Problem-specific Local Search Research Articles

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

Modeling and optimization of the hybrid flow shop scheduling problem with sequence-dependent setup times

The hybrid flow shop scheduling problem (HFSP) is an extension of the classic flow shop scheduling problem and widely exists in real industrial production systems. In real production, sequence-dependent setup times (SDST) are very important and cannot be neglected. Therefore, this study focuses HFSP with SDST (HFSP-SDST) to minimize the makespan. To solve this problem, a mixed-integer linear programming (MILP) model to obtain the optimal solutions for small-scale instances is proposed. Given the NP-hard characteristics of HFSP-SDST, an improved artificial bee colony (IABC) algorithm is developed to efficiently solve large-sized instances. In IABC, permutation encoding is used and a hybrid representation that combines forward decoding and backward decoding methods is designed. To search for the solution space that is not included in the encoding and decoding, a problem-specific local search strategy is developed to enlarge the solution space. Experiments are conducted to evaluate the effectiveness of the MILP model and IABC. The results indicate that the proposed MILP model can find the optimal solutions for small-scale instances. The proposed IABC performs much better than the existing algorithms and improves 61 current best solutions of benchmark instances.

Learning-based two-phase cooperative optimizer for distributed machine scheduling with heterogeneous factories and order priorities

In the realm of customized manufacturing, production cycles are often compressed to capture market opportunities swiftly. The blanking system stands as the inaugural and pivotal phase in the realm of large equipment customized manufacturing. This study abstracts a novel problem from real-world blanking systems, as the distributed unrelated parallel machine scheduling with heterogeneous factories and order priorities (DUPMS-HP). The presented work formulates the bi-objective DUPMS-HP, aiming to minimize both the total weighted tardiness and the workload gap of each machine. A learning-based two-phase cooperative optimizer (LCTPO) is introduced to address this NP-hard problem, featuring: i) a cooperative evolutionary algorithm during the first stage for global search to ensure diversity; ii) the incorporation of five problem-specific local search strategies in the first stage to balance priority and due date constraints. Additionally, reinforcement learning is applied to learn and select the best neighborhood search operator for each elite solution, further enhancing diversity. The effectiveness of the proposed algorithm is validated through a comparative analysis with five state-of-the-art algorithms on 20 instances. Experimental results affirm that LCTPO is more adept at solving DUPMS-HP compared to the alternative algorithms.

Multi-Objective Design of UAS Air Route Network Based on a Hierarchical Location–Allocation Model

This research concentrates on the Unmanned Aircraft System (UAS) demand sites’ hierarchical location–allocation problem in air route network design. With demand sites (locations where UAS operations are requested) organized and allocated according to the spatial hierarchy of UAS traffic flows, the hierarchical structure guarantees resource conservation and economies of scale through traffic consolidation. Therefore, in this paper, the UAS route network with a three-level hierarchy is developed under a multi-objective decision-making framework, where concerns about UAS transportation efficiency from the user side and construction efficiency from the supplier side are both simultaneously considered. Specifically, a bi-level Hybrid Simulated Annealing Genetic Algorithm (HSAGA) with global and local search combined is proposed to determine the optimal number, location, and allocation of hierarchical sites. Moreover, using the information of site closeness and UAS demand distribution, two problem-specific local search operators are designed to explore elite neighborhood regions instead of all the search space. A case study based on the simulated UAS travel demand data of the Beijing area in China was conducted to demonstrate the effectiveness of the proposed method, and the impact of critical parameter settings on the network layout was explored as well. Findings from this study will offer new insights for UAS traffic management in the future.

A Learning-Based Memetic Algorithm for Energy-Efficient Flexible Job-Shop Scheduling With Type-2 Fuzzy Processing Time

Green flexible job shop scheduling problem (FJSP) aims to improve profit and reduce energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy processing time is proposed to predict the uncertainty in timing constrain for better simulating the practical production. This study addresses the multi-objective energy-efficient flexible job shop scheduling problem with type-2 processing time (ET2FJSP), where the minimization of makespan and total energy consumption are considered simultaneously. The previous studies do not propose the model verification and energy-saving strategy. Moreover, the best parameters required by an algorithm in different stage is different. Therefore, we propose a mixed integer linear programming model and design a learning-based reference vector memetic algorithm (LRVMA). Its main features are: i) four problem-specific initial rules are presented for initialization to generate diverse solutions; ii) four problem-specific local search methods are incorporated to enhance the exploitation; iii) an effective solution selection method depending on the Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; iv) a reinforcement learning-based parameter selection strategy is proposed to improve the diversity of nondominated solutions, and v) an energy-saving strategy is designed to reduce energy consumption. To verify the effectiveness of LRVMA, it is compared against other related algorithms. The results demonstrate that LRVMA outperforms the compared algorithms for solving ET2FJSP.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Multi-objective grasp pose optimisation for robotic 3D pipe assembly manipulation

This paper considers the problem of grasp pose optimisation for manipulating 3D pipe assemblies during the manufacturing process. The method presented in this paper is specifically developed for manufacturing cryogenic pipe assemblies autonomously in a Factory-In-A-Box scenario (i.e., a compact factory built inside an industrial container). However, it also can be used for robotic manipulation of general frame structures. The problem is formulated as a constrained multi-objective optimisation problem. The optimisation algorithm searches for solutions that satisfy two constraints: (i) robot workspace reachability (i.e., feasible inverse kinematics solution for a given end-effector pose); and (ii) any possible collision when executing the post-grasp trajectory, and continuously improves them based on three objectives: (i) minimise robot joint motion for executing a specified pipe trajectory; (ii) minimise the sum of maximum deformation of pipe assembly along the trajectory; and (iii) minimise the sum of robot force manipulability along the trajectory. A special constraint handling method is used to decouple the constraint and objective evaluation process, allowing expensive objectives to be evaluated only when constraints are satisfied. The algorithm explicitly considers the possibility of multiple inverse kinematics solutions and uses a graph search algorithm (Dijkstra's Algorithm) to find the optimal trajectory amongst all feasible trajectories. The weighted sum approach is used to combine the three objectives with weights determined by the Analytical Hierarchy Process. The optimisation problem is solved using the Bees Algorithm with a proposed problem-specific local search strategy. Extensive benchmarks show that the proposed strategy achieves better overall results than the default strategy of the Bees Algorithm and other metaheuristics.

Collaborative scheduling of spare parts production and service workers driven by distributed maintenance demand

We analyse the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. For each failure, a service worker and a necessary spare part must be assigned for on-site maintenance. In particular, the spare part is not supplied from stock, but is delivered directly from the factory to the maintenance site through just-in-time production. Spare parts production and worker allocation are integrated to optimize two objectives: customer satisfaction and total service cost. To solve the problem, we first construct a mathematical model. Subsequently, a modified non-dominated neighbor immune algorithm (MNNIA) is proposed, in which a knowledge-based initialization rule, an idle time insertion method, and two problem-specific local search operators are developed to enhance exploitation. Extensive experiments and comparisons are conducted to demonstrate the superiority of MNNIA. Furthermore, compared with that of the separate decision-making mode of spare parts production and worker arrangement, the effectiveness of the integrated scheduling mode is verified.

An enhanced two phase estimation of distribution algorithm for solving scheduling problem

ABSTRACT Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, there is a high level of uncertainty in practical processing procedure, particularly in flexible manufacturing systems. This study addresses the multi-objective flexible job shop scheduling problem with fuzzy processing time (MOFFJSP) to minimize the makespan and the total workload simultaneously. A mixed integer liner programming model is presented and a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) is proposed to handle this problem. HPEA has the following features: (i) two problem-specific initial rules considering triangular fuzzy number are presented for hybrid initialization to generate diverse solutions; (ii) five problem-specific local search methods are incorporated to enhance the exploitation; (iii) an effective solution selection method based on Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; and (iv) a parameter selection strategy is proposed to improve the quality of non-dominated solutions. To verify the effectiveness of HPEA, it is compared against other well-known multi-objective optimization algorithms. The results demonstrate that HPEA outperforms these five state-of-the-art multi-objective optimization algorithms in solving MOFFJSP.

Local search driven periodic scheduling for workflows with random task runtime in clouds

When data processing applications from various fields are deployed in cloud computing, workflow scheduling is vital in satisfying users’ requirements and improving cloud platforms’ performance. Up to the present, some works have put forward heuristic methods to handle dynamic and uncertain factors when scheduling workflows in cloud platforms. Although these heuristics can generate feasible schedules quickly, their performance can be further improved. It is noteworthy that not all the workflow tasks can be executed immediately due to their data dependencies. Then, their waiting time can be utilized to further optimize schedules generated by heuristic methods. Motivated by the above fact, this paper proposes a Local Search driven Periodic Scheduling, LSPS, for workflows having deadlines and random task runtime. Specifically, in each scheduling period, the LSPS only schedule the tasks to start running, thus shortening the length of local waiting queues on resources to alleviate the negative effects of dynamics and uncertainty. Moreover, we design a problem-specific local search strategy for LSPS to fully use the scheduling period to improve the quality of schedules iteratively. At last, in the context of real cloud platforms, four groups of compared experiments are carried out to measure the effectiveness of the proposed LSPS concerning monetary cost and resource efficiency.

Improving Ant Colony Optimization efficiency for solving large TSP instances

Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics often applied to finding approximate solutions to difficult optimization problems. Despite being significantly faster than exact methods, the ACOs can still be prohibitively slow, especially if compared to basic problem-specific heuristics. As recent research has shown, it is possible to significantly improve the performance through algorithm refinements and careful parallel implementation benefiting from multi-core CPUs and dedicated accelerators. In this paper, we present a novel ACO variant, namely the Focused ACO (FACO). One of the core elements of the FACO is a mechanism for controlling the number of differences between a newly constructed and a selected previous solution. The mechanism results in a more focused search process, allowing to find improvements while preserving the quality of the existing solution. An additional benefit is a more efficient integration with a problem-specific local search. Computational study based on a range of the Traveling Salesman Problem instances shows that the FACO outperforms the state-of-the-art ACOs when solving large TSP instances. Specifically, the FACO required less than an hour of an 8-core commodity CPU time to find high-quality solutions (within 1% from the best-known results) for TSP Art Instances ranging from 100000 to 200000 nodes.

A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization

Under the current volatile business environment, the requirement of flexible production is becoming increasingly urgent. As an innovative production mode, seru-system with reconfigurability can overcome the lack of flexibility in traditional flow lines. Compared with pure seru-system, the hybrid seru-system composed of both serus and production lines is more practical for adapting to many production processes. This paper addresses a specific hybrid seru-system scheduling optimization problem (HSSOP), which includes three strongly coupled sub-problems, i.e., hybrid seru formation, seru scheduling and flow line scheduling. To minimize the makespan of the whole hybrid seru-system, we propose an efficient cooperative coevolution algorithm (CCA). To tackle three sub-problems, specific sub-algorithms are designed based on the characteristic of each sub-problem, i.e., a sub-space exploitation algorithm for hybrid seru formation, an estimation of distribution algorithm for seru scheduling, and a first-arrive-first-process heuristic for flow line scheduling. Since three sub-problems are coupled, a cooperation coevolution mechanism is proposed for the integrated algorithm by information sharing. Moreover, a batch reassign rule is designed to overcome the mismatch of partial solutions during cooperative coevolution. To enhance the exploitation ability, problem-specific local search methods are designed and embedded in the CCA. In addition to the investigation about the effect of parameter setting, extensive computational tests and comparisons are carried out which demonstrate the effectiveness and efficiency of the CCA in solving the HSSOP.

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.

Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping

Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.

Energy-efficient distributed heterogeneous welding flow shop scheduling problem using a modified MOEA/D

In this study, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed for energy-efficient scheduling of distributed heterogeneous welding flow shop (DHWFSP). This problem is extended from distributed flow shop with different amounts of machines in different factories. In addition, it is allowed that multiple machines could operate one job simultaneously. Considering energy efficiency and productivity, this problem could be treated as three sub-problems: job assignment among factories, job scheduling within each factory and deciding the amount of multi-machines upon each job. A multi-objective mathematical model and modified MOEA/D are proposed to minimize the total energy consumption and makespan simultaneously. In modified MOEA/D, various genetic operators and problem-specific local search strategies are designed for multi-level optimization. The comparison experiment with some well-known algorithms shows the effectiveness of the proposed MOEA/D in optimizing and balancing two contradictory objectives.

The capacitated dispersion problem: an optimization model and a memetic algorithm

The challenge of maximizing the diversity of a collection of points arises in a variety of settings, and the growing interest of dealing with diversity resulted in an effort to study these problems in the last few years. Generally speaking, maximizing diversity consists in selecting a subset of points from a given set in such a way that a measure of their diversity is maximized. Different objective functions have been proposed to capture the notion of diversity, being the sum and the minimum of the distances between the selected points the most widely used. However, in all these models, the number of points to be selected is established beforehand, which in some settings can be unrealistic. In this paper, we target a variant recently introduced in which the model includes capacity values, which reflects the real situation in many location problems. We propose a mathematical model and a heuristic based on the Scatter Search methodology to maximize the diversity while satisfying the capacity constraint. Scatter search is a memetic algorithm hybridizing evolutionary global search with a problem-specific local search. Our empirical analysis with previously reported instances shows that the mathematical model implemented in Gurobi solves to optimality many more instances than the previous published model, and the heuristic outperforms a very recent development based on GRASP. We present a statistical analysis that permits us to draw significant conclusions.

A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem

Research on the development of Pareto-based multi-objective algorithms to address scheduling problems has attracted a lot of attention in recent years. In this work, a multi-objective discrete Jaya algorithm (MODJA) is proposed to address the flexible job shop scheduling problem (FJSSP) considering the minimization of makespan, total workload of machines, and workload of critical machine as performance measures. A discrete Jaya algorithm is proposed to handle the problem under consideration. A problem specific neighborhood-based local search technique is integrated into the proposed approach to enhance its exploitation capability. Further, a dynamic mutation operator and a modified crowding distance measure are proposed to enhance the diversity in the search process. Extensive computational experiments are carried out considering 203 instances of FJSSP from literature. The Taguchi method of design is employed to identify the best set of key parameters based on three instances. In the experimentation phase, initially, the contribution of the proposed local search technique and crowding distance measure is investigated. Then, a comparison of MODJA with the weighted sum version of the approach and other multi-objective evolutionary algorithms is performed. Computational results demonstrate the effectiveness of the proposed MODJA in obtaining diverse and improved Pareto-optimal solutions.

Multi-trip time-dependent vehicle routing problem with time windows

In this study, we investigate a routing problem in urban transportation which considers time-dependent travel time, multiple trips per vehicle, and loading time at the depot simultaneously. Its objective is to minimize the total travel distance while satisfying the time windows, vehicle capacity, and maximum trip duration constraints. We model the problem as a multi-trip time-dependent vehicle routing problem with time windows (MT-TDVRPTW). We formulate the time-dependent ready time function and duration function for any segment of consecutive nodes as piecewise linear functions and develop an iterative algorithm to derive them efficiently. Then, these two functions are embedded in the segment-based evaluation scheme to accelerate the local search operators. Based on them, we design a hybrid meta-heuristic algorithm to solve the problem, leveraging the adaptive large neighborhood search (ALNS) for guided exploration and the variable neighborhood descend (VND) for intensive exploitation. Moreover, we propose problem-specific local search operators and removal operators to enhance the effectiveness of the algorithm. Extensive experiments are conducted to assess the performance of the algorithm on instances of varied sizes. The algorithm is shown to be robust and efficient under different speed profiles and maximum trip duration limits. Finally, we evaluate the performance of the algorithm on a special case: the multi-trip vehicle routing problem with time windows.

An estimation of distribution algorithm with branch-and-bound based knowledge for robotic assembly line balancing

Robotic assembly lines are widely used in manufacturing industries. The robotic assembly line balancing (RALB) problem aims to balance the workloads among different workstations and optimize the assembly line efficiency. This paper addresses a particular type of RALB problem, which minimizes the assembly line cycle time by determining the task and robot assignment in each workstation under precedence constraints. To solve the problem, we present an effective hybrid algorithm fusing the estimation of distribution algorithm and branch-and-bound (B&B) based knowledge. A problem-specific probability model is designed to describe the probabilities of each task being assigned to different workstations. Based on the probability model, an incremental learning method is developed and a sampling mechanism with B&B based knowledge is proposed to generate new feasible solutions. The fuse of B&B based knowledge is able to reduce the search space of EDA while focusing the search on the promising area. To enhance the exploitation ability, a problem-specific local search is developed based on the critical workstation to further improve the quality of elite solutions. The computational complexity of the proposed algorithm is analyzed, and the effectiveness of the B&B based knowledge and the problem-specific local search is demonstrated through numerical experiments. Moreover, the performance of the proposed algorithm is compared with existing algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness and efficiency of the proposed algorithm.