was generated as follows with the number n of stations and number m of blocks shown in column II of Table 8. All random choices were made with uniform distribution.

For each pair {n, m} we first construct a complete graph [K.sub.n] = (V, E), whose vertex set V corresponds to a set of n integer points randomly generated in a square of side [2.sup.15]; the length of each edge (u, V)[member of]E is equal to the Euclidean distance between vertices u and V. Also, we determine the set [E.sub.t] [subset] E of edges in a minimum length spanning tree on [K.sub.n], as well as the set [E.sub.h] [subset] E of edges in the convex hull of V. Finally, we make the railroad network of the instance correspond to graph G = (V, [E.sub.h] [union] [E.sub.t]).

Then, crew segments are randomly created so that every edge of the network belongs to one crew segment. The swap cost s(v) in every station v [member of] V is also randomly assigned in the integer range[10, 100].

We create next a set of m blocks, one after the other, verifying that each does not exceed the maximum number of swaps allowed to arrive to destination when traveling along a minimal length path. The number of cars r(b), length l(b), and weight w(b) of every block b are randomly chosen in the integer ranges [1, 100], [56, 65], and [74, 86], respectively, as these ranges are similar to those found in real instances. Each time we tentatively form a block, Step 1 of Section 4 is performed for feasibility verification.

Also, for every edge e [member of] E, the values of [bar.L](e), [bar.W](e), and [bar.T](e) are randomly chosen in the ranges [7,000, 14,000] and [9,000, 18,000], [6, 12], respectively.

Once random instances were generated, SA run 50 times for each. Results are displayed in columns III-V of Table 8 (indeed, instance p5_7D was taken as Example 1 in Section 2).

5.3. Lower Bounds. Establishing good lower bounds for the optimal solution of the TDOP seems a very difficult task. However, to assess the SA performance, we propose here a lower bound [sigma] for each solved instance; see Tables 7 and 8. To this aim we computed as explained below lower bounds for total travel cost ([[sigma].sub.1]), train start cost ([[sigma].sub.2]), train travel cost ([[sigma].sub.3]), work-event cost ([[sigma].sub.4]), and missed car cost ([[sigma].sub.5]). Thus, [sigma] = [[summation].sup.5.sub.j=1] [[sigma].sub.j].

In regard to travel cost let [[sigma].sub.1] = [C.sub.4] [[summation].sub.b[member of]B]([psi](b) x r(b)), where [psi](b) is the length of the shortest route from o(b) to d(b). In the case of the two real instances [[sigma].sub.1] is identical to the bound computed in [23].

For the train start cost a trivial lower bound is [[sigma].sub.2] = [C.sub.1] x [m/[M.sub.B]], where [M.sub.B] stands for the maximum number of blocks per train.

Let ([[psi].sub.1], ..., [[psi].sub.m]) be an order on [PSI] = {[psi](b) : b [member of] B}, with [[psi].sub.j] [greater than or equal to] [[psi].sub.j+1], for j = 1, ..., m - 1. Thus, for the train travel cost our proposed lower bound is [[sigma].sub.3] = [C.sub.2] [[summation].sub.j[member of]J] [[psi].sub.j], where J = {j : (1 = j mod [M.sub.B]) and 1 [less than or equal to] j [less than or equal to] m}. Bound [[sigma].sub.3] slightly improves on that from [20].

For every station v [member of] V, let [rho](V) be the number of blocks b such that o(b) = v or d(b) = v. Then [[sigma].sub.4] = [C.sub.3] [[summation].sub.v[member of]X] [[rho](v)/[M.sub.B]], where X is the set of stations belonging to a crew segment with the exclusion of its end points. As far as we know this lower bound for the work-event cost is proposed here for the first time.

Let Y denote the set of blocks b such that o(b) or d(b) does not belong to H (refer to Section 3 for a definition of vertex set H). Thus, it is impossible to deliver any block in Y. Our lower bound for the missed car cost is then [[sigma].sub.5] = [C.sub.7] [summation]b[member of]Y r(b). Although in practice it is not likely to get [[sigma].sub.5] > 0, instance Data Set 02 contains some blocks belonging to Y.

Thus, in regard to the best solutions found for instances Example, Data Set 1, and Data Set 2 (see Table 7) our lower bound [sigma] yields differences of 36.14%, 20.79%, and 14.57%, respectively, namely, 23.83% on average. Column VII of Table 8 shows a similar behavior of [sigma] for the synthetic instances. These facts lead us to believe that [sigma], although the highest lower bound known to us is far from the true optimum.

6. Conclusion

In this paper we proposed a simulated annealing approach for the Train Design Optimization Problem. This approach was computationally tested with three instances well-known in the specialized literature, and with 20 instances randomly generated by us, of sizes up to 320 stations and 640 car blocks. Its results show superiority--in regard to the objective function value--over those obtained elsewhere for instance Data Set 01, and competitivity (in fact the runner-up) for instance Data Set 02, both results using reasonable computing resources.

We think however that much research, be it theoretical or empirical, must still be carried out to successfully deal with this most challenging, combinatorial optimization problem. The TDOP being so fundamental in the railroad industry, there is a need to develop new heuristics or metaheuristics, which, working alone or hybridized, improve the best results obtained so far. Also, a pending and difficult task is to produce efficient procedures that yield good lower bounds for the TDOP objective function, so as to help assess the performance of proposed approaches.


David Romero is on sabbatical leave at Laboratorio Nacional de Informatica Avanzada, Xalapa, Veracruz, Mexico.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.


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Federico Alonso-Pecina (1) and David Romero (2)

(1) Universidad Autonoma del Estado de Morelos, 62209 Cuernavaca, MOR, Mexico

(2) Instituto de Matematicas, Universidad Nacional Autonoma de Mexico, 62210 Cuernavaca, MOR, Mexico

Correspondence should be addressed to Federico Alonso-Pecina;

Received 10 March 2017; Revised 20 June 2017; Accepted 9 July 2017; Published 10 August 2017

Academic Editor: Jorge Magalhaes-Mendes

Caption: Figure 1: Diagram of railroad networks. Example 1.

Caption: Figure 2: Diagram of railroad networks. Example 2.
Table 1: Data of Example 1.


Station                                            Block swap cost
A                                                        60
B                                                        60
C                                                        80
D                                                        20
E                                                        20


                                                Length     Weight
Block        Origin   Destination   # of cars   (feet)     (tons)

[b.sub.1]      C           A            5        290        420
[b.sub.2]      C           D            48       2,976      3,696
[b.sub.3]      A           D            13       819        962
[b.sub.4]      D           B            4        228        316
[b.sub.5]      E           D            12       708        936
[b.sub.6]      D           C            63       3,969      4,914
[b.sub.7]      B           A            42       2,730      3,570


          Length    Maximum train    Maximum train    Max number
Segment   (miles)   length (feet)    weight (tons)    of trains

BC           76         4,100            5,600            6
AB          132         4,400            6,300           12
DE          151         5,700            5,400            6
CE          202         4,500            7,900           11
CD          210         4,000           10,000            9
AE          250         6,200            6,500            6


End points defining five crew segments

{B, A}
{B, D}
{A, E}
{C, E}
{D, E}


Description                                                    Value

Unit locomotive cost (Q)                                        $400
Train travel cost per mile ([C.sub.2])                           $10
Cost per work-event ([C.sub.3])                                 $350
Car travel cost per mile ([C.sub.4])                            $0.75
Unit crew imbalance penalty ([C.sub.5])                         $600
Unit train imbalance penalty ([C.sub.6])                       $1,000
Cost per missed car ([C.sub.7])                                $5,000
Maximum number ofblocks per train ([M.sub.B])                     8
Maximum number of swaps per block ([M.sub.S])                     3
Maximum number of work-events per train ([M.sub.W])               4

Table 2: Train routes solution of Example 1.

                                      Cumulative   Deliver   Pick up
Train         Sequence      Station     miles       cars      cars

[t.sub.1]         1            D          0           0        63
                  2            C         210         63         5
                  3            B         286          0        42
                  4            A         418         47        13
                  5            B         550         13         0

[t.sub.2]         1            E          0           0        12
                  2            D         151         12         4
                  3            C         361          0         0
                  4            B         437          4        13
                  5            C         513          0        48
                  6            D         723         61         0
            Total mileage               1,273

                             Cars in     Crew
Train         Sequence      the train   change

[t.sub.1]         1            63         NO
                  2             5         NO
                  3            47        YES
                  4            13         NO
                  5             0         NO

[t.sub.2]         1            12         NO
                  2             4        YES
                  3             4         NO
                  4            13         NO
                  5            61         NO
                  6             0         NO
            Total mileage

Table 3: Block-to-train assignment of Example 1.

                                    Start      End
Block       Sequence     Train     station   station

[b.sub.1]      1       [t.sub.1]      C         A
[b.sub.2]      1       [t.sub.2]      C         D
[b.sub.3]      1       [t.sub.1]      A         B
[b.sub.3]      2       [t.sub.2]      B         D
[b.sub.4]      1       [t.sub.2]      D         B
[b.sub.5]      1       [t.sub.2]      E         D
[b.sub.6]      1       [t.sub.1]      D         C
[b.sub.7]      1       [t.sub.1]      B         A

            swap    Segment   # of    Car
Block       cost     miles    cars   miles

[b.sub.1]     0       208      5     1,040
[b.sub.2]     0       210      48    10,080
[b.sub.3]    60       132      13    1,716
[b.sub.3]     0       286      13    3,718
[b.sub.4]     0       286      4     1,144
[b.sub.5]     0       151      12    1,812
[b.sub.6]     0       210      63    13,230
[b.sub.7]     0       132      42    5,544
Totals       60                      38,284

Table 4: Crew-to-train assignment of Example 1.

segment     Train     Forward   Backward

BD        [t.sub.1]      0         1
BA        [t.sub.1]      1         1
DE        [t.sub.2]      0         1
BD        [t.sub.2]      1         1

Table 5: Total costs of Example 1.

Locomotive cost            2 (number of locomotives) x 400 = 800
Train travel cost            1,273 x 10 = 12,730 (see Table 2)
Car travel cost             38,284 x 0.75 = 28,713 (see Table 3)
Work-event cost        6 (number of train work-events) x 350 = 2,100
Blocks swap cost                      60 (see Table 3)
Crew imbalance cost            2 x 600 = 1,200 (see Table 4)
Train imbalance cost                 2 x 1,000 = 2,000
Missed cars cost                             0

Table 6: Average results--objective function value rounded to nearest
integer--of ten runs for instance Data Set 2. Time shown in seconds
and italics.

\[alpha]     0.70        0.75        0.80        0.85

500        3,209,687   3,210,058   3,212,176   3,209,065
             2,790       2,226       2,446       2,666
1,000      3,205,775   3,204,303   3,203,858   3,200,948
             2,459       2,593       2,771       3,351
1,500      3,200,260   3,201,520   3,192,792   3,190,837
             2,605       2,634       2,901       3,450
2,000      3,201,157   3,196,194   3,192,166   3,195,499
             3,070       2,890       2,540       4,332
4,000      3,195,857   3,187,514   3,187,182   3,185,158
             3,808       4,771       5,339       6,844
6,000      3,192,045   3,189,039   3,185,947   3,180,655
             5,857       5,976       7,398       8,711
8,000      3,188,106   3,185,158   3,184,032   3,181,325
             6,741       6,844       9,216      12,206
10,000     3,186,663   3,184,824   3,182,085   3,178,864
             7,466       8,622      10,846      14,772

\[alpha]     0.90        0.95        0.99

500        3,205,954   3,195,864   3,185,775
             2,783       3,934      10,877
1,000      3,201,772   3,191,151   3,184,551
             4,259       5,181      19,899
1,500      3,187,281   3,190,916   3,173,312
             4,355       7,264      29,082
2,000      3,188,878   3,188,311   3,175,760
             4,997       9,066      36,945
4,000      3,184,931   3,176,745   3,173,650
             8,904      16,043      76,952
6,000      3,181,356   3,175,733   3,169,176
            16,026      25,530      116,304
8,000      3,179,988   3,172,099   3,167,935
            18,319      37,695      153,931
10,000     3,176,411   3,168,984   3,164,844
            21,081      42,862      218,506

Table 7: Performance comparison of our SA approach and other methods;
computing times are given in seconds. The bottom row shows a lower
bound on the optimal cost, computed as explained in Section 5.3. (all
figures rounded to integers).

Number of                Example                      Set1

Nodes                       4                          94
Blocks                      5                         239
Arcs                        5                         134
Crew Segments               5                         154

                 cost              time     cost              time

[21]                       --             2,043,471            45
[20]                       --             2,032,516          2,201
[18]                       --             2,018,644          80,000
[23]            14,916              0     2,069,752           342
SA              14,916              1     2,005,514           181
Lower bound     10,956                    1,660,361

Number of                   Set 2

Nodes                        221
Blocks                       369
Arcs                         294
Crew Segments                154

                  cost               time

[21]            3,187,999            225
[20]            3,188,500           2,067
[18]            3,152,019           75,000
[23]            3,240,177           2,700
SA              3,170,815           1,991
Lower bound     2,751,213

Table 8: Relevant data (columns I, II) and results (columns III, IV,
and V) of 50 runs of SA on 20 random instances. A lower
bound--explained in Section 5.3--on the optimal solution cost is shown
in column VI. The last column is an indicator of the solution quality.

I                 II             III           IV

               Number of
               stations,       Minimum
             blocks, arcs,       cost       Average
Instance     crew segments      ([mu])        cost

p5_7D           5 7 5 6         47,193       47,193
p5_10D         5 10 4 6         77,691       77,726
p10_15D       10 15 11 14      224,186      226,845
p10_20D       10 20 8 13       294,595      299,021
p20_30D       20 30 13 25      297,952      301,013

p20_40D       20 40 15 25      336,651      339,876
p40_60D       40 60 22 45      565,954      576,906
p40_80D       40 80 21 45      568,089      573,991
p80_120D     80 120 38 86      791,072      800,660
p80_160D     80 160 38 89      987,646      997,005

p160_240D   160 240 62 169    2,214,593    2,240,989
p160_320D   160 320 72 169    2,201,347    2,221,606
p200_300D   200 300 82 216    4,043,845    4,071,617
p200_400D   200 400 81 210    1,591,058    1,607,980
p250_375D   250 375 96 261    2,518,148    2,548,918

p250_500D   250 500 104 260   1,881,881    1,905,522
p300_450D   300 450 117 311   8,919,042    8,969,957
p300_600D   300 600 115 313   10,419,523   10,453,791
p320_480D   320 480 138 333   8,172,082    8,218,034
p320_640D   320 640 138 332   2,053,295    2,077,627

I             V        VI             VII

             Avg      Lower     Difference in %
            time      bound      100 x [[mu] -
Instance    (sec)   ([sigma])   [sigma]]/[sigma]

p5_7D         1      32,957          43.18
p5_10D        1      60,336          28.76
p10_15D       4      187,862         19.34
p10_20D       4      248,649         18.48
p20_30D      13      243,932         22.15

p20_40D      13      267,840         25.69
p40_60D      49      446,538         26.74
p40_80D      46      446,411         27.26
p80_120D     124     614,225         28.79
p80_160D     168     791,041         24.85

p160_240D    376    1,791,395        23.62
p160_320D    320    1,765,637        24.68
p200_300D    409    3,306,843        22.29
p200_400D    463    1,203,586        32.19
p250_375D    856    1,958,093        28.60

p250_500D    723    1,445,175        30.22
p300_450D    606    7,480,671        19.23
p300_600D    744    8,782,219        18.64
p320_480D    662    6,676,505        22.40
p320_640D    900    1,516,815        35.37
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Title Annotation:Research Article
Author:Alonso-Pecina, Federico; Romero, David
Publication:Mathematical Problems in Engineering
Article Type:Report
Date:Jan 1, 2017
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