Iterated Greedy Research Articles

Iterated greedy algorithms for a complex parallel machine scheduling problem

This paper addresses a complex parallel machine scheduling problem with jobs divided into operations and operations grouped in families. Non-anticipatory family setup times are held at the beginning of each batch, defined by the combination of one setup-time and a sequence of operations from a unique family. Other aspects are also considered in the problem, such as release dates for operations and machines, operation’s sizes, and machine’s eligibility and capacity. We consider item availability to define the completion times of the operations within the batches, to minimize the total weighted completion time of jobs. We developed Iterated Greedy (IG) algorithms combining destroy and repair operators with a Random Variable Neighborhood Descent (RVND) local search procedure, using four neighborhood structures to solve the problem. The best algorithm variant outperforms the current literature methods for the problem, in terms of average deviation for the best solutions and computational times, in a known benchmark set of 72 instances. New upper bounds are also provided for some instances within this set. Besides, computational experiments are conducted to evaluate the proposed methods’ performance in a more challenging set of instances introduced in this work. Two IG variants using a greedy repair operator showed superior performance with more than 70% of the best solutions found uniquely by these variants. Despite the simplicity, the method using the most common destruction and repair operators presented the best results in different evaluated criteria, highlighting its potential and applicability in solving a complex machine scheduling problem.

Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing

To realise the intelligent decision-making of dynamic scheduling and reconfiguration, we studied the intelligent scheduling and reconfiguration with dynamic job arrival for a reconfigurable flow line (RFL) using deep reinforcement learning (DRL), for the first time. The system architecture of intelligent scheduling and reconfiguration in smart manufacturing is proposed, and the mathematical model is established to minimise total tardiness cost. In addition, a DRL system of scheduling and reconfiguration is proposed by designing state features, actions, and rewards for scheduling and reconfiguration agents. Moreover, the advantage actor-critic (A2C) is adapted to solve the studied problem. The training curve shows the A2C-based agents have effectively learned to generate better solutions for unseen instances. The test results show that the A2C-based approach outperforms two traditional meta-heuristics, iterated greedy (IG) and genetic algorithm (GA), in solution quality and CPU times by a large margin. Specifically, the A2C-based approach outperforms IG and GA by 57.43% and 88.30%, using only 0.46‱ and 2.20‱ CPU times of IG and GA. The trained model can generate a scheduling or reconfiguration decision within 1.47 ms, which is almost instantaneous and can satisfy real-time optimisation. Our work shows a promising prospect of using DRL for intelligent scheduling and reconfiguration.

An iterated greedy algorithm for the parallel blocking flow shop scheduling problem and sequence-dependent setup times

This paper deals with the problem of scheduling jobs in a parallel flow shop configuration under the blocking constraint, in which the setup time of machines depends not only on the job to be processed but also on the previously processed one, i.e., there are sequence-dependent setup times. The performance analysis of several iterated greedy algorithms with different initial solution procedures and local searches lets us define an efficient algorithm to minimize the maximum job completion time. Moreover, the computational evaluation showed the efficiency of searching in different neighborhood structures and noted the significant influence of the initial solution. However, contrary to other scheduling problems, starting with a high quality solution does not guarantee better performance of the algorithm.

A new method for a class of parallel batch machine scheduling problem

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.

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

• We study a distributed blocking flowshop scheduling problem. • We present six effective constructive heuristics and an iterated greedy algorithm . • Two different destructions focusing on the problem characteristic are designed. • Three local search procedures based on the various neighbourhood are presented. • The computational experiments prove the effectiveness of presented algorithms. Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.

Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms

The growing concerns of companies to economic savings, optimal utilization of resources, and increased environmental protection regulations prompt the manufacturers to be more focused on the recycling of the products that are at the end of their useful life. This study considers a job shop scheduling problem with reverse flows under uncertainty. Since the main parameter of the model (i.e., the processing time of operations) is tainted with a great degree of uncertainty in real-world applications, a robust programming approach is utilized. This paper proposes a computationally efficient model. Due to the complexity and difficulty of solving the presented model, an exact solution method for small-sized instances and simulated annealing (SA) and discrete harmony search (DHS) algorithms for medium- and large-sized instances are proposed. The model performance is evaluated by comparing the computational results with the literature. Furthermore, the performance of the proposed meta-heuristic algorithms is evaluated by comparing the resulted solutions with the exact method for small-sized instances and with three other meta-heuristics algorithms, such as discrete particle swarm optimization (DPSO) and invasive weed optimization (DIWO), and iterated greedy (IG) algorithms, for medium- and large-sized instances. The satisfying results show that the presented model and proposed algorithms ensure good quality solutions within a reasonable time for all test problems and the SA algorithm outperforms the DIWO, DPSO, DHS, and IG algorithms in most cases.

Just-in-time scheduling for a distributed concrete precast flow shop system

This paper focuses on the distributed concrete precast flow shop scheduling problem to minimize total weighted earliness and tardiness. It is crucial for a precast manufacturer to schedule jobs effectively to meet shipping dates because of tight due date, limited inventory capacity and huge tardiness penalties. In order to respond quickly to customer demands, many concrete precast manufacturers have adopted the distributed-multiple-factory mode in which job assignment and scheduling have to be decided jointly. To solve this problem, through analyzing the characteristics of distributed precast production, we first develop a novel mixed integer nonlinear programming (MINLP) model and then transform it into an effective mixed integer linear programming (MILP) model by linearization techniques. Additionally, since the problem is more complex than the classical distributed flow shop scheduling problem, which is NP-hard in most cases, we propose a hybrid iterated greedy algorithm (HIG) by integrating due date related NEH-based heuristics, problem-specific knowledge, different local search methods, and mechanism of destruction and reconstruction. Subsequently, we propose a hybrid tabu search and iterated greedy (HTS_IG) in which a hybrid tabu search (HTS) is run at first and then an iterated greedy (IG) starts from the best solution obtained by HTS. Aimed at improving search efficiency, some structural properties of the schedules are explored and integrated into the local search steps of HIG and HTS_IG. We also develop a hybrid genetic algorithm and variable neighborhood search (HGA_VNS) and a two-phase heuristic method (TPHM) for comparison. Finally, extensive experiments and deep analysis are conducted on instances with different combination of problem parameters. Computational results show the effectiveness of the MILP model and the proposed algorithms. The computational analysis indicates that, on average, HTS_IG performs best among all the proposed metaheuristics. The effectivenesses of local search based on problem-specific knowledge (PSK) in HIG and HTS_IG are also verified.

Minimization of total tardiness in no-wait flowshop production systems with preventive maintenance

Efficient business organizations must balance quality, cost, and time constraints in competitive environments. Reflecting the complexity of this task, we consider manufacturing systems including several stages of production chains requiring time measurement. When production scheduling is not prioritized in such enterprises, several negative effects may occur. A corporation may suffer financial penalties as well as negative brand exposure, and thus may find its credibility challenged. Therefore, in this study, we propose constructive methods to minimize a total tardiness criterion, considering preventative maintenance constraints to reflect the reality of industrial practice, focusing on a no-wait flowshop environment in which jobs are successively processed without operational interruptions. In addition to proposing constructive methods to solve the no-wait flowshop production scheduling problem, a metaheuristic is presented as an approach to improve results obtained by constructive methods. Computational experiments were designed and performed to compare several production scheduling algorithms. Among various constructive heuristics considered, an algorithm called HENLL using an insertion logic showed the best performance. The proposed metaheuristic is based on the iterated greedy (IG) search method, and the results obtained demonstrated significant improvement compared to the heuristics alone. It is expected that this study may be used by production planning and control (PPC) professionals to apply the proposed method to schedule production more efficiently. We show that the proposed method successfully presented a better solution in relation to total tardiness, considering the above mentioned environment.

Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

An effective iterated greedy algorithm for solving a multi-compartment AGV scheduling problem in a matrix manufacturing workshop

In this paper, we address a multi-compartment automatic guided vehicle scheduling (MC-AGVS) problem from a matrix manufacturing workshop that has attracted more and more attention of manufacturing firms in recent years. The problem aims to determine a solution to minimize the total cost including the travel cost, the service cost, and the cost of vehicles involved. For this purpose, a mixed-integer linear programming model is first constructed. Then, a novel iterated greedy (IG) algorithm including accelerations for evaluating objective functions of neighboring solutions; an improved nearest-neighbor-based constructive heuristic; an improved sweep-based constructive heuristic; an improved destruction procedure; and a simulated annealing type of acceptance criterion is proposed. At last, a series of comparative experiments are implemented based on some real-world instances from an electronic equipment manufacturing enterprise. The computational results demonstrate that the proposed IG algorithm has generated substantially better solutions than the existing algorithms in solving the problem under consideration.

An event-based reactive scheduling approach for the Resource Constrained Project Scheduling Problem with unreliable resources

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

Heuristic Scheduling of Batch Production Processes Based on Petri Nets and Iterated Greedy Algorithms

Wire rod and bar rolling is an important batch production process in steel production systems. A scheduling problem originated from this process is studied in this work by considering the constraints on sequence-dependent family setup time and release time. For each serial batch to be scheduled, it contains several jobs and the number of late jobs within it varies with its start time. First, we model a rolling process using a Petri net (PN), where a so-called rolling transition describes a rolling operation of a batch. The objective of the concerned problem is to determine a firing sequence of all rolling transitions such that the total number of late jobs is minimal. Next, a mixed-integer linear program is formulated based on the PN model. Due to the NP-hardness of the concerned problem, iterated greedy algorithm (IGA)-based methods by using different neighborhood structures and integrating a variable neighborhood descent method are developed to obtain its near-optimal solutions. To test the accuracy, speed, and stability of the proposed algorithms, we compare their solutions of different-size instances with those of CPLEX (a commercial software) and four heuristic peers. The results indicate that the proposed algorithms outperform their peers and have great potential to be applied to industrial production process scheduling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work deals with a scheduling problem of a batch production process, i.e., wire rod and bar rolling, which is modeled by a Petri net (PN). Due to the NP-hardness of the concerned problem, four iterated greedy algorithm-based methods are developed to solve it. The proposed methods are validated and tested by comparing their solutions with those of four heuristic peers and the exact ones (when available via CPLEX). Extensive experimental results show that they can fast solve one-week-scale instances with better performance than their peers’, thereby proving the readiness to put them in industrial use. When solving a one-month-scale instance, the proposed methods show much better performance than others.

An effective Iterated Greedy algorithm for the distributed permutation flowshop scheduling with due windows

Distributed Permutation Flowshop Scheduling Problem (DPFSP) has become a research hotspot in recent years. However, as a service level objective, the Total Weighted Earliness and Tardiness (TWET) has not been addressed so far. Due to the importance of the service level objective in modern industry, we deal with the minimization of the TWET for the DPFSP with due windows . An Iterated Greedy (IG) algorithm, namely IG with Idle Time insertion Evaluation (IGITE), is proposed. In the algorithm, an adapted NEH heuristic with five rules based on the unit earliness weight and unit tardiness weight, the due date, and the smallest slack on the last machine is used to generate an initial solution. Destruction procedure with a dynamic size is provided to enhance the exploration capability of the algorithm. Idle time insertion method is utilized to make the completion time of jobs within the due windows or as close to the due windows as possible. A large number of experiments show that the presented algorithm performs significantly better than the five competing algorithms adapted in the literature. The performance analysis shows that the IGITE is the most appropriate for the DPFSP with due windows among the tested algorithms.

Effective heuristics and metaheuristics for the distributed fuzzy blocking flow-shop scheduling problem

Abstract In consideration of the uncertainty of manufacturing system, this paper investigates a distributed fuzzy blocking flow-shop scheduling problem (DFBFSP) in which there are multiple homogeneous factories and each one is set as a flow shop with no intermediate buffers between any consecutive machines. The processing time is uncertain and represented by the fuzzy number. The objective is to minimize the fuzzy makespan among all factories. To address this problem, two constructive heuristics (i.e., INEH and DPFNEH) are firstly proposed based on the problem-specific knowledge and the NEH heuristic. The INEH employs the spread value of fuzzy processing time to generate the initial job sequence. The DPFNEH assigns the partial jobs to factories by reducing the total expected idle time and blocking time. Afterwards, two iterated greedy (IG) methods are presented in which the proposed constructive heuristic is employed to generate the initial solution with high quality. A novel plateau exploration-based local search is incorporated to enhance the quality of solutions. To keep the search vitality, an improved acceptance criterion based on the fuzzy characteristic is designed to avoid falling into the local optimum. Finally, a comprehensive computational experiment and comparisons with the state-of-the-art methods in the literature are conducted based on an extended benchmark set and a new evaluation indicator. The results show that the proposed constructive heuristics and IG methods can effectively and efficiently solve the considered problem.

Local search metaheuristic for solving hybrid flow shop problem in slabs and beams manufacturing

Solving optimization problems in the modern manufacturing industry is one of the most attractive areas of operational research. In this paper, we propose an industrial optimization case of slabs and beams production problem. The suggested approach consists in modeling the process as a hybrid flow shop with unrelated parallel machines under constraints of sequence dependent setup time. Jobs launched in production are of different characteristics and require a machine preparation time at each phase during their production cycle. The goal is to minimize the total tardiness of all jobs when they are completed on the last stage. The hybrid flow shop problem is considered as one of the NP-hard issues since it consists of at least two stages. To solve numerically this problem, we propose two methods, the iterative local search (ILS) and the iterated greedy (IG) metaheuristics. In addition, we suggest two new improvements, the first relates to the initial step while the second concerns the steps of the neighborhood exploration. In the initial phase, we consider an initial solution set generated from the priority rules whose scheduling is ensured by the Nawaz-Enscore-Ham (NEH) algorithm and the greedy randomized adaptive search procedure (GRASP). In the second phase, we suggest a new version of exploration of the neighborhood. In fact, from a single solution, we will generate a set of neighboring solutions and we will choose the best solution. In this new resolution approach, we develop a total of twelve algorithms based on neighborhood exploration. We note that the proposed algorithms are hybrid metaheuristics and are among the most powerful optimization algorithms in local search. A simulation study is conducted to verify the effectiveness of the suggested algorithms.This simulation is performed on a set of instances by varying the number of jobs, the number of machines per stage and the number of stages. We find that the IG algorithm based on NEH initialization heuristic gives good results in terms of quality and convergence time to the best solution.

Using iterated greedy and randomized iterated greedy algorithms to solve urban area waste collection in Riyadh city

This paper addresses the real-life waste collection vehicle routing problem by applying Iterated Greedy (IG) and Randomized Iterated Greedy (RIG) in order to improve the processes. This kind of problem becomes more complex in developing countries in several aspects such as costs and fuel. Nowadays, the waste collection is considered as one of the interesting areas. There are three types of waste: commer-cial, residential and roll-on-roll-off. In this paper, we mainly consider the residential waste collection problem. The problem can be summa-rized as follows: a vehicle has to satisfy the demand at each customer location while satisfying the capacity of the vehicle for reducing the total cost. We report a case study that is related to waste collection in Riyadh, Kingdom of Saudi Arabia. To solve the case study problem, IG and RIG were employed. Experiments have been done on the case study data and show a better performance when compared IG algo-rithm results with RIG algorithm results. Â

The distributed assembly permutation flowshop scheduling problem with flexible assembly and batch delivery

The scheduling problems of distributed production have received increasing attention in recent years. To coordinate the scheduling of production and transportation, we propose a novel distributed assembly permutation flowshop scheduling problem with flexible assembly and batch delivery (DAPFSP-FABD). The objective is to minimise the total cost of delivery and tardiness. Given that the problem involves six successive decisions, several batch allocation strategies for both production and distribution are proposed, and four neighbourhood structures are designed to search for the best batch sequence, batch allocation, product sequence, and job sequence. Seven algorithms, including four heuristics, a variable neighbourhood descent (VND) algorithm, and two iterated greedy (IG) algorithms, are proposed. In the IG, two types of destruction and construction operations are present to facilitate escape from local optima, thereby resulting in two IG algorithms: IG_desP and IG_desJ. Finally, extensive computational and statistical experiments are carried out. The results show that the proposed batch allocation strategies can improve the solution significantly. Additionally, the heuristics can obtain reasonable solutions in a short time, while IG_desJ performs the best regarding the quality of solutions. This study can help cross-regional companies coordinate the scheduling of production and transportation and enhance the performance of supply chains.

Combined Nearest Greedy Algorithm With Randomized Iterated Greedy Algorithm to Solve Waste Collection Problem

The waste collection considers as one of the common transportation problems in the operation research and management area and also it is a significant activity in each city. In this kind of the paper, we study a version of a real-life waste collection and try to find an efficient way to reduce the costs such as the cost of the operation such as fuel and maintenances, the cost of the environment such as noise and traffic congestions, the cost of the investment such as vehicles fleet. The waste collection problem can be formulated as a well-known Vehicle Routing Problem (VRP). The basic idea is to attempt to develop a daily truck routing which will improve the efficiency of the vehicle distribution in Riyadh. The solution will be done in a good way that it can serve all the customers, while in the meantime, it will attempt to improve the total cost. The main contribution of this paper is to improve the solution of total costs for the waste collection while using the existing resources through the combination of the Nearest Greedy algorithm with both Iterated Greedy (IG) and Randomized Iterated Greedy (RIG). We execute our proposed method with real data that collect waste from more than 100 customers in Riyadh city. In terms of the experiments, the results received by those methods are successfully implemented and improved the overall waste collection in Riyadh. In conclusion, these algorithms able to reduce the total costs to this kind of case study with the same number of vehicles.

An iterated greedy algorithm for the obnoxious p-median problem

The obnoxious p-median problem (OpM) is one of the NP-hard combinatorial optimization problems, in which the goal is to find optimal places to facilities that are undesirable (e.g. noisy, dangerous, or pollutant) such that the sum of the minimum distances between each non-facility location and its nearest facility is maximized. In this paper, for the first time in the literature, Iterated Greedy (IG) metaheuristic has been applied at a higher level to solve this problem. A powerful composite local search method has also been developed by combining two fast and effective local search algorithms, namely RLS1 and RLS2, which were previously used to solve the OpM. Comprehensive experiments have been conducted to test the performance of the proposed algorithm using a common benchmark for the problem. The computational results show the effectiveness of the IG algorithm that it can find high-quality solutions in a short time. Based on the set of selected instances, the results also reveal that the developed IG algorithm outperforms most of the state-of-the-art algorithms and contributes to the literature with 5 new best-known solutions.

A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop

With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem.