Precedence Relations Research Articles

Geminate blockage in Logoori harmony with no added machinery

Following the demise of the SPE theory of phonology, little attention was paid to how rules should be formulated. Instead, there was a general trend to minimize the language-specific component of a grammar, to factor out recurring patterns and state them as independent constraints or parameters. The richer representations of autose­gmental phonology additionally led to uncertainty in rule formulation, primarily because of questions about what the correct representations are, but also because specifying dominance and precedence relations is more complex than just specifying precedence relations.
 
 This paper discusses a vowel harmony rule in the Bantu language Logoori, which is blocked by an intervening gemi­nate consonant. It is typologically surprising that an intervening geminate has an effect on harmony, and there is no clear model for how such an effect in a rule should be encoded. It is shown that the effect follows naturally from correctly formalizing the rule, with attention to how rule and representational substrings are matched, and no rule-external conditions on the rule are necessary.

Industry 4.0, digitization, and opportunities for sustainability

The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0, is progressing exponentially. The digital revolution is reshaping the way individuals live and work fundamentally, and the public remains optimistic regarding the opportunities Industry 4.0 may offer for sustainability. The present study contributes to the sustainability literature by systematically identifying the sustainability functions of Industry 4.0. In doing so, the study first reviews the fundamental design principles and technology trends of Industry 4.0 and introduces the architectural design of Industry 4.0. The study further draws on the interpretive structural modelling technique to model the contextual relationships among the Industry 4.0 sustainability functions. Results indicate that sophisticated precedence relationships exist among various sustainability functions of Industry 4.0. ‘Matrice d’Impacts Croisés Multiplication Appliquée àun Classement’ (MICMAC) analysis reveals that economic sustainability functions such as production efficiency and business model innovation tend to be the more immediate outcome of Industry 4.0, which pays the way for development of more remote socioenvironmental sustainability functions of Industry 4.0 such as energy sustainability, harmful emission reduction, and social welfare improvement. This study can serve Industry 4.0 stakeholders – leaders in the public and private sectors, industrialists, and academicians – to better understand the opportunities that the digital revolution may offer for sustainability, and work together more closely to ensure that Industry 4.0 delivers the intended sustainability functions around the world as effectively, equally, and fairly as possible.

Drone-Based Material Transfer System in a Robotic Mobile Fulfillment Center

Deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has inspired this research. One conceivable scenario is to use the drone to transfer jobs in a robotic mobile fulfillment center. Each job might be characterized by origin, destination, time window, and precedence relation. In particular, a take-away conveyor is continuously pushing forward shipping cartons, wherein drones must pick up jobs and drop them off to the cartons within the time window since the cartons would be closed and labeled at the end of the conveyor. Furthermore, each job contains two subtransfers for drone: flight from drone’s current location to pickup location and flight to dropoff location. Two exact approaches are proposed—a mixed integer programming and a constraint programming—and tested for a real-time perspective. Note to Practitioners —Inspired by Amazon’s significant expansion of its army of warehouse robots and the adaptation of drones for final-mile small parcel delivery, this article has mixed those two emerging technologies and proposed a drone-powered material transfer system in a warehouse, wherein jobs would be picked up by drone, transferred, and dropped off directly to shipping cartons. The most encouraging finding is that the proposed method impressively proved the optimality of 144 jobs with 10 drones that requires 28.8 transfers per drone on average, within 1 s.

Intelligent generation method of 3D machining process based on process knowledge

ABSTRACTIntelligent manufacturing has become the trend of global manufacturing industry, and the core of the new industrial revolution and the commanding point of Made in China 2025. Process planning is an important part of intelligent manufacturing. To improve the speed and accuracy of intelligent generation of machining process, a systematic intelligent generation method of machining process based on process knowledge is proposed. Firstly, a method of modelling and management of process knowledge is presented. Secondly, by combining case-based reasoning with rule-based reasoning, accurate process information of machining features is obtained. Then, the skeleton process is formed. Thirdly, through the analysis of the precedence constraints and the processing experience constraints, the precedence relation between machining features is determined. Moreover, the precedence between process operations is formulated by process constraint matrix. Finally, a genetic algorithm (GA) approach is presented to obtain the global optimal process route for the part based on working step with process information. Taking the connecting rod and piston of marine diesel engine and the spindle box shell as the research object, the feasibility of this method is verified.

A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017

Assembly line balancing (ALB) deals with how tasks are assigned in an assembly line to reach the required production rate and to optimize goals. Tasks in assembly line are assigned in an ordered sequence of workstations, where constraints such as precedence relations of tasks are satisfied and performance indicators are optimized. Implementing ALB could bring about optimal or semi-optimal responses that reduce initiation costs and operational costs in production systems. The purpose of this study was to analyze the contents of research papers addressing ALB and to predict the direction of future studies concerned with the ALB problems and those of U-type ALB problems. As such, this study drew on a systematic approach to reviewing the papers dealing with ALB published within 1990–2017. The papers under investigation were selected from the Web of Science database and were analyzed with aid of related software based on their content, collaboration network, and citation analysis. Through this process, the top productive countries, institutions and authors were explored. Furthermore, cooperative content analysis, keyword frequency analysis, and co-occurrence (co-word) analysis were applied to explore the current hot topics, and predict future trend of studies. This study could considerably contribute to researchers’ perspective of the research topics in the field and of the gaps that demand further investigation.

Resource levelling problem in construction projects under neutrosophic environment

Planning and managing resources is one of the most important topics in project management science. Resource leveling is used for improving work efficiency and minimizing cost throughout the life of the project. Fuzzy resource leveling models assume only truth-membership functions to deal uncertainties conditions surrounded by the projects and their activities duration. In this paper, we consider the objective function of scheduling problem is to minimize the costs of daily resource fluctuations using the precedence relationships during the project completion time. We design a resource leveling model based on neutrosophic set to overcome the ambiguity caused by the real-world problems. In this model, trapezoidal neutrosophic numbers are used to estimate the activities durations. The crisp model for activities time is obtained by applying score and accuracy functions. A numerical example is developed to illustrate the validation of the proposed model in this study.

An efficient method for generating assembly precedence constraints on 3D models based on a block sequence structure

An efficient method is introduced for generating assembly precedence constraints on three-dimensional (3D) models based on a block sequence structure, with the purpose of enhancing the simulation quality of assembly precedence constraints and reducing the development cost of virtual assembly (VA)/virtual disassembly (VD) software. Firstly, we propose a block sequence structure to store and process the attribute data characterizing assembly precedence constraints and the reference variable of paths. Then the approaches to convert a block sequence to the next state for controlling the subsequent VA/VD operations are expounded. In the random-access memory, each assembly reference object is regarded as a node for distributed management of the precedence constraints of its paths. Therefore, the tedious preprocessing for each part model is not required. Meanwhile, the VA/VD process can be flexibly switched due to calculation and processing on the precedence relation data instead of separately setting and executing the assembly/disassembly sequences. The block sequence and state transition approaches can be programmed as the software development kit (SDK) providing parameter-input interfaces for different virtual simulation platforms, so the interactive VA/VD simulation can be efficiently developed without programming skills. For the practical application, we program and package an SDK for Unity3d and further evaluate its usability in aiding in the development of precedence constraints for VA/VD applications.

A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration

Uncertainty is one of the main parts of the project management environment that can strongly affect the project objectives and cause unpredictable delays. This study presents a multi-objective optimization approach for constructing resilient project schedules under resource constraints to cope with uncertain activity durations. In this paper, the concept of resilient project scheduling is defined to measure the ability of schedules to deal with duration disruption. Since the direct evaluation of resiliency is computationally complicated and time-consuming, a new surrogate resilience measure is introduced. The proposed resiliency criteria measure the floating of activities and the risks associated with the completion of the project. Furthermore, a new model based on a combination of time buffer and float allocation approach is developed. To extend existing project scheduling models with uncertainty, general precedence relationships between activities have been considered. To validate the proposed approach, the construction project of a combined cycle power plant is used as a case study. Due to a large number of project activities in this case study, the non-dominated sorting genetic algorithm (NSGA II) has been used to solve the problem. The results of solving the mathematical model using the proposed method are assessed through extensive simulation experiments and compared with those of the baseline schedule. The results show that by taking the proposed resiliency measure and the optimal allocation of buffer time to activities, the project completed at the same duration with higher reliability.

CPM and PERT techniques for small-scale R&D projects

Contemporary Project Management has conceived tools based on mathematical models for planning, scheduling and controlling the projects, the costs and resources. Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) are two network-based methods which were independently developed to assist the project managers in order to schedule complex real-life projects. A scheduling of a small-scale R&D project using these optimization time-oriented methods was accomplished. In many models of project network some activities are closely related to each other such as procurement activities of basic resources and research activities. If this precedence relationship is on the critical path, that means without no event slack or float time for activities, then some procurements delay may cause lag in the project’s completion time. Thus a good estimation for procurement and research activities duration is needed for a Just-in-Time project.

Iterated local search method and mathematical model for sequence-dependent U-shaped disassembly line balancing problem

Disassembly lines play an important role in disassembling end-of-life products and retrieving the valuable components. Sequence-dependent time increments are inevitable due to the interactions between two tasks in some occasions. This research provides the first attempt to study the sequence-dependent U-shaped disassembly line balancing problem (SUDLBP) with multiple objectives. A mixed-integer programming model is developed to solve the small-size instances optimally, where new expressions are developed to formulate the AND/OR precedence relations. Due to the NP-hard nature of this problem, a simple and effective iterated local search algorithm is developed for the first time. This algorithm utilizes modified NEH heuristic to achieve a high-quality initial solution. A new local search technic with referenced permutation and two neighbor structures is also embedded into it to obtain the local optimum solution. Computational tests demonstrate that the U-shaped lines outperform traditional straight lines in terms of the objective values. The comparative study on two sets of instances demonstrates that the proposed method outperforms the CPLEX solver in search speed and achieves a competing performance in comparison with other eight re-implemented algorithms.

Simple assembly line balancing problem of Type 1 with grey demand and grey task durations

PurposeThe purpose of this paper is to present a mixed integer programming model for simple assembly line balancing problems (SALBP) with Type 1 when the annual demand and task durations are uncertain and encoded with grey numbers.Design/methodology/approachGrey theory and grey numbers are used for illustrating the uncertainty of parameters in an SALBP, where the objective is to minimize the total number of workstations. The paper proposes a 0-1 mathematical model for SALBP of Type 1 with grey demand and grey task durations.FindingsThe uncertainty of the demand and task durations are encoded with grey numbers and a well-known 0-1 mathematical model for SALBP of Type 1 is modified to find the minimum number of workstations in order to meet both the lower and upper bounds of the uncertain demand. The results obtained from the proposed mathematical model show a task-workstation assignment that does not distribute precedence relations among tasks and workstations and the sum of task durations in each single workstation is less than or equal to the grey cycle time.Originality/valueThe grey theory and grey numbers have not been previously used to identify uncertainties in assembly line balancing problems. Therefore, this study provides an important contribution to the literature.

Multi-mode Resource Constrained Project Scheduling with Alternative Prerequisites: New Models and Computational Studies

We study the multi-mode resource constrained project scheduling problem (MRCPSP), which we generalize to account for the case of alternative prerequisite activities (MRCPSP-AP). The MRCPSP-AP arises in many real-world applications when two or more activities can serve as the required precursor to some subsequent activity, and it aims to determine not only the optimal schedule but also the optimal activity network. We propose a total of seven discrete-time models and compare their numerical tractability through comprehensive computational studies on literature benchmarks. We also extend the well-known critical path method to handle the existence of alternative prerequisites, allowing us to precalculate tight time windows for each activity. Our computational study reveals that a formulation implementing the generalized precedence relationships in a time-aggregated fashion dominates the rest, in terms of solution quality and runtime.

Disassembly sequence planning using a fast and effective precedence-based disassembly subset-generation method

Disassembly sequence planning plays a crucial role in the reuse and remanufacturing of end-of-life products. However, it is challenging to obtain optimal disassembly sequences as it is a difficult non-deterministic polynomial-time–hard combinatorial optimisation problem. This study proposes a precedence-based disassembly subset-generation method to solve the disassembly sequence-planning problem. The proposed method first generates disassembly subsets based on disassembly precedence relationships. Subsequently, multiple high-level feasible subsets are generated, and some of them are selected to generate higher level subsets using specially designed operators. After several iterations, different optimal disassembly sequences can be obtained. The principal merit of this method is that high-quality disassembly sequences can be generated in the solution construction process. Numerical experiments were conducted by applying the proposed method to three case studies, and the performance of the proposed method for finding optimal solutions and reducing computational time was evaluated in comparison with state-of-the-art methods. The parameters employed in this method were also analysed to explain the mechanism adopted.

Rock–paper–scissors dynamics from random walks on temporal multiplex networks

Abstract We study diffusion on a multiplex network where the contact dynamics between the nodes is governed by a random process and where the waiting-time distribution differs for edges from different layers. We study the impact on a random walk of the competition that naturally emerges between the edges of the different layers. In opposition to previous studies, which have imposed a priori inter-layer competition, the competition is here induced by the heterogeneity of the activity on the different layers. We first study the precedence relation between different edges and by extension between different layers, and show that it determines biased paths for the walker. We also discuss the emergence of cyclic, rock–paper–scissors effects on random walks, when the precedence between layers is non-transitive. Finally, we numerically show the slowing-down effect due to the competition on a multiplex network with heterogeneous layers activity as the walker is likely to be trapped for a longer time either on a single layer, or on an oriented cycle.

Tracing Software Development with UML—A Pragmatic Workflow

This paper proposes a software development tracing technique as a means for source of requirement verification. It is a pragmatic process since the methods employed in this research are well-known and practiced by software developers. The proposed technique helps trace any missing unimplemented or erroneously implemented requirements to insure their completeness. The underlying principles are governed by the proposed apriori algorithm and precedence relation that link all the cross-reference action items in the UML diagrams. These links are then recorded in a checklist that serves as the reference to requirement items and action sign-off confirmation. Thus, the contributions of this research work are two folds. First, the proposed tracing technique is simple, pragmatic, and inexpensive to learn and implement. Second, it helps reduce human errors caused by manual process that are often performed by different parties. The result statistics show that many errors incurred in the development process can be identified and rectified accordingly. Future work should focus on automating the proposed technique to be a development support tool for the benefits of software practitioners and technology.

A Genetic Algorithm with a New Local Search Method for Solving the Multimode Resource-constrained Project Scheduling Problem

The existence of limited resources and precedence relations between activities makes project scheduling a difficult task. In this paper, the multimode resource-constrained project scheduling problem (MRCPSP) as one of the important project scheduling problems is focused. In this regard, a genetic algorithm (GA) is proposed for solving the multimode resource-constrained project scheduling problem (MRCPSP) improved by a new local search method. The Activity Mode List (AML) is used as encoding and the multimode serial schedule generation scheme (MSSGS) is employed as the decoding procedure. The aim of the suggested local search is maximum use of existed nonrenewable resources of the project such that its makespan is decreased. For this purpose, the local search procedure allocates the unused nonrenewable resources of each feasible solution to the activities with lower total float. Comparing results of the proposed method with other ones using the set J20 in Project Scheduling Problem Library (PSPLIB) validates the effectiveness of the proposed algorithm to solve the MRCPSP.

Exact and heuristic approaches to detect failures in failed k-out-of-n systems

This paper considers a k-out-of-n system that has just failed. There is an associated cost of testing each component. In addition, we have apriori information regarding the probabilities that a certain set of components is the reason for the failure. The goal is to identify the subset of components that have caused the failure with the minimum expected cost. In this work, we provide exact and approximate policies that detects components’ states in a failed k-out-of-n system. We propose two integer programming (IP) formulations, two novel Markov decision process (MDP) based approaches, and two heuristic algorithms. We show the limitations of exact algorithms and effectiveness of proposed heuristic approaches on a set of randomly generated test instances. Despite longer CPU times, IP formulations are flexible in incorporating further restrictions such as test precedence relationships, if need be. Numerical results illustrate that dynamic programming for the proposed MDP model is the most effective exact method, solving up to 12 components within one hour. The heuristic algorithms’ performances are presented against exact approaches for small to medium sized instances and against a lower bound for larger instances.

Genetic algorithm based optimization for resource leveling problem with precedence constrained scheduling

Resource Leveling Problem (RLP) in construction projects mainly deals with reducing the peaks and valleys of daily resource requirements. RLP is an interesting field of research and several researchers have tried to solve it over the past few decades. Numerical methods and heuristics do not provide good results for solving Non-deterministic polynomial-hard (NP-hard) problems. So that, a metaheuristic approach is highly required to predict the solution with greater efficiency. This research work aims to solve RLP using Genetic Algorithm as the meta-heuristic approach, developed in a programming platform. Modified critical path scheduling method, was adopted to develop a schedule that determines the partial critical nature of activity with a start to start precedence relationship. Activities from a real-time construction project were considered to evaluate the proposed idea. The result obtained was significantly better than the results provided by existing commercial project management software and helps to solve RLP with the advantage of considering real-time floats.

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.

A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem

Abstract Value recovery from end-of-life products plays a key role in sustainability and circular economy, which starts with disassembly of products into components for reuse, remanufacturing, or recycling. As the process is often complex, a disassembly sequencing problem (DSP) studies how to optimally disassemble products considering the physical constraints between subassemblies/disassembly tasks for maximum profit. With a growing attention on energy conservation, this paper addresses a profit-oriented and energy-efficient DSP (PEDSP), whereby not only the profit is maximized, but also energy consumption is accounted as an important decision criterion. In this work, a disassembly AND/OR graph (DAOG) is used to model a disassembly diagram for a product, in which the ‘AND’ and ‘OR’ relations illustrate precedence relationships between subassemblies. Based on the DAOG, we propose a hybrid multi-objective metaheuristic that integrates an artificial bee colony algorithm, a non-dominated sorting procedure, and a variable neighborhood search approach to solve the PEDSP for Pareto solutions. The proposed method is applied to real-world cases (i.e., a simple ballpoint pen and a relatively complex radio) and compared with other multi-objective algorithms. The results indicate that our method can quickly produce a Pareto front that outperforms the alternative approaches.