Platelets are valuable, but highly perishable, blood components used in the treatment of, among others, viral dengue fever, blood-related illness, and post-chemotherapy following cancer. Given the short shelf-life of 3–5 days and a highly volatile supply and demand pattern, platelet inventory allocation is a challenging task. This is especially prevalent in emerging economies where demand variability

The Reconfigurable Transfer Line Balancing Problem (RTLB) is considered in this paper. This problem is quite recent and motivated by the growing need of reconfigurability in the new industry 4.0 context. The problem consists into allocating a set of operations necessary to machine a single part to different workstations placed into a serial line. Each workstation can contain multiple machines operating

Ribonucleic acid (RNA) molecules play informational, structural, and metabolic roles in all living cells. RNAs are chains of nucleotides containing bases {A, C, G, U} that interact via base pairings to determine higher order structure and functionality. The RNA folding problem is to predict one or more secondary RNA structures from a given primary sequence of bases. From a mathematical modeling perspective

In this paper, we describe a matheuristic to solve the stochastic facility location problem which determines the location and size of storage facilities, the quantities of various types of supplies stored in each facility, and the assignment of demand locations to the open facilities, which minimize unmet demand and response time in lexicographic order. We assume uncertainties about demands, inventory

A general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through the improved use of large neighbourhood search heuristics within mixed-integer programming solvers. While mixed-integer programming solvers are endowed with an array of large neighbourhood search heuristics, few, if any, have been designed for BD. Further, typically the use of large neighbourhood search heuristics

In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic algorithm (R-NSGA-II) was proposed for such problem-solving tasks. R-NSGA-II aims to finding Pareto-optimal points close, in the sense of Euclidean distance in the objective space, to the

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results

Motivated by the celebrated paper of Hooker (J Heuristics 1(1): 33–42, 1995) published in the first issue of this journal, and by the relative lack of progress of both approximation algorithms and fixed-parameter algorithms for the classical decision and optimization problems related to covering edges by vertices, we aimed at developing an approach centered in augmenting our intuition about what is

Proximity search is an iterative method to solve complex mathematical programming problems. At each iteration, the objective function of the problem at hand is replaced by the Hamming distance function to a given solution, and a cutoff constraint is added to impose that any new obtained solution improves the objective function value. A mixed integer programming solver is used to find a feasible solution

Finding short and convenient routes for vehicles is an important issue on efficient operations of Automated Guided Vehicle (AGV) systems at container terminals. This paper proposes an anisotropic Q-learning method for AGVs to find the shortest-time routes in the guide-path network of cross-lane type according to real-time vehicle states, which includes current and destination positions, heading direction

In this paper, we propose a heuristic search algorithm based on maximum conflicts to find a weakly stable matching of maximum size for the stable marriage problem with ties and incomplete lists. The key idea of our approach is to define a heuristic function based on the information extracted from undominated blocking pairs from the men’s point of view. By choosing a man corresponding to the maximum

In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations

This paper presents an iterated local search (ILS) algorithm for the single machine total weighted tardiness batch scheduling problem. To our knowledge, this is one of the first attempts to apply ILS to solve a batching scheduling problem. The proposed algorithm contains a local search procedure that explores five neighborhood structures, and we show how to efficiently implement them. Moreover, we

In the just-in-time job-shop scheduling (JIT–JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT–JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm

We propose a solution approach for stochastic network design problems with uncertain demands. We investigate how to efficiently use reduced cost information as a means of guiding variable fixing to define a restriction that reduces the complexity of solving the stochastic model without sacrificing the quality of the solution obtained. We then propose a matheuristic approach that iteratively defines

The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121

In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the

This paper proposes a reactive VNS metaheuristic for the maximum intersection of k-subsets problem (kMIS). The kMIS is defined as: Given a set of elements, a subset family of the first set and an integer k. The problem consists of finding k subset so that the intersection is maximum. Our VNS metaheuristic incorporates strategies used in GRASP metaheuristics, such as the GRASP construction phase and

Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of 5 years, and few restrictions with time window constraints

The speed by which the COVID-19 pandemic spread throughout the world caught some national and local governments unprepared. Healthcare systems found themselves struggling to increase capacity and procure key supplies, such as personal protective equipment. Protective face shields became essential for healthcare professionals. However, most hospitals and healthcare facilities did not have them in adequate

The original version of this article has unfortunately contained an error in the Acknowledgement section.

This paper considers a generalized version of the planar storage location problem arising in the stowage planning for Roll-on/Roll-off ships. A ship is set to sail along a predefined voyage where given cargoes are to be transported between different port pairs along the voyage. We aim at determining the optimal stowage plan for the vehicles stored on a deck of the ship so that the time spent moving

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger

Consider a rooted directed graph G with a subset of vertices called terminals, where each arc has a positive integer capacity and a non-negative cost. For a given positive integer k, we say that G is k-survivable if every of its subgraphs obtained by removing at most k arcs admits a feasible flow that routes one unit of flow from the root to every terminal. We aim at determining a k-survivable subgraph

The weather has a major impact on the profitability, safety, and environmental sustainability of the routes sailed by seagoing vessels. The prevailing weather strongly influences the course of routes, affecting not only the safety of the crew, but also the fuel consumption and therefore the emissions of the vessel. Effective decision support is required to plan the route and the speed of the vessel

Directed graphs are widely used to model data flow and execution dependencies in streaming applications. This enables the utilization of graph partitioning algorithms for the problem of parallelizing execution on multiprocessor architectures under hardware resource constraints. However due to program memory restrictions in embedded multiprocessor systems, applications need to be divided into parts

This paper proposes a local search algorithm for a specific combinatorial optimisation problem in graph theory: the Hamiltonian completion problem (HCP) on undirected graphs. In this problem, the objective is to add as few edges as possible to a given undirected graph in order to obtain a Hamiltonian graph. This problem has mainly been studied in the context of various specific kinds of undirected

We present several techniques that use distances between candidate solutions to achieve intensification in Local Search (LS) algorithms. An important drawback of classical LS is that after visiting a very high-quality solution the search process can “forget about it” and continue towards very different areas. We propose a method that works on top of a given LS to equip it with a form of memory so as

When resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or

The shift minimization personnel task scheduling problem is an NP-complete optimization problem that concerns the assignment of tasks to multi-skilled employees with a view to minimize the total number of assigned employees. Recent literature indicates that hybrid methods which combine exact and heuristic techniques such as matheuristics are efficient as regards to generating high quality solutions

This paper addresses a special bin packing problem in which each item can only be assigned to a subset of the bins. We name this problem as the restricted bin packing problem (RBPP). This paper is designed to explore the relationships of RBPP with classic NP-complete problems, and to resolve the restrictions of assignment through heuristic and meta-heuristic algorithms. A new heuristic algorithm named

This work considers the one-dimensional cutting stock problem in which the non-used material in the cutting patterns may be used in the future, if large enough. We show that a multiobjective criteria to classify the solutions could be more accurate than previous classifications attempts, also we give a heuristic algorithm and two meta-heuristic approaches to the problem and we use them to solve practical

Pareto-based evolutionary multiobjective approaches are methods that use the Pareto dominance concept to guide the search of evolutionary algorithms towards the Pareto frontier of a problem. To address the challenge of providing an entire set of optimal solutions they use specially designed mechanisms for preserving search diversity and maintaining the non-dominated solutions set. The limitation of

This paper deals with robust optimization applied to network flows. We consider two robust variants of the minimum-cost integer flow problem. Thereby, uncertainty in problem formulation is limited to arc costs and expressed by a finite set of explicitly given scenarios. It turns out that both problem variants are NP-hard. To solve the considered variants, we propose several heuristics based on local

The problem of finding a shortest curvature-constrained closed path through a set of targets in the plane is known as Dubins traveling salesman problem (DTSP). Applications of the DTSP include motion planning for kinematically constrained mobile robots and unmanned fixed-wing aerial vehicles. The difficulty of the DTSP is to simultaneously find an order of the targets and suitable headings (orientation

Service network design problems are used to address a variety of services in transportation and logistics planning. In the present paper, we consider the service network design problem with design-balanced requirements. This problem is particularly relevant to operations for consolidation transportation systems and determines the transportation network configuration and the characteristics of the corresponding

Variable ordering heuristics play a central role in solving constraint satisfaction problems. Combining two variable ordering heuristics may generate a more efficient heuristic, such as dom/deg. In this paper, we propose a novel method for combining two variable ordering heuristics, namely Pearson-Correlation-Coefficient-based Combination (PCCC). While the existing combination strategies always combine

This study proposes a strategic linkage method with mathematical model, simulation model and heuristic approaches to an adaptive workforce scheduling problem reflecting the work site situation of the air cargo terminal. For the application of the proposed method, we first generate an initial workforce schedule using the optimization model for minimizing labor costs. The simulation model is then used

The Hunter Valley coal export supply chain in New South Wales, Australia, is of great importance to the Australian economy. Effectively managing its logistics, however, is challenging, because it is a complex system, covering a large geographic area and comprising a rail network, three coal terminals, and a port, and has many stakeholders, e.g., mining companies, port authorities, coal terminal operators

The minimum sitting arrangement (MinSA) problem is a linear layout problem consisting in minimizing the number of errors produced when a signed graph is embedded into a line. This problem has been previously tackled by theoretical and heuristic approaches in the literature. In this paper we present a basic variable neighborhood search (BVNS) algorithm for solving the problem. First, we introduce a

The convex hull heuristic is a heuristic for mixed-integer programming problems with a nonlinear objective function and linear constraints. It is a matheuristic in two ways: it is based on the mathematical programming algorithm called simplicial decomposition, or SD (von Hohenbalken in Math Program 13:49–68, 1977), and at each iteration, one solves a mixed-integer programming problem with a linear

This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends

We consider the two-machine total completion time flow shop problem with additional requirements. These requirements are the so-called no-idle constraint where the machines must operate with no inserted idle time and the so-called no-wait constraint where jobs cannot wait between the end of an operation and the start of the following one. We propose a matheuristic approach that uses an ILP formulation

We consider a wireless network where a given set of stations is continuously generating information. A single vehicle, located at a base station, is available to collect the information via wireless transfer. The wireless transfer vehicle routing problem (WTVRP) is to decide which stations should be visited in the vehicle route, how long shall the vehicle stay in each station, and how much information

The problem of maximizing the lifetime of a wireless sensor network which uses video cameras to monitor targets is considered. These video cameras can rotate and have a fixed monitoring angle. For a target to be covered by a video camera mounted on a sensor node, three conditions must be satisfied. First, the distance between the sensor and the target should be less than the sensing range. Second,

A large number of application problems involve two levels of optimization, where one optimization task is nested inside the other. These problems are known as bilevel optimization problems and have been studied by both classical optimization community and evolutionary optimization community. Most of the solution procedures proposed until now are either computationally very expensive or applicable to

At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood

Hybrid flow shops can be encountered in various industrial settings. In this paper we develop methods for scheduling hybrid flow shops with hard time windows. Specifically, we study a two-stage hybrid flow shop scheduling problem with time windows to minimize the total weighted completion times. Each stage consists of one or more identical parallel machines, and each job visits two processing stages

In this paper, we propose an extension of the vehicle routing problem with cross-docking that takes into account resource constraints at the cross-dock. These constraints limit the number of docks that can be used simultaneously. To solve this new problem, we adapt a recently proposed matheuristic based on large neighborhood search. In particular, we focus on the feasibility tests for insertions and

The problem of reconfiguration in electrical power distribution systems deals with changes in the network topology using maneuvers switches. This is an optimization problem where one of the goals is to minimize losses following constraints such as faults isolation, load feeders balancing and voltage profile improvement. The present paper solves such problem by introducing a mixed-integer quadratic

In this paper, a coordinated production scheduling and vehicle routing problem aiming at minimizing the sum of order delivery times is considered, where there are a single machine for production and limited number of homogenous capacitated vehicles for transportation. Given the complexity of the studied problem, a variable neighborhood search (VNS) algorithm is proposed to address this problem. To

OFDMA is a popular coding scheme for mobile wireless communications. In OFDMA, one must allocate the available resources (bandwidth and power) dynamically, as user requests arrive and depart in a stochastic manner. Several exact and heuristic methods exist to do this, but they all perform poorly in the “over-loaded” case, in which the user demand is close to or exceeds the system capacity. To address

The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space. An empirical analysis on publicly available benchmark problems demonstrate

A methodology is introduced which uses three simple objective function features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objective functions using these features. Information on prior performance of various control parameters for each classification is then used to determine which control parameters to use in

Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules

Optimisation methods are widely used in complex data analysis, and as such, there is a need to develop techniques that can explore huge search spaces in an efficient and effective manner. Generalised simulated annealing is a continuous optimisation method which is an advanced version of the commonly used simulated annealing technique. The method is designed to search for the global optimum solution

Heuristically accelerated reinforcement learning (HARL) is a new family of algorithms that combines the advantages of reinforcement learning (RL) with the advantages of heuristic algorithms. To achieve this, the action selection strategy of the standard RL algorithm is modified to take into account a heuristic running in parallel with the RL process. This paper presents two approximated HARL algorithms

In this paper, a heuristic method for the optimal placement of monitors in communication networks is proposed. In order to be able to make informed decisions, a first step towards securing a communication network is deploying an adequate sensor infrastructure. However, appropriate monitoring should take into account the priority of the communication links as well as the location of monitors. The goal

Complex networks are known to be vulnerable to the failure of components in terms of structural robustness. An as yet less researched topic is dynamical robustness, which refers to the ability of a network to maintain its dynamical activity against local disturbances. This paper introduces a new type of attack—the overload attack—to disturb the network’s dynamical activity. The attack is based on the