Sequence-dependent Setup Times Research Articles

An iterated local search for customer order scheduling in additive manufacturing

This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis.

An Outer Approximation Method for Scheduling Elective Surgeries with Sequence Dependent Setup Times to Multiple Operating Rooms

In this paper, operating room planning and scheduling problems have been studied. In operating room planning, the allocation of patients to operating rooms and their sequencing are critical in determining the performance of operating rooms. In this paper, three surgery scheduling decisions are considered, including the number of operating rooms to open, the allocation of surgeries to operating rooms, and the sequencing of surgeries in allocated operating rooms. All the surgeries under consideration are elective, and surgery durations are considered deterministic. Further, it is considered that the surgeries have different specialties, and each operating room can accommodate a particular specialty of surgeries, i.e., heterogeneous operating rooms are considered in the current study. Before performing a surgery, setup time is required for operating room turnover and sterilization, and it is considered sequence dependent. A mixed integer nonlinear programming (MINLP) model is developed to minimize the overtime costs of operating rooms for allocation and surgery sequencing with sequence dependent setup times. An outer approximation (OA) method is proposed to solve the problem near optimally. Experiments are conducted to compare the performance of the proposed OA method with the standard mixed integer nonlinear programming model. Computational results show the efficiency of the proposed OA method. Later, a case data from a case hospital is collected and a case study is solved.

A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems

Genetic algorithms (GAs) belong to the category of evolutionary algorithms and are frequently utilized for resolving challenging combinatorial problems. However, they typically require customization to suit a particular problem type, and their performance is heavily influenced by numerous hyperparameters and reproduction operators. In this work, we propose a Reinforcement Learning approach for fine-tuning Genetic Algorithms in Flexible Job Shop Scheduling problems (FJSP), where the main parameters involved in the genetic algorithm operators are trained to allocate the most promising values. The approach returns an optimized schedule taking into account given constraints specific to the scenario, such as the relationship among release date, due date, and processing time, which machines must be selected out of a set of alternative machines, or which sequence-dependent setup time can be filtered.
 The approach takes input data in the form of FJSP instances by varying the numbers of jobs and machines and then uses the NSGA-II algorithm to generate solutions. These solutions are stored in a Solutions module and they are analyzed using a Principal components analysis (PCA) to identify clusters of similar instances and solutions. The Q-Learning module then generates hyperparameters for each iteration of the NSGA-II algorithm based on information from the previous modules. A toy example is presented to better understand the behavior of the proposal and the results obtained for optimizing further instances of the problem in a more efficient way.

Integrating sustainability into production scheduling in hybrid flow-shop environments.

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factorsin production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers' flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.

An asymmetric traveling salesman problem based matheuristic algorithm for flowshop group scheduling problem

The flowshop group scheduling problem (FGSP) has become a hot research problem owing to its practical applications in modern industry in recent years. The FGSP can be regarded as a combination of two coupled sub-problems. One is the group scheduling sub-problem with sequence-dependent setup times. The other is the job scheduling sub-problem within each group. A mixed integer linear programming model is built for the FGSP with the makespan criterion. Based on the problem-specific knowledge, i.e., the sequence-dependent group setup times are greater than the processing time of jobs, and the number of machines is small, the group scheduling sub-problem is approximated into an asymmetric traveling salesman problem (ATSP). Then, a matheuristic algorithm (MA) is proposed by integrating a branch-and-cut algorithm and an iterated greedy (IG) algorithm, where the branch-and-cut algorithm is used to generate the optimal Hamiltonian circuit for sub-group sequences of a group sequence obtained by the IG. On 405 test instances, the proposed MA performs significantly better than several state-of-the-art algorithms in the literature.

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

An Iterated Population-Based Metaheuristic for Order Acceptance and Scheduling in Unrelated Parallel Machines with Several Practical Constraints

This study considers order acceptance and scheduling problems in unrelated parallel machines with several practical constraints, including order release times, sequence-dependent setup times, machines’ unequal ready times, and preventive maintenance. In a make-to-order production environment, issues with order acceptance and scheduling are mainly caused by the limited production capacity of a factory, which makes it impossible to accept all orders. Consequently, some orders must be rejected in order to maximize profits and the accepted orders must be completed by the due date or no later than the deadline. An iterated population-based metaheuristic is proposed to solve the problems. The algorithm begins with an efficient initial solution generator to generate an initial solution, and then uses the destruction and construction procedure to generate a population with multiple solutions. Then, a solution is selected from the population, and a variable neighborhood descent search algorithm with several new reduced-size neighborhood structures is applied to improve the selected solution. Following the completion of the local search, a method for updating the members of the population was devised to enhance its diversity. Finally, the metaheuristic allows the populations to evolve for several generations until the termination condition is satisfied. To evaluate the performance of the proposed metaheuristic, a heuristic rule and an iterated local search algorithm are examined and compared. The computational experimental results indicate that the presented metaheuristic outperforms the other heuristics.

An efficient iterated greedy algorithm for a multi-objective no-wait flow shop problem with sequence dependent setup times

An efficient iterated greedy algorithm for a multi-objective no-wait flow shop problem with sequence dependent setup times

A Matrix-Cube-Based Estimation of Distribution Algorithm for No-Wait Flow-Shop Scheduling With Sequence-Dependent Setup Times and Release Times

The no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release times (RTs) is applicable in many areas, such as steel production, food processing, and chemical processing. Estimation of the distribution algorithm (EDA) has recently been recognized as a prominent metaheuristic methodology in the field of evolutionary computation due to its excellent performance of global exploration. In this article, an innovative matrix-cube-based (i.e., 3-D) EDA (MCEDA) is first proposed to minimize the total earliness and tardiness (TET) of the NFSSP with SDSTs and RTs. This problem is NP-hard in the strong sense. First, a 3-D matrix cube is devised to learn the valuable information from promising solutions or excellent individuals. Second, an EDA model or probabilistic model based on the matrix cube and a special sampling method is presented to perform effective exploration in solution space and find promising regions. Third, based on a series of newly defined subneighborhoods, a new local search with both a speed-up scanning method and one search strategy is developed to execute exploitation from promising regions. Fourth, a speed-up evaluation method based on the problem’s property is designed to reduce the computational complexity for calculating criterion and accelerate the search process. Owing to the reasonable hybridization of exploration and exploitation, MCEDA can perform very efficient search in solution space. Extensive test results on instances of such a just-in-time problem first show that MCEDA can achieve better solution than state-of-the-art algorithms in obviously less computation time. Additional experiments on instances of various NFSSPs further confirm the efficiency and robustness of MCEDA.

Multiobjective Sequence-Dependent Job Sequencing and Tool Switching Problem

This article considers the extension of the job sequencing and tool switching problem with sequence-dependent setup times (SDSSP) and with the requirement to comply with the due date of the processed jobs. This extension is motivated by the practical application in manufacturing environment where the products manufactured may be subjected to a specific due date. This study develops a multiobjective SDSSP model (MO-SDSSP) to simultaneously minimize the total setup time of tool switches and the tardiness of the machining process. Then, a two-stage Multiobjective Adaptive Large Neighborhood Search (MOALNS) and Simulated Annealing (SA) is presented as a heuristic algorithm for the MO-SDSSP, in which the MOALNS is proposed to solve the job sequencing subproblem while the SA is used to solve the tool switching subproblem. Subsequently, the performance of MOALNS-SA is compared to several popular multiobjective optimization algorithms. Comparison on four performance criteria has shown the applicability of the MOALNS. Finally, managerial analysis is performed to analyze the relationship between the total tool setup time and the compliance of due dates.

An exact branch-and-bound algorithm for seru scheduling problems with sequence-dependent setup time

An exact branch-and-bound algorithm for seru scheduling problems with sequence-dependent setup time

A hybridization of PSO and VNS to solve the machinery allocation and scheduling problem under a machinery sharing arrangement

This paper presents the hybrid Particle Swarm Optimization and the Variable Neighborhood Search (PSO-VNS) to solve the machinery and equipment allocation and scheduling to help the growers expand the production level to meet the increased demands and growing interest, and to increase profitability. This problem can be formulated as the Machinery and Equipment Allocation and Scheduling Problem (MEASP) which is special type of a hybrid flow shop scheduling problem (HFS) with sequence dependent setup times, machine eligibility, machine grouping, blocking, tooling constraint, and time windows (HFS |SDST, rcrc, Grouping, blocking, Tool, Tw | Cmax). The objective of this research is to minimize the total completion time for sugarcane cultivation. A Mixed Integer Linear Programming (MILP) model was developed to handle small-scale problems. The hybrid Particle Swarm Optimization and the Variable Neighborhood Search (PSO-VNS) was used for large-scale problems. Two new velocity update formulae and a position update formula were incorporated into the Particle Swarm Optimization (PSO), and four types of neighborhood strategies were developed for the VNS. The experimental results show that all the PSO-VNS methods outperform the traditional PSO due to the effectiveness of the newly proposed velocity and position update formulae in finding the optimal solutions, the PSO-VNS-6 methods, on the average, can reduce the computational time by 58.43% from the original PSO and improve the solution quality by 11.71%.

A cooperative whale optimization algorithm for energy-efficient scheduling of the distributed blocking flow-shop with sequence-dependent setup time

The distributed blocking flow-shop sequence-dependent scheduling problem (DBFSDSP) with the production efficiency measures has been extensively concerned as its wide industrial applications. Whereas, energy efficiency indicators are often ignored in the literature. The DBFSDSP considered makespan, total tardiness, and total energy consumption is investigated in this paper. A cooperative whale optimization algorithm (CWOA) is proposed to solve the DBFSDSP as the complexity of the distributed and multi-objective optimization. First, the critical path of DBFSDSP is defined. An energy-saving operation for the non-critical path is designed to reduce the energy consumption of the system. Second, an accelerated operation for the critical path is designed to reduce the makespan and total tardiness. Third, three acceptance criteria for multi-objective optimization are proposed to improve the diversity of the population. The statistical and computational experimentation in an extensive benchmark testified that the CWOA outperforms the state-of-the-art algorithms regarding efficiency and significance in solving DBFSDSP.

Unrelated Parallel Machine Photolithography Scheduling Problem With Dual Resource Constraints

This paper studies an unrelated parallel machine photolithography scheduling problem with dual resource constraints (DRC). This is an emerging problem in the semiconductor industry, where the masks can be transferred from one working station to another, increasing the flexibility of production through sharing the auxiliary resource. It gives rise to a complex DRC scheduling problem involving jobs, machines, and masks, restricted by sequence-dependent setup time and station-dependent mask transfer time. This study proposes two mixed-integer programming (MIP) models (Model 1 and Model 2) and an improved naked mole-rat algorithm that hybrids with a genetic algorithm (GA) and variable neighborhood search algorithm (VNS). Numerical experiments show that Model 2 performs better compared with Model 1, and the improved naked mole-rat algorithm brings a significant improvement over GA and VNS.

Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

AbstractWe deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of answer-set programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising knowledge representation and reasoning (KRR) paradigm for this problem and is competitive with state-of-the-art constraint programming (CP) and Mixed-Integer Programming (MIP) solvers.

Acceleration-based artificial bee colony optimizer for a distributed permutation flowshop scheduling problem with sequence-dependent setup times

The distributed permutation flowshop scheduling problem (DPFSP) has attracted a lot of attentions in recent years. Furthermore, sequence-dependent setup time (SDST) often occurs in the job’s production, and total flowtime becomes a more important and meaningful objective for the modern dynamic productive environment. Therefore, the DPFSP with SDST under minimization of total flowtime is investigated in this paper, and the mathematical model is established. To solve the problem, this paper presents three constructive heuristics and an effective discrete artificial bee colony (EDABC) method based on the acceleration. In the EDABC, the constructive heuristics are used to initialize population. Moreover, based on the problem-specific of sequence-dependent, the employed bees stage, onlooker bees stage and scout bees stage are improved by exploring the acceleration methods to evaluate the neighboring solutions. Further, to strengthen the performance of the proposed algorithm, three effective problem-oriented local search methods are developed, including referenced local search (RLS), improved local search 1 (ILS1) and improved local search 2 (ILS2). RLS is used to the best solution after the initialization, and ILS1 and ILS2 are designed in the framework of improved variable neighborhood descent. The extensive computational experiences are carried out, and the results show that the proposed methods, including three heuristics and EDABC, have outstanding performance for the SDST/DPFSP to minimize the total flowtime.

A multi-class teaching–learning-based optimization for multi-objective distributed hybrid flow shop scheduling

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP.

Bounded dynamic programming approach to minimize makespan in the blocking flowshop problem with sequence dependent setup times

This paper aims at presenting an algorithm for solving the blocking flow shop problem with sequence dependent setup times (BFSP-SDST) with minimization of the makespan. In order to do so, we propose an adapted Bounded Dynamic Programming (BDP-SN) algorithm as solution method, since the problem itself does not present a significant number of sources in the state-of-art references and also because Dynamic Programming and its variants have been resurfacing in the flowshop literature. Therefore, we apply the modified method to two sets of problems and compare the results computationally and statistically for instances with a MILP and a B&B method for at most 20 jobs and 20 machines. The results show that BDP-SN is promising and outperforms both MILP and B&B within the established time limit. In addition, some suggestions are made in order to improve the method and employ it in parallel research regarding other branches of machine scheduling.

Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times

For the green hybrid flow shop scheduling problem considering sequence dependent setup times (SDST) and transportation times, a mixed integer programming model is established with the objectives of minimizing the maximum completion time (makespan) and total energy consumption simultaneously, and an improved memetic algorithm is proposed with the problem characteristics. First of all, an encoding method combining the jobs sequence code at the first stage with the machine allocation code is designed to ensure that the algorithm can search the entire solution space to the greatest extent; then, a mixed population initialization method is designed to improve the quality of the initial population solution; Thirdly, the crossover and mutation operators, and four neighborhood search strategies are designed to balance the global search and local search capabilities of the algorithm; Finally, the effectiveness of the algorithm is verified by numerical experiments.

Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

<p style='text-indent:20px;'>In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi-objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.</p>