Reducing the carbon footprint of global supply chains is a challenge for many companies. Governmental emission regulations are increasingly stringent, and consumers are increasingly environmentally conscious. Companies should therefore integrate carbon emissions in their supply chain decision making. In this paper, we study the inbound supply mode and inventory management decision making for a company

In practice, consumption quality refers to “production quality + category image”, where the former is determined by a contract manufacturer (CM) and the latter is promoted by an industry leader. In this paper, we explore whether the industry leader should cooperate with the CM deeply by pooling their component procurement orders when the CM sells its self-branded products with the same consumption

Rapid growth in the world of e-commerce is forcing traditional retailers to rethink their operational efficiency and revenue/cost streams. Retailers are facing increasing pressure to better utilize in-store inventory and reduce end-of-season markdowns. Transshipments between stores can re-balance inventory levels while preventing shortages and backorders. However, the joint inventory and transshipment

Risk budgeting is a portfolio strategy where each asset contributes a prespecified amount to the aggregate risk of the portfolio. In this work, we propose an efficient numerical framework that uses only simulations of returns for estimating risk budgeting portfolios. Besides a general cutting planes algorithm for determining the weights of risk budgeting portfolios for arbitrary coherent distortion

We consider a simple assembly line balancing problem with given cycle time and number of stations. A quadratic objective function based on a so-called smoothness index SX levels the workloads of the stations. For this problem, called SALBP-SX, only a few solution procedures have been proposed in literature so far. In this paper, we extend and improve the branch-and-bound procedure SALSA (Simple Assembly

In the platform economy, many brands have already supplied products to platforms (e.g., Amazon.com and JD.com) who operate “self-run (SR)” stores. They face the challenges of whether or not operating flagship stores on the platforms and how to determine the optimal logistics choice (provided by the platforms or 3PL providers). To address these challenges, we explore the optimal channel and logistics

Prepositioning of relief supplies has been widely addressed to cover the demands of humanitarian emergencies. However, cost inefficiency, item type limitation, and damage risk make solely relying on prepositioning unrealistic. We develop two-stage stochastic models that incorporate prepositioning, physical capacity reservation, and production capacity reservation for reactive procurement. As an alternative

We consider the classical single-level multi-item capacitated lot-sizing problem (CLSP) which is the core model for production planning. It is NP-hard and if setup operations consume capacity the feasibility problem itself is NP-complete. Several construction heuristics have been proposed in the research literature, but none of them achieves a sufficient solution quality and generality at the same

Abstract not available

The Time Dependent (TD) Traveling Salesman Problem (TSP) is a generalization of the TSP which allows one to take traffic conditions into account when planning tours in an urban context, by making the travel time between locations dependent on the departure time instead of being constant. The TD-TSPTW further generalizes this problem by adding Time Window constraints. Existing exact approaches such

Violations in business-to-peer (B2P) product sharing such as theft and vandalism may cause firm bankruptcy and social welfare reductions. Taking bike-sharing as an example, this paper develops a non-atomic game model to characterize the collective behaviors of rational consumers and investigates how to inhibit violations in the B2P market. We assume that individuals are heterogeneous in the expected

Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical parallel machines to simultaneously minimize the maximum completion time and the total energy consumption over a time horizon partitioned into a set of discrete slots. The energy costs are

Unlike refueling an internal combustion engine vehicle, charging electric vehicles is time-consuming and results in higher energy consumption. Hence, charging stations will face several challenges in providing high-quality charging services when the adoption of electric vehicles increases. These charging infrastructures must satisfy charging demands without overloading the power grid. In this work

We consider strategic retail pricing in markets, where retail companies buy commodities at fluctuating wholesale prices and resell them to final consumers by applying dynamic retail tariffs. This is of especially large relevance in the context of energy markets where substantial wholesale price fluctuations are observed. Policy makers currently foster the introduction of such dynamic tariff schemes

The measurement validity of analytic hierarchy process (AHP) questionnaires used for multi-criteria decision-making (MCDM) and their fuzzification-based extensions have not yet been definitively investigated. In this context, we investigated the effect of a fuzzification procedure on the variable measurement validity and statistical results of an AHP model. Based on market data regarding factors influencing

Data Envelopment Analysis (DEA) very often sets unrealistic targets, which require from the decision-making units (DMUs) a huge amount effort, perhaps non-assumable, for their achievement. For the identification of best practices in the benchmarking, this paper proposes considering as peers not only DEA efficient DMUs but also those that are Pareto efficient, and allowing for reference sets spanning

We present solutions to a continuous patrolling game played on network. In this zero-sum game, an Attacker chooses a time and place to attack a network for a fixed amount of time. A Patroller patrols the network with the aim of intercepting the attack with maximum probability. Our main result is the proof of a recent conjecture on the optimal patrolling strategy for trees. The conjecture asserts that

In the flowshop scheduling literature, the insertion-based neighborhood search method is often considered to obtain high-quality solutions. It will lead to expending extensive computational effort when evaluating the objective function. Rapid evaluation methods based on Taillard's acceleration can reduce the time complexity of function evaluation. However, existing rapid evaluation methods cannot be

In an make-to-order (MTO) supply chain, as consumers are increasingly sensitive to the delivery time and delivery reliability, quoting a short delivery time while delivering on-time is the major challenge for the retailer. To avoid tardy delivery, the retailer usually requires the manufacturer provide reliable delivery service by hedging against its production uncertainty, which is termed as production

In this research note, we show that a simple application of Breiman’s work on optimal stopping in 1964 leads to an elementary proof that (s,S) policies minimize the long-run average cost for periodic-review inventory control problems. The method of proof is appealing as it only depends on the fundamental concepts of renewal-reward processes, optimal stopping, dynamic programming, and root-finding.

The growth of milk production in the Netherlands which followed the abolition of the milk quota in 2015, put pressure on the sustainable development of dairy farms. In this paper, we use the by-production model to measure dynamic economic, environmental, and social inefficiency of Dutch dairy farms. Subsequently, we investigate associations between inefficiency and socio-economic factors using a bootstrap

This paper constitutes the very first treatment of the Shapley–Shubik (1977) market-game mechanism with a continuum of commodities. We develop an oligopolistic-competition model in which product prices are endogenously determined, via buyers’ and sellers’ strategic decisions, and we lay down and examine its mathematical structure. Taking agents’ market power into account, we restudy the Ricardian Law

It is common to use minimax rules to make planning decisions when there is great uncertainty about what may happen in the future. Using minimax rules avoids the need to determine probabilities for each future scenario, which is an attractive feature in many public sector settings. However there are potential problems in the application of a minimax approach. In this paper our aim is to give guidance

This study develops closed-form solutions for distortion risk measures (DRM) in extreme cases by utilizing the first two moments and the symmetry of underlying distributions. The resultant extreme-case distributions, encompassing the worst- and best-case distributions, are identified by the envelopes of the distortion functions. The findings of this study extend previous research on worst-case risk

Abstract not available

This study develops a game theoretical model with two-competing hardware/software platforms, to examine whether a platform should charge consumers for first-party content, or offer it for free. Four strategies are considered: both platforms charge, both platforms offer the content for free, one platform charges and the other offers the content for free. As the platforms are two-sided, third-party content

Water pollutants can be classified into three categories, each of which includes several classifications of substances. In this paper, we present a methodology based on bankruptcy models to determine the emission limits of polluting substances belonging to more than one category. We model the problem as a multi-issue allocation problem with crossed claims and introduce the constrained proportional

Diagnostic testing is a fundamental component in effective outbreak containment during every phase of a pandemic. Test samples are collected at testing facilities and subsequently analyzed at specialized laboratories. In high-income countries where health care providers are often privately owned, the assignments of samples from testing facilities to laboratories are determined by individual stakeholders

The flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem, which has wide applications in the real world. The complexity and relevance of the FJSP have led to numerous research works on its modeling and resolution. This paper reviews some of the research of the past 30 years on the problem, by presenting and classifying the different criteria, constraints, configurations

In this paper, we propose a new uncertainty set for robust models of linear optimization problems. We first study data-free and distribution-free statistical properties of continuous and independent random variables using the Probability Integral Transform. Based on these properties, we construct a new uncertainty set by placing constraints on the order statistics of random variables. We utilize the

Developments in sensor techniques enable the continuous monitoring of the health of an operating system. The resulting condition data provides an opportunity for better prediction of failures and thereby for improving maintenance decisions. In this study, we consider condition-based maintenance for a single unit with an unknown, non-decreasing deterioration process and unknown failure behavior. Building

The growing technological capability for real-time condition monitoring (CM) of industrial equipment has spurred significant interest in methods for optimal maintenance planning using CM signals. Existing approaches for maintenance policy development consider degradation to be either fully or partially observable. For the more general case of partial observability, it is usually assumed that the relationship

An attractive feature of standard data envelopment analysis (DEA) is that decision-making units (DMUs) are put in their best possible light. This is accomplished by not imposing any restrictions on how the inputs and outputs can be weighted together. Unfortunately, this flexibility also has drawbacks. The underlying best practice production structure is typically very complex, which can make it difficult

We propose a family of probabilistic ordinal regression methods for multiple criteria sorting. They employ an additive value function model to aggregate the performances on multiple criteria and the threshold-based procedure to derive the class assignments of alternatives. The Decision Makers (DMs) can provide certain and uncertain assignment examples concerning a subset of reference alternatives,

In this paper, we study the optimal time-consistent reinsurance-investment problem for a risk model with the thinning-dependence structure. The insurer’s wealth process is described by a jump-diffusion risk model with two dependent classes of insurance business. We assume that the insurer is allowed to purchase per-loss reinsurance and invest its surplus in a financial market consisting of a risk-free

Nowadays, to protect the interests of customers, quite a few warranted products are sold with some kinds of repair thresholds. For example, a mobile phone manufacturer may provide a replacement service if the phone sold still cannot work after several repair attempts or the cumulative repair time reaches 7 days or 1 month. Motivated by this observation, we study a non-renewing replacement warranty

Organic farming enhances food quality and public health, and contributes to a more sustainable environment. Although certified organic farmland grew from 11 to 72.3 million hectares between 1999 and 2019, it constituted only 1.5 percent of the world’s agricultural farmland in 2019. The main impediment to the conversion from conventional to organic farming is the financial difficulties that farmers

This paper will study the pricing problem of two competitive products in a market characterized by local externalities. For this purpose, a stochastic model of sales propagation among consumers is developed. This model utilizes a compartmentalized schema denoted as a Markov Chain where the local network effects impact transition rates. A key aspect of the proposed model is its multilayer structure

This article introduces a framework that unifies and generalizes well-known literature results related to local search for the job-shop and flexible job-shop scheduling problems. In addition to the choice of the metaheuristic and the neighborhood structure, the success of most of the influential local search approaches relies on the ability to quickly and efficiently rule out infeasible moves and evaluate

This paper studies a variant of the lot sizing problem that arises in the context of disaster management. In this problem, a fixed budget has to be allocated efficiently over multiple time periods to procure large quantities of a staple food that will be stored and later delivered to people affected by disaster strikes whose numbers are unknown in advance. Starting from the deterministic model where

This study aims to explore the impacts of manufacturers’ downstream distribution channels on upstream sourcing strategy and of the business relationship between manufacturers resulting from upstream sourcing transactions on downstream distribution strategy, through investigating the interplay of co-opetition and decentralization. We consider a framework with two manufacturers producing substitutable

This paper aims to answer an open question recently posed in the literature, that is to find a fast exact method for solving the max-sum diversity problem, a nonconcave quadratic binary maximization problem. We show that, for Euclidean max-sum diversity problems (EMSDP), the distance matrix defining the quadratic term is always conditionally negative definite. This interesting property ensures that

We study a small farmer’s optimal market choice in the presence of multiple markets in an emerging economy. A small farmer sells her product through either of the three markets available: government-regulated market, corporate-owned market (a private market having high-quality requirements), and fixed-price market (a government procurement center). Using a dynamic game of incomplete information, we

To model the Exposure At Default (EAD) of revolving credit facilities, such as credit cards, most of the research thus far has employed point estimation approaches, focusing on the central tendency of the outcomes. However, such approaches may have difficulties coping with the high variance of EAD data and its non-normal empirical distribution, whilst information on extreme quantiles, rather than the

Abstract Regularized structural equation models have gained considerable traction in the social sciences. They promise to reduce overfitting by focusing on out-of-sample predictions and sparsity. To this end, a set of increasingly constrained models is fitted to the data. Subsequently, one of the models is selected, usually by means of information criteria. Current implementations of regularized structural

Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutor...

Many product recalls are caused by quality-related product failures. When such recalls occur, the effects may not only be limited to the firm selling the product but also extend to competing firms in the category. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the

Abstract The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI is that it is overly greedy in exploiting the fitted Gaussian process model for optimization, which results in suboptimal solutions even with large sample

Abstract We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. NSP works with a wide range of distributional assumptions on the errors, and guarantees

Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression

Abstract not available

In the post-pandemic era, global supply chains are increasingly experiencing disruptions. To mitigate the effects of production disruptions, many multinational firms (MNFs) are centralising their supply chains by removing production subsidiaries back to the domestic countries (i.e. backshoring), at the expense of tax arbitrage benefits. This study conducts an analytical game-theoretical model to examine

Abstract Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. The paper takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared

Abstract Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors, including a time-dependent coefficient, which transforms the dataset into “pseudo-observation”

Abstract Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to utilize cancer registry data as a complement to improve the estimation

Abstract Conventionally, censored quantile regression stipulates a specific, pointwise conditional quantile of the survival time given covariates. Despite its model flexibility and straightforward interpretation, the pointwise formulation oftentimes yields rather unstable estimates across neighbouring quantile levels with large variances. In view of this phenomenon, we propose a new class of quantile-based

We consider the profit-maximization problem solved by an electricity retailer who aims at designing a menu of contracts. This is an extension of the unit-demand envy-free pricing problem: customers aim to choose a contract maximizing their utility based on a reservation bill and multiple price coefficients (attributes). A basic approach supposes that the customers have deterministic utilities; then

Cyber security and resilience are major challenges in our modern economies; this is why they are top priorities on the agenda of governments, security and defense forces, management of companies and organizations. Hence, the need of a deep understanding of cyber risks to improve resilience. We propose here an analysis of the database of the cyber complaints filed at the Gendarmerie Nationale. We perform

When an original equipment manufacturer (OEM) sources key components from one contract manufacturer (CM), the CM may produce its own-brand product to compete against the OEM. Therefore, these two firms have a co-opetitive relationship. We develop a co-opetitive newsvendor model in which two newsvendors (OEM and CM) compete against each other in the format of product substitution. Moreover, the CM also

The maximal covering location problem (MCLP) and the partial set covering location problem (PSCLP) are two fundamental problems in facility location and have widespread applications in practice. The MCLP determines a subset of facilities to open to maximize the demand of covered customers subject to a budget constraint on the cost of open facilities; and the PSCLP aims to minimize the cost of open