Targeting and scheduling problem for field artillery

Kwon, Ojeong; Lee, Kyungsik

doi:10.1016/s0360-8352(97)00224-6

Cited by 15 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This value can range from 0 min to 2 min. Accordingly, p j and s jj are randomly generated in [1,3] and [0,2], respectively. The numerical experiments were implemented using the IBM ILOG CPLEX 12.6.1 (IBM software, New York, NY, USA).…”

Section: Resultsmentioning

confidence: 99%

“…The firing sequence problem was first studied by Kwon et al [1], who proposed a greedy heuristic to minimize the firing completion time of multiple artillery units. In their work, multiple artillery units were restricted to fire at a single target simultaneously to achieve surprise effects and sufficient destruction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Owing to the lower destructive power and performance of older artillery shells, multiple artillery units were required to fire simultaneously at a single enemy target to achieve surprise effects and sufficient destruction. Thus, the determination of the firing sequence of multiple artillery units was considered to minimize the firing operation completion time [1]. However, modern artillery shells have high destructive power, and the shell launch system is automated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem

2017

View full text Add to dashboard Cite

Abstract:In this study, deterministic and robust optimization models for single artillery unit fire scheduling are developed to minimize the total enemy threat to friendly forces by considering the enemy target threat level, enemy target destruction time, and target firing preparation time simultaneously. Many factors in war environments are uncertain. In particular, it is difficult to evaluate the threat levels of enemy targets definitively. We consider the threat level of an enemy target to be an uncertain parameter and propose a robust optimization model that minimizes the total enemy threat to friendly forces. The robust optimization model represents a semi-infinite problem that has infinitely many constraints. Therefore, we reformulate the robust optimization model into a tractable robust counterpart formulation with a finite number of constraints. In the robust counterpart formulation with cardinality-constrained uncertainty, the conservativeness and robustness of the solution can be adjusted with an uncertainty degree, Γ. Further, numerical experiments are conducted to verify that the robust counterpart formulation with cardinality-constrained uncertainty can be made equivalent to the deterministic optimization model and the robust counterpart formulation with box uncertainty by setting Γ accordingly.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem

2017

View full text Add to dashboard Cite

show abstract

“…Nadler and Eilbott (1971) and Barr and Piper (1972) analyze shoot-adjust-shoot strategies where the shooter incorporates feedback from earlier shots to improve his aim. Kwon, Lee, and Park (1997) and Cha and Kim (2010) develop algorithms to schedule when each artillery cannon should fire at specific targets. All these articles are complementary to our work by providing realistic parameter estimates for our model.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of artillery shoot‐and‐scoot tactics

Shim

Atkinson

2018

Naval Research Logistics

View full text Add to dashboard Cite

Firing multiple artillery rounds from the same location has two main benefits: a high rate of fire at the enemy and improved accuracy as the shooter's aim adjusts to previous rounds. However, firing repeatedly from the same location carries significant risk that the enemy will detect the artillery's location. Therefore, the shooter may periodically move locations to avoid counter‐battery fire. This maneuver is known as the shoot‐and‐scoot tactic. This article analyzes the shoot‐and‐scoot tactic for a time‐critical mission using Markov models. We compute optimal move policies and develop heuristics for more complex and realistic settings. Spending a reasonable amount of time firing multiple shots from the same location is often preferable to moving immediately after firing an initial salvo. Moving frequently reduces risk to the artillery, but also limits the artillery's ability to inflict damage on the enemy.

show abstract

“…Reference [7] modified the WTAP into integer programming and network flow problems and developed an exact algorithm and a heuristic algorithm. The FSP was first introduced by Reference [8]; they proposed a greedy heuristic to minimize the makespan. Reference [9] assumed that the probability of target destruction decreased with time and proposed a scheduling heuristic to minimize the total threat from surviving targets.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization Approach Using Scenario Concepts for Artillery Firing Scheduling Under Uncertainty

et al. 2019

View full text Add to dashboard Cite

Real wars involve a considerable number of uncertainties when determining firing scheduling. This study proposes a robust optimization model that considers uncertainties in wars. In this model, parameters that are affected by enemy's behavior and will, i.e., threats from enemy targets and threat time from enemy targets, are assumed as uncertain parameters. The robust optimization model considering these parameters is an intractable model with semi-infinite constraints. Thus, this study proposes an approach to obtain a solution by reformulating this model into a tractable problem; the approach involves developing a robust optimization model using the scenario concept and finding a solution in that model. Here, the combinations that express uncertain parameters are assumed by scenarios. This approach divides problems into master and subproblems to find a robust solution. A genetic algorithm is utilized in the master problem to overcome the complexity of global searches, thereby obtaining a solution within a reasonable time. In the subproblem, the worst scenarios for any solution are searched to find the robust solution even in cases where all scenarios have been expressed. Numerical experiments are conducted to compare robust and nominal solutions for various uncertainty levels to verify the superiority of the robust solution.

show abstract

Targeting and scheduling problem for field artillery

Cited by 15 publications

References 6 publications

Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem

Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem

Analysis of artillery shoot‐and‐scoot tactics

Robust Optimization Approach Using Scenario Concepts for Artillery Firing Scheduling Under Uncertainty

Contact Info

Product

Resources

About