The capabilities of nanosatellites are constrained by their limited power availability and size, which poses challenges for mission planning and operation. This study addresses the Offline Nanosatellite Task Scheduling (ONTS) problem by introducing multi-mode capability into the scheduling process, enhancing its relevance for more realistic, adaptable, and robust mission planning. We propose a Mixed-Integer

Soft Operational Research (Soft OR) emerged as a distinctive approach emphasising stakeholder engagement to address complex, ill-defined problems, departing from traditional OR methods. This review explores the nature and potential of Soft OR over the past fifty years, focusing specifically on articles published in the European Journal of Operational Research (EJOR) to clarify some foundational principles

We study how common ownership affects the magnitude and dynamics of investments in a duopoly. Followers exhibit less aggressive timing and quantity reactions because they internalize their effects on leaders. Leaders are therefore more likely to opt for a deterrence strategy, but their own internalization of followers softens their decisions. If firm roles are exogenous, high common ownership links

This paper is motivated by the occurrence of vaccine nationalism in the setting of pandemics. Certain high-income countries (HICs) aggressively accumulated vaccinations while showing little concern for the vaccination challenges faced by low- and middle- income countries. This disparity fosters the proliferation and mutation of viruses, thus risking the global population’s health and welfare. Hence

The packing of irregular pieces is widely applied across various industries including metalworking, woodworking, clothing manufacturing, and leather goods production. Allowing rotation during packing, particularly in scenarios where materials are homogeneous, can yield superior outcomes by reducing material wastage, thus contributing to cost-saving and environmental preservation. This study investigates

This paper proposes a new model that integrates opportunistic maintenance and routine maintenance to enhance the effectiveness of predictive maintenance and inventory management in complex manufacturing systems subject to a reliability chance constraint. It considers both hard and soft failure modes and their mutual dependence. When a machine experiences a hard failure, an opportunistic maintenance

This paper considers the generalized maximal covering location problem (GMCLP) which establishes a fixed number of facilities to maximize the weighted sum of the covered customers, allowing customer weights to be positive or negative. Due to the huge number of linear constraints to model the covering relations between the candidate facility locations and customers, and particularly the poor linear

The Root Sequence Index (RSI) is a parameter used in mobile wireless networks to allocate uplink channels between user equipment and base stations. Inadequate RSI assignment to neighbor radios may lead to failure in service establishment and performance degradation. Wireless networks are also dynamic, with uncertain modifications in time due to, for instance, seasonal foliage. In this paper, we model

Trade wars and geopolitical conflicts have intensified the supply risks associated with high-quality critical components in cross-border high-tech supply chains. In response, companies have restructured their supply networks by adopting multiple sourcing strategies and shifting from global to regional models. Consequently, domestic manufacturers reliant on overseas procurement may turn to low-quality

The yard crane scheduling problem (YCSP) consists of optimizing container loading for storage and retrieval requests from yard cranes at port terminals. This paper studies a realistic generalization of the YCSP that incorporates the assignments of input/output (I/O) points during the optimization stage. I/O points serve as buffers between the different transportation modes in the port terminal. These

Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs whose outputs are both desirable for the decision maker, whilst also being robust to the underlying uncertainties in the problem. In this work, we study the multi-objective case of this problem. We identify that the majority

The development of on-demand global transportation service procurement (oGTSP) through digital trading platforms has accelerated due to frequent fluctuations in transport capacity. In the oGTSP model, the exporter must consider logistics service quality and transport prices when sourcing global logistics services. To satisfy the continuous transport needs, procurement is conducted sequentially throughout

Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on “better estimating model parameters” with training data. In this study, we focus on using BNL and

The systems analysis field has traditionally focused on identifying the essential elements within an interconnected system and analyzing the cause-and-effect relationships among them. However, most decision-making methods in system analysis have been one-sided. They primarily rely on the interactions between elements to make decisions, neglecting to account for the non-uniform influence attenuation

Emergency department (ED) crowding has become a common phenomenon worldwide. A number of interventions have been proposed to improve operations in EDs, such as scheduling physicians to manage varying patient demands. Motivated by a collaboration with a large ED, we study physician scheduling in the ED. The ED is modeled as a time-varying case managers system where the number of patients simultaneously

This paper proposes a collaborative optimization strategy for multiperiod procurement and multimodal transportation that considers cost factors such as procurement, transportation, transshipment, and storage costs incurred for early arrival. A mixed-integer planning model is established to minimize the overall operating costs of cross-border e-commerce enterprises by arranging procurement, transportation

The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that students understand the role of study design and how to formulate meaningful scientific and statistical research questions. While the traditional Design of Experiments

Shipping alliances have emerged as a cooperation platform between independent shipping companies, aiming to enhance customer satisfaction and exploit the economies of scale through capacity and information sharing. A sustainable shipping alliance should operate in a profitable, fair and environmentally friendly way under emerging Emissions Trading System (ETS). A non-convex mixed-integer nonlinear

Emerging geopolitical risks have begun to threaten global supply chains, including those that produce life-saving drugs. Export bans may prevent a company from shipping products internationally, and it is unclear how these new dynamics may affect company plans and persistent, worldwide drug shortages. To address these questions, we present a global pharmaceutical supply chain design model that considers

Meta-analysis is a well-established method for integrating results from several independent studies to estimate a common quantity of interest. However, meta-analysis is prone to selection bias, notably when particular studies are systematically excluded. This can lead to bias in estimating the quantity of interest. Motivated by a meta-analysis to estimate the rate of completed-suicide after bariatric

Governments in many countries offer fiscal incentives—such as subsidies or tax breaks—to consumers to encourage the purchase of environmentally-friendly products like solar panels and electric vehicles. Early adoption by consumers facilitates manufacturers’ learning-by-doing and reduces production cost over time, although the cost reduction itself is subject to uncertainty. Governments face a challenge:

In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction

The problem of packing unequal circles into a circular container is a classic and challenging optimization problem in the field of computational geometry. This study introduces a suite of innovative and efficient methods to tackle this problem. Firstly, we present a novel layout-graph transformation method that represents configurations as graphs, together with an inexact hash method facilitating fast

This study examines patient-initiated appointment rescheduling with consideration of patient preferences. Online rescheduling policies are investigated for the selection and sequential offering of new appointments upon the arrival of a rescheduling request via a telephone call. Appointments are offered until the patient accepts one or the maximum number of offers is reached. The aim is to reschedule

Two-stage distributionally robust optimization is a recent optimization technique to handle uncertainty that is less conservative than robust optimization and more flexible than stochastic programming. The probability distribution of the uncertain parameters is not known but is assumed to belong to an ambiguity set. The size of certain types of ambiguity sets - such as several discrepancy-based ambiguity

The difference-of-convex (DC) program is an important model in nonconvex optimization due to its structure, which encompasses a wide range of practical applications. In this paper, we aim to tackle a generalized class of DC programs, where the objective function is formed by summing a possibly nonsmooth nonconvex function and a differentiable nonconvex function with Lipschitz continuous gradient, and

A longstanding question in the judgment and decision making literature is whether experts, even in high-stakes environments, exhibit the same cognitive biases observed in controlled experiments with inexperienced participants. Massey and Thaler (2013) claim to have found an example of bias and irrationality in expert decision making: general managers’ behavior in the National Football League draft

Given a positive integer p and a weighted undirected graph G=(V,E), we study a problem in which the objective is to find a minimum weight set of up to p elementary cycles partitioning the vertices of G. We study several exponentially sized formulations including (i) edge variables only; (ii) edge and depot variables only; (iii) edge, depot and node-depot assignment (NDA) variables only; (iv) edge,

In his 1996 paper, Talagrand highlighted that the Law of Large Numbers (LLN) for independent random variables can be viewed as a geometric property of multidimensional product spaces. This phenomenon is known as the concentration of measure. To illustrate this profound connection between geometry and probability theory, we consider a seemingly intractable geometric problem in multidimensional Euclidean

Carrier collaboration has emerged as an important way to increase the efficiency of the logistics sector and has attracted significant interest as a research topic in the past decades. Most frameworks for carrier collaboration rely on combinatorial auctions to allocate bundles of transportation requests to carriers. In this paper, we analyze whether combinatorial auctions are indeed needed for this

We consider a commerce platform that consists of two queues: one for buyers of an item and the other for sellers of the item. The platform is operated under the first-join-first-trade discipline. Upon a trade, the buyer and the seller gain respective profits but incur the cost of waiting in the platform. To maximize their expected payoffs from trading, both buyers and sellers can choose their arrival

High levels of uncertainty faced by decision makers can be alleviated by characterizing multiple possible ways in which the future might unfold with scenario narratives. Aiming at describing alternative plausible chains of outcomes of key uncertainty factors, scenario narratives are often associated with graphical networks describing the relationships between the outcomes of the factors. We present

Motivated by the operational practice of JD.com, China’s largest online retailer, our study delves into the phenomenon of consumer stickiness. It measures the probability that consumers will remain loyal to a specific product, refraining from purchasing alternatives, even in the temporary absence of the focal product. Based on real data from JD.com, we show that consumer stickiness has a significantly

Investment board meetings typically include a macroeconomic review, and a discussion of the implications for asset allocation. Investors who are able to time the market can no-doubt obtain abnormal returns, but what is the cost for investors who attempt to time the market but have no genuine timing ability? We prove that for virtually any uninformed timing strategy there is a constant-allocation strategy

Many existing branch and bound algorithms for multiobjective optimization problems require a significant computational cost to approximate the entire Pareto optimal solution set. In this paper, we propose a new branch and bound algorithm that approximates a part of the Pareto optimal solution set by introducing the additional preference information in the form of ordering cones. The basic idea is to

Local search methods start from a feasible solution and improve it by successive minor modifications until a solution that cannot be further improved is encountered. They are a common component of most metaheuristics. Two fundamental local search strategies exist: first-improvement and best-improvement. In this work, we perform an in-depth computational study using consistent performance metrics and

The e-commerce industry is quickly transforming towards more automation and technological advancements. With the growing intricacy of warehouse operations, there is a need for control systems that can efficiently handle this complexity. This study considers a Robotic Mobile Fulfillment System (RMFS), a semi-automated warehousing system. This system employs autonomous mobile robots (AMRs) to retrieve

We study the role of blockchain in achieving information transparency and generating trust in a dyadic supply chain with one retailer sourcing from a single supplier. The retailer has superior information regarding the demand distribution, whereas the supplier sets the capacity in preparation for the selling season. Two sources of risk are identified: information risk, which captures the incentives

We study the general problem of orthogonally packing rectangles in a fixed size circular container. This is a computationally challenging combinatorial optimization problem with important real-world applications and has recently received much attention from the operations research community. We propose an effective evolutionary algorithm for four variants of the problem, which integrates an improved

The literature includes very few instances of scatter search applications to bilevel optimization. These implementations have been proposed for problems in the field of logistics involving integer variables and are based on a structure where scatter search sets the values of the decisions at the upper level followed by the solution of the lower level problem. In this work, we develop a scatter search

This paper studies how competing firms make technology choices and production decisions under the cap-and-trade policy when they engage in both product and emission trading markets. Using a two-stage game theoretical model, we analyze firms’ responses to stricter emission caps and efficiency improvements of clean technology. Interestingly, we identify a “reverse trading” phenomenon where the firm with