In the classic distributed permutation flowshop scheduling problem (DPFSP), there are more studies on the minimization of makespan, total flow time, total tardiness, etc. This paper studies a new problem with a new op...
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In the classic distributed permutation flowshop scheduling problem (DPFSP), there are more studies on the minimization of makespan, total flow time, total tardiness, etc. This paper studies a new problem with a new optimization goal, the DPFSP with delivery dates and cumulative payoffs. It is a variation of the DPFSP with job release dates that maximizes the total payoff with a stepwise job objective function. The main contributions are summarized as follows. (1) A mathematical model is built to formulate the new problem. (2) The characteristics of the problem are explored, and the upper and lower bounds of the problem are given. Based on the problem specific knowledge, an algorithm named Insert-Pruning is proposed to improve the efficiency of search. (3) Nine heuristic algorithms are proposed, including DRI, DRA, DEI, DEA, DNI, DNA, DII, DIA and DFF. (4) Combined with the characteristics of the problem, some modifications and improvements have been made to the IG algorithm to solve it, including the destruction method, the local search method and the acceptance criterion. (5) The experimental results show that the presented algorithm significantly outperforms the existing algorithms in the literature. In comparison with other competing algorithms in different dimensions, our algorithm has shown better performance, which verifies the effectiveness of this algorithm.
This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, wher...
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This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics-the tabu search algorithm and the iterated greedy algorithm-to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the bestknown solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages.
In this paper, we studied the distributed assembly permutation flowshop scheduling problem (DAPFSP) with the makespan minimization as objective. We proposed iteratedgreedy-based approach that is labelled the bounded-...
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In this paper, we studied the distributed assembly permutation flowshop scheduling problem (DAPFSP) with the makespan minimization as objective. We proposed iteratedgreedy-based approach that is labelled the bounded-search iterated greedy algorithm BSIG. In addition, four local search methods are designed to improve solution quality. The computational results show the effectiveness of BSIG in solving the DAPFSP specially with small instances where BSIG outperforms most of the existing algorithms.
Wire rod and bar rolling is an essential process in a steel production system (SPS). This work considers a lexicographic bi-objective scheduling problem originated from it. As a medium process in SPS, both the impacts...
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Wire rod and bar rolling is an essential process in a steel production system (SPS). This work considers a lexicographic bi-objective scheduling problem originated from it. As a medium process in SPS, both the impacts of its upstream process, i.e., release time constraints, and the requirements of its downstream process, i.e., due time constraints, on SPS performance, must be considered. The concerned problem aims to minimize the number of tardy tasks as a main objective and total setup time as a secondary one. It is solved via a two-stage method. The first-stage problem is handled with an off-the-shelf optimization software, and the second one with iterated greedy algorithms (IGAs). We compare the performance of IGAs under different strategies and select the best one to solve it. By comparing the experimental results an exact single-objective method’s, this paper shows that the proposed method can solve the problem fast with excellent results in terms of the number of tardy tasks and setup time, i.e., the secondary objective, on average. Its usage can result in significant performance improvement in steel production over the existing practice.
This paper presents two meta-heuristic algorithms to solve the quadratic assignment problem. The iterated greedy algorithm has two main components, which are destruction and construction procedures. The algorithm star...
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ISBN:
(纸本)9781467359047
This paper presents two meta-heuristic algorithms to solve the quadratic assignment problem. The iterated greedy algorithm has two main components, which are destruction and construction procedures. The algorithm starts from an initial solution and then iterates through a main loop, where first a partial candidate solution is obtained by removing a number of solution components from a complete candidate solution. Then a complete solution is reconstructed by inserting the partial solution components in the destructed solution. These simple steps are iterated until some predetermined termination criterion is met. We also present our previous discrete differential evolution algorithm modified for the quadratic assignment problem. The quadratic assignment problem is a classical NP-hard problem and its applications in real life are still considered challenging. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are superior to the migrating birds optimization algorithm which appeared very recently in the literature. Ultimately, 7 out of 8 printed circuit boards (PCB) instances are further improved.
A scheduling problem in a wire rod and bar rolling process is very important and hard to solve in practical steel production *** paper considers a new scheduling problem originated from a wire rod and bar rolling proc...
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A scheduling problem in a wire rod and bar rolling process is very important and hard to solve in practical steel production *** paper considers a new scheduling problem originated from a wire rod and bar rolling process considering job release time and due time,as well as setup time between consecutive *** objectives are to minimize both the number of late jobs and total setup *** linear combination is used to measure a *** solve such a practical scheduling problem,four effective algorithms were *** experimental results show that all of the presented algorithms can well solve the considered *** them,iterated greedy algorithm shows the best solution *** great performance implies its readiness to be used in practical industrial scheduling systems.
This paper studies the problem of multiple automatic guided vehicles (multi-AGVs) dispatching in a matrix manufacturing workshop. The goal is to minimize the transportation cost that includes the cost of travelling di...
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This paper studies the problem of multiple automatic guided vehicles (multi-AGVs) dispatching in a matrix manufacturing workshop. The goal is to minimize the transportation cost that includes the cost of travelling distance, the cost of penalty time and the cost of AGVs. For the purpose, a mixed integer linear programming model is set up and an improved iteratedgreedy (IIG) algorithm is proposed. In the algorithm, an AGV route merging strategy and a workshop partition strategy are designed to reduce the cost of AGVs and travelling distance. Two rules are designed to quickly identify infeasible solutions to save the operation time. A nearest neighbor heuristic is used to generate an initial solution with high quality. In the local search stage, four effective operators are used to improve the quality of the solution. A repair strategy is proposed to avoid the algorithm falling into local optima. Finally, we use 110 real instances to test the IIG and the other six algorithms in the literature. The comparative experiments show that the proposed algorithm and strategies have much better performance for solving this problem.
This paper presents a discrete artificial bee colony algorithm (DABC) for solving the team orienteering problem with time windows (TOPTW). The proposed algorithm employs a destruction and construction procedure to gen...
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ISBN:
(纸本)9781467359047
This paper presents a discrete artificial bee colony algorithm (DABC) for solving the team orienteering problem with time windows (TOPTW). The proposed algorithm employs a destruction and construction procedure to generate neighboring food sources in the framework of the DABC algorithm. In addition, a variable neighborhood descent (VND) algorithm is developed to enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is competitive to the best performing algorithms from the literature. Ultimately, 11 instances are further improved by the proposed DABC algorithm.
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