This study analyzes how the new online platform-selling format affects the optimal co-operative advertising choices of both the manufacturer and the e-commerce platform (e-tailer), and explores the effects of the e-tailer’s contractual forms and commission fees on these choices. In the model, the manufacturer can decide its advertising expenditure as well as the level of participation in the e-tailer’s

In this article, we study the influences of manufacturer rebate on the outcomes of the channel members in a green supply chain, where the retailer has private information about market demand. We find that, (1) manufacturer rebate is good for the channel members, however, the manufacturer takes a lower share of the incremental profit than the retailer; (2) the value of manufacturer rebate for the channel

DEA (Data Envelopment Analysis) is a nonparametric approach that has been used to estimate fuzzy portfolio efficiency. In this paper, we propose an approach under the fuzzy theory framework that can both improve the DEA frontier and suggest a replicable benchmark for investors. We first construct an improved DEA model using the proposed approach and then investigate the relationships among the evaluation

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfolio optimization model which attempts to exploit side information about the financial market in order to reach an optimal risk-return tradeoff. This side information might include for instance recent stock returns, volatility indexes, financial news

To reduce the environmental pollution of Waste Electrical and Electronic Equipment (WEEE), the government has promulgated take-back legislation. For the producer, he should consider collection effort and remanufacturing design response to collection goal and remanufacturing goal. In this article, we develop two models: (1) model only with remanufacturing design and (2) model with both collection effort

(2020). Preface to the special issue of INFOR on “continuous optimization and applications in machine learning and data analytics”. INFOR: Information Systems and Operational Research: Vol. 58, Special Issue on 'Continuous Optimization and Applications in Machine Learning and Data Analytics', pp. 167-167.

Sourcing overseas enables customers to experience high-quality products, especially for those in the developing countries/regions. This induces many brand-owners to decide product diversification by selling both imported products (high quality reliability) and local products (low quality reliability), although product self-competition occurs and may result in system profit loss. In this paper, we consider

In this paper, a closed-loop supply chain network design problem is developed. In the proposed model, expected returned products is estimated as a function of return price and if the amount of returned products is less than the expected amount, decision makers have some choices such as more advertising, incentives (extra cost) for returning more products. Different quality levels are considered for

We study a parallel-queue system with Poisson arrivals, in which a dispatcher sends the incoming traffic to K queues using Size Interval Task Assignment (SITA) policy that aims to equalize the performance of all queues. We study existence and uniqueness of the allocation thresholds for a large set of performance functions of the queues. We also provide a family of performance functions of the queues

In this study, a new mathematical programming approach for solving the joint timetabling and train stop planning problem in a railway line with double-track segments is proposed. This research aims to design an optimized train timetable subject to the station-capacity and time-dependent dwell time constraints. The objective function is to maximize the railway infrastructure capacity by minimizing the

Product innovation and service upgrades are effective measures for firms to improve their competitiveness. Considering the upgrading of the installed customers (IU), the upgrade strategy of a platform firm usually follows two principles, namely, the derived principle (i.e., the basic product still operates when the new derived product is released) or updated principle (i.e., the basic product stops

Real industrial studies often give rise to complex optimization problems involving mixed variables and time consuming simulators. To deal with these difficulties we propose the use of a Gaussian process regression surrogate with a suitable kernel able to capture simultaneously the output correlations with respect to continuous and categorical/discrete inputs without relaxation of the categorical variables

Abstract Robust optimization (RO) has attracted much attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which

A methodology is presented that determines the staff requirements for a baggage handling operation at an international airport facing high demand. It consists of two sequentially solved integer linear programming models. The first model determines the handler requirement using historical demand data and incorporates the operation’s spatial constraints. The second model, taking as input the results

In service industries such as restaurants and tourism, empirical findings show that uninformed customers may consider queues as a signal of service quality and choose to join a longer queue. Service managers become aware of this phenomenon and stimulate customer purchase by maintaining a queue. In this paper, we explore issues related to the balance between herding and congestion for service systems

This article addresses the daily scheduling of surgeries in an operating theatre. The problem consists of a set of surgical operations that have to be scheduled in identical operating rooms. The operations have uncertain durations, require setup activities before and after the surgical act and are assigned to surgeons who have to perform the surgical part. The objective is to find the best sequence

Analysing conflicts in large optimization problems is an intricate and difficult task. In this paper we present a tool for infeasible LP, to guide the decision maker towards an adequate strategy for dealing with the infeasibility. We propose a mathematical formulation for the ranking of the optimal values and solutions among all feasible subsets of constraints, that is, to find (feasible) clusters

HIV/AIDS prevention funds are often allocated at multiple levels of decision-making. Optimal allocation of HIV prevention funds maximizes the number of HIV infections averted. However, decision makers often allocate using simple heuristics such as proportional allocation. We evaluate the impact of using incentives to encourage optimal allocation in a two-level decision-making process. We model an incentive

(2020). Correction. INFOR: Information Systems and Operational Research: Vol. 58, Special Issue on 'Continuous Optimization and Applications in Machine Learning and Data Analytics', pp. 424-424.

Data envelopment analysis (DEA) models are applied in commercial firms to evaluate their technical, cost and revenue efficiencies. The new models introduced here are based on inverse DEA for preserving cost/revenue efficiency. The commercial institution management seeks to minimize the input costs for a given level of outputs or maximize the revenue of outputs for a specified level of inputs. It is

Adaptive cubic regularization (ARC) and trust-region (TR) methods use modified linear systems to compute their steps. The modified systems consist in adding some multiple of the identity matrix (or a well-chosen positive definite matrix) to the Hessian to obtain a sufficiently positive definite linear system, the so called shifted system. This type of system was first proposed by Levenberg and Marquardt

Abstract The Accumulating Priority Queue model, in which customers accumulate priority as a linear function of their time in the queue, was first introduced by Kleinrock in 1964 under another name. All publications addressing the APQ since then have assumed that customers start accumulating priority credits upon arrival. The model we present herein, called the Delayed Accumulating Priority Queue, entails

(2019). Correction. INFOR: Information Systems and Operational Research. Ahead of Print.

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required

We analyze symmetric subgame perfect equilibria of the dollar auction in its original format, without the modifications that the literature adopts to rule out overbidding in the game. The game has a continuum of subgame perfect equilibria, generating expected revenues that range from zero to the full value of the contested prize. Such multiplicity of equilibria suggests that the overbidding pattern

This article presents a two-stage workforce scheduling approach for security guards at motor traffic gates. In the first stage, staffing requirements are determined based on analyzing workload variations for each gate. In the second stage, an integer programming model is formulated to assign guards to different work shifts in order to satisfy the staffing requirements of each gate. The objective is

This article studies least-squares approximate policy iteration (API) methods with parametrized value-function approximation. We study several variations of the policy evaluation phase, namely, Bellman error minimization, Bellman error minimization with instrumental variables, projected Bellman error minimization, and projected Bellman error minimization with instrumental variables. For a general discrete-time

In the literature, it has been shown that autocorrelation in service times has a dramatic impact on performance measures in delay systems. Although some studies deal with systems having autocorrelated service times, a general renewal approximation methodology that can incorporate this information in analytical models has not been previously proposed. In this paper, we develop a discrete event simulation

Effective communication between nurses and physician teams on the internal medicine unit is crucial for high quality, safe, and efficient patient care. In our hospital of interest, a large academic health sciences centre, the physical layout of the unit, current admission process, and the presence of three separate physician teams contribute to uneven workload and communication barriers. We address

Data envelopment analysis (DEA) is a nonparametric data-based approach that is used to evaluate the performance of a set of homogeneous units; hence, inputs and outputs play a crucial role in this evaluation. In many technologies, the inputs and outputs are so volatile and complex that even other stochastic events can have an influence in the measurement accuracy. A suitable estimation method can be

We introduce an algorithm for the optimization of problems whose objective functions are evaluated by computationally expensive black-box simulations and for which an analytic description of the objective and its derivatives are not available. We consider the case where two levels of simulation model fidelity are available, namely a high fidelity model that is computationally very expensive to evaluate

This paper discusses the petrol-station replenishment problem with adjustable demands (PSRP-AD). This problem originates from a practical application of fuel delivery, where the main objective is to satisfy all petrol-station demands at a minimal cost. However, the problem includes tanks with adjustable loads while respecting the agreement made between the company and the customers. This agreement

The concept of the optimal partition was originally introduced for linear optimization and linear complementarity problems and subsequently extended to semidefinite optimization. For linear optimization and sufficient linear complementarity problems, from a central solution sufficiently close to the optimal set, the optimal partition and a maximally complementary optimal solution can be identified

We introduce the generalized constrained maximum-entropy sampling problem (GCMESP) as a common generalization of the ordinary constrained maximum-entropy sampling problem (CMESP) and the constrained D-optimality problem (CDOPTP). Exact algorithms for both CMESP and CDOPTP are based on branch-and-bound methods. We extend a spectral upper-bounding method for the CMESP to the GCMESP.

We propose an infeasible interior-point algorithm for constrained linear least-squares problems based on the primal-dual regularization of convex programs of Friedlander and Orban. Regularization allows us to dispense with the assumption that the active gradients are linearly independent. At each iteration, a linear system with a symmetric quasi-definite (SQD) matrix is solved. This matrix is shown

The concepts of immobile indices and their immobility orders are objective and important characteristics of feasible sets of semi-infinite programming (SIP) problems. They can be used for the formulation of new efficient optimality conditions without constraint qualifications. Given a class of convex SIP problems with polyhedral index sets, we describe and justify a finite constructive algorithm (algorithm

In this article, the design of a quality control network is considered in an entire supply chain, in the context of an international food and beverage company, denoted as ABC. For each possible control point of the network, two decisions have to be made: (1) establish a monitoring activity or not; (2) if a monitoring activity is placed, determine its acceptance level with a well-adjusted sampling plan