Duration Constraints Research Articles

Cost optimization of project schedules under constrained resources and alternative production processes by mixed-integer nonlinear programming

PurposeThe purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).Design/methodology/approachThe model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation.FindingsThe proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost.Practical implicationsA set of application examples are addressed on an actual construction project to display the advantages of proposed model.Originality/valueThe unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure.

From Real-time Logic to Timed Automata

We show how to construct temporal testers for the logic MITL, a prominent linear-time logic for real-time systems. A temporal tester is a transducer that inputs a signal holding the Boolean value of atomic propositions and outputs the truth value of a formula along time. Here we consider testers over continuous-time Boolean signals that use clock variables to enforce duration constraints, as in timed automata. We first rewrite the MITL formula into a “simple” formula using a limited set of temporal modalities. We then build testers for these specific modalities and show how to compose testers for simple formulae into complex ones. Temporal testers can be turned into acceptors, yielding a compositional translation from MITL to timed automata. This construction is much simpler than previously known and remains asymptotically optimal. It supports both past and future operators and can easily be extended.

A modular approach to the specification and management of time duration constraints in BPMN

The modeling and management of business processes deals with temporal aspects both in the inherent representation of activity coordination and in the specification of activity properties and constraints. In this paper, we address the modeling and specification of constraints related to the duration of process activities. In detail, we consider the Business Process Model and Notation (BPMN) standard and propose an approach to define re-usable duration-aware process models that make use of existing BPMN elements for representing different nuances of activity duration at design time. Moreover, we show how advanced event-handling techniques may be exploited for detecting the violation of duration constraints during the process run-time. The set of process models specified in this paper suitably captures duration constraints at different levels of abstraction, by allowing designers to specify the duration of atomic tasks and of selected process regions in a way that is conceptually and semantically BPMN-compliant. Without loss of generality, we refer to real-world clinical working environments to exemplify our approach, as their intrinsic complexity makes them a particularly challenging and rewarding application environment.

Short-Term Load Dispatching Method for a Diversion Hydropower Plant with Multiple Turbines in One Tunnel Using a Two-Stage Model

Short-term load dispatching (STLD) for a hydropower plant with multiple turbines in one tunnel (HPMTT) refers to determining when to startup or shutdown the units of different tunnels and scheduling the online units of each tunnel to obtain optimal load dispatch while simultaneously meeting the hydraulic and electric system constraints. Modeling and solving the STLD for a HPMTT is extremely difficult due to mutual interference between units and complications of the hydraulic head calculation. Considering the complexity of the hydraulic connections between multiple power units in one tunnel, a two-phase decomposition approach for subproblems of unit-commit (UC) and optimal load dispatch (OLD) is described and a two-stage model (TSM) is adopted in this paper. In the first stage, an on/off model for the units considering duration constraints is established, and the on/off status of the units and tunnels is determined using a heuristic searching method and a progressive optimal algorithm. In the second stage, a load distribution model is established and solved using dynamic programming for optimal load distribution under the premise of the on/off status of the tunnel and units in the first stage. The proposed method is verified using the load distribution problem for the Tianshengqiao-II reservoir (TSQII) in dry season under different typical load rates. The results meet the practical operation requirements and demonstrate the practicability of the proposed method.

Distribution with Quality of Service Considerations: The Capacitated Routing Problem with Profits and Service Level Requirements

Inspired by a problem arising in cash logistics, we propose the Capacitated Routing Problem with Profits and Service Level Requirements (CRPPSLR). The CRPPSLR extends the class of Routing Problems with Profits by considering customers requesting deliveries to their (possibly multiple) service points. Moreover, each customer imposes a service level requirement specifying a minimum-acceptable bound on the fraction of its service points being delivered. A customer-specific financial penalty is incurred by the logistics service provider when this requirement is not met. The CRPPSLR consists in finding vehicle routes maximizing the difference between the collected revenues and the incurred transportation and penalty costs in such a way that vehicle capacity and route duration constraints are met. A fleet of homogeneous vehicles is available for serving the customers. We design a branch-and-cut algorithm and evaluate the usefulness of valid inequalities that have been effectively used for the capacitated vehicle routing problem and, more recently, for other routing problems with profits. A real-life case study taken from the cash supply chain in the Netherlands highlights the relevance of the problem under consideration. Computational results illustrate the performance of the proposed solution approach under different input parameter settings for the synthetic instances. For instances of real-life problems, we distinguish between coin and banknote distribution, as vehicle capacities only matter when considering the former. Finally, we report on the effectiveness of the valid inequalities in closing the optimality gap at the root node for both the synthetic and the real-life instances and conclude with a sensitivity analysis on the most significant input parameters of our model.

Double-blind holography of attosecond pulses

A key challenge in attosecond science is the temporal characterization of attosecond pulses that are essential for understanding the evolution of electronic wavefunctions in atoms, molecules and solids1–7. Current characterization methods, based on nonlinear light–matter interactions, are limited in terms of stability and waveform complexity. Here, we experimentally demonstrate a conceptually new linear and all-optical pulse characterization method, inspired by double-blind holography. Holography is realized by measuring the extreme ultraviolet (XUV) spectra of two unknown attosecond signals and their interference. Assuming a finite pulse duration constraint, we reconstruct the missing spectral phases and characterize the unknown signals in both isolated pulse and double pulse scenarios. This method can be implemented in a wide range of experimental realizations, enabling the study of complex electron dynamics via a single-shot and linear measurement. Double-blind holography allows reconstruction of the missing spectral phases and characterization of the unknown signals in both isolated-pulse and double-pulse scenarios, facilitating the study of complex electron dynamics via a single-shot and linear measurement.

A Review of Ultrahigh Temperature Metamorphism

Ultrahigh-temperature (UHT) metamorphism represents extreme crustal metamorphism with peak metamorphic temperatures exceeding 900 oC and pressures ranging from 7 to 13 kbar with or without partial melting of crusts, which is usually identified in the granulite-facies rocks. UHT rocks are recognized in all major continents related to both extensional and compressive tectonic environments. UHT metamorphism spans different geological ages from Archean to Phanerozoic, providing information of the nature, petrofabric and thermal evolution of crusts. UHT metamorphism is traditionally identified by the presence of a diagnostic mineral assemblage with an appropriate bulk composition and oxidation state in Mg-Al-rich metapelite rocks. Unconventional geothermobarometers including Ti-in-zircon (TIZ) and Zr-in-rutile (ZIR) thermometers and phase equilibria modeling are increasingly being used to estimate UHT metamorphism. Concentrated on the issues about UHT metamorphism, this review presents the research history about UHT metamorphism, the global distribution of UHT rocks, the current methods for constraints on the UHT metamorphism, and the heat sources and tectonic settings of UHT metamorphism. Some key issues and prospects about the study of UHT metamorphism are discussed, e.g., identification of UHT metamorphism for non-supracrustal rocks, robustness of the unconventional geothermometers, tectonic affinity of UHT metamorphic rocks, and methods for the constraints of age and duration of UHT metamorphism. It is concluded that UHT metamorphism is of great importance to the understanding of thermal evolution of the lithosphere.

Bond Portfolio Optimization in the Presence of Duration Constraints

Although there exists an abundant literature on the benefits and limits of scientific diversification in the equity universe, little is known about the out-of-sample performance of portfolio optimization models in the fixed-income universe. In this article, the authors address two key challenges that are specific to bond portfolio optimization, namely, the presence of duration constraints and the presence of no-arbitrage restrictions on risk parameter estimates, for which no equivalent exists in the equity universe. In an application to sovereign bonds in the eurozone, they find that the use of portfolio optimization techniques based on robust estimators for risk parameters generates an improvement in investor welfare compared with the use of ad hoc bond benchmarks such as equally weighted or cap-weighted portfolios. These results are robust with respect to changes in the number of constituents in the portfolio and the rebalancing period, and in the presence of duration or weight constraints. <b>TOPICS:</b>Fixed income and structured finance, fixed-income portfolio management, statistical methods

Articulatory dynamics of (de)gemination in Dutch

So-called ‘fake’ or derived geminates differ in the extent to which they behave like lexical geminates, both across and within languages. Data that enable us to study the gestural as well as the durational properties of fake geminates can shed light on their status as long consonants or clusters, and on the effects of any degemination rules present in the language. Our focus is on Standard Dutch, which has been said to exhibit categorical degemination of derived identical consonant clusters. The specific interest is in the post-lexical gemination of /r/, which involves gesturally strongly distinct allophones in the language, such that the members of /r#r/ sequences are identical phonologically, but not phonetically. Ultrasound data from 8 speakers of Standard Dutch show that fake geminate /r#r/ sequences present combined articulatory characteristics of coda (bunched approximant) and onset (uvular fricative or alveolar tap) allophones, which can be captured in terms of gestural overlap or ambisyllabicity. At the same time, however, concatenated /r#r/ does not show an increased duration relative to singleton /r/. These results suggest that Dutch degemination does not involve categorical segmental deletion, as previously suggested, but instead it has constraints on phonetic consonant duration that limit temporal gemination. Such specific duration constraints have a more general role to play in any account of gemination, as accumulating evidence suggests that fake geminates at word boundaries tend to behave as consonant clusters, subject to constraints on degree of overlap.

Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.

A fast heuristic for large-scale capacitated arc routing problems

The purpose of this paper is to develop a fast heuristic called FastCARP for the solution of large-scale capacitated arc routing problems, with or without duration constraints. This study is motivated by a waste collection problem in Denmark. After a preprocessing phase, FastCARP creates a giant tour, partitions the graph into districts, and construct routes within each district. It then iteratively merges and splits adjacent districts and reoptimises the routes. The heuristic was tested on 264 benchmark instances containing up to 11,640 nodes, 12,675 edges, 8581 required edges, and 323 vehicles. FastCARP was compared with an alternative heuristic called Base and with several Path-Scanning algorithms. On small graphs, it was better but slower than Base. On larger graphs, it was much faster and only slightly worse than Base in terms of solution quality. It also outperforms the Path-Scanning algorithms.

Intertemporal Segmentation Via Flexible-Duration Group Buying

(1) Problem definition: We study a special form of group buying: the group buying succeeds only if the number of sign-ups reaches a preset threshold, with no duration constraint. Customers with heterogeneous valuations arrive sequentially and decide between signing up for the group buying or purchasing a regular product. To decide on whether to join the group buying, customers need to anticipate their expected waiting time, which varies depending on the cumulative sign-ups by the time of their arrival. The firm decides on the prices of the group-buying product and regular product, with the product quality levels and group-buying size exogenously determined. (2) Academic/Practical relevance: Such type of group buying is often adopted for a special edition of the product and offered alongside a constantly available regular product. (3) Methodology: We study the product line design with the group-buying sign-up behavior of customers characterized by the rational expectations equilibrium in a random pledging process. (4) Results: We show that group buying with flexible duration can result in intertemporal customer segmentation, as different segments might be admitted at different times in the dynamic sign-up process. Such intertemporal segmentation is a natural discrimination scheme and has non-trivial implications. First, the efficiency loss due to waiting for enough sign-ups may decrease in the batch size required for economic production. Second, as valuation heterogeneity in the market increases, the firm may not always benefit from offering group buying along with the regular product. Third, group buying can achieve a win-win-win situation for both high-end and low-end customers as well as the firm. (5) Managerial implications: In addition to demonstrating the profitability of offering flexible-duration group buying, the firm can strengthen its profitability by contingently setting prices or concealing sign-up information in group buying. We also confirm the robustness of our main insights by considering customers’ heterogeneous patience levels, and horizontally differentiated products, among others.

A task scheduling method for agent/activity-based models

Estimation of spatio-temporal travel demand requires accurate activity schedules as an input along with a mechanism to adapt the schedules to changing travel options. Individuals are assumed to own a duty list of activities to be accomplished within the simulated period. A partial order based on chronological and functional constraints determines the set of feasible activity execution sequences (plans). Trip and activity timing is determined by schedule prediction and adaptation. Event times in a schedule are constrained by conditions involving time-of-day (absolute time) and by duration constraints (relative time). Both types of constraints are expressed using time deviation functions (TDF). Each start and end event in a schedule induces a set of non-linear equations expressing the absolute and relative constraints. Time values are determined by solving the set of non-linear equations using a relaxation method. A discrepancy evaluation function is used both as a criterion to decide convergence of the relaxation and to compare alternative schedules for a given plan.

Medical missions for the provision of paediatric cardiac surgery in low- and middle-income countries.

This review will outline the role of visiting cardiac surgical teams in low- and middle-income countries drawing on the collective experience of the authors in a wide range of locations. Requests for assistance can emerge from local programmes at a beginner or advanced stage. However, in all circumstances, careful pre-trip planning is necessary in conjunction with clinical and non-clinical local partners. The clinical evaluation, surgical procedures, and postoperative care all serve as a template for collaboration and education between the visiting and local teams in every aspect of care. Education focusses on both common and patient-specific issues. Case selection must appropriately balance the clinical priorities, safety, and educational objectives within the time constraints of trip duration. Considerable communication and practical challenges will present, and clinicians may need to make significant adjustments to their usual practice in order to function effectively in a resource-limited, unfamiliar, and multilingual environment. The effectiveness of visiting trips should be measured and constantly evaluated. Local and visiting teams should use data-driven evaluations of measurable outcomes and critical qualitative evaluation to repeatedly re-assess their interim goals. Progress invariably takes several years to achieve the final goal: an autonomous self-governing, self-financed, cardiac programme capable of providing care for children with complex CHD. This outcome is consistent with redundancy for the visiting trips model at the site, although fraternal, professional, and academic links will invariably remain for many years.

Multipass Target Search in Natural Environments

Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as -admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time.

School Bus Routing with Stochastic Demand and Duration Constraints

The school bus routing problem (SBRP) is crucial because of its impact on economic and social objectives. A single bus is assigned to each route, picking up students and arriving at the school within a specified time window. SBRP aims to find the fewest buses needed to cover all of the routes while minimizing the total travel distance and meeting required constraints. We propose a mathematical formulation responding to the overbooking policies applied in a real-world school district. According to our empirical studies, the probability of a student riding a bus varies from 22% to 77%, opening the opportunity to overbook the buses to improve utilization of their capacity. However, SBRP with overbooking has not attracted much attention in previous studies. In this work, overbooking is modeled via chance constrained programming. Additionally, to account for the uncertainty of the total travel time of the buses, a constraint limiting the probability of being late to school is also proposed in this paper. As a result of the NP-hard nature of the problem, a cascade simplification algorithm is proposed to partition the multiple stage SBRP problems into multiple multi-depot and one-school subproblems that are solved sequentially, where the results for one are data inputs for the next. Furthermore, we develop column-generation-based algorithms to solve the scheduling problem, and different instances of the problem are examined. Our computational experiments on a real-world school district demonstrate desirable cost savings in terms of the total number of buses used.

An Exact Algorithm for the Green Vehicle Routing Problem

We propose an exact algorithm for solving the green vehicle routing problem (G-VRP). The G-VRP models the optimal routing of an alternative fuel vehicle fleet to serve a set of geographically scattered customers. Vehicles’ fuel autonomy and possible refueling stops en route are explicitly modeled and maximum duration constraints are imposed on each vehicle route. We model the G-VRP as a set partitioning problem in which columns represent feasible routes corresponding to simple circuits in a multigraph. Each node in the multigraph represents one customer and each arc between two customers represents a nondominated path through a set of refueling stations visited by a vehicle when traveling directly between the two customers. We strengthen the set partitioning formulation by adding valid inequalities including k-path cuts and describe a method for separating them. We provide computational results on benchmark instances showing that the algorithm can optimally solve instances with up to ∼110 customers. The online appendix is available at https://doi.org/10.1287/trsc.2016.0734 .

Estimation of baboon daily travel distances by means of point sampling - the magnitude of underestimation.

Daily travel distance (DTD), the distance an animal moves over the course of the day, is an important metric in movement ecology. It provides data with which to test hypotheses related to energetics and behaviour, e.g. impact of group size or food distribution on DTDs. The automated tracking of movements by applying GPS technology has become widely available and easy to implement. However, due to battery duration constraints, it is necessary to select a tracking-time resolution, which inevitably introduces an underestimation of the true underlying path distance. Here we give a quantification of this inherent systematic underestimation of DTDs for a terrestrial primate, the Guinea baboon. We show that sampling protocols with interval lengths from 1 to 120 min underestimate DTDs on average by 7 to 35 %. For longer time intervals (i.e. 60, 90, 120 min), the relative increase of deviation from the “true” trajectory is less pronounced than for shorter intervals. Our study provides first hints on the magnitude of error, which can be applied as a corrective when estimating absolute DTDs in calculations on travelling costs in terrestrial primates.

Multichannel Deconvolution for Earthquake Apparent Source Time Functions

Abstract Previous studies of earthquake apparent source time functions (ASTFs) removed propagation effects through seismogram deconvolution with a smaller earthquake known as an empirical Green’s function (EGF). We develop a multichannel deconvolution (MCD) algorithm for recovering ASTFs that does not require an EGF, but instead the availability of two or more earthquakes that share a common Green’s function. Under this condition, ASTFs satisfy U i * S j − U j * S i =0, in which U i and S i are the seismogram and ASTF for a given earthquake. This system can be augmented with a scaling equation and written as Ax = b , in which matrix A comprises the seismograms in a block‐Toeplitz structure and x contains the target ASTFs. We minimize an objective function for this linear system with a Newton‐projection algorithm that honors positivity, causality, and duration constraints. If the earthquakes have a suitable range in magnitude, EGF deconvolution may be used to estimate differences in the duration of the events and to obtain a starting model for the larger ASTF(s). We demonstrate the effectiveness of MCD using synthetic tests and apply it to seven M w ∼5 earthquakes from the Kamaishi sequence, Japan, related to the 2011 Tohoku‐Oki M w 9 event. We demonstrate that MCD is an effective way to recover earthquake ASTFs and that the details of rupture revealed by MCD ASTFs will be useful in furthering our understanding of the earthquake source. Electronic Supplement: Figures of seismograms and multichannel deconvolution (MCD) apparent source time functions (ASTFs) from the Kamaishi earthquake sequence.

A Special Vehicle Routing Problem Arising in the Optimization of Waste Disposal: A Real Case

We address a particular pickup and delivery vehicle routing problem arising in the collection and disposal of bulky recyclable waste. Containers of different types, used to collect different waste materials, once full, must be picked up to be emptied at suitable disposal plants and replaced by empty containers alike. All requests must be served, and routes are subject to a maximum duration constraint. Minimizing the number of vehicles is the main objective, while minimizing the total route duration is a secondary objective. The problem belongs to the class of rollon–rolloff vehicle routing problems (RR-VRPs), though some characteristics of the case study, such as the free circulation of containers and the limited availability of spare containers, allow us to exploit them in the solution approach. We formalize the problem as a special vehicle routing problem on a bipartite graph, we analyze its structure, and we compare it to similar problems emphasizing the impact of limited spare containers. Moreover, we propose a neighborhood-based metaheuristic that alternatively switches from one objective to the other along the search path and periodically destroys and rebuilds parts of the solution. The main algorithm components are experimentally evaluated on real and realistic instances, the largest of which fail to be solved by a mixed-integer linear programming solver. We are increasingly competitive with the solver as the instance size increases, especially regarding fleet size. In addition, the algorithm is applied to the benchmark instances for the RR-VRP.