Idle Time Costs Research Articles

An Interpretation of the Cumulative Impact of FASTag on the Reduction of Environmental Pollution and Traffic Delays at Toll Booths: A Case Study

Traffic congestion has been identified as a significant contributor to the environmental degradation and elevated fuel consumption. The causes of traffic delays are multifaceted, with toll booths positioned on highways being a notable factor. This study aims to analyze the impact of toll booths on traffic congestion, with a particular focus on the case study of the highway toll booth in question. The analysis will include a comparison of the delay times before and after the implementation of the FASTag system. The study will also undertake an economic evaluation to assess the cost implications of delay. The findings of this study indicate that congestion is more pronounced in the presence of manual toll booths compared to automated systems. The present study uses the 'Chakiya Toll Plaza on National Highway 27A, located between Motihari and Muzaffarpur, as a case study. This toll booth has been initially operated with a manual toll collection system, but has since transitioned to the FASTag implementation. A comprehensive analysis encompasses both traffic volume and delay studies, with a focus on their environmental and economic ramifications. The analysis reveals that for each Passenger Car Unit (PCU), the environmental cost is 8.3 rupees and the fuel cost is 15.34 rupees. The idle time cost is calculated as 8.34 rupees. The overall cost of the manual toll collection delay, including all the aforementioned factors, is found to be 52.3 rupees. However, the implementation of FASTag significantly reduces this cost to. 8.20 rupees.

Optimization of Taxi Allocation for Minimizing CO2 Emissions Based on Heuristics Algorithms

Recently, the rapid climate change caused by increasing CO2 emissions has become a global concern. Efficient transportation systems are necessary to reduce CO2 emissions in cities. Taxi services are an essential part of the transportation system, both in urban areas with high demand and in rural areas with inadequate public transportation. Inefficient taxi services cause problems such as increased idle times, resulting in increased CO2 emissions. This study proposes a taxi allocation model that minimizes taxi idle time costs for efficient taxi service operation. We also propose three heuristic algorithms to solve the proposed model. At last, we conduct a case study by using real taxi data in Nagaoka, Japan. By comparing the three algorithms, the dynamic greedy algorithm produced the best result in terms of idle time cost and CPU time. The findings indicate that by minimizing idle time costs and reducing the number of taxis, it is possible to achieve a significant 81.84% reduction in CO2 emissions within the transportation sector. Further, in order to estimate the idle time costs the sensitivity of demand is considered.

BLOCKCHAIN OF OPTIMAL MULTIPLE CONSTRUCTION PROJECTS PLANNING UNDER PROBABILISTIC ARRIVAL AND STOCHASTIC DURATIONS

With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task’s start times. The project arrival date and the task’s execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task’s execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task’s execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task’s execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task’s execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning.

Random misplacement and production process reliability: A sustainable industrial approach to deal with the discrepancy and deficiency

<p style='text-indent:20px;'>Inventory inaccuracy emerges as a significant management issue, leading to profit losses within the supply chain network. This study focused on randomly misplaced inventory. Here an integrated inventory model has been constructed with two-stage production with two manufacturing units. The first manufacturer produces semi-finished goods, while the second manufacturer produces the finished items. The reliability becomes a concern if the first manufacturer's production capacity is higher than that of the second one. Hence, process reliability has been considered to make the model more realistic. Proper handling of used products has been considered for sustainability purposes, and these items are either remanufactured or repaired. The manufacturer bears the stock out, and the remanufactured items are supposed to deal with the absence of material. Moreover, as the repairing may be cost-effective while remanufacturing, the manufacturer opts to repair the remaining items and resell them in the secondary market. The discrepancy resulted in a two-way loss to the retailer, as it reduced the product's business accessibility and built up the holding cost of the material. It is investigated with stochastic misplacement at the retailer's end, following the first kind's beta distribution. Idle time costs have also been considered. An analytical approach uses to derive the best policy for the provided model, and the numerical analysis illustrates the managerial perspicacity. The model's limitations have also been discussed, and a linear relationship is built between production cost and minimum selling price using the curve fitting least square method.</p>

Модель приоритетов обслуживания подач вагонов

Purpose: for the definition of optimal switch point of crane operation between platforms, to perform task simulation of priority choice in car delivery service, the task should feature penalty consideration for excessive idle delivery at freight front. Methods: Methods of probability theory, queuing and mathematical statistics as well as expert assessments are used to find distribution. Results: Priority model for car delivery service has been worked out. The dependences of average cost of idle time of two car delivery under freight operations - from the moment of switch between deliveries at distribution of Erlang of kth order of freight operation implementation time and the distribution special case — exponential one at mixed priority of car delivery service - are determined. Practical significance: Rational distribution of crane operation is proposed allowing to reduce terminal's costs for penalties for excessive idle delivery at freight front.

Modeling a remanufacturing reverse logistics planning problem: some insights into disruptive technology adoption

Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material flow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate different real-world scenarios. Through a set of numerical experiments, the influences of different demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era.

Application of chicken swarm optimization algorithm for multi objective scheduling problems in FMS

Scheduling plays an important role in Flexible Manufacturing System (FMS).Various Evolutionary Algorithms are used by different researchers to solve the multi objective scheduling problems. However, solutions obtained by some of these algorithms suffer from various issues such as struck in local optima, not support deadline constraint, poor convergence speed etc. In this paper, a new optimization technique called Chicken Swarm Optimization (CSO) algorithm is implemented for optimum scheduling of Multi objective Scheduling problems considered from the literature. The Combined Objective Function (COF) is formulated by considering two objectives such as Minimization of machine idle time and Penalty cost minimization with equal weight-ages. MATLAB code has developed to determine the COF values and also for implementing CSO to obtain the optimum solution. It is observed that, the results obtained by CSO algorithm are very competitive when compared with other well-known algorithms like Genetic Algorithms (GA), Cuckoos Search Algorithm (CSA), Modified Cuckoos Search Algorithm (MCSA) & Jaya Algorithm (JA)

A Study of Wagon Turnaround in the Current Context

Wagon turnaround remains the key performance indicator of railway transport operations characterizing the efficiency both of a wagon fleet owner and of “Russian Railways” Company (RZD). Freight logistics efficiency in the railway sector can also be assessed using this indicator. For determining the wagon turnaround time, the duration of operations at technical stations, freight handling time and the time in motion are taken into account. “Russian Railways” company and operator companies control the three components of the wagon turnaround time. The synergies of railways, intermodal types of transport, and private railways belonging to major freight owners are of particular interest. Their interaction efficiency can be calculated using the wagon turnaround formula that considers the freight idle time and the local operations factor. The local operations at the station for serving private tracks and port sidings are regulated by a number of documents that determine the procedure for supply and departure of wagons, the number of wagons in supply, the regularity of these operations, the number and series of shunting locomotives, etc. The optimal number of wagons in supply will be determined by the minimum cost of wagon movement and wagon idle time on the station tracks. The calculation of the cost function sensitivity will make it possible to determine the limits in the optimal wagon movement changes.

The effect of few historical data on the performance of sample average approximation method for operating room scheduling

AbstractWe model the scheduling problem of a single operating room for outpatient surgery, with uncertain case durations and an objective function comprising waiting time, idle time, and overtime costs. This stochastic scheduling problem has been studied in diverse forms. One of the most common approaches used is the sample average approximation (SAA). Our contribution is to study the use of SAA to solve this problem under few historical data using families of log t distributions with varying degrees of freedom. We analyze the results of the SAA method in terms of optimality convergence, the effect of the number of scenarios, and average computational time. Given the case sequence, computational results demonstrate that SAA with an adequate number of scenarios performs close to the exact method. For example, we find that the optimality gap, in units of proportional weighted time, is relatively small when 500 scenarios are used: 99% of the instances have an optimality gap of less than 2.6 7% (1.74%, 1.23%) when there are 3 (9, many) historical samples. Increasing the number of SAA scenarios improves performance, but is not critical when the case sequence is given. However, choosing the number of SAA scenarios becomes critical when the same method is used to choose among sequencing heuristics when there are few historical data. For example, when there are only three (nine, many) historical samples, 99% of the instances have less than 25.38% (13.15%, 6.87%) penalty in using SAA with 500 scenarios to choose the best sequencing heuristic.

Two-echelon imperfect production fuzzy supply chain model for reliability dependent demand with probabilistic deterioration and rework

This paper develops a fuzzy two-layer supply chain for manufacturer and retailer with defective and non-defective types of products. The manufacturer produces up to a specific time, including faulty and non-defective items, and after the screening, the non-defective item sends to the retailer. The retailer’s strategy is to do the screening of items received from the manufacturer; subsequently, the perfect quality items are used to fulfill the customer’s demand, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The supply chain considers probabilistic deterioration for the manufacturer and retailers along with the strategies such as production rate, unit production cost, cost of idle time of manufacturer, screening, rework, etc. The optimum average profit of the integrated model is evaluated for both the cases crisp and fuzzy environments. Managerial insights and the effect of changes in the parameters’ values on the optimal inventory policy under fuzziness are presented.

Imperfect and variable production cost in three-layer supply chain system incorporating deterioration and screening effect

This paper develops a three-layer supply chain for defective and non-defective types of produced items by supplier and manufacturer. The condition of the chain that without any financial liability on the manufacturer, defective items will be sent back to the supplier after screening. In the subsequent stage, the retailer accepts the non-defective items produced by the manufacturer after the screening and sends back the defective items to the manufacturer. Hence the retailer receives the perfect quality items for selling to the customers, but the retailer considers the effect of the deterioration of items. This model also considers the impact of several business strategies such as; optimal order size of raw materials, production rate, unit production cost, idle time costs of the supplier, and manufacturer in a collaborative marketing system, etc. to determine the optimum average profit of the integrated model. This study discusses the selling price of the retailer, demand rate of the customer, purchase cost of supplier and holding cost, which can be a significant breakthrough in expanding the profit of the business in real terms. Numerical example and sensitivity analysis are presented to illustrate the phenomenon of theoretical study and demonstrate the managerial implication of the model.

Coordinated appointment scheduling with multiple providers and patient-and-physician matching cost in specialty care

To achieve effective care, it is critical to match patients with capable physicians in specialty care. Motivated by the rising popularity of patient-and-physician matching applications in specialty care, this study optimizes the matching and appointment scheduling problems simultaneously in a stochastic environment, in which a decision-maker determines the patient-and-physician pair assignment and the starting times of services. We develop a stochastic optimization model to minimize the matching and operational costs (i.e., patients’ waiting time costs, service providers’ idle time and overtime costs). This paper is the first study that incorporates matching and appointment scheduling problems together. The benefits of combining these two problems are enormous. The experimental results show that the operational costs gap is as large as 51% between the ill-matched and the well-matched patient-and-physician scenarios. We first reformulate this problem as a two-stage optimization problem. With the analysis for the optimal solution of the second stage problem, a Benders decomposition algorithm is developed. To improve the efficiency of the proposed algorithm, we also prove a low bound of our problem and use it to construct a set of feasibility cuts. Then, we extend our method to incorporate no-shows. Our algorithm can solve problems efficiently, and it can obtain optimal solutions for medium-size problems within 2 or 3 min. In contrast, traditional optimal methods require nearly 2 h. For large-size problems, our algorithm can obtain optimal solutions within 5 or 6 min, whereas traditional optimal methods cannot generate a result within 5 h. Finally, numerical experiments are conducted to evaluate the performance of our proposed algorithm and to investigate the variation of the optimal solutions in different scenarios. To provide quality care as well as minimize the total cost of appointment scheduling in specialty care, we suggest that physicians should develop or train their specialties based on the local patients’ disease pattern. We also disclose that the no-show has less influence on the service system when the weight of the matching cost is substantial.

Multi-item a supply chain production inventory model of time dependent production rate and demand rate under space constraint in fuzzy environment

In this paper, we have developed an integrated production inventory model for two echelon supply chain consisting of one vendor and one retailer. Production rate and demand rate of retailer and customer are time dependent. Idle time cost of the vendor has been considered. Multi-item inventory has been considered. In integrated inventory model average cost has been calculated under limitation on stroge space. Two echelon supply chain fuzzy inventory model has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is illustrated to test the model. Finally to make the model more realistic, sensitivity analysis has been shown.

DEVELOPING THE PROCEDURE OF DIESEL FUEL CONSUMPTION AND TIMING CALCULATION FOR A SHUNTING HALF-RUN IN FLAT MARSHALLING YARDS

By today, the prerequisites for the design of marshalling devices have hardly changed in comparison with the standards applied in the last century. These standards were determined by comparing the reduced costs of design solutions with the prices that existed at that time. Current pricing policies and operating conditions are dramatically different. This is especially the case with the structure of the car fleet, the technical means used for car handling, and, most importantly, the cost of idle time of cars at stations, as well as the cost of fuel and energy resources. Thus, determining the amount of handling when it is advisable to use flat marshalling yards is an urgent scientific and applied problem, which cannot be solved without developing a procedure for calculating the duration of shunting half-runs and the diesel fuel consumption for shunting movements. In order to determine the amount of handling for which it is economically feasible to use flat marshalling facilities together with backward movement, diesel fuel consumption and the duration of shunting half-runs should be primarily calculated. These indicators are decisive in the feasibility study of the use of a sorting facility. To develop a procedure for calculating diesel fuel costs and duration of shunting half-runs, the type of shunting half-run is selected, which contains the following elements: acceleration, steady predetermined speed, inertia motion, and braking (if necessary). To estimate the diesel fuel consumption and the duration of a shunting half-run, an estimated train was made up. Rational positions of the driver’s controller and the rational length of the path the train passes by inertia and with braking are determined for the movement of this train.The developed procedure for calculation of diesel fuel consumption and timing of a shunting half-run can be used to determine the range of handling volumes, for which the use of flat sorting facilities and uncoupling of trains only by backward movement is economically justified. It should also be noted that in order to minimize operating costs for the execution of technological operations, the rational position of the driver’s controller for each shunting half-run and the path to be travelled by the shunting train by inertia and with braking should be determined. The substantiated design conditions for flat marshalling devices can be applied in the new edition of the Ukrainian industry building codes.

A Multi-Objective Cuckoo Search Algorithm Based on the Record Matrix for a Mixed-Model Assembly Line Car-Sequencing Problem

In the car-sequencing problem of mixed-model assembly lines, a series of cars with different model types will be put into the assembly line in a certain order considering a variety of goals and constraints. In this paper, a multi-objective cuckoo search algorithm based on the record matrix is proposed to solve this problem. In this algorithm, the factors, including the variation of parts usage rates, variation of workstation workload, idle time, overload time, and model switching cost are considered. The record matrix proposed in this paper is utilized to record the characteristic information of the optimal solutions and historical solutions. Meanwhile, two search strategies based on the record matrix are proposed to enhance the ability of local search and global search in the algorithm. The proposed algorithm is verified by a real case. The results show that the proposed model and algorithm have good results, and they have the potential to address other similar problems.

Multi-Objective Two Echelon Supply Chain Inventory Model with Lot Size and Customer Demand Dependent Purchase Cost and Production Rate Dependent Production Cost

This paper develops an integrated production inventory model for a two echelon supply chain consisting of one manufacturer and one retailer. Purchase cost for the manufacturer is dependent on inventory lot size, production cost of the manufacturer is dependent on production rate. Purchase cost of the retailer is dependent on demand rate of the customer. Idle time cost has been considered. Multi-item has been considered in this supply chain inventory model. Average cost in integrated inventory model has been calculated per unit time. The formulated problem has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is provided to justify the proposed model. Finally graphical illustrations are considered and its sensitivity analysis is provided to test feasibility of the model.

Minimization of Machine Idle Time and Penalty Cost in Flexible Manufacturing System Scheduling

This paper address the application of Jaya algorithm to solve Multi objective scheduling problem in Flexible Manufacturing System(FMS) to Minimize the Combined Objective Function(COF) Value. The effectiveness of this algorithm is tested on the problem of 43 jobs processed on 16 machines taken from literature. The MATLAB code is written to find best sequence and Combined Objective Function value by implementing Jaya Algorithm. Results obtained by Jaya Algorithm are compared with different algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Shortest Processing Time (SPT), Cuckoo Search (CS) and Modified Cuckoos Search (MCS) for the problem considered. It is observed from the results that COF value for the sequence obtained by Jaya Algorithm is better than other algorithms. It is concluded that the Jaya algorithm is best suitable for solving the Scheduling problem considered in Flexible Manufacturing System.

Individualized No‐Show Predictions: Effect on Clinic Overbooking and Appointment Reminders

Patient no‐shows and late cancellations lead to clinic inefficiency, high clinic costs and low patient satisfaction. The two main strategies clinics employed to alleviate the adverse effects of no‐shows are overbooking and patient appointment reminders. Developing effective overbooking schedules depends on accurately predicting each patient’s no‐show probability, while developing effective reminder systems requires a patient‐level estimate of communication sensitivity. Current methods of estimating no‐show probabilities do not produce such patient‐level predictions. To remedy this, we develop a Bayesian nested logit model which utilizes appointment confirmation data and estimates individual‐level coefficients for patient‐specific predictors. A log‐likelihood comparison of model fit on 12 months of appointment data shows that the Bayesian model outperforms the standard logit model by about 30% improvement in model fit. Additionally, our Bayesian model allows categorization of patients based on their appointment confirmation behavior. Finally, using patient‐specific no‐show probabilities as an input to a simulated appointment scheduler we find that the Bayesian model improves clinic profit over the standard logit model. The benefit comes mainly from waiting cost reduction when no‐show probability is low and from physician overtime and idle time cost reduction when no‐show probability is high. Our study has two managerial implications. First, the Bayesian method allows customizing appointment reminder effort based on patient’s confirmation behavior. Second, the Bayesian method also allows improved overbooking scheduling especially in clinics that experience large patient throughput.

A variability reduction method for the operating room scheduling problem under uncertainty using CVaR

Uncertain surgery durations in Operating Rooms (OR) can cause a large deviation from the expected completion time of all surgery cases scheduled for each day. When the deviation is significantly large, it causes an extended overtime for the surgical team to complete the scheduled cases, and it often creates unnecessarily excessive idle times. As a result, the hospital will lose revenue opportunities. To address this issue, this paper presents a risk-based solution approach using the concept of Conditional Value-at-Risk (CVaR) to reduce variability on overtime, idle time, and associated costs in a daily OR scheduling problem. The OR scheduling problem is formulated as a stochastic mixed-integer linear programming (SMILP) model, where a surgery duration follows a probability distribution function. The objective of the SMILP model is to minimize the CVaR of overtime and idle time costs. Numerical experiments are conducted on real-life benchmark instances, and showed that CVaR outperformed the widely used expected value (EV) approach in reducing variance of the total cost. As compared to the EV in terms of the total cost, the CVaR reduced the variance by 37%, produced a 25% lower interquartile range, and 24% lower median absolute deviation at a slight increase (4%) in the expected value.

An integrated cost and worker fatigue evaluation model of a packaging process

This paper proposes a model for managing a packaging process of small products at a production line. The aim of the paper is to determine the optimal size of a box the products are stored in considering both financial (in terms of cost) and ergonomic (in terms of worker fatigue) aspects. The paper first develops a biomechanical model to estimate the expected fatigue-recovery parameters associated with the packaging process. Secondly, it proposes an optimization model that minimizes the total relevant cost of the packaging process consisting of the cost of packaging material and the cost of working time, which includes the time required for packing items, setting up and handling boxes, transporting boxes to the shipping area, and idle time cost. Thirdly, the fatigue-recovery approach is integrated into the optimization model to determine the optimal box size from a given set of alternative boxes as well as the work schedule that minimize the total relevant cost while satisfying the upper bound on the estimated total (accumulated) fatigue level. The developed model is then analysed in a numerical experiment inspired by a case observed in industry. The results of the numerical analysis show that smaller box sizes are less sensitive to changes in the maximum permitted fatigue level and that they lead to lower total relevant cost for low wage cost. Larger boxes, in turn, are recommended for higher wage cost. The maximum permitted fatigue level and the box sizes also influence the fatigue range experienced by the worker as well as the share of productive time the worker spends on the job.