Christopher Garcia

Exact and approximate methods for parallel multiple-area spatial scheduling with release times

Spatial scheduling problems involve scheduling jobs that each require certain amounts of two-dimensional space within a processing area of limited width and length. Thus, this requires not only assigning time slots to each job but also locations and orientations within the limited physical processing space as well. Such problems, often encountered in shipbuilding and aircraft manufacturing, are generally difficult to solve, and there is a relatively small amount of literature addressing these problems compared to other types of scheduling. In this paper, we consider a particularly complex class of spatial scheduling problems that involve scheduling each job into one of several possible processing areas in parallel to minimize the total amount of tardy time. In addition, each job has a release time before which it may not be processed. We introduce two methods for solving this type of problem: an integer programming (IP) model and a heuristic algorithm. We perform computational tests and comparisons of each method over a large number of generated benchmark problems with varying characteristics, and also compare these to a more naïve heuristic. Solving the IP model was effective for small problems but required excessive amounts of time for larger ones. The heuristic was effective and produced solutions of comparable quality to the IP model for many problems while requiring very little computational time.

A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity

Purpose – The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem. Design/methodology/approach – The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time re...

Optimal multiunit transfer over adversarial paths with increasing intercept probabilities

Abstract We consider a problem involving transporting a set of items over a set of hostile paths where an adversary seeks to intercept them, with the goal of maximizing the probability that all items successfully cross. Items leave a unique footprint as they cross a path, and the probability that an item is intercepted on a given path increases according to an intercept probability function as the cumulative footprint on that path increases. We provide a problem formulation and demonstrate several properties important for its solution. We then use these to develop four optimization algorithms: an exact algorithm (E), a greedy heuristic (G), a greedy heuristic with local search (LS), and a genetic algorithm (GA). These algorithms were evaluated via computational experiments on a large set of benchmark problems spanning different sizes and characteristics. LS provided the largest number of best solutions while outperforming GA in terms of solution time.

Approximation Algorithms for Spatial Scheduling

Spatial scheduling problems involve a set of jobs which have spatial dimensions in addition to traditional scheduling considerations such as due dates or processing times. In these problems processing space is a limited resource, and the scheduling decisions must determine both when and where the each job will be processed as well as each job’s layout orientation. Spatial scheduling problems find many real-world applications in industries such as shipbuilding and aircraft assembly, where there is limited working space available and tasks utilize significant amounts of spatial resources in their completion. In this chapter we discuss spatial scheduling and present several heuristic and metaheuristic algorithms for this problem class.

A nearest-neighbor algorithm for targeted interaction design in social outreach campaigns

Purpose Organizations rely on social outreach campaigns to raise financial support, recruit volunteers, and increase public awareness. In order to maximize response rates, organizations face the challenging problem of designing appropriately tailored interactions for each user. An interaction consists of a specific combination of message, media channel, sender, tone, and possibly many other attributes. The purpose of this paper is to address the problem of how to design tailored interactions for each user to maximize the probability of a desired response. Design/methodology/approach A nearest-neighbor (NN) algorithm is developed for interaction design. Simulation-based experiments are then conducted to compare positive response rates obtained by two forms of this algorithm against that of several control interaction design strategies. A factorial experimental design is employed which varies three user population factors in a combinatorial manner, allowing the methods to be compared across eight distinct scenarios. Findings The NN algorithms significantly outperformed all three controls in seven out of the eight scenarios. Increases in response rates ranging from approximately 20 to 400 percent were observed. Practical implications This work proposes a data-oriented method for designing tailored interactions for individual users in social outreach campaigns which can enable significant increases in positive response rates. Additionally, the proposed algorithm is relatively easy to implement. Originality/value The problem of optimal interaction design in social outreach campaigns is scarcely addressed in the literature. This work proposes an effective and easy to implement solution approach for this problem.

Resource-constrained scheduling with hard due windows and rejection penalties

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.

SQL Injection: A Demonstration and Implications for Accounting Students

ABSTRACT The purpose of this paper is to present a pedagogical case that demonstrates how a prevalent cybersecurity threat, SQL Injection (SQLi), operates. Prompted by questions from students such as: “How do cybersecurity threats work?” and “What specific actions can organizations take to mitigate cybersecurity threats?”, this paper demonstrates the technical inner-working of SQLi. Students first answer background questions on SQLi and then simulate SQLi in both a Microsoft Access and web-based environment.

The optimal exam experience: a timetabling approach to prevent student cheating and fatigue

University exam planning is a notoriously difficult endeavour that involves satisfying numerous classroom capacity and conflict-prevention constraints. Accordingly, exam timetabling has received much attention in the literature over the years. Despite research advances, student cheating and fatigue continue to persist and are typically addressed separately from timetabling considerations. In this paper, we show how both problems can be addressed effectively by incorporating such considerations into the timetabling. We address a real problem occurring at the Turkish Air Force Academy (TUAFA) and develop two versions of an integer programming model. The first finds a feasible timetable while respecting cheating and fatigue-prevention constraints. The second maximises classroom utilisation, decreasing both the number of classrooms and proctors required. Computational experiments using TUAFA data show that both models are tractable in practice and that by incorporating the objective function, classroom utilisation can be increased by 55% leading to a 43% reduction in required classrooms.

Optimising coordinated multi-team volunteer scheduling: a combined constraint programming and goal programming approach

Volunteer scheduling is often more difficult than other types of labour scheduling because the non-compulsory nature of such work requires organisations to respect many more preferences and constraints. Furthermore, there is very little existing research in this type of scheduling. In this paper, we describe a software system based on constraint programming (CP) and goal programming (GP) we developed to enable a volunteer-run church to schedule its weekly operations. Multiple volunteer teams operate each week within this organisation, and each team has a required number of weekly volunteers. Each team has its own scheduling objective: either to maximise the amount of time each worker has off or to maximise worker participation. Conflicts between jobs and workers must also be prevented in addition to respecting several types of worker preferences. We develop a CP–GP approach for solving this problem and discuss the computational results obtained.