Improvement Heuristics Research Articles

Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation

Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation

Optimising distributed heterogeneous flowshop group scheduling arising from PCB mounting: integrating construction and improvement heuristics

ABSTRACT In real-world manufacturing systems for processing printed circuit boards (PCBs), the workshops integrating various flowline-based cells are common in large-scale enterprises. The scheduling problem within the systems is modelled as the distributed heterogeneous flowshop group scheduling problem (DHFGSP) in this study. Departing from practical requirements and considering the grouping characteristics among PCB components, we account for carryover sequence-dependent setup time (CSDST). Moreover, recognising the critical importance of just-in-time production in semiconductor manufacturing, the total tardiness, a previously unexplored objective within the DHFGSP context, is addressed. To tackle this problem, a mixed-integer linear programming (MILP) model, capable of obtaining optimal solutions for small-scale instances, is proposed. Due to the NP-hard nature of the problem, obtaining high-quality solutions in a reasonable time using the MILP model becomes challenging for large-scale instances. Therefore, a solution algorithm, comprising construction and improvement heuristics, is developed. Capitalising on the problem’s characteristics, the construction heuristic efficiently generates high-quality feasible solutions in a very short time. Built primarily around artificial bee colony (ABC) optimisation, the improvement heuristic can significantly further enhance solutions by integrating the collaborative and restart operators. Comprehensive experiments on instances of varying scales demonstrate the effectiveness of the proposed algorithm for the problem under investigation.

BPM application in clinical process improvement: a women 'hospital case study

PurposeThe childbirth process is a complex and vital event that requires careful analysis and improvement. This experience can shape a woman's perspective on motherhood and even affect her mental health. Healthcare providers must prioritize improving the birth experience for women. In this interdisciplinary research, a combination of business process modeling (BPM) and medicine have been used with the aim of realizing an improved delivery experience and increased maternal satisfaction.Design/methodology/approachThe data collection of this study was done by observing 518 childbirth processes and interviewing the chief of labor, chief residents, and midwives in the obstetrics and gynecology department of a hospital in Tehran from October 2022 to February 2023.FindingsThe research has been done in four main stages. The first phase is to model the primary process and sub-processes of normal vaginal delivery (NVD). The second phase is validation using expert confirmation and process mining (PM). The third phase is the analysis of the causes of maternal dissatisfaction in labor. The fourth phase of the heuristics redesigning and improving the process, in which for the first time three new categories have been presented including hospital-based, patient-based, and medical technique-based results show BPM intervention effect can be far-reaching in improving patient care and optimizing operational efficiency.Originality/valueThis study is one of only a few to adopt a process-oriented perspective to show how BPM can be used in clinical processes and has specifically examined an essential clinical process, i.e. childbirth.Highlights Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.Providing a framework for clinical process modeling and improvement containing steps and tools.

Matheuristic for the lot-sizing and scheduling problem in integrated pulp and paper production

Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of by-product treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for real-world instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.

A Twin Learning Framework for Traveling Salesman Problem Based on Autoencoder, Graph Filter, and Transfer Learning

Efficient solvers for traveling salesman problem (TSP) have great significance in the field of consumer electronic systems and devices. Existing studies require independent and repetitive runs for similar TSPs. To utilize useful knowledge buried in the twin TSP which is similar to the target TSP, a twin learning framework based on task matching and mapping strategy is proposed. We use an autoencoder to extract the feature vectors of the historical tasks and the target task to find the twin task. If not found, construct a twin task based on a graph-filter. Further, we get the solution of the target task with the optimized twin task by learning a mapping matrix from the twin task to the target task. Finally, we incorporate local search algorithms into the twin learning framework, which called strategy mapping solver (SMS) to further improve the quality of the solution. The efficacy of the SMS is evaluated through comprehensive empirical studies with commonly used TSP benchmarks, against meta-heuristic algorithms and a learning improvement heuristics algorithm, demonstrating its effectiveness for TSP. Moreover, a real-world combinatorial optimization application, laser engraving path planning, is presented to further confirm the efficacy of SMS.

Optimal and heuristic solutions for placing multiple finite-size rectangular facilities in an existing layout

In a companion paper (Date, K., and R. Nagi. [(2023) Optimal Placement of Multiple Finite-size Rectangular Facilities in an Existing Layout. International Journal of Production Research, THIS ISSUE]) we investigated a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. We introduced the requisite theory to arrive at a solution by dividing the feasible region into sub-regions whose boundaries provide the candidates for the optimal placement. In this paper, we focus on developing optimal and heuristic solution methods for solving problem instances with various facility counts. To solve small instances with fewer facilities, we develop two optimal methods: (1) efficient explicit enumeration based on a tree state-space representation and (2) an implicit enumeration scheme that uses a flow-decomposition-based lower bound to reduce the search space and time. To solve industrial-sized instances, we have to resort to a family of construction and improvement heuristics given that the problem is NP-hard. The heuristic procedures perform well for non-pathological cases with an acceptable optimality gap. The main contribution of this paper is a range of optimal to heuristic methods that can be applied by the practitioner depending on their problem characteristics and desired efficiency.

Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning

Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning

Neural Improvement Heuristics for Graph Combinatorial Optimization Problems.

Recent advances in graph neural network (GNN) architectures and increased computation power have revolutionized the field of combinatorial optimization (CO). Among the proposed models for CO problems, neural improvement (NI) models have been particularly successful. However, the existing NI approaches are limited in their applicability to problems where crucial information is encoded in the edges, as they only consider node features and nodewise positional encodings (PEs). To overcome this limitation, we introduce a novel NI model capable of handling graph-based problems where information is encoded in the nodes, edges, or both. The presented model serves as a fundamental component for hill-climbing-based algorithms that guide the selection of neighborhood operations for each iteration. Conducted experiments demonstrate that the proposed model can recommend neighborhood operations that outperform conventional versions for the preference ranking problem (PRP) with a performance in the 99 th percentile. We also extend the proposal to two well-known problems: the traveling salesman problem and the graph partitioning problem (GPP), recommending operations in the 98 th and 97 th percentile, respectively.

Augmented tour construction heuristics for the travelling salesman problem

Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study aims to enhance tour construction heuristics without compromising their theoretical complexity. Specifically, an iterative step of partial tour deconstruction has been introduced to the existing heuristics. This additional step has been implemented and evaluated with three highly performing tour construction heuristics: the farthest insertion heuristic, the max difference insertion heuristic, and the fast max difference insertion heuristic. The results demonstrate that augmenting these heuristics with the partial tour deconstruction step improves the best, worst, and average solutions while preserving their theoretical complexity

The single robot line coverage problem: Theory, algorithms, and experiments

AbstractLine coverage is the task of servicing a given set of one‐dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of the required graph, that is, the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3 s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs on the UNC Charlotte campus road network.

Improving the efficiency of patient diagnostic specimen collection with the aid of a multi-modal routing algorithm

The Sustainable Specimen Collection Problem (SSCP), in which diagnostic specimens are collected from GP surgeries (doctor’s office/clinics) and subsequently transported to a hospital laboratory for analysis using more sustainable transport modes, is introduced in this paper. Using a weighted objective function, we solve a new multi-objective problem using cycle consolidation to limit driving time and the numbers of vans used whilst improving overall service quality, reducing costs and emissions. This particular heterogeneous vehicle routing problem is explored and applied to two real-world case studies in the UK, where 97 and 22 sites (respectively) are currently served, using a column generation based heuristic algorithm with some additional improvement heuristics. The results demonstrated a potential improvement in the system’s maximum delivery time between 41% and 74% compared to business-as-usual activity using solely road vehicles. Road vehicle (van) fleets could be reduced by up to 40%, and the total driving time across the fleet by between 41% and 65%. Operational costs were estimated to increase by up to 38%, though additional workloads for gig-economy cycle couriers and improvement in specimen quality and service reliability may make this trade-off worthwhile. Tailpipe CO2 emissions were also reduced by up to 43%. The proposed algorithm was effective, reducing computational time by up to 99% whilst achieving an average of 5% deviation from optimality.

History and Future Directions of rTMS for Treatment of Depressive Disorders

History and Future Directions of rTMS for Treatment of Depressive Disorders

A framework to select heuristics for the rectangular two-dimensional strip packing problem

Defining the algorithm capable of best fit the characteristics observed for a problem is a complex task in the context of combinatorial optimization problems. As a decision-making process, one of the most practical and useful ways to treat and solve algorithm selection problems is using supervised machine learning techniques to search for patterns between explanatory variables characteristics of the problem and the algorithms available to be selected. The present article deals with the development of a framework to fit classification models based on supervised machine learning techniques to select improvement heuristics for the rectangular 2D strip packing problem (2D-SPP) with 90-degrees rotation. Classification models were fitted to predict the best improvement heuristic for constructive heuristics bottom-left, bottom-left-fill, best-fit, best-fit with bottom-left-fill, fast-heuristic, and fast-heuristic with bottom-left-fill, using 19 features provided by problem characteristics. A total of 15,666 benchmark problem instances from the literature were used to represent the rectangular 2D-SPP characteristics variations found in real-world applications to train and test the fitted classification models. The framework proved to be consistent to predict improvement heuristics with acceptable accuracy, being able to be applied for the prediction of other cutting and packing problems algorithms.

On the Design and Optimization of Test Cases Using an Improved Artificial Bee Colony Algorithm-Based Swarm Intelligence Approach

In this research work, a swarm intelligence-based approach, namely an improved artificial bee colony (IABC), has been proposed to design and optimize the test cases during the software testing process. The novelty of the proposed IABC algorithm is that it has three major improvement heuristics over the general ABC algorithm: (1) it replaces random population generation during the initial phase into a systematic initial solution generation by means of a novel heuristic, namely ‘Chaotic Map'; (2) to eliminate the redundant test cases, another novel heuristic, namely ‘Euclidean Distance', is applied to maintain the diversity of population; (3) to increase the convergence speed, the fitness value of the previous solution is used in the new solution generation. Further, the proposed algorithm has been evaluated with several case studies and compared with the existing works using path coverage-based test adequacy criterion. Hence, the proposed work is improved, and it outperforms the existing works and provides optimal or near optimal test case generation for efficient software testing.

Learning Improvement Heuristics for Solving Routing Problems.

Recent studies in using deep learning (DL) to solve routing problems focus on construction heuristics, whose solutions are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by handcrafted rules that may limit their performance. In this article, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention-based deep architecture as the policy network to guide the selection of the next solution. We apply our method to two important routing problems, i.e., the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art DL-based approaches. The learned policies are more effective than the traditional handcrafted ones and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions, and even real-world data set.

Energy minimizing order picker forklift routing problem

The material handling systems used in warehouses involve important operations such as the usage of order picker forklifts. These order picker forklifts provide efficient utilization of the storage space by their ability in moving in narrow aisles and picking items from high level racks. Routing the order picker forklifts to pick ordered items belongs to the operational decision level and is done in high frequency. Therefore, finding an energy-efficient route for an order picker forklift can yield significant savings in the energy consumption in warehouses and the resulting CO2 emission. In this paper, we introduce and study the energy minimizing order picker forklift routing problem (EMFRP) which aims to find an energy-efficient route for an order picker forklift to pick a given list of items. To our knowledge, this is the first study that considers the order picker forklifts in the context of the order picking problem. We calculate the forklift’s energy consumption in both horizontal and vertical moves considering the effects of friction forces, the acceleration and deceleration of the forklift, and its load. A mixed integer programming formulation and a dynamic programming approach are developed to solve small size instances of the EMFRP exactly. To solve larger instances, we provide tour construction and tour improvement heuristics and integrate them into a single solution approach. Computational results show that the proposed heuristic approach finds high quality solutions. Moreover, it is observed that significant energy savings can be achieved by solving the EMFRP instead of the classical distance minimization problem.

An Improvement Heuristic Based on Variable Neighborhood Search for Dynamic Orienteering Problems with Changing Node Values and Changing Budgets

We study the Dynamic Orienteering Problem (DOP) with changing node values and changing budgets. It is a complex combinatorial optimization problem with many applications, e.g., in tour planning. To solve the DOP, an improvement heuristic based on Variable Neighborhood Search mathrm {VNS}_{mathrm {DOP}} is proposed. In addition, three methods for handling solutions that became invalid by budget changes are presented. Heuristic mathrm {VNS}_{mathrm {DOP}} is experimentally compared with two improvement heuristics based on state-of-the-art algorithms for the static Orienteering Problem. In addition, the influence of the three invalid solution handling methods on the algorithms’ optimization behavior is evaluated experimentally. For the experiments, benchmark instances as well as instances generated from existing road networks are used. As a quality measure for the algorithms, their performance over time is used. The results show that both types of dynamic changes, i.e., changes in the node values and changes in the budget, lead to higher volatility in the results for all compared algorithms. However, the latter type has a more negative effect on the performance. Out of the compared algorithms, the proposed heuristic mathrm {VNS}_{mathrm {DOP}} obtains the best results in most cases on a variety of problem instances with dynamic node values and dynamic budgets, showing that it has a high performance over time in dynamic environments and is able to deal with different levels of dynamic changes. For DOPs with changing budgets, the invalid solution handling method that repairs solutions by fixing the violation of the budget constraint as fast as possible performs best for the considered algorithms.

A Constructive Heuristic for Automated Parallel Tests Assembly

Parallel tests contain different items but have the same measurement properties. They are administered at the same or different time slots and their measurement results must be comparable. The problem of automated parallel tests assembly is studied for a long time, and many (mostly improvement) heuristic solutions are proposed and elaborated in literature. Such approaches frequently suffer from algorithms of unpredictable execution time, forcing the methods to terminate execution when some time limit or solution quality is reached. This paper proposes an efficient method of polynomial complexity, as a complete solution to the automated parallel tests assembly problem. The method uses the idea of Nawaz, Enscore, and Ham constructive heuristic algorithm to reduce the number of examined permutations, originally exploited for solving the permutation flow-shop sequencing problem. We compared the experimental results of the proposed method with two methods based on improvement heuristics that solve the same problem formulation, simulated annealing and variable neighborhood search. The main advantages of the proposed method are predictable execution time and implementation simplicity. Achieved quality of assembled tests, combined with predictable test assembly execution time, may be of particular interest in cases when computational resources for test assembly and administering are overloaded.

A theoretical framework to accelerate scheduling improvement heuristics using a new longest path algorithm in perturbed DAGs

ABSTRACT Job-shop scheduling problems are complex and still well-studied manufacturing problems. Improvement heuristic algorithms have been proposed to solve the scheduling problems using makespan as their performance measure. All these heuristics iteratively perturb trial schedules by selecting a new schedule from a set of nearby schedules (neighbourhood); then, recalculate and compare the makespan until a sufficient schedule is determined. Unlike previous studies, we did not generate a new heuristic or a novel neighbourhood calculation. Instead, we proposed a theoretical framework, Algorithm to Visit Affected Node (AVAN), which can be incorporated in qualified heuristics while using their current neighbourhood structure to accelerate the recalculation of the makespan in each iteration. We modelled the system by Directed Acyclic Graph (DAG) where the length of the longest path equals the makespan. The scheduling perturbations are represented by adding and deleting edges. AVAN investigates the configuration of scheduling perturbations (added/deleted edges) to find an appropriate starting point to traverse the graph. AVAN is mathematically more efficient than previous longest path algorithms for perturbed DAG. Its time complexity is , where is the number of affected nodes and is the number of incoming and outgoing edges of the affected nodes.

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time‐consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small‐size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group‐based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time.