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The customer order scheduling problem has garnered considerable attention in the recent scheduling literature. It is assumed that each of several customer orders consists of several jobs, and each customer order is completed only if each job of the order is completed. In this paper, we consider the customer order scheduling problem in a machine environment where each customer places exactly one job on each machine. The objective is to minimize the earliness–tardiness, where tardiness is defined as the time an order is finished past its due date, and earliness is the time a job is finished before its due date or the completion time of the corresponding order, whichever is later. Even though the earliness–tardiness criterion is an important objective for just-in-time production, this problem has not been studied in the context of the customer order scheduling problem. We provide a mixed-integer linear programming (MILP) formulation for this problem and derive multiple problem properties. Furthermore, we develop six different heuristics for this problem configuration. They follow the structure of the iterated greedy algorithm and additionally use a refinement function in which they differ. In a computational experiment, the algorithms were compared with each other and outperformed a solver solution of the MILP, which proves their ability to efficiently solve the problem configuration. [ABSTRACT FROM AUTHOR]

The distributed no-idle permutation flowshop scheduling problem has gained significant attention as a prominent area of research in recent years, particularly in industries where setup operations are so expensive that reactivating the machines is not cost-effective. This study addresses an extension of the distributed permutation flowshop scheduling problem with no-idle and due window constraints. The aim is to determine the job assignments to the factories and their sequences in each factory that provide the minimum total weighted earliness and tardiness (TWET) penalties considering due windows. This study is the first to formulate this problem, offering four different mathematical models, and presents a benchmark to examine different problem cases that may arise in practical applications. Furthermore, to effectively solve the diverse problem instances, two hybrid metaheuristic algorithms based on the Iterated Greedy are proposed. These metaheuristics exhibit promising capabilities, enabling the solution of problem instances involving up to 500 jobs. To assess the effectiveness of the proposed models and algorithms, extensive numerical experiments are conducted, facilitating a thorough evaluation and comparison of their performances. [ABSTRACT FROM AUTHOR]

In recent years, both distributed scheduling and energy-efficient scheduling have attracted great attention in production systems. This paper studies an energy-efficient distributed blocking flowshop scheduling problem where several heterogeneous factories cooperate to process jobs. A knowledge-based iterated Pareto greedy algorithm (KBIPG) is proposed to minimize simultaneously the makespan and total energy consumption. Based on a speed scaling framework that allows machines to process different jobs at different speed levels or remain in the standby mode, the KBIPG has two stages, where the difference lies in whether to adjust the processing speed. First, two multi-objective insertion procedures are proposed to form construction procedures. Second, we presented an efficient destruction procedure for each stage separately. Third, two local intensification methods are designed based on adjusting machine speeds, including the energy-saving procedure that optimizes the total energy consumption and the speedup-based local search procedure that optimizes the makespan. The KBIPG algorithm starts with generating solutions under various initial machine speed matrixes with different levels and then goes through a two-stage loop based on the proposed procedures. Computational experiments and comparisons with five algorithms demonstrate the effectiveness of the proposed KBIPG algorithm. [ABSTRACT FROM AUTHOR]

In this research, we are studying the scheduling problem of a professional ball manufacturing company in Taiwan. The problem is related to scheduling a flexible flow shop that includes setups with the aim of minimizing total weighted tardiness. We first present a mathematical model for the studied problem. Next, we conduct data processing and generate initial solutions. This involves extracting ordering data from the company, aggregating the total demand based on the product types of all orders, and splitting the demand into sublots. For production purposes, each subplot will be matched with a production lot, abbreviated as PLot. We assign a score to each order based on customer priority, order amount, due date, and order profitability. This has allowed us to propose a strategy in which orders compete for production priority based on their scores. We also proposed four dispatching rules to assign PLots to machines based on their production priority. Finally, we have suggested an iterated greedy algorithm with mutation (IGm algorithm) to improve the initial solutions obtained through dispatching rules. Extensive computational runs based on real data have been conducted to compare the performances of the proposed orders competing for production priority, the four dispatching rules, and the IGm algorithm with sequences generated by the EDD (Earliest Due Date) and WEDD (Weighted Earliest Due Date) rules. Computational results demonstrate that the proposed orders competing for production priority, along with the dispatching rules and the IGm algorithm, are effective in providing promising results for the scheduling problem. [ABSTRACT FROM AUTHOR]

This paper studies the scheduling problem of jobs with release times, non-identical sizes, and incompatible job families on unrelated parallel batch machines. The capacities of batch machines and the processing times of each job on the batch machines are different. The processing time of one batch is equal to the longest processing time of jobs in this batch. Different types of jobs are not allowed to be assigned into the same batch, which is known as incompatible job families. Mixed integer linear programming and constraint programming (CP) models are proposed. A new batch-based local search method is designed and an iterated greedy (IG) algorithm is developed to avoid unreasonable exchanging of jobs during the local search. Numerical results show that the CP method can obtain high quality solutions in the small-scale instances. For the large-scale instances, the IG algorithm with the new local search method has a competitive performance. [ABSTRACT FROM AUTHOR]

Community structure is one of the most important properties in complex networks. Detecting such communities plays an important role in a wide range of applications such as information sharing and diffusion, recommendation, and classification. In this paper, we propose a novel iterated greedy algorithm for detecting communities in complex networks. The algorithm is based on an iterative process that combines a destruction phase and a reconstruction phase. During the destruction phase, the algorithm destructs community indexes of a certain percent nodes having lower modularity contribution. In the reconstruction phase, their community indexes are reconstructed using the well-known Louvain construction heuristic. A local search procedure can be applied after the reconstruction phase to improve the performance of the algorithm. Experiments on the computer-generated networks and real-world networks show that our algorithm is very efficient and competitive compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today’s mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. [ABSTRACT FROM AUTHOR]

Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem(PFSP with time lags) is concentrated but non-permutation problem(non-PFSP with time lags) seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algorithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation(IGTLP) and iterated greedy algorithm for non-permutation (IGTLNP). The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% computational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation. [ABSTRACT FROM AUTHOR]

An improved iterated greedy algorithm (IIGA) is proposed in this paper to solve the no-wait flow shop scheduling problem with the objective to minimize the makespan. In the proposed IIGA, firstly, a speed-up method for the insert neighborhood is developed to evaluate the whole insert neighborhood of a single solution with ( n − 1)2 neighbors in time O( n 2), where n is the number of jobs; secondly, an improved Nawaz-Enscore-Ham (NEH) heuristic is presented for constructing solutions in the initial stage and searching process; thirdly, a simple local search algorithm based on the speed-up method is incorporated into the iterated greedy algorithm to perform exploitation. The computational results based on some well-known benchmarks show that the proposed IIGA can obtain results better than those from some existing approaches in the literature. [ABSTRACT FROM AUTHOR]