We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided

In this article, we use the hypervolume indicator as a scalarizing function for biobjective combinatorial optimization problems. In particular, we describe a generic solution approach that determines the nondominated set of a biobjective optimization problem by solving a sequence of hypervolume scalarizations with appropriate choices of the reference point. Moreover, this solution technique can also

Personalized tourist route planning (TRP) and navigation are online or real-time applications whose mathematical modeling leads to complex optimization problems. These problems are usually formulated with mathematical programming and can be described as NP hard problems. Moreover, the state-of-the-art (SOA) path search algorithms do not perform efficiently in solving multi-objective optimization (MO)

The complexity of the socio-economic environment requires the involvement of a group of decision makers in the decision-making process. They can contribute the diversity of knowledge and experience that allow all the important aspects of the decision-making problem to be taken into account. In the aggregation process, the weights of the decision makers play an important role. In this paper we have

We study a chemical tanker scheduling problem in which a multi-compartment chemical tanker picks up chemicals (within specific time windows) and delivers them to their destinations. Additional safety and ship balancing requirements related to the compartment storage need to be met. We refer to this problem as the single ship pick-up and delivery problem with pick-up time windows, tank allocations and

NEH heuristic remains one of the most efficient heuristics for permutation flowshop scheduling problems (PFSSP). While scheduling, it breaks the ties randomly. Several tie-breaking mechanisms have been proposed to improve the makespan. In this paper, three simple idle times based tie-breaking mechanisms are proposed and analysed using Taillard and Vallada-Framinan-Ruiz (VFR) benchmark problems. The

The slot allocation mechanism aims to match flight demand and airport resources from a strategic perspective. However, current research mainly focused on airlines' interests, ignoring the influencing factors that lead to primary delays, which makes the temporal and spatial distribution of flight schedules unreasonable. This paper proposes a data-driven approach to reduce operational delays at a strategic

Most existing facility location and network design models assume the facility cost as either a fixed cost term or a linear/concave function of the volume of demand assigned to a facility. In particular, a concave cost function implies decreasing marginal cost and exhibits economies of scale. However, when the assigned demand volume exceeds a certain level, diseconomies of scale may occur due to facility

Given a group of individuals, each one with a single skill, and a social network capturing the mutual affinity among them, the Multiple Team Formation Problem (MTFP) consists in finding a set of teams, as harmonious as possible, each one comprising a given number of required skills. Dedication time of individuals can be partitioned into time fractions, thus allowing an individual to work in more than

In this paper, we will shed light on when to pack and use 3D-printers in disaster response operations. For that, we introduce a new type of problem, which we call the two-stage stochastic 3D-printing knapsack problem. We provide a two-stage stochastic programming formulation for this problem, for which both the first and the second stage are NP-hard integer linear programs. We reformulate this formulation

The rapid growth of e-commerce has led to an increase of home delivery requests. Providing efficient distribution systems for services on the last mile has become a challenging issue for logistics companies, where a trade-off between the classical approaches, attended home delivery (AHD) and usage of shared delivery locations (SDLs) has been identified. AHD provides a higher quality of service but

This paper studies the Aquaculture Service Vessel Routing Problem (ASVRP), which is an important planning problem arising in sea-based fish farming. In the ASVRP, there is a set of fish farms located in the sea, where each fish farm has one or more service tasks to be performed by a given heterogeneous fleet of service vessels with different capabilities. Some service tasks require simultaneous operation

It is well-known that the demand ratefor some products depends on several factors, such as price, time, and stock, among others. Moreover, the holding cost can varyover time. More specifically, it increases with time since a long period of storage requires more expensive warehouse facilities. This research introduces an inventory model with shortages for a singleproduct wherethe demand ratedepends

Robust scheduling for target tracking with a wireless sensor network (WSN), focuses on the deployment of a WSN in a remote area to monitor a set of moving targets. Each sensor operates on a battery and is able to communicate with reachable sensors in the network. The targets are typically moving vehicles (planes, trains, cars,…) passing through the area. In order to monitor the targets, an activation

We study a single-machine scheduling problem with two competing agents. The objective function of one agent is minimizing total late work, whereas the maximum cost of the jobs of the second agent cannot exceed a given upper bound. We introduce and test a pseudo-polynomial dynamic programming algorithm for this NP-hard problem. Our tests indicate that the algorithm can handle large-size instances (containing

Even though order picking is the most costly operation in a warehouse, current design practices have used the same principles (straight rows with parallel pick aisles and perpendicular cross aisles) to reduce travel distances between pick locations for more than sixty years. We present an open-source computational software system for facilitating the design of warehouse layouts to near-optimality considering

Mixed-model assembly lines enable a flow-oriented mass production of individualized products such as cars. The most investigated operational decision problems in this domain are the sequencing problem, which plans the initial sequence of cars launched down the line, and the resequencing problem, which alters a given car sequence by applying resequencing buffers. This dichotomy of existing research

The aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers. The aim of this paper is to present a focused review on the most relevant techniques in the recent literature

The capacitated lot-sizing and machine scheduling problem with sequence-dependent setup time and setup carryover is a challenging problem with a wide application in industries. For the problem, two mixed integer programming models are proposed in order to explore their relative efficiencies in obtaining optimal solutions and linear programming relaxation lower bounds. Furthermore, due to the fact that

A bi-objective branch-and-bound and an ∊-constraint algorithms are proposed for the unit-time job shop scheduling problem. These two objectives are the minimization of the makespan and the total completion time. We model this problem as a bi-objective mixed graph coloring using both the chromatic number and the sum of path-endpoints coloring (i.e. sum of the colors assigned to the endpoints of maximal

By considering the market competition among retailers, the object of this study is to measure the bullwhip effect in a two-stage supply chain with one supplier and multiple retailers. A model is detailed for measuring the bullwhip effect in which multiple retailers exhibit AR(1) demand processes and the degree of market competition is captured with copula. Through the model developed in this paper

We propose a Simulated Annealing approach for the Examination Timetabling problem, in the classical uncapacitated formulation of Carter et al. (1996). Our solver is based on a novel combination of many neighborhoods and a principled tuning procedure performed on artificial training instances. The experimental results on real-world benchmarks show that our solver is able to improve upon all state-of-the-art

This study proposes a new uncertain risk index model with background risk and presents its deterministic equivalents. The security returns and background asset returns are assumed as uncertain variables and estimated by experts. To discuss the influence of background risk on investment decisions, we compare the proposed model with a variant without background risk and find that the portfolio with background

With recent emergence of machine learning problems with massive number of features, feature selection (FS) has become an ever-increasingly important tool to mitigate the effects of the so-called curse of dimensionality. FS aims to eliminate redundant and irrelevant features for models that are faster to train, easier to understand, and less prone to overfitting. This study presents a wrapper FS method

We present a location–allocation problem for wastewater treatment in a cluster of processing units. The problem involves installing wastewater treatment plants of appropriate capacities at suitable locations and allocating processing units to these plants. The operational cost of treatment plants is a nonconvex function of wastewater volume and pollutant concentration. This problem is formulated as

With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic scenario of a smart manufacturing system, which concerns the production scheduling of complex multi-level products under a dynamic flexible job shop environment with shop floor disruptions incorporated. The products are assembled from multiple

We consider an uncapacitated location problem where p facilities have to be located in order to serve a given set of customers, and we assume that a customer requesting for a service has to reach a facility at his/her own cost. In this setting, a central issue is that of fairness among customers for the accessibility to the services provided. Every choice regarding the location of facilities corresponds

Semi-supervised or constrained graph clustering incorporates prior information in order to improve clustering results. Pairwise constraints are often utilized to guide the clustering process. This work addresses a constrained graph clustering problem in biological networks where (1) subgraph connectivity constraints are strictly required to be satisfied and (2) clustering quality is assessed with respect

We propose an exact algorithm for solving biobjective integer programming problems, which arise in various applications of operations research. The algorithm is based on solving Pascoletti-Serafini scalarizations to search specified regions (boxes) in the objective space and returns the set of nondominated points. We implement the algorithm with different strategies, where the choices of the scalarization

Inspired by real life maintenance operations, we introduce a vehicle maintenance scheduling problem in an urban light rail line which considers service level agreement (SLA), preventive maintenance cycles and corrective jobs on a single dead-end track. We show that the problem is strongly NP-Hard. Experts in real life make use of a heuristic. Yet, the heuristic calls vehicles in their preventive maintenance

This study investigates a multi-project, multi-period, multi-factory and multi-objective supplier selection problem with project life cycles and demand uncertainty. For this problem, we establish a multi-stage mixed integer stochastic programming model with three objectives (i.e., the procurement cost, number of quality defects and delayed delivery quantity). An adapted chaos optimization algorithm

The Minimum Gap Graph Partitioning Problem (MGGPP) consists in partitioning a vertex-weighted undirected graph into a given number of connected subgraphs with the minimum difference between the largest and the smallest weight in each subgraph. We propose a two-level Tabu Search algorithm and an Adaptive Large Neighborhood Search algorithm to solve the MGGPP in reasonable time on instances with up to

The use of electric buses is expected to rise due to its environmental benefits. However, electric vehicles are less flexible than conventional diesel buses due to their limited driving range and longer recharging times. Therefore, scheduling electric vehicles adds further operational difficulties. Additionally, various labor regulations challenge public transport companies to find a cost-efficient

We present a value function-based formulation for solving a two-stage robust optimization model for surgery-to-OR allocation planning where only upper and lower bounds on surgery duration are known. We solve this formulation using a constraint and column generation algorithm and interpret it as an iterative bi-level interdiction game. We derive valid inequalities based in this interpretation and compare

Appointment scheduling has many applications (e.g., surgery scheduling, airport gate scheduling, container vessel dockings and radiation therapy bookings) and it has a direct and significant operational and economic impact. For example, in healthcare, surgical departments are one of the main drivers of hospital costs and revenue, and appointment scheduling is used to book surgeries. Effective scheduling

This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural

The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of multiple machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the

This paper deals with a real manufacturing scheduling problem that is particularly encountered in the tannery industries. This problem often integrates employee timetabling and production scheduling. The employee timetabling problem is addressed in the context of skill requirements and under availability and legislative constraints. The production scheduling is considered as a re-entrant hybrid job-shop

In this study, we address the capacitated stochastic lot-sizing problem under α service level constraints. We assume that processing times can be decreased in return for compression cost that follows a convex function. We consider this problem under the static uncertainty strategy suggesting to determine replenishment plans at the beginning of the planning horizon. We develop an extended mixed integer

Cross-docking is a logistic strategy which can increase rates of consolidation, and reduce distribution and storage costs and delivery times. The optimization literature on cross-docking has mostly focused on the modeling and solution of problems considering a single cross-docking facility. Networks with multiple cross-docks remain rather unexplored and the few papers that deal with the problem do

In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances

In this paper, a scheduling problem on non-identical parallel machines with auxiliary resources and sequence-dependent and machine-dependent setup times is studied. This problem can be found in various manufacturing contexts, and in particular in workshops of wafer manufacturing facilities. Three different criteria are defined and analyzed: The number of products completed before the end of a given

In this paper, a new version of the aircraft maintenance routing problem, which is an important component of the airline planning process, is studied. We define the big-cycle aircraft maintenance routing problem on a connection network and formulate it as an asymmetric travelling salesman problem with fleet size and maintenance violation constraints, which takes maintenance resource availability into

In the problem of extracting maximal items/objects from three-dimensional materials, we are given a piece of solid material and an object with predefined ratios. The goal is to extract the largest possible convex or non-convex object from the solid material. There are two variants of the problem: the general case where the solid material is non-convex and its special case consisting of a convex solid

The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor length objectives. Within each floor, departments are arranged in two parallel rows on opposite sides along a central corridor without overlapping. In this study, the bDFCAP is formulated

We consider a special case of resource leveling problem with variable job duration and period-aggregated resource consumption. The objective function is to minimize the total extra resource cost required to complete all jobs before the hard project deadline. All jobs must be implemented without any preemption. The planning horizon is divided into periods, each period is characterized by an available

Multi-agent systems are generally applicable in a wide diversity of domains, such as robot engineering, computer science, the military, and smart cities. In this paper, we introduce a mobile multi-agent sensing problem and present a mathematical formulation. The model can be represented as a submodular maximization problem under a partition matroid constraint, which is NP-hard in general. The optimal

Permutation flow shop scheduling problem with makespan criterion is known to be NP-hard if the number of machines is greater than two. The most known polynomial complexity algorithm for solving PFSP is the constructive NEH heuristic. In this paper we propose the N-NEH+ algorithm which extends the classical NEH heuristic and which is, to our best knowledge, the first deterministic constructive algorithm

A problem of scheduling n unit-time jobs with a small number of distinct release dates and deadlines, on identical parallel machines, to minimize the number of active machines is studied. The feature that makes this problem challenging is that no machine can stand idle between its start and completion times. A number of properties of this problem is established, and heuristic and optimal algorithms

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associated threshold. This greedy approach trains trees very fast, but, by its nature, their classification accuracy may not be competitive against other state-of-the-art

We consider the Boolean Quadratic Programming problem with Generalized Upper Bound constraints (BQP-GUB). This problem belongs to the NP-hard complexity class of problems and is a generalization of the Quadratic Semi-Assignment Problem (QSAP), which has applications in different areas such as production planning and image segmentation. Non-exact methods for solving BQP-GUB instances are commonly based

In this paper, we propose an approximate dynamic programming (ADP) algorithm to solve a Markov decision process (MDP) formulation for the admission control of elective patients. To manage the elective patients from multiple specialties equitably and efficiently, we establish a waiting list and assign each patient a time-dependent dynamic priority score. Then, taking the random arrivals of patients

We study a multi-stage appointment scheduling problem with limited distributional information of stochastic service times, and formulate the distributionally robust (DR) model. In our studied problem, only the means and supports of service times are known to the planner, and the planner has to determine a job allowance for each appointment in the first stage. The objective is to minimize the maximum

In this article, we propose an extension of the multi-Weber facility location problem with rectilinear-distance in the presence of passages over a non-horizontal line barrier. For the single-facility case, we develop an exact heuristic based on a divide-and-conquer approach that outperforms alternative heuristics available in literature. The multiple facilities case is solved by means of the application

Clustering addresses the problem of assigning similar objects to groups. Since the size of the clusters is often constrained in practical clustering applications, various capacitated clustering problems have received increasing attention. We consider here the capacitated p-median problem (CPMP) in which p objects are selected as cluster centers (medians) such that the total distance from these medians

The bin packing problem is one of the most studied combinatorial optimization problem. This paper proposes two novel bin packing problem settings with many practical applications, in particular in logistics capacity planning. Both problems explicitly consider, besides the classical bin-selection costs, the item and bin-specific item-to-bin assignment costs. These assignment costs depend not only on

In this paper we analyze a continuous version of the maximal covering location problem, in which the facilities are required to be linked by means of a given graph structure (provided that two facilities are allowed to be linked if a given distance is not exceed). We propose a mathematical programming framework for the problem and different resolution strategies. First, we provide a Mixed Integer Non

Course timetables are often rendered infeasible due to unexpected changes in requirements and must be repaired. Given an initial timetable, planners prefer a repaired timetable whose quality is worsened as little as possible while its structural difference is also as little as possible. This paper formulates this problem as one of minimizing the quality degradation subject to an upper limit on the

Additive manufacturing – also known as 3D printing – is a manufacturing process that is attracting more and more interest due to high production rates and reduced costs. This paper focuses on the scheduling problem of multiple additive manufacturing machines, recently proposed in the scientific literature. Given its intractability, instances of relevant size of additive manufacturing (AM) machine scheduling

We consider the Inventory Routing Problem (IRP) with one vendor replenishing the inventories of many retailers who face stochastic demands. To hedge against demand uncertainty, dynamic lot-sizing and safety stock planning are integrated using chance-constrained programming to adapt to the varying demand uncertainty across planning periods and allow for variable replenishment periods. We present a tactical