Operations Research Research Articles

The Yellow Peril and the Ontology of the Enemy

How and when did war come to be understood as a “science”—one that could be quantitatively calculated and measured in such a way that one could derive universal laws? This paper turns to the archives of the Operations Research Office (ORO) during the Korean War to argue that race war orders and produces a scientific “truth” of war. This techno-scientific “truth” drives war to increasingly brutal forms of imperial and racial violence while simultaneously naturalizing and sanitizing this violence as the nature of war. Techno-scientific war produced a racialized enemy: the numerically superior Asian Communist attacked in human waves, willing to sacrifice soldiers because of an Asian disregard for life. But the enemy’s tactics, however seemingly irrational, could be rendered predictable through quantitative analysis. Race ordered techno-scientific war. Combat was rendered sensible through perfecting and managing the superior techno-scientific capabilities of the white race to overcome the problem of the Asian horde and ensure victory. By treating Korea as a “laboratory” for future combat, the ORO ordered and produced these racialized visions through technical manuals on infantry weapons, chemical weapons, and atomic weapons.

How to achieve sustainable operation of transport infrastructure development: a case study of 30 transport infrastructure projects in China

Transport infrastructure projects are distinguished by their high levels of investment, lengthy construction periods, and reliance on government subsidies during operation. These factors have far-reaching impacts, not only on the sustainable operation and success of the projects themselves, but also on the orderly functioning of society and the sustainable development of the economy. As such, it is of paramount importance to thoroughly investigate the sustainable operation of transport infrastructure. This paper examines the impact of sustainable operation on transport infrastructure by analysing 30 projects in China’s transport infrastructure sector. The study employs the qualitative comparative analysis method. To achieve the aim of this paper, we identified five dimensions for assessing a project’s sustainability. These include the economic environment of the construction site, operational market demand, government support capacity, and enterprise management level. This analysis offers valuable guidance for future engineering projects. Finally, the study identified seven strategies for achieving sustainable operation of the project. The findings revealed that the economic development status, population size, and government subsidies of the construction site do not effectively promote sustainable operation of the project. Rather, they can hinder enterprises’ market competitiveness. The study’s results can assist decision-makers in determining the feasibility of implementing sustainable management practices in the new project. Examine the factors that influence the achievement of sustainable business while enhancing the operational efficiency of ongoing initiatives. This research aims to produce a valuable addition to risk assessments in sustainable operation research and the advancement of transportation infrastructure reduction.

Modeling and generating user‐centered contrastive explanations for the workforce scheduling and routing problem

AbstractIn the last decade, explainability has been attracting much attention in the machine learning community. However, this research topic extends beyond this field to encompass others such as operations research and combinatorial optimization (CO). This paper addresses this issue in the case of the workforce scheduling and routing problem (WSRP), a CO problem involving human resource allocation and routing decisions. We first introduce a novel mathematical framework that models the process of explaining solutions to the end‐users of a WSRP‐solving system. Then, we present original algorithmic methods to generate explanation texts employing a high‐level vocabulary adapted to such end‐users. Explanations are user‐centered, local, and contrastive. They are triggered by end‐user questions about various topics regarding a solution of a WSRP instance. Both questions and explanations are expressed as texts thanks to templates. Numerical experiments show that the algorithms generating explanation texts have execution times that are mostly compatible with the online use of explanations in an interactive system.

Vehicle Travel Time Prediction Based on SSA-LSTM

Highway travel time, defined as the time difference between a vehicle entering and exiting the highway, is primarily influenced by factors such as the highway's characteristics, current road conditions, and the driver's behavior. Travel time is a crucial parameter for assessing the operational state of a highway and serves as an important reference for both drivers when selecting routes and for traffic managers in implementing control measures. Therefore, predicting highway travel time is of significant importance. Long Short-Term Memory (LSTM) is a type of recurrent neural network, an improved version of the traditional recurrent neural network, known for its excellent performance in predicting sequential data, such as time-series data on highway travel times. In operations research, search optimization algorithms have been a key area of focus in conjunction with machine learning. The Sparrow Search Algorithm (SSA), a relatively new search optimization algorithm, boasts high convergence speed and accuracy, along with strong global search capabilities, reducing the risk of getting trapped in local optima. This study utilizes vehicle entry and exit data collected by the ETC system at the Jin-Gang Expressway toll stations to analyze the highway's basic information. Building upon the traditional LSTM model, we integrate the SSA to optimize the LSTM model parameters. By using SSA to search for the optimal parameters, we form a new SSA-LSTM model. Finally, we apply this model to predict travel time data, showing a 120% improvement in prediction accuracy compared to the original LSTM model.

Fully Polynomial Time Approximation Schemes for Robust Multistage Decision Making

We design a framework to obtain Fully Polynomial Time Approximation Schemes (FPTASes) for adjustable robust multistage decision making under the budgeted uncertainty sets introduced by Bertsimas and Sim. We apply this framework to the robust counterpart of three problems coming from operations research: (i) ordered knapsack, (ii) single-item inventory control, and (iii) single-item batch dispatch. Our work gives the first FPTAS for these problems, and for adjustable robust multistage decision making in general. The proposed approximation schemes are constructed with the technique of K-approximation sets and functions, relying on careful robust dynamic programming formulations for a master problem (corresponding to the decision maker) and for an adversary problem (corresponding to nature, which chooses bad realizations of uncertainty for the decision maker). The resulting algorithms are short and simple, requiring just a few concise subroutines. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported in part by the United States-Israel Binational Science foundation [Grant 2018095). N. Halman was also supported in part by the Israel Science Foundation [Grants 399/17 and 1074/21].

Project Management Using Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT)

Project delivery within the established time line and project cost is one of the persistent issues in current project management practices. This work therefore aimed at assessing the appropriateness of Project Evaluation Review Technique and Critical Path Method in project management with a case study of a UNN lecture hall construction. Consequently, the research establishes the impact of these quantitative operations research tools on the efficient project management. Both of these project management techniques were explained and used in relation to the data that was obtained from the lecture hall construction project manager, concerning the project activities and the time taken for each of these activities. As indicated by the findings of this study, both the methods were posited to achieve success in project management where by relationship and connectivity of the activities that define a project life cycle persist as the key issues. As such, it was suggested that in view of the project critical activities determined by the CPM and/or PERT analyses, more resources and attention should be directed towards effective management of such activities to avoid the occasion of project delay as well as ensure the successful completion of projects.

Decision-Making in Grain Supply Chains for Sustainable Food System

Grain supply chains (GSCs) are crucial for global food security, economic development, and environmental sustainability. This study investigates the complexities and challenges of enhancing the resilience of GSCs. The research unravels decision-making intricacies, assesses post-harvest risks and stakeholder interactions, and identifies future research avenues. This systematic literature review (SLR), following PRISMA guidelines, and conducted with two search rounds, addressed questions of resilience and efficiency of post-harvest GSCs, key decision factors of stakeholders influencing the sustainability and operational success of GSCs, and methods that can be used to develop robust decision-making for managing risks and uncertainties in GSCs. A comprehensive database of post-2000 journal articles from the Web of Science and Scopus was analysed using bibliometric and content analysis. This research identified five key themes: (1) operational complexity requiring strategic resilience, (2) comprehensive risk management strategies, (3) critical role of transport and storage infrastructure, (4) significant impact of stakeholder decisions on GSC dynamics, and (5) diverse methodological approaches for robust analysis. These themes illustrate the multifaceted challenges and opportunities within GSCs, suggesting that integrating operations research with behavioural economics is vital for improving strategic decision-making. The results advocate for adopting advanced technologies and innovative multidisciplinary methods to improve GSC efficiency and resilience, which are essential for navigating geopolitical tensions, market fluctuations, and complex stakeholder behaviours. It identified under-researched areas such as farmer transport decisions and logistics provider selection, and integrated emerging themes (such as resilience and behavioural aspects), contributing to the body of knowledge and supporting enhanced decision-making in GSCs for a stable global food supply system.

Still more surveys in operations research…

Still more surveys in operations research…

An Operations Research–Based Teaching Unit for Grade 12: The ROAR Experience, Part III

In this paper, we finish describing the project and the experimentation of Ricerca Operativa Applicazioni Reali (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools. ROAR is composed of three teaching units addressed to grades 10, 11, and 12, respectively. To improve students’ interest, motivation, and skills related to science, technology, engineering, and mathematics disciplines, ROAR integrates the teaching of mathematics and computer science through operations research. Its implementation started in 2021 in a grade 10 class at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). We provided the details of the first two units in previous papers. Here, we focus on the third and last unit, carried out from October 2022 to January 2023, with the same students, then in a grade 12 class. Similarly to the first two units, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project that, this time, involved the manufacturer company Filtrec S.p.A. with a case. After analyzing the feedback from students, teachers, and practitioners engaged in the experimentation, we reflect on the entire experimentation and provide some insights to replicate a similar experience. Funding: G. Colajanni was partially supported by the research project Programma Ricerca di Ateneo UNICT 2020-22 linea 2, Ottimizzazione di Modelli di Network slIce 5G con UAV, the Italian Ministry of University and Research and the European Union for the Programma Operativo Nazionale project on Research and Innovation 2014-2020, D.M. 1062/2021, and the GNAMPA-INdAM Group. A. Raffaele was partially supported by the National Group for Scientific Computation. E. Taranto was partially supported by the National Group for Algebraic and Geometric Structures, and their Applications. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ited.2023.0065 .

The use of the Maple CMS in solving linear programming problems in the Methods of Operations Research discipline

The article demonstrates the effectiveness of using the Maple computer mathematics system for solving linear programming problems in the «Methods of Operations Research» discipline. The use of Maple allows us to automatically perform routine calculations, significantly reducing the time required to solve problems and reducing the risk of errors. The integration of numerical methods, symbolic computing, and graphical visualization tools creates a convenient environment for solving a wide range of optimization problems.

Point-of-care testing improves care timeliness in the emergency department. A multicenter randomized clinical trial (study POCTUR).

Emergency department (ED) crowding is a widespread problem that positions patients at risk. The desire to improve the ED throughput requires novel approaches. Point-of-care testing (POCT) has emerged as useful technology that could contribute to create more efficient patient flow and better timeliness in the ED. The main objective of our study is to demonstrate, in a multicenter study, that POCT benefits care timeliness in the ED. We conducted a multicenter and cluster randomized study. A total of 3,200 patients. We randomly assigned patients to a POCT group or Central Laboratory Group. The primary outcome was the ED time to clinical decision. The secondary outcome included the length of stay and the laboratory turnaround time. Readmission within the seven after discharge was also calculated. The primary finding of this study is a strategy based on POCT that aims to significantly improve care timeliness in the ED. We found significant reductions in all outcomes regardless of presentation reason, patient disposition or hospital type. Time to clinical decision decreased by 75.2 min (205-129.8), length of stay by 77.5 min (273.1-195.6) and laboratory turnaround time by 56.2 min (82.2-26) in the POCT group. No increase in readmission was found. Our strategy represents a good approach to optimize timeliness in the ED. It should be seen as a starting point for further operational research focusing on POCT for improving throughput and reducing crowding in the ED.

Exploring Zig-Zag Triangles: H-Coverings and Practical Applications

Graph theory is widely used to analyze the structure models in chemistry, biology, computer science, operations research and sociology. Molecular bonds, species movement between regions, development of computer algorithms, shortest spanning tree in a weighted graphs, aircraft scheduling and exploration of diffusion mechanism are some of these structure models. A computing system C can execute an algorithm A if A is graph-isomorphic to a subgraph of CG. This is equivalent of defining a graph isomorphism between A and subgraph of CG. This research article gives a small description of graph theory applications and one of graph model (zig-zag triangle) to define a suitable computer algorithm. Let Γ be a connected, simple graph with finite vertices v and edges e. A family {Γ1, Γ2, . . . , Γp} ⊂ Γ of subgraphs such that for all e ∈ E, e ∈ Γl, for some l, l = 1, 2, . . . , p is an edge-covering of Γ. If Γl ∼= Γ′, ∀l, then Γ has an Γ′-covering. Graph Γ with Γ′-covering is an (ad, d)-Γ′-antimagic if f : V ∪ E → {1, 2, . . . , |V | + |E|} a bijection exists and the sum over all vertex-weights and edge-weights of Γ′ form a set {ad, ad + d, . . . , ad + (p − 1)d}. The labeling ξ is super for ξ(VΓ) = {1, 2, 3, . . . , |VΓ|} and graph Γ is Γ′-supermagic for d = 0. This manuscript investigates super (ad, d)-Γ′-antimagic labelings of zig-zag triangles for differences d = 1, 2, . . . , 8.

Perspectives on the implementation of post-validation surveillance for lymphatic filariasis in the Pacific Islands: A nominal group technique-based study protocol

Introduction Lymphatic filariasis (LF) is a neglected tropical disease caused by parasitic worms, transmitted via mosquito bites. Significant global efforts have led to the interruption of LF transmission, with eight of the 16 previously endemic Pacific Island countries and territories (PICTs) validated by WHO as having eliminated the disease as a public health problem. Post-validation surveillance (PVS) is recommended to verify the absence of a resurgence in transmission; however, there are no guidelines on how to implement such surveillance effectively. Aim This protocol outlines a research study that aims to explore, synthesise, and prioritise the perspectives of LF program staff from LF-eliminated PICTs. The study will focus on identifying the challenges faced in implementing PVS in the PICT settings and determining corresponding context-relevant operational research priorities. Method The study will employ a nominal group technique (NGT) involving representatives from LF-eliminated PICTs. The NGT will adhere to a structured process for generating, ranking, and prioritising ideas, followed by group discussions, debates, and the collation of shared views. Discussion The research will provide insights into the most significant challenges faced in implementing PVS in the PICTs. It will also identify priority areas where operational research is required to inform policy and practice. To our knowledge, this study is the first to apply a rigorous consensus group method to distil challenges and research priorities for LF PVS in PICTs. We expect that this research will inform the development of national and regional LF guidelines.

Associations between forest harvest scheduling and artificial intelligence

Contemporary tactical forest harvest scheduling efforts address planning problems that generally have an economic or commodity production objective, accounting routines to accumulate outcomes or to assess the extent of forest conditions, and constraints (policy or resource) that limit the assignment of management actions to subdivisions of a forest (stands or strata). Operations research methods have proven useful for addressing these problems and providing guidance, in the form of a harvest schedule, to people managing forests. Heuristic search and simulation methods have also shown promise for addressing these types of problems. Artificial intelligence includes some of these forms of search processes. When concepts of learning, adaptation, and emulation of human thought describe certain search processes, they can be considered under the broad umbrella of artificial intelligence. In this work, many of the operations research, heuristic search, and simulation methods that have been demonstrated as useful for forest harvest scheduling efforts are assessed for their association with artificial intelligence. In some cases, it is argued that the forestry profession has been using artificial intelligence for quite some time to develop tactical forest harvest schedules.

A Combined DEAV-BWM Approach for Effective Evaluation and Ranking of Biomass Materials in Charcoal Briquette Production

The utilization of agricultural waste for producing charcoal briquettes is gaining significant attention as a sustainable alternative energy source. Converting these residues into charcoal briquettes not only addresses energy shortages but also provides an efficient solution for managing agricultural waste, contributing to environmental sustainability. This study proposes a novel methodology integrating a Data Envelopment Analysis Variant (DEAV) with the Best-Worst Method (BWM) to assess and rank biomass materials for charcoal briquette production. The DEAV-BWM model enhances the evaluation process by considering multiple criteria simultaneously and incorporating both efficiency and consensus among different evaluation methods. The key highlights of the methodology are as follows:• A novel method, called the DEAV model, for evaluating efficiency scores has been established, incorporating both Data Envelopment Analysis (DEA) and Multi-Attribute Decision Making (MADM) concepts.• This paper introduces a novel hybrid method that combines the DEAV model with the BWM to evaluate and rank biomass materials for charcoal briquette production, enhancing the reliability and robustness of the decision-making process.• The proposed method can be adapted and applied to other areas of biomass utilization and beyond, providing a versatile tool for researchers and practitioners in the field of sustainable energy, engineering, and operations research.

Topology reconstruction in telecommunication networks: Embedding operations research within deep learning

Topology reconstruction in telecommunication networks: Embedding operations research within deep learning

Corrigendum to “Two-stage no-wait proportionate flow shop scheduling with minimal service time variation and optional job rejection” [European Journal of Operational Research, 305, 608-616

Corrigendum to “Two-stage no-wait proportionate flow shop scheduling with minimal service time variation and optional job rejection” [European Journal of Operational Research, 305, 608-616

Towards sustainable energy selection for ships in response to maritime regulation

Ship owners and operators are continuously seeking financially viable ways to comply with the Sulphur emission limits set out by the IMO, which calls for a reduction of sulphur content in ship fuel to less than 0.5 % in 2020. The prudent decision in managing required energy sources by shipowners/operators will lead to an efficient ship operating decision, subsequently resulting in long-term financial return. The study aims to identify the factors influencing ship owners and operators to select the energy types in operating their vessels. Furthermore, the research explores the most desired energy sources to address the IMO Sulphur Cap Regulations 2020. One operation research method, namely the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and one econometrics tool, namely the conditional logit model, have been used to explore the research objectives. The PROMETHEE analysis suggests that clean fuel (VLSFO) is the best alternative to respond IMO 2020, followed by scrubber installation. The conditional logit analysis suggests almost the same, indicating clean fuel as the only alternative for responding to regulation. The decision is driven by the vessel's age negatively and the size of the vessel positively. The study will enormously impact the ship’s energy procurement practices in Bangladesh.

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

Fifty years of operational research applied to healthcare

Fifty years of operational research applied to healthcare