Planning Scheduling Research Articles

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.

Guarantee Stability of Supply Chain with Dual Channels under Supply Failure of Goods

The supply chain is an extremely important and complex system, which is widely related to the social system and the management system. The hierarchical scheduling and production planning of a dynamic supply chain can enable enterprises in the supply chain to obtain, and maintain, stable and lasting competitive advantages; thus, improving the overall competitiveness of the supply chain. Simultaneously, research on supply chains is also a hotspot in system engineering. At present, research on complex supply chain systems with dual channels is insufficient, and analysis of dynamic supply chains with supply failure of goods is rare. In the epidemic era, dynamic supply chain management was affected by the epidemic and became a very challenging topic. The control of the supply chain system under the impact of an epidemic situation is particularly important. This paper focused on H∞ the fault-tolerant control of a complex supply chain system with dual channels. First, from the perspective of engineering practice and green production, we established a new green supply chain model. This supply chain model includes the dual-channel supply of goods and also considers the supply failure of goods. At the same time, production delay and supply delay are also considered in this model. Second, a supply scheduling scheme was designed through cybernetics to ensure the stability of a supply chain with dual channels under supply failure of goods, and we completed supply chain management. Finally, the effectiveness of our results was verified by specific Chinese enterprise data.

Evaluation of Active Labor Market Policy Reform: Employment Outcomes of Vocational Rehabilitation Services.

The purpose of this study is to examine the effect of the Estonian active labor market reform in 2016, which introduced a new policy concerning vocational rehabilitation services. As a research question, we investigate how such services may have affected the employment outcomes of people with mental and/or physical impairments. Our sample includes 9244 people from 2016 to 2020, with a mean age of 46years. Due to multiple entries to the services, we have more than 11,000 cases with over 100,000 monthly observations. We use propensity score matching in combination with fixed effects panel regressions to analyze how the completion of the scheduled rehabilitation plan affected monthly employment duration. Our findings indicate that completing the rehabilitation service results on average in 2.6months longer post-rehabilitation employment, compared to matched individuals who discontinued the service. This effect was larger when already employed and male participants entered the service, while weaker effects were observed in the case of individuals with only mental disabilities. Overall, we conclude that while completing the scheduled rehabilitation plan has a positive effect on employment outcomes, still maintaining employment status seems to remain a challenge, based on the relatively modest effect sizes. Thus, we question the economic arguments behind the reform.

Comparison of Online-Onboard Adaptive Intensity-Modulated Radiation Therapy or Volumetric-Modulated Arc Radiotherapy With Image-Guided Radiotherapy for Patients With Gynecologic Tumors in Dependence on Fractionation and the Planning Target Volume Margin

Patients with newly diagnosed locally advanced cervical carcinomas or recurrences after surgery undergoing radiochemotherapy whose tumor is unsuited for a brachytherapy boost need high-dose percutaneous radiotherapy with small margins to compensate for clinical target volume deformations and set-up errors. Cone-beam computed tomography-based online adaptive radiotherapy (ART) has the potential to reduce planning target volume (PTV) margins below 5 mm for these tumors. To compare online ART technologies with image-guided radiotherapy (IGRT) for gynecologic tumors. This comparative effectiveness study comprised all 7 consecutive patients with gynecologic tumors who were treated with ART with artificial intelligence segmentation from January to May 2022 at the West German Cancer Center. All adapted treatment plans were reviewed for the new scenario of organs at risk and target volume. Dose distributions of adapted and scheduled plans optimized on the initial planning computed tomography scan were compared. Online ART for gynecologic tumors. Target dose coverage with ART compared with IGRT for PTV margins of 5 mm or less in terms of the generalized equivalent uniform dose (gEUD) without increasing the gEUD for the organs at risk (bladder and rectum). The first 10 treatment series among 7 patients (mean [SD] age, 65.7 [16.5] years) with gynecologic tumors from a prospective observational trial performed with ART were compared with IGRT. For a clinical PTV margin of 5 mm, IGRT was associated with a median gEUD decrease in the interfractional clinical target volume of -1.5% (90% CI, -31.8% to 2.9%) for all fractions in comparison with the planned dose distribution. Online ART was associated with a decrease of -0.02% (90% CI, -3.2% to 1.5%), which was less than the decrease with IGRT (P < .001). This was not associated with an increase in the gEUD for the bladder or rectum. For a PTV margin of 0 mm, the median gEUD deviation with IGRT was -13.1% (90% CI, -47.9% to 1.6%) compared with 0.1% (90% CI, -2.3% to 6.6%) with ART (P < .001). The benefit associated with ART was larger for a PTV margin of 0 mm than of 5 mm (P = .004) due to spreading of the cold spot at the clinical target volume margin from fraction to fraction with a median SD of 2.4 cm (90% CI, 1.9-3.4 cm) for all patients. This study suggests that ART is associated with an improvement in the percentage deviation of gEUD for the interfractional clinical target volume compared with IGRT. As the gain of ART depends on fractionation and PTV margin, a strategy is proposed here to switch from IGRT to ART, if the delivered gEUD distribution becomes unfavorable in comparison with the expected distribution during the course of treatment.

A Proposal of Scheduling Method Based on the Decision Criteria of Schedule Planners Considering Productivity and Electric Power Consumption

A Proposal of Scheduling Method Based on the Decision Criteria of Schedule Planners Considering Productivity and Electric Power Consumption

Optimized scheduling with prioritization to enhance network sustainability in edge-cloud environment

Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms.

Energy Consumption Optimization of Milk-Run-Based In-Plant Supply Solutions: An Industry 4.0 Approach

Smart factories are equipped with Industry 4.0 technologies including smart sensors, digital twin, big data, and embedded software solutions. The application of these technologies contributes to better decision-making, and this real-time decision-making can improve the efficiency of both manufacturing and related logistics processes. In this article, the transformation of conventional milk-run-based in-plant supply solutions into a cyber–physical milk-run supply is described, where the application of Industry 4.0 technologies makes it possible to make real-time decisions regarding scheduling, routing, and resource planning. After a literature review, this paper introduces the structure of Industry 4.0 technologies supported by milk-run-based in-plant supply. A mathematical model of milk-run processes is described including both scheduling and routing problems of in-plant supply. This mathematical model makes it possible to analyze the impact of Industry 4.0 technologies on the efficiency, performance, and flexibility of in-plant supply logistics. The scenarios’ analysis validates the mathematical model and shows that significant performance improvement and energy savings can be achieved using Industry 4.0 technologies. This performance improvement can lead to a more cost-efficient and sustainable in-plant supply solution, where not only logistics aspects but also energy efficiency and emissions can be taken into consideration.

Distributed shared energy storage scheduling based on optimal operating interval in generation-side

Shared energy storage (SES) is proposed base on the sharing economy. It can effectively improve the utilization rate of energy storage system (ESS) and reduce costs. This paper mainly discusses a novel application mode of generation-side SES, including the multiple utilization of single ESS and the centralized utilization of distributed ESS. Renewable energy power plants (REPPs) establish SES collation by sharing their own ESS. On the basis of satisfying the utilization of REPPs, they further share energy storage resources for auxiliary services like secondary frequency regulation (SFR) to obtain benefits. Secondly, the scheduling method of SES on generating side is discussed. Traditional day-ahead robust optimization algorithm can only provide the specific output power of ESS in each period. However, it is difficult to provide an adjustment scheme for day-ahead scheduling plan due to the influence of time coupling constraints such as renewable energy power output and ESS state constraints. Based on this, a distributed SES scheduling method based on the optimal operating interval is proposed. The optimal interval variable of energy storage state of charge (SOC) is added into the traditional min–max–min three-layer optimization problem to decouple the time coupling constraints. In addition, a novel method nested column and constraint generation (Nested C&CG) algorithm is introduced to solve the proposed mathematical model. Finally, a revenue sharing model based on cooperative game is proposed. An example is as well presented to show the feasibility of the proposed method to deal with uncertainty.

Two-Stage Optimal Scheduling of Highway Self-Consistent Energy System in Western China

Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method is proposed, which can effectively track the day-ahead scheduling plan and reduce the impact of forecast errors and energy fluctuations by coordinating the unit output within the HSCES system. It is verified that the HSCES can operate economically and safely in Western China, and self-consistently, without grid support.

PartitionTuner: An operator scheduler for deep‐learning compilers supporting multiple heterogeneous processing units

AbstractRecently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep‐learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep‐learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep‐learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator‐scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type‐based operator‐scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling‐based operator‐scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

Construction project management monitoring system using s-curve

Every construction project generally has a specific implementation plan and implementation schedule: when to start the implementation of the project, when to complete the project, how to do the project, and how to provide resources. Making a plan for a construction project always refers to the estimates that exist when the scheduled development plan is made. Therefore, problems can arise if there is a mismatch between the plan that has been made and its implementation. With the limited resources in the company, thorough and good planning is needed as a guide in carrying out the project to use resources efficiently and organize work to be completed according to the time specified in the contract. To achieve this goal, the manager controls the project with an s-curve by comparing the actual curve graph with the plan curve graph. The method to be used in monitoring the progress of construction projects is the s-curve. The use of the s-curve method is very supportive because it is suitable for monitoring project progress in terms of performance, cost, and timeliness of project work. The results of this study show that the construction project management monitoring website using the s-curve can create a cost budget plan that follows the contract document, a schedule plan, a weekly project report, and a project report recapitulation. The output results are displayed in the form of a Gantt Chart, and S-curves are completed with plan curve lines and realization curve lines. This project management monitoring website has a multi-user feature so that more than one user can use it.

IOT Based Bridge Collapse and Flood Detection System

Knowledge on the dynamic properties of bridges improves condition assessment, maintenance scheduling and emergency planning to better serve the public. The Crack inspection is an important task in the maintenance of bridge and it is closely related to structural health of the bridge. It is done through a very manual procedure, an experienced human inspector monitors the whole bridge visually and try to detect cracks on the bridge and marks the location of the crack. The proposed system is implemented using a real time wireless sensor network for bridge monitoring is of lossless data transmission over several minutes continuously. Overall information about the bridge will be stored on the cloud. The cracks will be detected automatically and send the real time data with location of crack to the PWD department. If bridge will be collapse because of any accident or a flood that time the barriers will be open and the Vehicles will stop at some distance, and also notify about the accident to the nearby government departments like Police station, Hospitals and Society. After the accident detected by system up to previous and next 5KM the alert notification will shows another route for travellers.

An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs

The industrial sector is the largest consumer of total energy in the world, with the majority of its consumption in the form of electricity. Recently, to strengthen the capacity building of peak load regulation, many countries have implemented time-of-use (TOU) tariffs to encourage manufacturing enterprises to shift their electricity consumption from on-peak hours to mid-peak and off-peak hours. It brings the urgent requirement for energy-efficient operation and maintenance (O&M) schedules in manufacturing enterprises, among which the maintenance planning and production scheduling (MPPS) are both highly correlated to electricity consumption under this time-varying electricity charging modes. To tackle this key issue, a complex MPPS problem for serial-parallel manufacturing systems under TOU tariffs is studied in this paper. To solve the problem, an energy-efficient two-stage maintenance (ETM) strategy is developed to minimize the sum of the total electricity cost and tardiness cost. In the first stage, the preventive maintenance (PM) planning is presented to obtain the multi-attribute PM intervals for each machine, considering machine availability, maintenance cost, and the potential impact of planned PM actions on average electricity price. Based on the PM intervals, a mixed-integer programming model is proposed for a hybrid flow shop schedule with PM actions in the second stage. Finally, the results confirm the effectiveness of this ETM strategy in achieving electricity cost savings and ensuring system productivity, which can provide instructions for the operation of industrial enterprises.

On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model

Travel time is an essential indicator for trip planning, transportation service planning, and operation. This study aims to propose a novel Burr mixture autoregressive (BMAR) model for the intermediate-to-long term period of bus section travel time prediction, which is useful for bus service and schedule planning. The BMAR model exhibits greater flexibility, allowing it to effectively capture the multi-peak and non-peak, non-linear with heteroscedasticity characteristics of travel time. The model is trained and well-validated with 6-month bus section travel time data collected via the automatic vehicle location system. Results show that the BMAR model gives promising results in travel time point and interval prediction, especially for the higher degree of variability and irregular pattern of travel time observed on urban roads and highways. The reliability ratio index derived from the BMAR model could be used to measure the bus service reliability and aid in the bus scheduling of maintenance activities.

Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer

Background and purposeAdaptive radiotherapy (ART) in locally advanced cervical cancer (LACC) has shown promising outcomes. This study investigated the feasibility of cone-beam computed tomography (CBCT)-guided online ART (oART) for the treatment of LACC. Material and methodsThe quality of the automated radiotherapy treatment plans and artificial intelligence (AI)-driven contour delineation for LACC on a novel CBCT-guided oART system were assessed. Dosimetric analysis of 200 simulated oART sessions were compared with standard treatment. Feasibility of oART was assessed from the delivery of 132 oART fractions for the first five clinical LACC patients. The simulated and live oART sessions compared a fixed planning target volume (PTV) margin of 1.5 cm around the uterus-cervix clinical target volume (CTV) with an internal target volume-based approach. Workflow timing measurements were recorded. ResultsThe automatically-generated 12-field intensity-modulated radiotherapy plans were comparable to manually generated plans. The AI-driven organ-at-risk (OAR) contouring was acceptable requiring, on average, 12.3 min to edit, with the bowel performing least well and rated as unacceptable in 16 % of cases. The treated patients demonstrated a mean PTV D98% (+/-SD) of 96.7 (+/- 0.2)% for the adapted plans and 94.9 (+/- 3.7)% for the non-adapted scheduled plans (p<10−5). The D2cc (+/-SD) for the bowel, bladder and rectum were reduced by 0.07 (+/- 0.03)Gy, 0.04 (+/-0.05)Gy and 0.04 (+/-0.03)Gy per fraction respectively with the adapted plan (p <10−5). In the live.setting, the mean oART session (+/-SD) from CBCT acquisition to beam-on was 29 +/- 5 (range 21–44) minutes. ConclusionCBCT-guided oART was shown to be feasible with dosimetric benefits for patients with LACC. Further work to analyse potential reductions in PTV margins is ongoing.

A branch-price-and-cut algorithm for a time-dependent green vehicle routing problem with the consideration of traffic congestion

This paper studies a time-dependent green vehicle routing problem (TDGVRP) with the consideration of traffic congestion. In this work, the objective is to design the vehicle scheduling plan, with the goal of reducing carbon emissions, which has a positive linear relationship with fuel consumption of vehicles. In order to mathematically model traffic congestion, the travel speed is set as time-dependent, thus travel time and carbon emissions are also time-dependent. We develop a set partitioning formulation (SPF) for the TDGVRP, and propose a branch-price-and-cut (BPC) algorithm to solve it, in which a tailored labeling algorithm is designed for solving the pricing problem. Furthermore, we introduce dominance rule to discard unpromising labels. In order to get a more reasonable lower bound, valid inequalities are used to strengthen the SPF in this paper. Extensive computational experiments validate the effectiveness of the proposed BPC algorithm. This study could make a contribution on the theoretical research on vehicle routing problem (VRP), and at the same time presents a mathematical and reasonable method for logistics companies to formulate logistics plans with the consideration of fuel consumption, climate change and carbon emissions reduction.

Facility planning and schedule design in the pandemic: Eliminating contacts at construction workplace

The construction industry has been severely affected by the COVID-19 pandemic and the associated restrictions on person-to-person contacts issued by the government. A construction site usually has a high number of workers working at the same time; therefore, the question of how to ensure their safety during the pandemic—that is, how to protect them from getting infected—has become an urgent problem. In this study, we propose a bi-objective integer programming model to establish the optimal schedule plan under COVID-19 regulations. We develop a solution method and conduct numerical experiments to solve and validate our model. The optimal schedule plan can avoid contacts between workers of different groups while minimizing the total costs of complying with government policy. Our proposed model can be applied in practice to help project managers establish a reasonable and cost-effective schedule plan. This study contributes to reducing the operating costs of contractors and protecting the health of construction workers.

Demand-Driven Harvest Planning and Machinery Scheduling for Guayule

Demand-Driven Harvest Planning and Machinery Scheduling for Guayule

D-ITAGS: A Dynamic Interleaved Approach to Resilient Task Allocation, Scheduling, and Motion Planning

Complex, multi-task missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning. This challenge is exacerbated by dynamic changes, such as sensor and actuator failures, communication loss, and unexpected delays. We introduce Dynamic Iterative Task Allocation Graph Search (D-ITAGS) to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> address coalition formation, scheduling, and motion planning in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</i> settings involving heterogeneous teams. D-ITAGS achieves resilience via two key characteristics: i) interleaved execution, and ii) targeted repair. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interleaved execution</i> enables an effective search for solutions at each layer while avoiding incompatibility with other layers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Targeted repair</i> identifies and repairs parts of the existing solution impacted by a given disruption, while conserving the rest. In addition to algorithmic contributions, we derive accurate bounds on schedule suboptimality and provide insights into the inherent trade-off between time and resource optimality in these settings. Our experiments reveal that i) D-ITAGS is significantly faster than recomputation from scratch in dynamic settings, with little to no loss in solution quality, and ii) the theoretical bounds on optimality gap consistently hold in practice.

A Simulation Optimization Approach to Optimize Dredger Fleet Schedule Plan for Dredging Works in Existing Ports

Making an optimal dredger fleet scheduling (DFS) plan is difficult for dredging works in existing ports, where the dredging activities are interrupted by stochastic vessel traffic. Therefore, a simulation optimization (SO) approach is adopted to solve the DFS problem. In the SO approach, an agent-based simulation model is first developed to simulate the stochastic traffic flow and possible spatiotemporal traffic conflicts between vessels and dredgers. Then, this simulation model is integrated with an optimization model to find the optimal DFS plan with the objective of minimum total dredging equipment usage fees. Finally, the proposed SO approach is applied to a 4-dredging-job dredging work. The results demonstrate its applicability in identifying the bottlenecks of the DFS plan and finding the optimal DFS plan. This research contributes to gaining more insights into using the SO approach to provide decision making for dredging work planning under uncertainty.