A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria

In this paper, we tackle the problem of total flowtime and makespan minimisation in a permutation flowshop. For this, we introduce a multi-criteria iterated greedy search algorithm. This algorithm iterates over a multicriteria constructive heuristic approach to yield a set of Pareto-efficient solutions (a posteriori approach). The proposed algorithm is compared against the best-so-far heuristic for the problem under consideration. The comparison shows the proposal to be very efficient for a wide number of multicriteria performance measures. Aside, an extensive computational experience is carried out in order to analyse the different parameters of the algorithm. The analysis shows the algorithm to be robust for most of the considered performance measures.     

Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study

Increasing global energy consumption, large variations in its cost and the environmental degradation effects are good reasons for the manufacturing industries to become greener. Green shop floor scheduling is increasingly becoming a vital factor in the sustainable manufacturing. In this paper, a green permutation flowshop scheduling problem with sequence-dependent setup times is studied. Two objectives are considered including minimisation of makespan as a measure of service level and minimisation of total energy consumption as a measure of environmental sustainability. We extend a bi-objective mixed-integer linear programming model to formulate the stated problem. We develop a constructive heuristic algorithm to solve the model. The constructive heuristic algorithm includes iterated greedy (CHIG) and local search (CHLS) algorithms. We develop an efficient energy-saving method which decreases energy consumption, on average, by about 15%. To evaluate the effectiveness of the constructive heuristic algorithm, we compare it with the famous augmented ?-constraint method using various small-sized and large-sized problems. The results confirm that the heuristic algorithm obtains high-quality non-dominated solutions in comparison with the augmented ?-constraint method. Also, they show that the CHIG outperforms the CHLS. Finally, this paper follows a case-study, with in-depth analysis of the model and the constructive heuristic algorithm.     

Scheduling identical wafer sorting parallel machines with sequence-dependent setup times using an iterated greedy heuristic

Wafer sorting is one of the most critical processes involved in semiconductor device fabrication. This study addresses the wafer sorting scheduling problem (WSSP), with minimisation of total setup time as the primary criterion and minimisation of the number of testers used as the secondary criterion. In view of the strongly NP-hard nature of this problem, a simple and effective iterated greedy heuristic is presented. The performance of the proposed heuristic is empirically evaluated by 480 simulation instances based on the characteristics of a real wafer testing shop-floor. The experimental results show that the proposed heuristic is effective and efficient as compared to the state-of-art algorithms developed for the same problem. It is believed that this study has developed an approach that is easy to comprehend and satisfies the practical needs of wafer sorting.     

Effective dynamic dispatching rule and constructive heuristic for solving single-machine scheduling problems with a common due window

This study addresses the single-machine scheduling problem with a common due window (CDW) that has a constant size and position. The objective is to minimise the total weighted earliness–tardiness penalties for jobs completed out of the CDW. To determine a schedule as close to optimum as possible, this study develops a dynamic dispatching rule and an effective constructive heuristic. The better performance of the proposed heuristic is demonstrated by comparing the results of it with those of a state-of-the-art greedy heuristic on a well-known benchmark problem set. In addition, we incorporate the constructive heuristic into a best-so-far meta-heuristic to examine the benefit of the proposed heuristic. The results show that the best known solutions in 144 out of the 250 benchmark instances are improved.     

Improved imperialist competitive algorithms for rebalancing multi-objective two-sided assembly lines with space and resource constraints

In this paper, a mathematical model and an improved imperial competition algorithm (IICA) are proposed to solve the multi-objective two-sided assembly line rebalancing problem with space and resource restrictions (MTALRBP-SR). The aim is to find lines’ rebalance with the trade-off between efficiency, rebalancing cost and smoothing after reconfiguration. IICA utilises a new initialisation heuristic procedure based on classic heuristic rules to generate feasible initial solutions. A novel heuristic assimilation method is developed to vigorously conduct local search. In addition, a group-based decoding heuristic procedure is developed to fulfil the final task reassignment with the additional restrictions. To investigate the performance of the proposed algorithm, it is first tested on MTALRBP of benchmark problems and compared with some existing algorithms such as genetic algorithm, variable neighbourhood search algorithm, discrete artificial bee colony algorithm, and two iterated greedy algorithms. Next, the efficiency of the proposed IICA for solving MTALRBP-SR is revealed by comparison with a non-dominated sorting genetic algorithm (NSGA-II) and two versions of original ICA. Computational results and comparisons show the efficiency and effectiveness of IICA. Furthermore, a real-world case study is conducted to validate the proposed algorithm.     

Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm

This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based on the iterated Pareto greedy (IPG) algorithm but exploits the accessible Tabu list (TL) to enhance its performance. To demonstrate the superior performance of the proposed Tabu-enhanced iterated Pareto greedy (TIPG) algorithm, its computational results are compared with IPG and existing algorithms on the same benchmark problem set. Experimental results reveal that incorporating the accessible TL can eliminate ineffective job moves, causing the TIPG algorithm to outperform state-of-the-art approaches in the light of five multi-objective performance metrics. This work contributes a useful theoretical and practical optimisation method for solving this problem.     

An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system

This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a two-dimensional vector, where the first dimensional vector is the factory and the second dimensional vector lists the operation scheduling sequence of each factory. Second, considering the distributed problem feature, a distributed iterated greedy heuristic (DIG) is developed where destruction and construction processes are designed in detail while considering the distributed structures. Third, an efficient population initialisation method that considers the factory workload balance is presented. Then, a local search approach that randomly replaces two factories with two randomly selected jobs and that finds an optimal position for the two inserted operations via the DIG method is proposed. For the canonical ABC algorithm, using the DIG approach, the main three parts are improved, namely, the employee, onlooker, and scout bees. Finally, the proposed algorithm is tested on sets of extended instances based on the well-known benchmarks. Through an analysis of the experimental results, the highly effective proposed IABC algorithm is compared to several efficient algorithms drawn from the literature.     

Minimising makespan in distributed mixed no-idle flowshops

The rapid growth of distributed manufacturing in industry today has recently attracted significant research attention that has focused on distributed scheduling problems. This work studied the distributed mixed no-idle flowshop scheduling problem using makespan as an optimality criterion. To the best of the authors’ knowledge, this is the first paper to study the multi-flowshop extension in which each flowshop has mixed no-idle constraints. A novel cloud theory-based iterated greedy (CTBIG) algorithm was proposed for solving the problem. Computational experiments conducted on a set of test instances revealed that the proposed CTBIG algorithm significantly outperformed classic iterated greedy algorithms.     

Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm

The distributed permutation flowshop scheduling problem (DPFSP) is a newly proposed topic in the shop scheduling field, which has important application in globalised and multi-plant environments. This study presents a modified iterated greedy (MIG) algorithm for this problem to minimise the maximum completion time among all the factories. Compared with previous approaches, the proposed algorithm is simpler yet more effective, more efficient, and more robust in solving the DPFSP. To validate the performance of the proposed MIG algorithm, computational experiments and comparisons are conducted on an extended benchmark problem set of Taillard. Despite its simplicity, the computational results show that the proposed MIG algorithm outperforms all existing algorithms, and the best-known solutions for almost half of instances are updated. This study can be offered as a contribution to the growing body of work on both theoretically and practically useful approaches to the DPFSP.     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

Scheduling algorithms for remanufacturing systems with parallel flow-shop-type reprocessing lines

This study considers a scheduling problem for remanufacturing systems in which end-of-life products are separated into their major components at a disassembly workstation, each of them is reprocessed at its dedicated flow-shop-type reprocessing line with serial workstations, and finally, the reprocessed components, together with new components if required, are reassembled into remanufactured products at a reassembly workstation. Among various system configurations, we focus on the one with parallel flow-shop-type reprocessing lines since it is a typical remanufacturing configuration. The problem is to determine the sequence of products to be disassembled, the sequence of components to be reprocessed at each workstation of flow-shop-type reprocessing lines and the sequence of products to be reassembled for the objective of minimising the total flow time. An integer programming model is developed to represent the problem mathematically, and then, three types of heuristics, i.e. priority rule-based heuristic, Nawaz–Enscore–Ham-based heuristic and iterated greedy algorithm, are proposed due to the problem complexity. To show the performances of the heuristics, a series of computational experiments were done on various test instances, and the results are reported.     

Bi-objective reentrant hybrid flowshop scheduling: an iterated Pareto greedy algorithm

The multi-objective reentrant hybrid flowshop scheduling problem (RHFSP) exhibits significance in many industrial applications, but appears under-studied in the literature. In this study, an iterated Pareto greedy (IPG) algorithm is proposed to solve a RHFSP with the bi-objective of minimising makespan and total tardiness. The performance of the proposed IPG algorithm is evaluated by comparing its solutions to existing meta-heuristic algorithms on the same benchmark problem set. Experimental results show that the proposed IPG algorithm significantly outperforms the best available algorithms in terms of the convergence to optimal solutions, the diversity of solutions and the dominance of solutions. The statistical analysis manifestly shows that the proposed IPG algorithm can serve as a new benchmark approach for future research on this extremely challenging scheduling problem.     

New block properties for flowshop scheduling with blocking and their application in an iterated greedy algorithm

This paper proposes new block properties for the flowshop scheduling problem with blocking to minimise makespan. A pruning procedure based on these proposed properties is used in the construction phase of an iterated greedy algorithm to decrease the total number of solutions to be examined to find an optimal schedule. Computational results using Taillard’s benchmark problem instances show that the new block properties help to eliminate more ‘unpromising’ solutions than the classic properties. In addition, the effectiveness of the proposed algorithm is verified by comparison with some high-performing algorithms for the considered problem.     

A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations

Assembly lines of big-size products such as buses, trucks and helicopters are very different from the lines studied in the literature. These products’ manufacturing processes have a lot of tasks most of which have long task times. Since traditional assembly line models including only one worker in each station (i.e. simple assembly lines) or at most two workers (two-sided assembly lines) may not be suitable for manufacturing these type of products, they need much larger shop floor for a number of stations and long product flow times. In this study, an assembly line balancing problem (ALBP) with parallel multi-manned stations is considered. Following the problem definition, a mixed integer programming formulation is developed. A detailed study of priority rules for simple ALBPs is also presented, and a new efficient constructive heuristic algorithm based on priority rules is proposed. In order to improve solutions found by the constructive heuristic, a genetic algorithm-based solution procedure is also presented. Benchmark instances in the literature are solved by using the proposed mathematical programming formulation. It has been seen that only some of the small-size instances can be solved optimally by this way. So the efficiency of the proposed heuristic method is verified in small-size instances whose optimal solutions are found. For medium- and big-size instances, heuristics’ results and CPU times are demonstrated. A comparative evaluation with a branch and bound algorithm that can be found in the literature is also carried out, and results are presented.     

Scheduling multistage hybrid flowshops with multiprocessor tasks by an effective heuristic

This study considers the scheduling problem of multistage hybrid flowshops with multiprocessor tasks, which is a core topic for numerous industrial applications. An effective and efficient heuristic, namely the heuristic of multistage hybrid flowshops (HMHF) is proposed to solve this problem. To verify the developed heuristic, computational experiments are conducted on a well-known benchmark problem set. The results are compared with 10 constructive heuristics and a tabu search (TS) based meta-heuristic from the relevant literature. These computational results show that the proposed HMHF heuristic is highly effective when compared to these algorithms for this problem on the same benchmark instances.     

Synchronization of yard truck scheduling and storage allocation in container terminals

Yard truck scheduling and storage allocation, as two separate subproblems in port operations, have been extensively studied in the past decades. However, from the operational point of view, they are highly interdependent. This article proposes an integer programming model in which yard truck scheduling and storage allocation problems are formulated as a whole for heterogeneous import containers. Different stacking times at yard blocks is modelled as well. The objective of the proposed model is to reduce the congestion and waiting time of yard trucks in the terminal so as to decrease the makespan of discharging containers. Owing to the inherent computational complexity, a genetic algorithm and a greedy heuristic algorithm have been designed. Computational experiments show that the proposed genetic algorithm and greedy algorithm are both effective in solving the studied problem.     

A batching and scheduling algorithm for the diffusion area in semiconductor manufacturing

This paper proposes an efficient heuristic algorithm for solving a complex batching and scheduling problem in a diffusion area of a semiconductor plant. Diffusion is frequently the bottleneck in the plant and also one of the most complex areas in terms of number of machines, constraints to satisfy and the large number of lots to manage. The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on machines. The problem is modelled and solved using a disjunctive graph representation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and Simulated Annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of the iterative sampling algorithms to significantly improve the initial solution, and that Simulated Annealing enhances the results. Furthermore, our algorithm compares favourably to an algorithm reported in the literature for a simplified version of our problem. The constructive algorithm has been embedded in software and is currently being used in a semiconductor plant.     

New heuristics for no-wait flow shops with a linear combination of makespan and maximum lateness

In this work we study a flow shop scheduling problem in which jobs are not allowed to wait between machines, a situation commonly referred to as no-wait. The criterion is to minimise a weighted sum of makespan and maximum lateness. A dominance relation for the case of three machines is presented and evaluated using experimental designs. Several heuristics and local search methods are proposed for the general m-machine case. The local search methods are based on genetic algorithms and iterated greedy procedures. An extensive computational analysis is conducted where it is shown that the proposed methods outperform existing heuristics and metaheuristics in all tested scenarios by a considerable margin and under identical CPU times.     

Scheduling with multi-attribute set-up times on unrelated parallel machines

This paper studies a problem in the knitting process of the textile industry. In such a production system, each job has a number of attributes and each attribute has one or more levels. Because there is at least one different attribute level between two adjacent jobs, it is necessary to make a set-up adjustment whenever there is a switch to a different job. The problem can be formulated as a scheduling problem with multi-attribute set-up times on unrelated parallel machines. The objective of the problem is to assign jobs to different machines to minimise the makespan. A constructive heuristic is developed to obtain a qualified solution. To improve the solution further, a meta-heuristic that uses a genetic algorithm with a new crossover operator and three local searches are proposed. The computational experiments show that the proposed constructive heuristic outperforms two existed heuristics and the current scheduling method used by the case textile plant.     

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.