Operations Research Research Articles

Optimising picking operations in distribution centres: a simulation and algorithm-based approach for travel distance reduction

Abstract This study examines the pivotal role of the logistics sector in corporations, particularly those with diverse product portfolios. It focuses on the time-intensive “picking” process, driven by various products and material handling requirements for order fulfilment. The research presents a methodology that combines distribution centre modelling for simulation with an optimisation algorithm to enhance operational efficiency. The goal is to determine an optimal route for product retrieval, minimising employee travel distances, using the simulated annealing algorithm. The results showcase a significant 7 % reduction in employee travel distance during collection. This research is relevant to academia and practical applications as it presents an opportunity to reduce operational costs in distribution centres, enhance the efficiency of picking operations, and improve the competitiveness of companies. Furthermore, it contributes to operations research by addressing a complex problem with significant practical implications using a rather unused method to solve this problem of picking operations. The approach is empirically applied in a prominent Brazilian beverage company. This methodology proves valuable for optimising logistics operations in various industrial contexts, highlighting its practical applicability.

Challenges in the Quality of Malaria Case Management in Sub-Saharan Africa: A Narrative Review

Malaria continues to be a parasitic disease that poses a persistent public health concern in countries of the Southern region. Despite the deployment of strategies aimed at its elimination by 2030, significant gaps still impede the achievement of this goal. A critical gap is the quality of case management. This article presents a narrative review specifically focusing on the quality of malaria case management in SubSaharan African countries. It delves into the complex challenges associated with managing this disease, encompassing aspects such as healthcare service organization, diagnosis, treatment, and patient follow-up. The article highlights underlying factors that hinder progress, including insufficient resources, inadequate skills, limited monitoring and evaluation capacities, lack of motivation and commitment among stakeholders, and challenges related to implementation and governance. It explores various initiatives to strengthen and enhance the quality of care, including continuous training programs, epidemiological and operational research endeavors, fostering a culture of quality, and promoting collaboration among stakeholders. Furthermore, the article emphasizes the importance of sustainable approaches at the country level to contribute to effective malaria elimination. It underscores the need for further exploration and implementation of strategies to achieve this crucial goal.

Advanced method for solving the transportation problem

Transportation problem is one of the predominant areas of operations research. It is a type of linear programming problem designed to minimize the cost of distributing a single product from m sources to n destinations. Transportation problem is widely used as a decision making tool in different fields such as engineering, business management and many others. These types of problems can be solved by general network methods. In this paper The Advanced Method for Solving the Transportation Problem (AMSTP) is proposed to find initial basic feasible solution and optimal solution of transportation problem and compared its results with other existing methods.

Calculation of 3D Shape of a Hyperelastic Body for Nonlinear Elasticity Models Using the Newton Method

This article discusses methods for calculating the deformations of objects with hyperelastic materials within the framework of the nonlinear elasticity theory. This topic is relevant due to the use of new technological materials in industry, and as a result, the emerging task of preliminary numerical calculations for operational reliability, research and modeling of deformation behavior. The first part provides a brief overview of the main provisions and formulas of the nonlinear elasticity theory. Then the spatial discretization of 3-dimensional objects and the calculation of the deformation gradient using the finite element method are considered. The article provides an algorithm for solving this problem, such as minimizing the functional of stored energy, and also considers the class of permissible deformations. Afterwards, a detailed description is given, along with pseudocode, of the method of implementing and calculating the minimization by Newton’s method. The last part demonstrates an example of the calculations performed, based on the developed software that allows for numerical experiments and computer modeling of deformations of hyperelastic bodies, and one of the capabilities of which is to perform these calculations using Newton’s method.

Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

ABSTRACT The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

The Chinese Postman Problem and Simulated Annealing applied to urban forest inventory

Routing is the process of defining paths between geographically distinct points, and it has been applied in decision-making processes for services that include displacement, with potential application in the planning of urban forest inventories. Linear Programming is an operations research technique often applied to solve complex problems, such as those related to routing. Thus, the objective of this study was to verify whether the application of a routing model via Integer Linear Programming (ILP) increases the efficiency of an urban forest inventory using real data. First, we compared the empirical division used in the inventory with the application of the ILP method for routing. We also tested an approximate method (Simulated Annealing, SA) to solve the routing problem and reduce the processing time. Finally, we simulated the consequences of applying ILP in different scenarios. The results showed that the ILP improved the inventory efficiency by 6.36%, and the use of SA reduced the processing time by 43,200 times for the most complex scenario, resulting in a solution that was 1.87% of the ILP response value. Our findings show the potential application of ILP in the planning of urban forest inventories, with gains directly proportional to the number of lots in the inventory.

Optimal operation of soft open point devices and distribution network reconfiguration in a harmonically polluted distribution network

Soft open point (SOP) and distribution network reconfiguration (DNR) are crucial for modern power systems as they address significant challenges in managing power losses and maintaining power quality. SOP is a power electronic device used in distribution networks (DNs) instead of tie lines. It injects reactive power into feeders and transfers active power between them, while DNR involves altering the network topology to improve DN index. In this paper, the effect of DNR, optimal operation of SOP, and simultaneous operation of DNR and SOP are thoroughly investigated and compared on IEEE 33 bus harmonic polluted DN via particle swarm optimization (PSO). These comparisons were performed by considering three loss-based objectives including fundamental and harmonic losses and summation of them, along with one voltage-based objective function assessing voltage stability, and four Total Harmonic Distortion (THD)-based objective functions quantifying harmonic pollution. Simulation results not only demonstrate the importance of optimal operation of SOP in improving losses and power quality but also highlight the synergistic effect of simultaneous operation of DNR and SOP in further enhancing these two objectives. Results provide valuable insights for future research and practical applications in operation and optimizing power distribution systems.

Research Progress and Hot Frontier Analysis of Container Terminal Transportation Operations

Using CiteSpace software and bibliometric method, 652 articles related to container terminal transportation operations in WOS core collection database were visualized and analyzed. Based on the network knowledge graph of document distribution of time and space, core author, research institution and key words, this paper analyzes the overall distribution, research hotspot, evolution context and frontier trend of container terminal transport operations research, in order to enrich the perspective of container terminal transport operations review study, and provide reference for promoting the construction of container terminals in our country.

Admission, discharge, and transfer control in patient flow logistics: Overview and future research

Patient flow logistics involves managing and coordinating the movement of patients within a healthcare system. It aims to optimize the patients’ flow from their arrival to discharge or transfer, ensuring efficient and effective use of resources while minimizing delays and bottlenecks. Key components of patient flow logistics include capacity planning, resource allocation, appointment scheduling, and notably, admission, discharge, and transfer control. Our focus is on admission, discharge, and transfer control policies, which manage and regulate the flow of individual patients into and out of various facilities delivering care. We review a selection of 37 analytical, empirical, and experimental papers in this area published in leading operations research, operations management, and medical journals. These papers are categorized based on the specific department they target and the type of policies they consider. In particular, we identify: (i) four types of policies for intensive care units, (ii) three types of policies for emergency departments, and (iii) four types of policies for general wards. For each paper, we provide an overview of the research questions, formulations, solution methodologies, and results. This comprehensive review culminates in identifying future research directions for academics in this field.

A survey on optimization studies of group centrality metrics

AbstractCentrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization.

Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5

Abstract. Meteorological fronts are important due to their associated surface impacts, including extreme precipitation and extreme winds. Objective identification of fronts is therefore of interest in both operational weather prediction and research settings. The aim of this study is to produce a front identification algorithm based on earlier studies that is portable and scalable to different resolution datasets. We have made a number of changes to an earlier objective front identification algorithm, applied these to reanalysis datasets, and present the improvements associated with these changes. First, we show that a change in the order of operations yields smoother fronts with fewer breaks. Next, we propose the selection of the front identification thresholds in terms of climatological quantiles of the threshold fields. This allows for comparison between datasets of differing resolutions. Finally, we include a number of numerical improvements in the implementation of the algorithm and better handling of short fronts, which yield further benefits in the smoothness and number of breaks. This updated version of the algorithm has been made fully portable and scalable to different datasets in order to enable future climatological studies of fronts and their impacts.

Urban Air Mobility for Last-Mile Transportation: A Review

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks.

Overview of Hungarian operations research based on the VOCAL 2022 conference

Overview of Hungarian operations research based on the VOCAL 2022 conference

Navigating Intelligence: A Survey of Google OR‐Tools and Machine Learning for Global Path Planning in Autonomous Vehicles

We offer a new in‐depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost‐efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)‐Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR‐Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR‐Tools, assessing their computational effectiveness and real‐world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q‐Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.

Trolley Optimisation for Loading Printed Circuit Board Components

A trolley is a container for loading printed circuit board (PCB) components, and a trolley optimisation problem (TOP) is an assignment of PCB components to trolleys for use in the production of a set of PCBs in an assembly line. In this paper, we introduce the TOP, a novel operation research application. To formulate the TOP, we derive a novel extension of the bin packing problem. We exploit the problem structure to decompose the TOP into two smaller, identical, and independent problems. Further, we develop a mixed integer linear programming model to solve the TOP and prove that the TOP is an NP-complete problem. A case study of an aerospace manufacturing company is used to illustrate the TOP which successfully automated the manual process in the company and resulted in significant cost reductions and flexibility in the building process.

Average Waiting Time of Customers in Single-Server Uncertain Queueing System

Uncertain queueing system is an important research object in the field of operations research, and widely exists in logistics, production, insurance and other fields. In order to further analyze the performance of the single-service uncertain queueing system, this paper derives the generalized inverse uncertainty distributions of the waiting times of customers, and provides the calculation formulas for the average waiting times of customers on this basis. Finally, this paper also provides two examples to illustrate the generalized inverse uncertainty distributions of waiting times and the calculation formulas of the average waiting times under specific uncertainty distributions.

Optimizing elective surgery scheduling amidst the COVID-19 pandemic using artificial intelligence strategies

The COVID-19 pandemic profoundly affects elective surgery and healthcare resources. Efficient management of resources, like ward capacity and operating theaters, is crucial. The operations research community explores solutions, notably leveraging artificial intelligence, to address scheduling challenges amid COVID-19 restrictions. In this situation, applying AI becomes essential to getting the best results. In this paper, we address the problem of daily scheduling elective surgeries while accounting for hospital ward capacity. It is possible to reduce this issue to a scheduling puzzle that, given a variety of restrictions, resembles a four-stage hybrid flow shop. These limitations include the availability of resources, patient flow control, wait time avoidance, patient prioritizing, and resource coordination. With the crucial aid of artificial intelligence, our main goal is to assign patients to different surgical resources to minimize the length of time they spend on average in the hospital ward. We suggest putting into practice effective optimization strategies that make use of AI-based algorithms, particularly the variable neighborhood search (VNS) and variable neighborhood descent (VND) algorithms, which are inextricably linked with artificial intelligence concepts. Our studies demonstrate the effectiveness and efficiency of the general VNS in addressing the daily elective surgical scheduling issue (SSP) with the priceless assistance of artificial intelligence. The experiments are based on novel data instances that were inspired by current literature guidelines. The test results conclusively demonstrate the ability of our algorithms to find virtually perfect solutions. Moreover, our results highlight that the use of these methods, strengthened by AI, can significantly increase the size of the solved issue by a remarkable factor of 19.54. In light of the current COVID-19 pandemic, AI thus becomes a key factor in optimizing the scheduling of elective surgeries and the allocation of resources.

An intuitionistic fuzzy graph’s variation coefficient measure with application to selecting a reliable alliance partner

Group decision-making (GDM) is crucial in various components of graph theory, management science, and operations research. In particular, in an intuitionistic fuzzy group decision-making problem, the experts communicate their preferences using intuitionistic fuzzy preference relations (IFPRs). This approach is a way that decision-makers rank or select the most desirable alternatives by gathering criteria-based information to estimate the best alternatives using a wider range of knowledge and experience. This article proposes a new statistical measure in a fuzzy environment when the data is ambiguous or unreliable to solve a decision-making problem. This study uses the variation coefficient measure combined with intuitionistic fuzzy graphs (IFG) and Laplacian energy (LE) to solve a GDM problem that utilizes intuitionistic fuzzy preference relations (IFPRs) to select a reliable alliance partner. Initially, the Laplacian energy determines the weight of individual standards, and the obtained weight average further estimates the overall criterion weight vector. We establish the authority criteria weights using the variation coefficient measure and then ultimately rank the alternatives for each criterion using the same measure. We examine four distinct companies Alpha, Beta, Delta, and Zeta to conduct a realistic GDM to choose which alliance partner would be ideal. We successfully implemented the suggested technique, determining that Alpha satisfies company standards and is ranked first among other companies. Moreover, this technique is useful for all kinds of Intuitionistic fuzzy group decision-making problems to select optimal ones.

Research on Teaching and Learning of Operations Research in the Engineering Costing Program Based on MATLAB Technology

Research on Teaching and Learning of Operations Research in the Engineering Costing Program Based on MATLAB Technology

Corrigendum to “Labeling methods for partially ordered paths” [European Journal of Operational Research 318 (2024) 19–30]

Corrigendum to “Labeling methods for partially ordered paths” [European Journal of Operational Research 318 (2024) 19–30]