Showing papers by "Quan-Ke Pan published in 2020"

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs

Abstract: In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem

Abstract: This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance.

An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

Abstract: The distributed permutation flowshop scheduling problem (DPFSP) has attracted much attention in recent years. In this paper, we extend the DPFSP by considering the sequence-dependent setup time (SDST), and present a mathematical model and an iterated greedy algorithm with a restart scheme (IGR). In the IGR, we discard the simulated annealing-like acceptance criterion commonly used in traditional iterated greedy algorithms. A restart scheme with six different operators is proposed to ensure the diversity of the solutions and help the algorithm to escape from local optimizations. Furthermore, to achieve a balance between the exploitation and exploration, we introduce an algorithmic control parameter in the IG stage. Additionally, to further improve the performance of the algorithm, we propose two local search methods based on a job block which is built in the evolution process. A detailed design experiment is carried out to calibrate the parameters for the presented IGR algorithm. The IGR is assessed through comparing with the state-of-the-art algorithms in the literature. The experimental results show that the proposed IGR algorithm is the best-performing one among all the algorithms in comparison.

An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop

Abstract: This paper addresses a new multiple automatic guided vehicle dispatching problem (AGVDP) from material handling process in a matrix manufacturing workshop. The problem aims to determine a solution with the objective of minimizing the transportation cost including travel cost, penalty cost for violating time and AGV cost. For this purpose, a mixed integer linear programming model is first formulated based on a comprehensive investigation. Then, a discrete artificial bee colony algorithm (DABC) is presented together with some novel and advanced techniques for solving the problem. In the proposed DABC algorithm, a nearest-neighbor-based heuristic based on the problem-specific characteristics is presented to generate an initial solution with a high level of quality. Five effective neighborhood operators are presented to generated neighboring solutions with a high level of diversity. Four theorems are proposed to avoid the unfeasible solutions generated by the neighborhood operators. Two new control parameters are introduced. One is to balance the global exploration and local exploitation in employed bee and onlooker bee phases. The other is to enhance the local exploitation capability of the neighborhood operators. Besides, an insertion-based local search method is provided for the scout bee phase to lead the algorithm to a promising region of the solution space. A comprehensive and thorough evaluation with 110 instances collected from a real-world factory shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms in the close related literature.

An energy-efficient permutation flowshop scheduling problem

Abstract: The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies, multiple objectives related to production efficiency have been considered simultaneously. However, studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work, we studied two contradictory objectives, namely, total flowtime and total energy consumption (TEC) in a green permutation flowshop environment, in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime, the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore, the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard, two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially, the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then, it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model.

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

Abstract: This paper deals with a new automatic guided vehicle (AGV) scheduling problem from the material handling process in a linear manufacturing workshop. The problem is to determine a sequence of Cells for AGV to travel to minimize the standard deviation of the waiting time of the Cells and the total travel distance of AGV. For this purpose, we first propose an integer linear programming model based on a comprehensive investigation. Then, we present an improved nearest-neighbor-based heuristic so as to fast generate a good solution in view of the problem-specific characteristics. Next, we propose an effective discrete artificial bee colony algorithm with some novel and advanced techniques including a heuristic-based initialization, six neighborhood structures and a new evolution strategy in the onlooker bee phase. Finally, the proposed algorithms are empirically evaluated based on several typical instances from the real-world linear manufacturing workshop. A comprehensive and thorough experiment shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms.

A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling

Abstract: In this study, a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL, insertion, and exchange operators are used to shaking the permutation. On the other hand, in the inner loop of variable neighborhood descent procedure, variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.

Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption

Abstract: No-wait flowshop scheduling problem (NWFSP) is a well-known strongly NP-hard problem, where in-process waiting is not allowed between any two consecutive machines in such a way that once a job is started, subsequent processing must be carried out on all machines until completion. In this paper, we propose an energy-efficient NWFSP in order to investigate the trade-off between makespan and total energy consumption. The energy-efficient NWFSP aims to seek to obtain Pareto solution sets to minimize the makespan and the total energy consumption conflicting with each other. Unlike the classical NWFSP, there are different speed levels for each job on machines and the processing times of jobs can differ according to the assigned speed levels. Therefore, we modify the formulation of NWFSP by introducing a speed scaling strategy in order to approximate Pareto solution sets, i.e., non-dominated solution sets. In this paper, we propose a mixed-integer linear programming model (MILP), an energy-efficient variable block insertion heuristic (EE-VBIH), an energy-efficient iterated greedy algorithm (IG) and an energy-efficient & IG-ALL) to solve the energy-efficient NWFSP. Extensive computational analyses on Taillard’s benchmark suite show that the proposed algorithms are very effective for approximating Pareto solution sets.

An iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime

Abstract: In recent years, the distributed permutation flowshop scheduling problem (DPFSP) has been widely studied. In this paper, we extended the DPFSP by considering preventive maintenance (PM) operation to prevent machines from breaking down after the long process. An iterated greedy (IG) algorithm is developed to minimize total flowtime. A heuristic with swapping operator is proposed to initialize the IG. After that, the destruction phase and construction phase are modified to fit our problem. A local search is then applied to further improve the solution generated in the construction stage. At last, a simple simulated annealing-like acceptance criterion is used to prevent local optimal situations. Comparison with three state-of-the art algorithms in the recent literature based on 225 instances shows the high performance of our IG algorithm for solving the DPFSP with PM operation.

Evolutionary Optimization Under Uncertainty: The Strategies to Handle Varied Constraints for Fluid Catalytic Cracking Operation.

Abstract: This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.

An improved imperialist competitive algorithm for hybrid flowshop rescheduling in steelmaking-refining-continuous casting process:

Abstract: Steelmaking-refining-Continuous Casting (SCC) is a key process in iron and steel production. SCC scheduling is to determine an optimal schedule for the SCC process, which is a worldwide and importa...

NEH-Based heuristics for the distributed blocking flowshop with makespan criterion

Abstract: The distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem (BFSP), in which the blocking constraint has to be considered. The NEH heuristic is regarded as the best constructive heuristic for the permutation flowshop scheduling problem. Naderi and Ruiz proposed the NEH2 for the distributed permutation flowshop scheduling problem by adding a factory assignment rule to the Nawaz-Enscore-Ham (NEH) heuristic. In this paper, a priority rule is first presented for generating initial sequence for the NEH2. Then, the proposed rescheduling operator is applied in NEH insertion procedure. Additionally, we generated a sequence containing all job twice, and then the solution for the problem is constructed by applying the NEH insertion procedure on each element of the sequence. Based on the above ideas, we proposed three NEH-based heuristics, namely NEH2E, NEH2Een and NEH2EE. Computational results demonstrate that the proposed heuristics perform significantly better than the original NEH2.

An improved discrete artificial bee colony algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance

Abstract: The distributed permutation flowshop scheduling problem with preventive maintenance operator (PM/DPFSP) is closely related to modem industry. This paper presents an improved discrete artificial bee colony (IDABC) algorithm for solving this problem. The criterion to be optimized is the makespan. An improved NEH heuristic method is proposed to initialize the population effectively. We adapted a local search method with insertion and swap operator to produce neighboring solutions in employ bee phase and onlooker bee phase. A global search method with destruction and reconstruction phase is introduced to avoid local optima in scout bee phase. The parameters for the proposed IDABC are calibrated by means of a design of experiments and analysis of variance. We conduct extensive experiments to test the performance of IDABC. Computational results indicate that IDABC has promising advantages on PM/DPFSP.

An iterated local search algorithm for distributed assembly permutation flowshop problem

Abstract: Nowadays, the distributed assembly permutation flowshop problem (DAPFSP) has important applications in practice. In this paper, we propose a group iterated local search (gILS) algorithm to solve the problem with total flowtime (TF) criterion. We use the heuristic method based on a ascending order, which is originated from the NEH. In order to simplify and optimize the algorithm, we introduce two kinds of local search methods based on products and jobs respectively. In addition, considering the diversity of search area, we propose a probabilistic random selection based on the TF value and the number of iterations to determine the optimized solution. Acceptance criterion is a simple comparison to determine whether a new solution is acceptable or not. Finally, we calculate 180 instances with our proposed algorithm and compare the results with those from the recent effective algorithms. The results verify the superiority of the presented gILS algorithm.

An Iterated Greedy Algorithm for Distributed Blocking Flowshop Problems with Makespan Minimization

Abstract: In this paper, we study a distributed blocking flowshop scheduling problem (DBFSP) that is an extension of the traditional blocking flowshop scheduling problem (BFSP), in which an additional decision of which factory to process each job. We propose an iterated greedy (IG) algorithm to minimize makespan among all the factories. First of all, an effective initialization method based on the PW algorithm is used in order to make better use the problem-specific characteristics. Then, an enhanced construction method is developed to further improve the solution obtained at each iteration. At last, after calibration of algorithm parameters, comparison of algorithms is carried out using the well-known 720 instances from the literature. The results demonstrate the effectiveness of the proposed IG algorithm for solving the DBFSP with makespan criterion.

A New Heuristic for PCBs Grouping Problem with Setup Times

Abstract: In this paper, we present a new heuristic to divide a batch of printed circuit boards (PCBs) into subgroups to save the setup time for loading and unloading components from the assembly machine. In the heuristic, we propose several concepts about similarity to make the number of groups as few as possible. To better show the relationship between the PCB types and the component types of a group, we introduce a new solution representation. In addition, considering the characteristics of the PCBs grouping problem (PGP), a method for pairing PCBs is presented. With the PCB pairs, an iterative scheme is applied to start a new group. We try the rest PCBs one by one according to the similarity between it and the PCB group. Finally, the experiments and comparisons show the good performance of the proposed heuristic.