The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). To fulfill a mission, the NRF operates a number of technical systems, mostly vehicles. Each system is composed of several parts which might fail over time, and it can only be used again in the mission if all broken parts are replaced. For short deployments (e.g., one

This article gives closed-form analytic expressions as well as a computational analysis of the stationary system-length distribution for the renewal-input, bulk-arrival, and multi-server continuous-time queueing model. The service times are equal to the constant D for any customer. The queueing model may be denoted as GIX/D/c queue. Using the steady-state equations, the system-length probability generating

This study addresses a multi-period product-line-mix problem considering product interdependencies and uncertainties regarding price, demand, production cost, and the cannibalization effect of new products. The problem is modeled as a two-stage stochastic program. In the first-stage, decisions for the release times of new product-lines and capacity expansion are made without waiting for the realization

This paper proposes a production planning approach for a job shop type manufacturing company in electronics sector that operates with make-to-order (MTO) convention and has a high-mix production range. A novel Mixed Integer Linear Programming (MILP) model is proposed that finds workload-dependent planning horizon by making order acceptance decisions. Partial acceptance of orders and delayed internal

The personalized crew rostering problem (CRP) consists of assigning pairings (sequences of flights, deadheads, connections, and rests, forming one or several days of work) to individual crew members to create a feasible roster that maximizes crew satisfaction. This problem is often solved using a branch-and-price algorithm. In this paper, we propose a partial pricing scheme for the CRP in which the

Stockouts of repairable spares usually lead to significant downtime costs. Managers of Maintenance Repair Organizations (MROs) seek advance indicators of future stockouts which might allow them to take proactive actions that are beneficial for achieving target service levels with reasonable costs. Among such (proactive) actions, the most common, and the cheapest one is expediting existing repair processes

This paper deals with the no-wait multiproduct multistage product scheduling problem (NWMMSP) with minimum makespan and maximum mean customer’s satisfaction level criteria. The mean customer’s satisfaction level criterion related to flexible due date is introduced to approach an imprecise or flexible nature of the data in a practical manufacturing environment. A novel discrete multi-objective evolutionary

We address an integrated airline scheduling problem that combines three airline planning processes: fleet assignment, aircraft routing, and crew pairing. For a given daily flight schedule, the problem requires simultaneously assigning aircraft and crews to each scheduled flight, taking into account aircraft maintenance restrictions and crew work rules. We propose to solve this complex problem of integrated

Order picking (OP) is a time and cost consuming operation in many warehouses. The optimization of order picking planning is crucial to speed up customers’ delivery process and reduce warehouse expenses. A commonly adopted strategy is called mixed-shelves storage strategy (MSSS) that is highly recommended for e-commerce warehouses. Applying this, the units of the same stock keeping unit (SKU) are scattered

Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the

This paper proposes an iteratively doubling local search approach for the two-dimensional irregular bin packing problem (2DIRBPP) with limited rotations, whose objective is to pack a given set of irregular pieces into the minimum number of rectangular bins. The allowable angles of each piece are limited. To solve this problem, a waste least first decreasing strategy is introduced to assign the piece

This paper introduces discrete production systems where units are produced one at a time. These systems exist in industry but to the best of our knowledge, they have not been addressed in the open literature. Manufacturing of large iron castings and special precast panels are examples of such production systems. We consider the case where the production process is imperfect resulting in non-conforming

Minimizing queue waiting time in multi-class multi-server systems, where the service time depends both on the job type and the server type, has wide applications in transportation systems such as emergency networks and taxi networks, service systems such as call centers, and distributed computing platforms. However, the optimal dynamic policy for this problem is not known and remains a hard open problem

The manufacturing of oil extraction pipes involves several steps of pipe processing in a sequence of machines. The pipes are attached to reels that are moved between the machines by cranes. As the physical space and the reach of the cranes are limited, the motion must follow specific paths, which must comply with several movement constraints. A poorly designed reel movement plan will require a long

In general, data sets are growing larger and larger, and handling related issues is topic of big data. Similar trends and tendencies are evident in data envelopment analysis (DEA). DEA is a well-known instrument for determining the efficiencies of decision-making units (DMUs), applying linear programming. Still, as we will show, DEA suffers notably from the curse of dimensionality. Therefore, we propose

Disasters can disrupt road networks by blocking some of the roads and consequently impeding accessibility to critical locations. In the immediate post-disaster response phase, while the blockage information is yet to be collected, relief distribution crews (RDCs) should dispatch from warehouses to supply critical locations with first aid items. The RDCs are not capable of unblocking damaged roads and

We introduce a learning-based algorithm to solve the drone routing problem with recharging stops that arises in many applications such as precision agriculture, search and rescue, and military surveillance. The heuristic algorithm, namely Learn and Fly (L&F), learns from the features of high-quality solutions to optimize recharging visits, starting from a given Hamiltonian tour that ignores the recharging

Elective surgery patients have surgery in the operating room (OR), and then recover in one or more downstream recovery units for several consecutive hours or days after surgery. Upstream scheduling that focuses on OR alone or a resource-constrained scheduling approach that fails to account for the inherent uncertainty in surgery durations and postoperative downstream recovery times yield sub-optimal

The Maritime Inventory Routing Problem (MIRP) has gained attention in combinatorial optimization in the last decades. The problem has several variations, mostly originated from real-world problems. This work presents a literature review of the solution approaches proposed for different MIRP variations, focusing on matheuristic methods that combine heuristic and optimal approaches and are frequently

We study a class of single-objective non-linear optimization problems, the so-called Integer Linear Generalized Maximum Multiplicative Programs (IL-GMMP). This class of optimization problems has a significant number of applications in different fields of study including but not limited to game theory, systems reliability, and conservation planning. An IL-GMMP can be reformulated as a mixed integer

A new “rich” variation on the multi-objective vehicle routing problem (VRP), called the multi-tiered vehicle routing problem with global cross-docking (MTVRPGC), is introduced in this paper. With respect to previously studied VRPs, the MVRPTGC includes the following novel features: (i) segregation of facilities into different tiers that distinguish them in terms of different processing and storage

Cross-docking is a strategy to facilitate a persistent process from suppliers to the consumer points, without long-term storage of products at a distribution warehouse. Products are collected from various origins by inbound trucks, unloaded to the cross-dock, reconsolidated with other products, and finally loaded onto outbound trucks within the same or next day. Because of the limited number of dock-doors

Since the battery remains a significant cost component of electric vehicles (EVs), controlling the depreciation costs of EVs’ batteries is of great importance, especially from the perspective of the electric vehicle routing problem (EVRP). However, most existing studies on the EVRP have not explicitly considered the battery depreciation in the cost function; instead, it has been treated as a linear

The real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and timeliness guarantees can be achieved by time-triggered communication schedules that are enabled by the IEEE 802.1Qbv standard. However, generating such a schedule is a computationally

This paper considers an integrated supplier selection and inventory control problem for a multi-echelon inventory system with an order-splitting policy. A buyer firm consisting of one warehouse and N identical retailers procures a type of product from a group of potential suppliers; the acquisition of the warehouse takes place when the inventory level depletes to a reorder point R, and the order Q

Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research—unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first design a new flexible non-interpolating parsimonious surrogate model using a partitioning-based

In this paper, we investigate vehicle routing problems with third-party transshipment facilities that arise in the context of city logistics. Contrary to classical vehicle routing problems, where each customer request is delivered directly to its destination, the problems considered in this paper feature the alternative possibility of delivering customer requests to third-party transshipment facilities

This paper studies a scheduling problem in which serial and parallel batching decisions must be simultaneously taken. There is a single machine with a fixed capacity that can process multiple jobs at the same time (i.e., parallel batching). Throughout the planning horizon, jobs are released at different instants and they belong to different families. Each job requires one unit of the machine capacity

In this paper, a hybrid metaheuristic is developed to solve the smart waste collection problem with workload concerns. It is composed by: (i) a look-ahead heuristic aiming at deciding the days in which collection is necessary and which bins need to be collected (must-go) considering the present bin fill levels and future bin fill level predictions; and (ii) a simulated annealing/neighborhood search

We consider the problem of designing vehicle routes in a distribution system that are at the same time cost-effective and visually attractive. In this paper we argue that clustering, a popular data mining task, provides a good proxy for visual attractiveness. Our claim is supported by the proposal of a bi-objective capacitated vehicle routing problem in which, in addition to seek for traveling cost

Over the years, methods and algorithms have been extensively studied to solve variations of the rectangular two-dimensional strip packing problem (2D-SPP), in which small rectangles must be packed inside a larger object denominated as a strip, while minimizing the space necessary to pack all rectangles. In the rectangular 2D-SPP, constraints are used to restrict the packing process, satisfying physical

Recent trends towards larger and more complex systems necessitate the use of heterogeneous and flexible automated guided vehicles (AGVs) to fulfill the transport demand within a factory. To operate the fleet of AGVs efficiently, it is also important to consider their limited battery capacity. In this context, we tackle the problem of scheduling transport requests on multi-load and multi-ability AGVs

This paper introduces the transit time constrained fixed charge multi-commodity network design problem. Transit times are origin-to-destination time limits for the commodities, which appear for example in transport systems with perishable goods. We discuss how to model the problem and present three different formulations of it. The first formulation is an exponential size path formulation, which we

This paper aims to propose an adaptive nonmonotone line search for direction-based multiobjective optimization (MO) algorithms. All direction-based MO algorithms can be equipped with this line search, especially the BFGS quasi-Newton algorithm is applied in this study. To validate the proposed line search some well-known line searches, including Armijo, maximum nonmonotone and average nonmonotone line

Dynamic routing occurs when customers are not known in advance, e.g. for real-time routing. Two heuristics are proposed that solve the balanced dynamic multiple travelling salesmen problem (BD-mTSP). These heuristics represent operational (tactical) tools for dynamic (online, real-time) routing. Several types and scopes of dynamics are proposed. Particular attention is given to sequential dynamics

The support vector machine (SVM) model is one of the most well-known machine learning models, which is based on the structural risk minimization (SRM) principle. The SRM principle, formulated by Vapnik in a statistical learning theory framework, can be naturally expressed as an Ivanov regularization-based SVM (I-SVM). Recent advances in learning theory clearly show that I-SVM allows a more effective

When solving hard multicommodity network flow problems using an LP-based approach, the number of commodities is a driving factor in the speed at which the LP can be solved, as it is linear in the number of constraints and variables. The conventional approach to improve the solve time of the LP relaxation of a Mixed Integer Programming (MIP) model that encodes such an instance is to aggregate all commodities

This paper treats a replenishment problem where an order in a single period is to be determined. Demand is assumed to be known for multiple periods. The first periods’ demands are required to be satisfied by the order, satisfying remaining demand is optional. Furthermore, a threshold needs to be satisfied, e.g., a minimum total order volume is given and orders can be placed only if they reach (or exceed)

Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from

This paper addresses the optimization of routing problems with drones. It analyzes the coordination of one mothership with one drone to obtain optimal routes that have to visit some target objects modeled as general graphs. The goal is to minimize the overall weighted distance traveled by both vehicles while satisfying the requirements in terms of percentages of visits to targets. We discuss different

The Automatic Scene Detection Problem (ASDP) is a combinatorial optimization problem that arises in the context of video processing and that has a central role in the management, storing and content retrieval of videos. The problem consists of partitioning the shots of a given video into scenes by optimizing a measure related to the similarity between the given shots. In this article, we build up upon

This paper addresses the customer order scheduling problem in parallel production environment commonly appearing in the pharmaceutical and paper industries. The problem aims to minimize the total completion time of the orders with their jobs processed on dedicated machines in parallel. To deal with the computational challenge of large-scale problems, we propose a learning-based two-stage optimization

This paper deals with a multi-period extension of the p-center problem, in which arc traversal times vary over time, and facilities are mobile units that can be relocated multiple times during the planning horizon. The problem arises in several applications, such as emergency services, in which vehicles can be relocated in anticipation of traffic congestion. First, we analyze the problem structure

The berth allocation problem (BAP) has been extensively studied in the literature, which seeks to determine berths and times for upcoming ships. The previous studies do not distinguish berths and ships among different liner carriers. By taking into account the increasing use of dedicated berths in practice, this paper proposes a special BAP where berths and ships could be distinguished among different

The design of a distribution network under uncertainty is addressed by using the minmax regret paradigm. Economic or technical uncertain factors may have a strong influence on the behavior of a distribution system and should be taken into account in the design process. These factors can affect to the construction and operating costs, origin or destination of the supplied commodities, required demands

We study the Multiple Allocation Tree of Hubs Location Problem where a tree topology is required among the hubs and transportation cost of sending flows between OD pairs is minimized. Unlike most studies in the literature that assume a complete network with costs satisfying the triangle inequality to formulate the problem, we define the problem on non-complete networks and develop a modeling approach

This work focuses on firms that sell perishable products to risk-averse strategic consumers over two periods: the full-price period and the clearance period. The risk-aversion level of strategic consumers affects their expected utility in both periods, which will determine their purchasing behaviour. Typically, firms implement either quantity commitment (QC), price commitment (PC) or rational-expectation

With the advent of automated vehicles (AVs), new infrastructure planning concepts such as subnetworks of AV-ready roads have been proposed to improve the performance of transportation networks and to promote the adoption of AVs. However, these subnetworks should evolve over time in response to the growing AV demand, which necessitates a multi-stage modeling approach. In this study, we propose multi-stage

We consider the problem of jointly optimizing train stop plan and timetable for a set of trains on a railway corridor, with focus on time-dependent passenger demand: these are very relevant elements in railway operations, and closely related to the service quality provided to the passengers. The train stop planning problem requires to determine the stopping pattern for each train, while the train timetabling

Portfolio optimization has long been used in asset management to mitigate risks of fluctuating asset prices. In this study, we use copula models and portfolio optimization to investigate how inventory financing providers (IFP) can utilize the timely market information of collaterals to optimize their portfolios of collaterals to mitigate default risks. Through comparing the predictive performance of

In this study, we consider an integrated production and transportation scheduling problem faced by make-to-order manufacturers that adopt a commit-to-delivery business mode and cooperate with third-party logistics providers to deliver processed products to customers. The third-party logistics providers typically offer multiple shipping modes chosen by the manufacturers to deliver products, each shipping

The (environmental) selection procedure in Evolutionary Multiobjective Optimization Algorithms (EMOAs) can be interpreted as a subset selection problem, where the goal is to determine a subset of a given size that maximizes a quality indicator. The hypervolume indicator possesses desirable theoretical properties (e.g. monotonicity properties) that make it well-suited for indicator-based selection,

Oil and gas companies are facing low output prices and are forced to focus on the development of mature fields. Relevant investment decisions for operators include lifetime-enhancing activities, such as drilling new wells or permanent shutdown. We study the problem of optimal timing of investments in mature oil and gas fields in the presence of price uncertainty, which is an example of a complex real

Field service operation is a theoretically complex problem. Numerous tasks at various locations need to be assigned to service staff. Skills, capabilities, and the level of local knowledge have to be considered to estimate the time required to complete a task by a group of selected technicians. Multiple objectives involve cost, task priorities, and consumer satisfaction. The cost includes the salary

The disjunctively constrained knapsack problem consists in packing a subset of pairwisely compatible items in a capacity-constrained knapsack such that the total profit of the selected items is maximized while satisfying the knapsack capacity. DCKP has numerous applications and is however computationally challenging (NP-hard). In this work, we present a threshold search based memetic algorithm for

In this work, we consider a recycling facility location-routing planning problem with feedstock volume and composition uncertainty. A salient feature of the problem is that the feedstock condition (volume and composition) exhibits both ambiguity and ‘unobservability’. That is, the distribution of (future) feedstock condition cannot be known at the planning stage, which is regarded as ambiguity or ‘here-and-now

This work proposes a partial optimization metaheuristic under special intensification conditions (POPMUSIC) for the classical capacitated vehicle routing problem. The proposed approach uses a branch-cut-and-price algorithm as a powerful heuristic to solve subproblems whose dimensions are typically between 25 and 200 customers. The whole algorithm can be seen as the application of local search over