Many combinatorial problems addressed in the literature are modeled using binary matrices. It is often of interest to verify whether these matrices hold the consecutive ones property (C1P), which implies that there exists a permutation of the columns of the matrix such that all nonzero elements can be placed contiguously, forming a unique 1-block in every row. The minimization of the number of 1-blocks

In this paper, we extend, analyze and evaluate the exponential multi-insertion neighborhood for the vehicle routing problem with unit demands, originally proposed by Angel et al. (2008). In this neighborhood, a neighbor solution is obtained by the removal of a set of mobile nodes from a solution and a subsequent best reinsertion. At first, we examine theoretical properties of the neighborhood, such

The Quota Travelling Salesman Problem with Passengers, Incomplete Ride, and Collection Time is a new version of the Quota Travelling Salesman Problem. In this problem, the salesman uses a flexible ridesharing system to minimize travel costs while visiting some vertices to satisfy a pre-established quota. We consider operational constraints regarding vehicle capacity, travel time, passenger limitations

This paper concerns the design of optimized single-finger keyboard layouts on smartphones. In the literature, the optimization problem associated with assigning characters to keys on a single-finger keyboard is denoted as single-finger keyboard layout problem (SK-QAP), which can be seen as a variant of the quadratic assignment problem (QAP), a classical combinatorial optimization problem. To solve

The scheduling of an Agile Earth Observation Satellite (AEOS) consists of selecting and scheduling a subset of possible targets for observation in order to maximize the collected profit related to the images while satisfying its operational constraints. In this problem, a set of candidate targets for observation is given, each with a time-dependent profit and a visible time window. The exact profit

A heuristic method based on column generation is presented for the nurse rostering problem. The method differs significantly from an exact column generation approach or a branch and price algorithm because it performs an incomplete search which quickly produces good solutions but does not provide valid lower bounds. It is effective on large instances for which it has produced best known solutions on

This work analyzes an inventory system with stock-dependent demand and non-linear holding cost. It presents a new approach to maximize the return on investment, that is, the profit/cost ratio. When an inventory manager can invest in different projects and the resources are limited, it seems sensible to select those projects that provide a higher return on investment. Thus, the goal of the manager will

Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem

Practical constrained optimization models are often large, and solving them in a reasonable time is a challenge in many applications. Further, many industries have limited access to professional commercial optimization solvers or computational power for use in their day-to-day operational decisions. In this paper, we propose a novel approach to deal with the issue of solving large operational stochastic

This paper addresses the joint optimization of production capacity and safety stocks in supply chains under the guaranteed service approach (GSA). The integrated problem is formulated as a mixed integer nonlinear program (MINLP) and solution procedures are proposed in the cases of general acyclic and spanning tree networks. For general acyclic supply chains, the integrated problem is solved using a

Typically, Graph Coloring Problem (GCP) is one of the key features for graph stamping in graph theory. The general approach is to paint at least edges, vertices, or the surface of the graph with some colors. In the simplest case, a kind of coloring is preferable in which two vertices are not adjacent to the same color. Similarly, the two edges in the same joint should not have the same color. In addition

In this paper, robust optimization methodologies for solving incomplete-information two-person zero-sum and nonzero-sum games are developed that consider single or multiple interval inputs (i.e., interval-valued payoffs). Unlike complete-information games, where all parameters of the game such as individual players’ payoffs are assumed common knowledge, incomplete-information games deal with uncertain

This paper models the problem of providing an unmanned aerial vehicle (UAV)-based wireless network in a disaster area as a set covering problem that takes into consideration the operational constraints and benefits of UAVs. The research presents a branch-and-price algorithm and two approximation models of the quadratic coverage radius constraint in a simple discretization and a linear pairwise-conflict

The problem of choosing an optimal codon sequence arises when synthetic protein-coding genes are added to cloning vectors for expression within a non-native host organism: to maximize yield, the chosen codons should have a high frequency in the host genome, but particular nucleotide bases sequences (called “motifs”) should be avoided or, instead, included. Dynamic programming (DP) has successfully

The crew scheduling and routing problem (CSRP) consists of determining the best route and schedule for a single crew to repair damaged nodes in a network affected by extreme events. The problem also involves the design of paths to connect a depot to demand nodes that become accessible only after the damaged nodes in these paths are repaired. The objective is to minimize the total time that demand nodes

We consider a non-linear version of the Generalized Assignment Problem, a well-known strongly NP-hard combinatorial optimization problem. We assume that the variables are continuous and that objective function and constraints are defined by non-linear functions of the variables. A mathematical model is introduced and used to derive upper bounds on the optimal solution value. We present constructive

Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges

The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability of renewable

In this paper, we study a new Workforce Scheduling and Routing Problem, denoted Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks. In this problem, customers request services from a company. Each service is composed of dependent tasks, which are executed by teams of varying skills along one or more days. Tasks belonging to a service may be executed by different teams, and customers

The maximum balanced biclique problem (MBBP) is to find the largest complete bipartite subgraph induced by two equal-sized subsets of vertices in a bipartite graph. MBBP is an NP-hard problem with a number of relevant applications. In this work, we propose a general swap-based multiple neighborhood adaptive search (SBMNAS) for MBBP. This algorithm combines a general k-SWAP operator which is first used

Congestion pricing has become an effective instrument for traffic demand management on road networks. This paper proposes an optimal control approach for congestion pricing for day-to-day timescale that incorporates demand uncertainty and elasticity. Travelers make the decision to travel or not based on the experienced system travel time in the previous day and traffic managers take tolling decisions

Operations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context

In so-called random preference models of probabilistic choice, a decision maker chooses according to an unspecified probability distribution over preference states. The most prominent case arises when preference states are linear orders or weak orders of the choice alternatives. The literature has documented that actually evaluating whether decision makers' observed choices are consistent with such

We consider an uncertain variant of the knapsack problem in which the weight of the items is not exactly known in advance, but belongs to a given interval, and an upper bound is imposed on the number of items whose weight differs from the expected one. For this problem, we provide a dynamic programming algorithm and present techniques aimed at reducing its space and time complexities. Finally, we computationally

In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final

Demographic change towards an ever aging population entails an increasing demand for specialized transportation systems to complement the traditional public means of transportation. Typically, users place transportation requests, specifying a pickup and a drop off location and a fleet of minibuses or taxis is used to serve these requests. The underlying optimization problem can be modeled as a dial-a-ride

We formulate a bi-objective covering tour model with stochastic demand where the two objectives are given by (i) cost (opening cost for distribution centers plus routing cost for a fleet of vehicles) and (ii) expected uncovered demand. In the model, it is assumed that depending on the distance, a certain percentage of clients go from their homes to the nearest distribution center. An application in

In this paper, we introduce two algorithms to address the two-echelon capacitated location-routing problem (2E-CLRP). We introduce a branch-and-cut algorithm based on the solution of a new two-index vehicle-flow formulation, which is strengthened with several families of valid inequalities. We also propose an adaptive large-neighbourhood search (ALNS) meta-heuristic with the objective of finding good-quality

This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes

This article comprises the first theoretical and computational study on mixed integer programming (MIP) models for the connected facility location problem (ConFL). ConFL combines facility location and Steiner trees: given a set of customers, a set of potential facility locations and some inter-connection nodes, ConFL searches for the minimum-cost way of assigning each customer to exactly one open facility