Heuristic Algorithm for the Parallel Machine Total Weighted Tardiness

Heuristic Algorithm for the Parallel Machine Total Weighted Tardiness Scheduling Problem Rosiane Rodrigues [email protected] COPPE - Engenharia de Sistemas e Computa¸ca˜o Universidade Federal do Rio de Janeiro Departamento de Ciˆencia da Computa¸ca˜o Universidade Federal do Amazonas Artur Pessoa, Eduardo Uchoa {artur, uchoa}@producao.uff.br Departamento de Engenharia de Produ¸ca˜o Universidade Federal Fluminense Marcus Poggi de Arag˜ao [email protected] Departamento de Inform´atica Pontif´ıcia Universidade Cat´olica do Rio de Janeiro

Abstract This paper presents P a heuristic algorithm for the parallel machine weighted tardiness scheduling problem (P || wj Tj ). The main innovative feature of the algorithm is its representation of a multi-machine schedule by a single sequence, greatly simplifying the treatment of that problem. The single sequence is optimized using an iterated local search over generalized pairwise interchange moves, improved with a suitable tie breaking criterion. Extensive tests on instances, with 2 and 4 machines, and with up to 50 jobs, obtained very good results, finding optimal solutions in almost all cases.

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1

Introduction

Let J = {1, . . . , n} be a set of jobs to be processed in a set of parallel identical machines M = {1, . . . , m} without preemption. Each machine can process at most one job at a time and each job must be processed by a single machine. Each job j has a positive processing time pj , a due date dj , and a positive weight wj . The tardiness of a job j with respect to its due date is defined as Tj = max{0, Cj − dj }, where Cj is the job completion time. The scheduling problem considered in this paper consists in sequencing P P the jobs in the machines in order to minimize nj=1 wj Tj . This problem, referred as P || wj Tj in the P3-field notation [10], is strongly NP-hard, since even the single-machine case (referred as 1|| wj Tj ) hasPthat complexity [12]. On the other hand, the single-machine problem without weights (1|| Tj ) is NP-hard in the ordinary sense [7], but can solved in pseudo-polynomial time [12]. All those problems may play an important role in real applications, such as in the manufacturing industry. P We are not aware of other heuristic algorithms specially devised for the P || wj Tj . Recent works on related problems include Anghinolfi and Paolucci (2007) [2], that proposes a hybrid metaheuristic mixing Tabu Search, Simulated Annealing and Variable NeigborhoodP Search for P the P || Tj ; and Bilge et al. (2004) [4], that proposes a Tabu Search for the P |sij | Tj (considering sequence dependent setup times). P On the other hand, there is a rich literature on heuristics for the 1|| wj Tj . The most competitive heuristics for that problem are based on local searches that use both insertion moves (remove a job from the sequence and reinsert it in another position) and swap moves (swap the positions of a pair of jobs in the sequence). Those combined moves are also known P as generalized pairwise interchange (GPI) moves [6]. Some 1|| wj Tj heuristics use the GPI moves in a traditional way, making one move at a time [20]. However, better results are obtained using dynamic programming to explore a large neighborhood that consists in certain sequences of single moves, the so-called dynasearch technique. While the size of the neighborhood is potentially exponential, the dynamic programming may determine such an optimal sequence of moves in polynomial time. This technique was introduced by Potts and Val de Verde [17] in the Traveling Salesman Problem P (see the survey by Ahuja et al. [1]). Congram et al. [5] first applied dynasearch to the 1|| wj Tj , but only considering swaps as single moves. Grosso et al. [11] adapted the dynasearch technique in order to consider all GPI moves. Both papers present dynamic programming procedures with O(n3 ) time complexity per search. Recently, Ergun and Orlin [9] improved this time complexity to O(n2 ), by giving a faster dynasearch that combines GPI moves and also twist moves. P Our heuristic algorithm for the P || wj Tj features the representation of a multi-machine schedule by a single sequence, that can be improved by a local search using the well-known GPI moves. Unfortunately, the single sequence representation does not appear to be suitable to dynasearch. This happens because this indirect representation makes the evaluation of moves to be much more costly, even a single insertion move may change the completion times of most jobs in a complex way, so the tardiness costs may have to be recomputed from scratch in O(n log m) time. Evaluating the tardiness costs of many sequences of moves is prohibitively expensive. In order to partially remedy the lack of a dynasearch, we introduce a tie breaking criterion in the GPI search. The idea is that even when the current solution has no GPI neighbor with better tardiness cost, the search is not stopped if there are neighbor solutions improving this criterion. The criterion was devised with the hope of guiding the search into solutions that more likely to be further improved. P Several exact methods were proposed for the 1|| wj Tj , including [8, 19, 22, 21, 3, 14, 15]. The last three works describe techniques that can solve instances with up to 100 jobs consistently. However, the best heuristics typically find the optimal solutions for those instances in much less Vers˜ao Final Recebida em 18/11/2008 - Publicado em 22/12/2008

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time. By the way, those heuristics are often embedded as part of those branch-and-bound based exact algorithms. A good heuristic solution can P reduce the search tree, leading to significant speedups. The only exact algorithm for the P || wj Tj that we know is proposed in [15]. It can solve most instances with up to 50 jobs. The heuristic presented in this paper was created in order to embedded in that exact algorithm.

2

The Algorithm

P A scheduling for the P || wj Tj is defined as a sequence of jobs on each machine, where each job appears in exactly one sequence. It is assumed that a machine is only idle after all jobs in its sequence are processed. Moreover, we say that a scheduling is minimal if no machine is idle before all jobs have started their processing. Of course, there is at least one optimal scheduling that is minimal. P The main idea of our heuristic algorithm is to represent a minimal P || wj Tj scheduling as a single sequence of jobs. Let π = (π1 , . . . , πn ) be a permutation of the job indices 1, . . . , n. A minimal scheduling can be obtained from π as follows. Start with an empty schedule S, where all machines are idle at time 0. Then, for i = 1, . . . , n, schedule the job Jπi in the first machine that becomes idle according to S. When ties occur, choose the machine with the smallest index. Denote the obtained schedule and its cost by Sπ and w(π), respectively. Note that every minimal schedule S can be generated in this way. To see this, construct π so that the sequence of jobs (Jπ1 , . . . , Jπn ) is in a non-decreasing order their starting times according to S. When ties occur, put the jobs scheduled in machines with smaller indices first. It is easy to see that Sπ = S. Given an initial permutation π of jobs, our algorithm performs a local search over a neighborhood defined by GPI moves. They are defined as either an exchange of two (not necessarily adjacent) jobs in π or the removal of a job from π followed by its insertion in another position. Each local search is performed until no GPI move improves the current solution. During the local search, a move is accepted only when the new solution improves upon the previous one. However, locally optimal solutions often have many neighbor solutions with the same cost. Hence, a premature stop in such local optima may be avoided using a suitable tie breaking criterion. Our criterion is based on the values of the due dates and the reverse position of the job in the sequence. In a formal way, given a sequence π = (π1 , . . . , πn ), we define n P b(π) = dπj × (n − j + 1). Thus, given two neighbor sequences of jobs π and ρ with the same j=1

cost, if b(ρ) < b(π), then ρ is considered to improve over π. Note that our criterion induces the jobs having earlier due dates to be scheduled before the ones with later due dates. This criterion is motivated by the well-known P Earliest Due Date first (EDD) rule [13, 16], which generates an optimal solution for the 1|| wj Tj when the optimum value is zero (i.e., when there is a solution without tardy jobs). Algorithm 1 below presents the general steps of our heuristic, where N, r and k are parameters.

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Algorithm 1 Single sequence based heuristic for the P ||

P

3

wj Tj

• i ← 1; π ∗ ← a permutation following the EDD rule. • While i ≤ N – If i is a multiple of r, then π ← a random permutation. – Apply GPI moves in π, until no improvement is possible. – If w(π) < w(π ∗ ), then π ∗ ← π. – Apply k randomly chosen 2-change moves in π. – i ← i + 1.

First, it generates a feasible solution following the EDD rule and store in π ∗ . In the first iteration, it generates a random permutation π, where the probabilities are identically distributed among all permutations. Then, GPI moves are applied to π. The search on the GPI neighborhood of the current π is stopped when the first improvement move is found, in this case π is updated and a new search starts. This is done until a search is completed without improvement (considering the tie breaking criterion). If the obtained solution improves upon the best solution found so far, then it is kept as π ∗ . Finally, k randomly chosen GPI moves are applied (regardless of whether they generate improvements or not), in an attempt to escape from bad local optimal regions. On every r iterations, a completely random permutation replaces the solution generated in the previous iteration. A complete search on the GPI neighborhood tests O(n2 ) moves. The evaluation of each move requires the construction of the corresponding scheduling, this takes O(n log m) time.

3

Computational Experiments

In all our experiments, we set N = 30mn, r = 5, and k = 3. The reported times were obtained with a Intel Xeon 2.33 GHz processor.

3.1

Single-Machine

P P Even though our algorithm was devised for the P || wj Tj , we also tested it on 1|| wj Tj instances in order to benchmark it against other methods from the literature. The experiments were performed on the set of 375 instances of the problem available at the OR-Library. This set was generated by Potts and Wassenhove [18] and contains 125 instances for each n ∈ {40, 50, 100}. In fact, for each such n, they created 5 similar instances (changing the seed) for each of 25 parameter configurations of the random instance generator. Therefore, for each value of n there are 25 groups composed by 5 similar instances. Those parameter configurations have influence on the distribution of the due dates. Processing times and weights are always picked from the discrete uniform distribution on [1,100] and [1,10], respectively. The best known results for those instances were obtained by Grosso et al. [11] using the GPI-based Dynasearch algorithm (GPI-DS). In that paper, a comparative analysis was done between this method and an older method called Ant-Colony Optimization (ACO), proposed by St¨ utzle et al. [20], that uses the GPI moves in an ACO framework. Table 1 contains a comparison among our algorithm and the two methods above mentioned. The comparison is done on the time (average and maximum) needed to find the optimal value. The CPU times of

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GPI-DS were obtained in a HP Kayak 800 MHz workstation, while ACO was run on a Pentium III 450 MHz. Table 1: CPU time to find an optimal solution, GPI-DS [11], ACO [20], and our algorithm, on ORLibrary instances with 40, 50 and 100 jobs (125 instances of each size).

40

50

100

GPI-DS ACO Our method GPI-DS ACO Our method GPI-DS ACO Our method

Topt,avg 0.003 0.088 0.012 0.010 0.320 0.084 0.107 6.990 1.104

Topt,max 0.125 1.720 0.870 0.562 10.740 5.760 3.907 86.260 29.180

Although our heuristic (as GPI-DS and ACO) eventually obtains the optimal solutions for all OR-Library instances, our CPU times are significantly higher than those by GPI-DS. However, even taking taking the difference of the processors into account, the performance of our heuristic is similar to ACO. This is quite remarkable, because when m = 1 our method reduces to a naive implementation of an iterated GPI local search. The factor that may explain why such a simple method matches the performance of a sophisticated metaheuristic using the same neighborhood is the improvement obtained by the tie-breaking criterion. In fact, without that criterion our method would fail to find the optimal solutions for 2 instances with 100 jobs, even after 3000 iterations and 160 seconds of CPU time. On the instances with 100 jobs, the average quality of the solutions found after each GPI local search iteration is 0.67% above the optimal, without the tie-breaking criteria this number would be 1.26% above the optimal.

3.2

Multi-Machines

P In order to perform experiments on the P || wj Tj problem, we derived 100 new instances P from the previously mentioned 1|| wj Tj OR-Library instances. For m ∈ {2, 4}, n ∈ {40, 50}, we pick the first instance in each group (those with numbers ending with the digit 1 or 6) and divided each due date dj by m (and rounded down the result), processing times pj and weights wj are kept unchanged. For example, from instance wt40-1, we produced instances wt40-2m-1 and wt40-4m-1 by dividing due dates by 2 and 4, respectively. The exact algorithm presented in [15] solved to optimality 98 out of those 100 instances, so we have a good basis to assess the performance of our heuristic. Tables 2 to 5 present detailed results of our heuristic algorithm. The columns have the following meaning: (1) the instance number; (2) the optimal solution value; (3) the value of the best solution found by the heuristic; (4) the difference between the previous values; (5) the iteration in which the best value was first found; (6) the elapsed CPU time (in seconds) when the best value was first found; (7) the number of iterations where the best value was found; (8) the average value of the solutions obtained in all iterations; (9) the total number of iterations performed (given by the algorithm parameter N ); and, (10) the total CPU time (in seconds). Table 2 and Table 3 present detailed results considering 2 machines, for 40 and 50 jobs, respectively. Only for a single instance (wt40-2m-116), the optimal solution (or the best known solution, in case of instance wt50-2m-31) was not found. Table 4 and Table 5 present detailed

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results considering 4 machines, for 40 and 50 jobs, respectively. Optimal solutions (or the best known solution, in case of instance wt50-4m-56) were found for all tested instances. We also run all instances with a simpler variant of our method, without the tie-breaking criterion. Table 6 compares statistics on those runs with statistics on the runs using the complete method, with the tie-breaking criterion. For each value of m and n, it shows: (1) the number of instances where the optimal or best known solution value was not found; (2) the average quality of the solutions found after each GPI local search iteration; and (3) the total time to complete all the N iterations. It can be seen that the tie-breaking criterion makes the algorithm significantly more robust, with a modest increase on the time spent per iteration (28% in average).

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Inst 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 Avg :

Opt 606 3886 9617 38356 41048 87 3812 10713 30802 34146 0 1279 11488 35279 47952 0 571 6048 26075 66116 0 0 17936 25870 64516

BestV 606 3886 9617 38356 41048 87 3812 10713 30802 34146 0 1279 11488 35279 47952 0 571 6048 26075 66116 0 0 17936 25874 64516

∆ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0

IterBest 1 1 1 2 1 1 4 1 2 4 1 7 2 32 2 1 972 3 6 37 1 1 7 25 32

TBest(s) 0.00 0.00 0.00 0.01 0.00 0.01 0.02 0.00 0.00 0.01 0.00 0.04 0.01 0.14 0.00 0.00 6.29 0.01 0.02 0.16 0.00 0.00 0.04 0.12 0.14 0.28

#Best 2009 1903 1531 265 394 1912 189 580 616 1173 2400 872 168 149 395 2400 1 20 520 110 2400 2400 228 20 122

AvgV 610.3 3888.3 9640.9 38405.2 41053.8 93.7 3842.6 10729.3 30837.8 34148.8 0.0 1294.2 11534.8 35313.6 47962.6 0.0 620.9 6084.2 26115.4 66137.8 0.0 0.0 17969.0 25934.5 64556.8

TotIter 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400

Table 2: Results on instances with 40 jobs and 2 machines. TotT(s) 13.89 14.76 13.94 12.17 9.77 13.52 13.66 13.36 11.39 9.72 7.64 13.78 12.41 10.82 9.77 7.16 15.58 13.82 11.29 9.77 6.79 6.96 12.00 11.53 10.43 11.44

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Inst 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 Avg :

Opt 1268 14272 23028 46072 111069 26 ≤5378 18956 38058 82105 0 761 13682 40907 78532 0 542 12557 47349 92822 0 0 15564 19608 41696

BestV 1268 14272 23028 46072 111069 26 5378 18956 38058 82105 0 761 13682 40907 78532 0 542 12557 47349 92822 0 0 15564 19608 41696

∆ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

IterBest 10 2 3 1 23 6 1 14 6 37 1 108 490 3 9 1 64 86 115 4 1 1 4 604 1

TBest(s) 0.10 0.02 0.03 0.01 0.17 0.06 0.01 0.16 0.06 0.26 0.00 1.24 6.00 0.03 0.07 0.00 0.68 0.98 1.13 0.03 0.00 0.01 0.05 6.76 0.01 0.71

#Best 1453 885 379 751 119 631 2796 167 303 149 3000 37 15 653 192 3000 58 75 17 34 3000 3000 171 5 901

AvgV 1281.9 14344.3 23085.4 46100.2 111082.2 29.2 5387.6 19086.9 38182.0 82125.3 0.0 765.4 13902.3 40964.0 78580.6 0.00 680.8 12674.8 47443.6 92882.3 0.0 0.0 15729.3 19681.9 41733.1

TotIter 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000

Table 3: Results on instances with 50 jobs and 2 machines. TotT(s) 33.87 31.01 34.06 29.55 21.77 29.86 36.33 34.22 24.77 21.08 17.06 34.87 36.45 29.32 24.05 16.60 32.60 33.43 28.16 25.38 16.78 16.67 37.34 33.29 30.12 28.35

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Inst 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 Avg :

Opt 439 2374 5737 21493 22793 88 2525 6420 17685 19124 0 826 7357 20251 26740 0 564 4725 15569 36266 0 0 11263 15566 35751

BestV 439 2374 5737 21493 22793 88 2525 6420 17685 19124 0 826 7357 20251 26740 0 564 4725 15569 36266 0 0 11263 15566 35751

∆ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

IterBest 1 1 1 1 1 2 59 2978 46 17 2 7 34 24 6 1 2204 146 216 22 1 1 26 559 4

TBest(s) 0.01 0.01 0.01 0.01 0.00 0.02 0.62 24.52 0.32 0.10 0.02 0.07 0.28 0.15 0.04 0.00 21.97 1.20 1.63 0.13 0.00 0.00 0.20 4.27 0.02 2.22

#Best 2189 1213 912 370 2367 2224 107 1 63 171 1918 320 142 313 684 4800 1 44 19 115 4800 4764 39 8 278

AvgV 456.3 2420.9 5769.1 21512.6 22797.6 115.0 2556.9 6479.6 17794.5 19128.3 32.8 873.9 7409.5 20295.9 26747.8 0.0 743.1 4824.8 15634.2 36280.7 0.0 0.1 11316.8 15652.6 35774.6

TotIter 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800

Table 4: Results on instances with 40 jobs and 4 machines. TotT(s) 52.36 49.21 47.29 33.55 34.27 53.41 47.20 39.36 33.10 29.38 48.04 53.17 39.52 32.20 31.29 21.70 47.71 39.28 35.92 30.12 21.48 31.41 35.82 36.59 32.58 38.24

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Inst 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 Avg :

Opt 785 8317 12879 25376 59440 54 3061 10796 21806 44455 0 ≤570 7898 23138 42645 0 495 8369 26551 50326 0 0 10069 11552 23792

BestV 785 8317 12879 25376 59440 54 3061 10796 21806 44455 0 570 7898 23138 42645 0 495 8369 26551 50326 0 0 10069 11552 23792

∆ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

IterBest 18 15 612 3 55 3 5 246 8 13 1 63 4433 27 118 1 14 24 1520 12 1 1 3636 23 1357

TBest(s) 0.47 0.36 11.19 0.04 0.68 0.08 0.10 4.32 0.10 0.17 0.01 1.51 81.60 0.39 1.54 0.01 0.26 0.37 22.38 0.17 0.00 0.01 65.70 0.43 19.63 8.46

#Best 695 218 31 446 71 5323 840 9 631 406 6000 261 1 198 78 6000 102 6000 1 122 6000 6000 2 6 9

AvgV 805.9 8387.0 12926.6 25416.9 59451.9 58.3 3192.1 10929.4 21869.3 44469.7 0.0 584.4 8054.0 23174.7 42684.4 0.0 746.7 8493.4 26638.3 50373.7 0.0 0.0 10164.1 11619.7 23839.9

TotIter 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000

Table 5: Results on instances with 50 jobs and 4 machines. TotT(s) 142.83 125.92 111.11 90.08 75.17 152.87 132.67 104.70 85.28 74.21 59.88 143.81 110.65 82.59 74.88 50.93 124.06 100.20 88.33 81.76 52.10 63.38 108.65 100.93 86.45 96.94

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m 2 2 4 4

n 40 50 40 50

10

Table 6: Effect of the tie-breaking criterion on the proposed algorithm Without tie-breaking With tie-breaking #N onOpt AvgQ(%) AvgTotT(s) #N onOpt AvgQ(%) AvgTotT(s) 1 2.49 9.18 1 0.87 11.44 0 3.32 22.40 0 1.83 28.35 1 5.77 29.83 0 3.29 38.24 1 4.30 73.50 0 3.08 96.94

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