## Center for Advanced Studies in Management (CASiM)

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This paper addresses the order- and rack-sequencing problem at a single picking station in the context of robotic mobile fulfillment systems, a warehouse technology typically applied in large distribution centers. Following the parts-to-picker concept, items are stored on movable racks that are lifted and transported by automated guided vehicles from the storage area to picking stations for order-processing. The order-picking process involves two linked decisions: How to sequence the processing of orders and how to sequence the rack visits to supply the picking station with the requested items. We present a novel mixed-integer linear programming formulation achieving stronger linear programming bounds than a previous formulation. Including preprocessing techniques it quickly solves instances of medium-size to proven optimality for the first time in literature. For large real-world instances, we provide a three-stage heuristic solution procedure suitable in a dynamic environment, while providing competitive solutions within a short run time. Computational experiments on a broad set of benchmark instances and a comparative study with approaches from literature verify our results.

The scale of freight forwarding to the hinterland becomes an issue from the perspective of both – transport policy and cost efficiency of service providers. This problem is sharply visible in areas where ports, depots, inland intermodal terminals, exporters and importers are located, and full and empty containers satisfying demand and supply are frequently distributed creating a lot of traffic. Therefore solutions meeting the challenges of sustainable transport, responding to climate change and regulation of CO2 emissions are in need. In this paper, a variant of a Mixed Fleet Heterogeneous Dial-a-Ride Problem is proposed for optimal routing of trucks carrying full and empty 20-foot and 40-foot containers, with multiple pick-ups and deliveries. Transportation is performed by alternatively fueled vehicles (AFVs) for environmental reasons which causes a constraint of a limited driving range and a need of refueling. The main objective is minimising the total distance subject to matching the empty container demand and supply, necessary refueling of the trucks, and service time windows.

A single machine scheduling problem with assignable job due dates to minimize total late work has recently been introduced by Mosheiov, Oron, and Shabtay (2021). The problem was proved NP-hard in the ordinary sense, and no solution algorithm was proposed. In this note, we present two pseudo-polynomial dynamic programming algorithms and an FPTAS for this problem. Besides, we introduce a new single machine scheduling problem to minimize maximum late work of jobs with assignable due dates. We develop an O(n log n) time algorithm for it, where is the number of jobs. An optimal solution value of this new problem is a lower bound for the optimal value of the total late work minimization problem, and it is used in the FPTAS.

Constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google’s OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice.

This special issue publishes contributions from the operations research (OR) community in the following areas and at the intersections of those areas, namely manufacturing and supply chain digitalization, resilience, and sustainability. The application areas of OR and analytics to digital, resilient, and sustainable manufacturing systems may contain descriptive and diagnostic analyses, predictive simulation and prescriptive optimization, real time control, and adaptive learning. Examples of OR and analytics applications include logistics and supply chain control with real-time data, inventory control and management using sensing data, dynamic resource allocation in Industry 4.0 customized assembly systems, improving forecasting models using big data, machine learning techniques for process control, network visibility and risk control, optimizing systems based on predictive information (e.g., predictive maintenance), combining optimization and machine learning algorithms, and supply chain risk analytics.

We study the scheduling problem with calibrations and time slot costs. In this problem, the machine has to be calibrated to run a job and such a calibration only remains valid for a fixed time period of length T, after which it must be recalibrated in order to execute jobs. On the other hand, a certain cost will be incurred when the machine executes a job and such a cost is determined by the time slot that is occupied by the job in the schedule. We consider jobs with release times, deadlines and identical processing times. The objective is to schedule the jobs on a single machine and minimize the total cost while calibrating the machine at most K times.
We investigate the structure of the optimal schedule and based on that we propose dynamic programs for different scenarios of the problem. At last, for another variant of the problem without the consideration of machine calibration, a greedy algorithm is proposed, which is based on matroid theory.

The intensity of local truck container transport results from the ubiquitous development of container shipping. Optimal routing of container trucks contributes to cost savings of the service provider but also the reduction of traffic and detrimental emissions. In this paper, a variant of a Mixed Fleet Heterogeneous Dial-a-Ride Problem is proposed for a container truck routing problem. Our aim is an optimal routing of trucks carrying full and empty 20-foot and 40-foot containers, with multiple pick-ups and deliveries. Transportation is performed by alternatively fuelled vehicles (AFVs) for environmental reasons. The AFVs have a limited driving range and are allowed to refuel in any alternative fuel station. The main objective is minimising the total distance subject to matching the empty container demand and supply, necessary refuelling of the trucks, and service time windows.

One of the most known results in the machine scheduling is Lawler’s algorithm to minimize the maximum cost of jobs processed by a single machine subject to precedence constraints. We consider an uncertain version of the same min-max cost scheduling problem. The cost function of each job depends on the job completion time and on an additional generalized numerical parameter, which may be a tuple of parameters. For each job, both, its processing time and the additional parameter are uncertain, only intervals of possible values of these parameters are known. We analyse certain classes of cost functions and develop polynomial algorithms which construct min-max regret solutions. The considered problems cover the most general range of studied cases of interval uncertainty. In the only two papers that present algorithms for minimizing the maximum regret for the problem with uncertain job processing times, the algorithms are based on extremal scenarios, where some uncertain parameters take their maximum values, while all others take their minimum possible values. We show that it is impossible to always limit the search to extremal scenarios. Our approach is based on new ideas different from those underlying previous work. Finally, we show that our approach outperforms all known results for constructing min-max regret solutions for the min-max cost scheduling problem under uncertainty of job processing times.

The train-to-yard assignment problem (TYAP) pertains to freight consolidation in a large rail transshipment yard—also called a multiple yard—that consists of two sub-yards. Inbound and outbound trains need to be assigned to one or the other sub-yard in a way that minimizes the total railcar switching costs. Each inbound and outbound train is processed in one of the two sub-yards, and time-consuming maneuvers may be necessary for railcars that are supposed to be part of an outbound train leaving from the other sub-yard. A lower number of railcar reassignments between the sub-yards reduce train dwell times and avoid train delays that affect the whole rail network. We develop a matheuristic algorithm with a learning mechanism, which we call MuSt, as well as a branch-and-bound procedure that incorporates elements of constraint propagation. We examine the performance of the developed algorithms through extensive computational experiments. Effective optimization approaches for the TYAP have high practical significance since they may reduce the number of avoidable railcar reassignments, which are resource-blocking, traffic-generating, and expensive, by about 20% compared to current practice, as we illustrate in our computational experiments. Our branch-and-bound algorithm solves problem instances for small or medium railyards in less than a minute or within several hours run time, respectively. The heuristic procedure MuSt finds optimal or nearly optimal solutions within just a couple of minutes, even for large railyards.

In this paper, we work on the scheduling problem with active time model. We have a set of preemptive jobs with integral release times, deadlines and required processing lengths, while the preemption of jobs is only allowed at integral time points. We have a single machine that can process at most g distinct job units at any given time unit when the machine is switched on. The objective is to find a schedule that completes all jobs within their timing constraints and minimizes the time when the machine is on, i.e., the active time. This problem has been studied by Chang et al. where they proposed an LP rounding approach which gives a 2-approximation solution. In this paper, we also give a 2-approximation algorithm based on LP rounding approach with a different rounding technique and analysis. Finally, we give a new linear programming formulation for which we conjecture that the integrality gap is 5/3, which might bring new hope for beating the barrier of 2 for the approximation ratio.

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.

Der Kunde als Mitentwickler
(2018)

Im Rahmen der marktorientierten Unternehmensführung übernimmt der Kunde zunehmend eine aktive Rolle des Mitentwicklers von Produkten und Dienstleistungen. Hier bestehen bereits Konzepte der interaktiven Wertschöpfung, wie etwa Open Innovation und Mass Customization. Mit JOSEPHS® wird eine Plattform für Kundeninteraktionen vorgestellt. Es handelt sich um ein offenes Innovationslabor in Nürnberg, das sich als Intermediär zwischen Unternehmen und Kunden versteht. Hier werden durch verschiedene Akteure bereits in frühen Phasen des Entwicklungsprozesses Kunden und Nichtkunden einbezogen. Am Unternehmen Mifitto wird das Konzept erläutert und die Vorteile werden dargestellt.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones
(2018)

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or eironmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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 coentional 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 search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

We consider single-crane scheduling at rail transshipment yards, in which gantry cranes move containers between trains, trucks and a storage area. The single-crane scheduling problem arises at single-crane transshipment terminals and as a subproblem of the multiple-crane scheduling problem. We consider a makespan objective function, which is equivalent to minimizing the train dwell time in the yard, and introduce time windows for container moves, for example, as a customer service promise. Our proposed decomposition algorithm with integrated dynamic branch-and-cut or dynamic programming solves practically relevant instances within short time limits.

The makespan of operations at container terminals is crucial for the lead time of cargo and consequently the reduction of transportation costs. Therefore, an efficient transhipment and short storage of containers are demanded. Our paper refers to the consolidation process of trains in a container transhipment terminal as well as to the intermediate storage of containers in seaports in order to accelerate the loading and unloading of the vessels. It can also be encountered in automated storage/retrieval systems. Each of these (container) storage and retrieval moves corresponds to a crane operation, carrying a load from its pickup to its drop-off position. The problem is to find a permutation of the loaded crane moves that minimises the total empty crane travel time, which is the sum of times the crane needs to get from the last drop-off point of a load to the next pickup point of a load. We address the problem as an extension of an asymmetric travelling salesman problem (ATSP), assuming that n ordered pairs of points in the two-dimensional Euclidean space need to be traversed. Each point corresponds to a crane operation carrying a load from its pickup to its drop-off position. Despite that the problem seems to be easier than the ATSP, because a simple constant factor approximation exists, which was for a long time an open question for the ATSP, we are the first to prove that there is no polynomial-time approximation algorithm with an approximation guarantee less than 1+0.23/n unless P=NP.

Container transportation has become an important part of global transportation and it may gain a new potential in the light of the Chinese One Belt One Road (OBOR) initiative, known in Europe also as the New Silk Road. It iolves development of transport corridors – rail and maritime, linking China with Europe and it can be anticipated that its development will cause considerable shifts in container transportation from a sea into a rail (intermodal) route. In the light of substantial trade imbalance between Europe and China the problem of empty container repositioning gains specific importance. The aim of this study is the analysis of models and a variety of solutions to empty container repositioning problems through the prism of Eurasian intermodal transportation.

A parallel machine schedule updating game with compensations and clients averse to uncertain loss
(2019)

There is a finite number of non-cooperating clients, who are averse to uncertain loss and compete for execution of their jobs not later than by their respective due dates in a parallel service eironment. For each client, a due date violation implies a cost. In order to address the minimization of the total scheduling cost of all clients as a social criterion, a game mechanism is suggested. It is designed such that no client has an incentive to claim a false due date or cost. The game mechanism allows the clients to move their jobs to complete earlier in a given schedule. However, they must compensate costs of those clients whose jobs miss their due dates because of these moves. Algorithmic aspects are analyzed. Furthermore, that determines an equilibrium of the considered game is suggested and embedded into the game mechanism. Computational tests analyze the performance and practical suitability of the resulting game mechanism.