Look-ahead Planning Research Articles

Analysis of The Relationship of Rework and Inventory to Waste in The Ciputra Hospital Surabaya Project

Currently, the construction sector is adopting the theory of production in the manufacturing industry, known as lean construction, to reduce waste and increase value. Last Planner System (LPS) has not been widely used and has good potential because the advantage of LPS is to identify a job along with obstacles to improve performance in a construction project. In the construction of Junior High School (SMP) Al-Falah, it has work obstacles due to erratic weather so that the project is delayed, the author conducts a field survey to analyze the actual progress in the field, LPS has work indicators / work flow to measure the extent to which work indicators can be realized properly, the LPS work flow are Master Plan, Phase Pull Planning, Lookahead Planning, Constraints Analysis, Shielding Production, and Percent Plan Complete (PPC) as a standard for measuring whether project productivity is realized properly or not. In this study, the results of the implementation using LPS on the Al-Falah Junior High School construction project show that the lowest PPC can be seen in week 7, which is 0% because there is no work achievement so that the work is delayed, while in week 16 it can be seen that PPC has increased dramatically to 96%. Then after averaging the PPC of 51% which means that LPS has not been able to increase the reliability of planning above 70%, (Ballard, 2000).

Harnessing LPS Metrics for Smarter Resource Allocation and Project Control through Gamification

Question: What is the significance of Last Planner System® (LPS®) metrics in capacity planning and project outcomes? Purpose: This study highlights the vital role of LPS metrics in optimizing resource allocation for improved capacity planning, lookahead scheduling, and waste reduction. Additionally, it showcases the efficacy of simulation-based serious games as an engaging and instructive pedagogical method. Research Method: Simulation-based serious game developed using a discrete-event simulation engine and a user-friendly interface. Findings: This study delineates the differences between individuals exposed to LPS metrics during capacity planning and those without such exposure, and the differences between individuals with and without prior LPS knowledge. Limitations: It’s essential to acknowledge that simulating real project conditions in a game necessitates certain assumptions. Implications: This game can evolve into a valuable educational tool for assessing users’ capacity planning and metric analysis competencies. Multiple versions could also be developed to assess diverse skills vital to project planning and control. Value for practitioners: This paper not only highlights the importance of LPS metrics in capacity and lookahead planning, but also sheds light on the availability of educational and evaluative games. Such games can be embraced by organizations to enhance their current educational and training practices. Keywords: LPS® metrics, capacity planning, resource allocation, serious games Paper type: Full Paper

Moving toward lean construction through automation of planning and control in last planner system: A systematic literature review

The Last Planner System (LPS) is a production planning and control system based on Lean Construction (LC) principles. Effectively implementing the LPS in the construction industry remains challenging, impeding the realization of LC principles. Automation of planning and control in the LPS has gained attention to address this challenge. However, research exploring the state-of-the-art of such automation and its impacts on realizing LC principles is still lacking. Therefore, this study performed a systematic literature review of 112 relevant studies, identifying 50 functionalities and associated benefits of automation across six LPS stages, with the lookahead planning stage having the maximum functionalities. Analyzing their interactions with LC principles revealed that automated planning and control in the LPS can support achieving ten LC principles. Future research directions are suggested in areas such as constraint management, knowledge reuse, dynamic replanning, and construction workforce training.

Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions

Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships’ flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations.

Prescriptive analytics for optimal multi-use battery energy storage systems operation: State-of-the-art and research directions

This paper presents the state-of-the-art and latest advances in implementing multi-use practices on BESS applications to the power system grid. Representative papers on modeling and optimization methods were selected, most of them working with realistic use cases, but none reporting on real-world implementations. Some major findings from reviewing key representative papers are that current optimization methods were able to handle uncertainty related to prices and other parameters either in the look-ahead planning stage or real-time control for obtaining an optimal dispatch schedule, thus addressing previously pointed-out gaps. Further, a recommendation for future work is to implement current multi-use methods, found to be mature enough, to real-world BESS use cases for validation and gaining experimental experience. Furthermore, the multi-use performance can further be improved by joining the strong contributions from each paper's method. Current methods have shown great potential for bringing simultaneous benefits to the grid, as well as being economical for the owner. A continued exploration and inclusion of new future BESS applications in prescriptive analytics for BESS multi-use suggest robustness for future market changes. However, further assessments are needed to evaluate whether the technology is economical and green for each particular use case and also for a potential systematic upscaling of the technology as a means to facilitate the green shift.

Improving Commissioning and Qualification Delivery Using Last Planner System®

Question: Can the Last Planner System® enhance the Commissioning and Qualification phase of project delivery? Purpose: Evaluation of Last Planner System in the Commissioning and Qualification phase of project execution; identifies challenges; proposes mitigations and opportunities for enhancing future implementations. Research Method: Mixed-methods approach encompassing critical literature review, site documentation data-analysis, focus groups, and semi-structured purposeful interviews. Findings: Last Planner System offers distinct value-add to Commissioning and Qualification. However, there is need for increased involvement of upstream and downstream players in planning construction production processes. Limitations: Conducted on a single project with limited sample size. Implications: Paper offers value for clients and practitioners as Last Planner System in the Commissioning and Qualification phase of project execution facilitates greater collaboration, increased visibility of workflow, and resulting productivity, schedule alignment, safety, cost, and client value-add benefits. Keywords: Lean Construction; Last Planner System®; collaboration; workflow; lookahead planning; handoff. Paper type: Case Study

Current Process Benchmark for the Last Planner® System of Project Planning and Control.

Abstract: The Last Planner® System (LPS)3 was initially designed as a system for planning and controlling production on projects, that is, to do what is necessary to achieve set targets (Ballard 2000). It was understood to differ from project controls, which sets targets (objectives and constraints on their delivery) and monitors progress toward them. Initially, LPS consisted only of lookahead planning (Ballard 1997), weekly work planning, and learning from breakdowns. In the early 2000s, planning and scheduling project phases (which provide inputs to lookahead planning) were added to its scope, as described in the 2016 Benchmark (Ballard and Tommelein 2016). This 2020 Current Process Benchmark further extends LPS in principle to both production4 (i.e., striving for targets) and project planning and control (i.e., setting targets). That does not mean there is no longer a role for technical specialists such as schedulers, estimators, inspectors, etc. It means that a single system is needed rather than two systems; a system for the project chain of command to both manage the project and continuously improve the project’s planning and control system. Technical specialists are still needed to collect and analyze information that managers at different levels need in order to make good decisions.

Automating look-ahead schedule generation for construction using linked-data based constraint checking and reinforcement learning

Look-ahead planning is the stage in construction planning where information from diverse sources is integrated and plans developed for the next six/eight weeks. Poor planning of construction site activities at this stage often results in cost overruns and schedule delays. This work presents a novel Look-Ahead Schedule (LAS) generation method, which uses reinforcement learning and linked-data based constraint checking within the reward, to address the issues associated with manual look-ahead planning and help construction professionals efficiently plan construction activities at this stage. Our proposal can generate conflict-free LAS significantly faster than conventional methods, demonstrating its capability as a decision support tool during look-ahead planning meetings. Therefore, this paper extends existing knowledge in the construction informatics domain by demonstrating the application of reinforcement learning to aid data-driven look-ahead planning.

Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction

In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.11Access to the code and a sample of the dataset can be granted upon direct request from the authors. Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.

Toward BIM and LPS Data Integration for Lean Site Project Management: A State-of-the-Art Review and Recommendations

Over recent years, the independent adoption of lean construction and building information modeling (BIM) has shown improvements in construction industry efficiency. Because these approaches have overlapping concepts, it is thought that their synergistic adoption can bring many more benefits. Today, implementing the lean–BIM theoretical framework is still challenging for many companies. This paper conducts a comprehensive review with the intent to identify prevailing interconnected lean and BIM areas. To this end, 77 papers published in AEC journals and conferences over the last decade were reviewed. The proposed weighting matrix showed the most promising interactions, namely those related to 4D BIM-based visualization of construction schedules produced and updated by last planners. The authors also show evidence of the lack of a sufficiently integrated BIM–Last Planner System® framework and technologies. Thus, we propose a new theoretical framework considering all BIM and LPS interactions. In our model, we suggest automating the generation of phase schedule using joint BIM data and a work breakdown structure database. Thereafter, the lookahead planning and weekly work plan is supported by a field application that must be able to exchange data with the enterprise resource planning system, document management systems, and report progress to the BIM model.

LAST PLANNER SYSTEM (LPS) IMPLEMENTATION ON JAKARTA-CIKAMPEK II ELEVATED TOLL ROAD PROJECT

In construction industry, its implementation of the construction has its own constraints. A good management or project management system is needed to minimize the risk of these constraints. The latest project management concept that has proven more effective and efficient is Last Planner System method. This study aims to investigate how the implementation of Last Planner System works on Jakarta-Cikampek II Elevated Toll Road Project. With the evaluation of weekly job evaluations using PPC (Percent Plan Completed) and Pareto Variance Constraint analysis, the constraint issues of this project will be identified. Then they are followed up to find the right solution in handling the constraints. The research method is done by conducting literature study, problem formulation, followed by implementing LPS method, which is through work flow control such as phase and pull plan, lookahead plan, constrain analysis, daily huddles, PPC (Percent Plan Completed) calculation, Pareto Chart analysis with data variation of issues that have been obtained. The results show that the average PPC weekly project is 83.04% and the most influential reason that must be concerned its priority is submittals.

Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction

In the construction sector, complex constraints are not usually modeled in conventional scheduling and 4D building information modeling software, as they are highly dynamic and span multiple domains. The lack of embedded constraint relationships in such software means that, as Automated Data Collection (ADC) technologies become used, it cannot automatically deduce the effect of deviations to schedule. This paper presents a novel method, using semantic web technologies, to model and validate complex scheduling constraints. It presents a Linked-Data based Constraint-Checking (LDCC) approach, using the Shapes Constraint Language (SHACL). A prototype web application is developed using this approach and evaluated using an OpenBIM dataset. Results demonstrate the potential of LDCC to check for constraint violation in distributed construction data. This novel method (LDCC) and its first prototype is a contribution that can be extended in future research in linked-data, BIM based rule-checking, lean construction and ADC.

Look-ahead planning of lexical heads in adjunct-head structures

This study investigates whether the scope of advance lexical planning is constrained by a head-adjunct dependency between words in a sentence. Participants produced Korean sentences beginning with a complex subject noun phrase consisting of a relative clause followed by a head phrase. The subject noun phrase had three nouns and were manipulated for whether the second noun was semantically related or unrelated to the first noun and whether it occurred inside the relative clause (two-noun modifier structures) or the head phrase (one-noun modifier structures). Speech onset latencies were modulated by both semantic relatedness and relative clause length. However, the two factors did not interact with each other. The results indicate that a head noun was planned in advance before their adjunct could be articulated even when it occurred outside a sentence-initial phrase. The data suggest that head-adjunct dependencies may necessitate advance access to major structural components and lead to a broad scope of lexical planning.

Autonomous Surface Vehicle Multistep Look-Ahead Measurement Location Planning for Optimal Localization of Underwater Acoustic Transponders

An underwater vehicle may utilize underwater transponders (UTs) for navigation in the absence of global navigation satellite system signals. However, the position of each UT must be known by the underwater vehicle. The problem of an autonomous surface vehicle (ASV) optimally planning measurement locations to localize a set of arbitrarily predeployed acoustic UTs is considered. The ASV is assumed to make noisy range measurements to the UTs. A maximum a posteriori estimator is derived to localize the UTs. In addition, a multistep look-ahead (MSLA) ASV optimal measurement location planning (OMLP) strategy is developed. This planning strategy prescribes future multistep measurement locations. A physical interpretation of the proposed planner in the single-step, single transponder case is provided. Simulation results are presented demonstrating the tradeoff between expected localization performance and computational time associated with various look-ahead horizons and travel distances. Experimental results are given illustrating the proposed MSLA OMLP strategy's performance in environments containing one and two UTs. The proposed OMLP strategy is able to localize UTs to within 4 m of their true locations. Additionally, increasing the planning horizon is demonstrated to yield better UT localization at the cost of increased computational burden.

Risk-aware graph search with dynamic edge cost discovery

In this paper, we introduce a novel algorithm for incorporating uncertainty into lookahead planning. Our algorithm searches through connected graphs with uncertain edge costs represented by known probability distributions. As a robot moves through the graph, the true edge costs of adjacent edges are revealed to the planner prior to traversal. This locally revealed information allows the planner to improve performance by predicting the benefit of edge costs revealed in the future and updating the plan accordingly in an online manner. Our proposed algorithm, risk-aware graph search (RAGS), selects paths with high probability of yielding low costs based on the probability distributions of individual edge traversal costs. We analyze RAGS for its correctness and computational complexity and provide a bounding strategy to reduce its complexity. We then present results in an example search domain and report improved performance compared with traditional heuristic search techniques. Lastly, we implement the algorithm in both simulated missions and field trials using satellite imagery to demonstrate the benefits of risk-aware planning through uncertain terrain for low-flying unmanned aerial vehicles.

Are Indonesia contractors ready to implement last planner system? – an early investigation

Last Planner System (LPS) is one of Lean Construction’s tools for scheduling to enhance project’s reliability, productivity, and performance. It has already been widely adopted in many developed countries and shown benefits, such as shorter duration, less rework, and better communication. Despite its benefits, the use of LPS is almost unheard of in Indonesian. The aim of this exploratory research is to investigate the readiness of LPS implementation for Indonesia’s projects. The assessment is based on five principles of LPS, i.e. Master Schedule, Pull Planning, Making Work Ready, Weekly Work Plan, and Learning, using four categories of assessment, i.e. not aware, aware, willing, and implemented. The levels of readiness are categorised into none, poor, moderate, high and excellent. Data was collected by interviews and questionnaire surveys with project managers and observations of four construction projects. The results show that on average the readiness level is moderate, particularly for Pull Planning and Look-ahead Plan. The main challenges identified include lack of confidence and resistance to change. These research findings provide an initial understanding of LPS as perceived by contractors, and later can be used as inputs to build a strategic framework to enable wider LPS adoption to improve Indonesia’s project performance.

A smooth tool path generation and real-time interpolation algorithm based on B-spline curves

In the machining progress of free-form surface, tool path is presented as continuous small line segments. To achieve high machining speed and fine machining quality, the tool path needs to be smoothed. This study presents a smooth tool path generation algorithm based on B-spline curves for small line segments machining. The algorithm includes two modules: smooth tool path generation module and real-time look-ahead interpolation module. In the smooth tool path generation module, the tool path is divided into non-fitting regions and fitting regions by three conditions: the length of small line segments, the angle of adjacent small line segments, and the change rate of the length and angle. To control contour error and get fine machining quality, the fitting regions are corrected by circle correction method and fitted into B-spline curves, while the non-fitting regions are smoothed with B-spline curves. In this module, the gained tool path has continuous curvature. In the interpolation module, the seven-phase jerk-limited look-ahead planning is adopted to generate smooth machining velocity, while the calculation accuracy of interpolation point generated by the interpolation period crossing two adjacent tool path is controlled. Simulations and experimental results demonstrate that the proposed algorithm is able to reduce the amount of numerical control codes, achieve high machining speed, and improve machining quality.

Design management using knowledge innovation and visual planning

An open platform used for industrialised house-building imposes restrictions on the flexibility of the product offering when developing design standardisation. How design process standardisation incorporates variations in products has not been widely studied. The aim of this research is to explain how design breakdown enables Lean Product Development Flow (LPDF) and look-ahead planning in an industrialised house-building context where an open platform is used. A case study was conducted of how one of the leading industrialised house-building companies in Sweden introduced the LPDF tool Knowledge Innovation/Visual Planning (KI-VP) into their design process. The implementation of KI-VP led to an increased cross-functional understanding of relationships between activities, which are an important factor in achieving flexibility and a synchronised workflow. By using design standardisation, look-ahead planning was implemented and used in the management of design flow. Standardisation through design breakdown provides a basis for knowledge innovation that enables improvement of the open platform using a bottom-up approach and increases the production flow.

AD HOC TEAMWORK BEHAVIORS FOR INFLUENCING A FLOCK

Ad hoc teamwork refers to the challenge of designing agents that can influence the behavior of a team, without prior coordination with its teammates. This paper considers influencing a flock of simple robotic agents to adopt a desired behavior within the context of ad hoc teamwork. Specifically, we examine how the ad hoc agents should behave in order to orient a flock towards a target heading as quickly as possible when given knowledge of, but no direct control over, the behavior of the flock. We introduce three algorithms which the ad hoc agents can use to influence the flock, and we examine the relative importance of coordinating the ad hoc agents versus planning farther ahead when given fixed computational resources. We present detailed experimental results for each of these algorithms, concluding that in this setting, inter-agent coordination and deeper lookahead planning are no more beneficial than short-term lookahead planning.

Removing Constraints to Make Tasks Ready in Weekly Work Planning

The Last Planner System (LPS) has been used on construction projects to improve reliable work planning. Lookahead planning is an intermediate process that connects the master or phase schedule to the weekly work plan. Construction teams employ lookahead planning to achieve various objectives including a breakdown down of activities into the level of operations, operations design, and constraint removal to make tasks ready for execution. Tasks Made Ready (TMR) measures the performance of lookahead planning in identifying and eliminating constraints to make activities ready for implementation. The purpose of this paper is to study through computer simulation the impact of TMR on task execution, reliability of weekly work planning, and project duration. Results show that TMR is a good predictor of project duration. These results advise planners on the importance of the constraint removal process, how it influences the reliability of construction planning, and its impact on project duration.