A two-stage skilled manpower planning model with demand uncertainty

2018 ◽  
Vol 11 (4) ◽  
pp. 526-551 ◽  
Author(s):  
Mohsen Sadeghi-Dastaki ◽  
Abbas Afrazeh
Keyword(s):  
Large Scale ◽  
Demand Uncertainty ◽  
Benders Decomposition ◽  
Latin Hypercube Sampling ◽  
Sample Average Approximation ◽  
Manpower Planning ◽  
Mixed Integer ◽  
Planning Model ◽  
Two Stage ◽  
Content Type

Purpose Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed. Design/methodology/approach To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method. Findings The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research. Originality/value Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

scholarly journals Optimal Torpedo Scheduling

2018 ◽  
Vol 63 ◽  
pp. 955-986 ◽  
Author(s):  
Adrian Goldwaser ◽  
Andreas Schutt
Keyword(s):  
Large Scale ◽  
Benders Decomposition ◽  
Steel Production ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Hot Metal ◽  
Rail Network ◽  
Sulfur Level ◽  
First Time ◽  
Resource Capacity

We consider the torpedo scheduling problem in steel production, which is concerned with the transport of hot metal from a blast furnace to an oxygen converter. A schedule must satisfy, amongst other considerations, resource capacity constraints along the path and the locations traversed as well as the sulfur level of the hot metal. The goal is first to minimize the number of torpedo cars used during the planning horizon and second to minimize the time spent desulfurizing the hot metal. We propose an exact solution method based on Logic based Benders Decomposition using Mixed-Integer and Constraint Programming, which optimally solves and proves, for the first time, the optimality of all instances from the ACP Challenge 2016 within 10 minutes. In addition, we adapted our method to handle large-scale instances and instances with a more general rail network. This adaptation optimally solved all challenge instances within one minute and was able to solve instances of up to 100,000 hot metal pickups.

Service capacity coordination in IT services supply chain

2018 ◽  
Vol 13 (4) ◽  
pp. 952-972 ◽  
Author(s):  
Sanjay Kumar Prasad ◽  
Ravi Shankar
Keyword(s):  
Supply Chain ◽  
Demand Uncertainty ◽  
Degree Of Substitution ◽  
Future Research ◽  
It Services ◽  
Two Stage ◽  
Content Type ◽  
Optimal Capacity ◽  
Automated Software ◽  
Capacity Cost

Purpose The purpose of this paper is to investigate capacity coordination in services supply chain (SSC). It provides discussion and application of various contracts in a two-stage single period SSC. Design/methodology/approach This paper considers a two-stage serial supply chain with demand uncertainty and price insensitivity. A model is developed to represent a global IT SSC incorporating services specific factors like over-capacity cost and higher degree of substitution resulting in flexibility to meet unplanned demand. At first, centralized and competitive solutions of the model are studied. Then, the paper studies coordination in this supply chain using some of widely used contract templates. Findings This paper finds several key insights for the researchers and practitioners in this area around adverse impact of over-capacity cost on demand, positive effect of delivery team’s exposure to market on contracting terms and better understanding of efficient frontiers for selected contracting mechanism. Research limitations/implications This paper has limited its analysis to three key and most widely used contracts and made assumptions about risk-neutrality of the firms. Future research can study other contracting templates and/or relax for the model as laid out in this paper. Practical implications An automated software agent can be built leveraging the closed form equations developed here to help decide on optimal capacity investment and devise coordinating contracts. Originality/value This paper established that because of higher degree of substitution, perishability and non-trivial over-capacity cost, SSC behave bit differently than the physical goods supply chain and coordination of participating firms needs to be studied in a services specific context for improving system-wide performance.

scholarly journals Stabilized Benders Methods for Large-Scale Combinatorial Optimization, with Application to Data Privacy

2020 ◽  
Vol 66 (7) ◽  
pp. 3051-3068 ◽  
Author(s):  
Daniel Baena ◽  
Jordi Castro ◽  
Antonio Frangioni
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Benders Decomposition ◽  
Mixed Integer ◽  
Master Problem ◽  
Continuous Variables ◽  
Typical Structure ◽  
Disclosure Control ◽  
Current State ◽  
Statistical Disclosure

The cell-suppression problem (CSP) is a very large mixed-integer linear problem arising in statistical disclosure control. However, CSP has the typical structure that allows application of the Benders decomposition, which is known to suffer from oscillation and slow convergence, compounded with the fact that the master problem is combinatorial. To overcome this drawback, we present a stabilized Benders decomposition whose master is restricted to a neighborhood of successful candidates by local-branching constraints, which are dynamically adjusted, and even dropped, during the iterations. Our experiments with synthetic and real-world instances with up to 24,000 binary variables, 181 million (M) continuous variables, and 367M constraints show that our approach is competitive with both the current state-of-the-art code for CSP and the Benders implementation in CPLEX 12.7. In some instances, stabilized Benders provided a very good solution in less than 1 minute, whereas the other approaches found no feasible solution in 1 hour. This paper was accepted by Yinyu Ye, optimization.

scholarly journals Micro Grid Energy Management using DE Algorithm with BDC theorem

2019 ◽  
Vol 8 (4S2) ◽  
pp. 334-338
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Benders Decomposition ◽  
Reactive Power ◽  
Differential Evolution Algorithm ◽  
Decomposition Theorem ◽  
Drop Out ◽  
Mixed Integer ◽  
De Algorithm ◽  
Micro Grid

this paper evaluates combination of DE algorithm and benders decomposition theorem of VMG is used to solving the large scale mixed integer programming problems. DE algorithm is implemented in Village area Micro grids. Village area micro grid and the required load is calculated regarding the sold out power or purchase power with the help of DE algorithm. Differential evolution algorithm is applied in the village area micro grid and measure the real power, reactive power of various power plants. . In this DE algorithm is implemented in village area micro grid and the survey period is two years. Final survey shows which month produce more power and sold out power in nearest city area electricity board, But in power shortage in village area micro grid ,it purchase the power from nearest electricity board.DE algorithm determine the one month power survey and benders decomposition determine the individual value of the power plants. But the benders decomposition theorem is not accept the non linearity items. To overcome this problem BDCT and DEA is implemented in village area micro grid. This combination is used to maintain the drop out voltage of any power plants.

Protecting vulnerable people during pandemics through home delivery of essential supplies: a distribution logistics model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eric Breitbarth ◽  
Wendelin Groβ ◽  
Alexander Zienau
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Service Providers ◽  
Home Delivery ◽  
Vulnerable Population ◽  
Mixed Integer ◽  
Baseline Scenario ◽  
Content Type ◽  
Distribution Logistics ◽  
The Impact

PurposeThis paper studies a concept for protecting vulnerable population groups during pandemics using direct home deliveries of essential supplies, from a distribution logistics perspective. The purpose of this paper is to evaluate feasible and resource-efficient home delivery strategies, including collaboration between retailers and logistics service providers based on a practical application.Design/methodology/approachA food home delivery concept in urban areas during pandemics is mathematically modeled. All seniors living in a district of Berlin, Germany, represent the vulnerable population supplied by a grocery distribution center. A capacitated vehicle routing problem (CVRP) is developed in combination with a k-means clustering algorithm. To manage this large-scale problem efficiently, mixed-integer programming (MIP) is used. The impact of collaboration and additional delivery scenarios is examined with a sensitivity analysis.FindingsRoughly 45 medically vulnerable persons can be served by one delivery vehicle in the baseline scenario. Operational measures allow a drastic decrease in required resources by reducing service quality. In this way, home delivery for the vulnerable population of Berlin can be achieved. This requires collaboration between grocery and parcel services and public authorities as well as overcoming accompanying challenges.Originality/valueDeveloping a home delivery concept for providing essential goods to urban vulnerable groups during pandemics creates a special value. Setting a large-scale CVRP with variable fleet size in combination with a clustering algorithm contributes to the originality.

scholarly journals An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2116 ◽  
Author(s):  
Zipeng Liang ◽  
Haoyong Chen ◽  
Xiaojuan Wang ◽  
Idris Ibn Idris ◽  
Bifei Tan ◽  
...  
Keyword(s):  
Wind Power ◽  
Transmission Lines ◽  
Benders Decomposition ◽  
Electrical Power ◽  
Mixed Integer ◽  
Two Stage ◽  
Operating Variables ◽  
Expansion Planning ◽  
Power Resources ◽  
Bus System

The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method.

scholarly journals A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3756 ◽  
Author(s):  
Saravanan Venkatachalam ◽  
Kaarthik Sundar ◽  
Sivakumar Rathinam
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fuel Consumption ◽  
Large Scale ◽  
Deterministic Model ◽  
Optimal Solution ◽  
Sample Average Approximation ◽  
Stage Model ◽  
Two Stage ◽  
Routing Problem ◽  
Aerial Vehicles

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.

A parallel algorithm for optimizing the capital structure contingent on maximum value at risk

Kybernetes ◽  
2015 ◽  
Vol 44 (3) ◽  
pp. 384-405 ◽  
Author(s):  
Ralf Östermark
Keyword(s):  
Parallel Processing ◽  
Capital Structure ◽  
Value At Risk ◽  
Financial Risk ◽  
Large Scale ◽  
Disjunctive Programming ◽  
Mixed Integer ◽  
Future Research ◽  
General Validity ◽  
Content Type

Purpose – The purpose of this paper is to measure the financial risk and optimal capital structure of a corporation. Design/methodology/approach – Irregular disjunctive programming problems arising in firm models and risk management can be solved by the techniques presented in the paper. Findings – Parallel processing and mathematical modeling provide a fruitful basis for solving ultra-scale non-convex general disjunctive programming (GDP) problems, where the computational challenge in direct mixed-integer non-linear programming (MINLP) formulations or single processor algorithms would be insurmountable. Research limitations/implications – The test is limited to a single firm in an experimental setting. Repeating the test on large sample of firms in future research will indicate the general validity of Monte-Carlo-based VAR estimation. Practical implications – The authors show that the risk surface of the firm can be approximated by integrated use of accounting logic, corporate finance, mathematical programming, stochastic simulation and parallel processing. Originality/value – Parallel processing has potential to simplify large-scale MINLP and GDP problems with non-convex, multi-modal and discontinuous parameter generating functions and to solve them faster and more reliably than conventional approaches on single processors.

scholarly journals Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints

2021 ◽  
Vol 11 (23) ◽  
pp. 11240
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee
Keyword(s):  
Waiting Time ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Flow Shop ◽  
Mixed Integer ◽  
Time Constraints ◽  
Two Stage ◽  
Second Stage ◽  
Type Flow

This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the final product. In the flow shop, the assembly operations in the second stage should start within the limited waiting times after those components complete in the first stage. For this problem, a mixed-integer programming formulation is provided, and this formulation is used to find an optimal solution using a commercial optimization solver CPLEX. As this problem is proved to be NP-hard, various heuristic algorithms (priority rule-based list scheduling, constructive heuristic, and metaheuristic) are proposed to solve a large-scale problem within a short computation time. To evaluate the proposed algorithms, a series of computational experiments, including the calibration of the metaheuristics, were performed on randomly generated problem instances, and the results showed outperformance of the proposed iterated greedy algorithm and simulated annealing algorithm in small- and large-sized problems, respectively.

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