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BEATRICE BOLSI

Dottorando
Dipartimento di Scienze e Metodi dell'Ingegneria

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Pubblicazioni

2024 - Assigning multi-skill configurations to multiple servers with a Scenario-Based Planning and Recombination Approach [Articolo su rivista]
Bolsi, B.; Alves de Queiroz, T.; de Lima, V. L.; Kramer, A.; Iori, M.
abstract

This work deals with a dynamic problem arising from an outpatient healthcare facility. Patients with varying priorities arrive throughout the day, each with specific service requests that must be satisfied within target times. Failure to meet these targets incurs weighted tardiness penalties. Additionally, patients may choose to leave the system if subjected to prolonged waiting times, leading to further weighted penalties. The outpatient facility is equipped with multiple identical servers, each capable of providing a finite subset of services, referred to as configurations. The objective is to dynamically assign configurations, selected from a predefined set, to servers by minimizing the sum of weighted tardiness and abandonment penalties. Assignments are not fixed statically but can be dynamically changed over time to cope with the service requests. To address this problem, we propose a Scenario-Based Planning and Recombination Approach (SBPRA) that integrates an inner Reduced Variable Neighborhood Search. Differently from the traditional Scenario-Based Planning Approach (SBPA), which makes decisions based only on the solutions of individual scenarios, our approach solves an optimization problem to produce an additional solution that offers the best balance among the scenario solutions. Extensive tests on realistic instances show that SBPRA generates solutions that are 38% on average more effective than those generated by SBPA. Overall, the proposed approach can optimize resource allocation, mitigate the impact of patient abandonment, and improve the performance of the outpatient healthcare facility.

2023 - A Storage Location Assignment Problem with Incompatibility and Isolation Constraints: An Iterated Local Search Approach [Relazione in Atti di Convegno]
Mendes, N. F. M.; Bolsi, B.; Iori, M.
abstract

Centralised warehouses are a widespread practice in the healthcare supply chain, as they allow for the storage of large quantities of products a short distance from hospitals and pharmacies, allowing both a reduction in warehouse costs and prompt replenishment in case of shortages. However, for this practice to lead to effective optimization, warehouses need to be equipped with efficient order fulfillment and picking strategies, as well as a storage policy that takes into account the specific procedures of the various types of items, necessary to preserve their quality. In this framework, we present a storage location assignment problem with product-cell incompatibility and isolation constraints, modeling goals and restrictions of a storage policy in a pharmaceutical warehouse. In this problem, the total distance that order pickers travel to retrieve all required items in a set of orders must be minimised. An Iterated Local Search algorithm is proposed to solve the problem, numerical experiments based on simulated data are presented, and a detailed procedure is provided on how to retrieve and structure the warehouse layout input data. The results show a dramatic improvement over a greedy full turnover procedure commonly adopted in real-life operations.

2023 - Assigning Multi-skill Configurations to Multiple Servers with a Reduced VNS [Relazione in Atti di Convegno]
de Queiroz, T. A.; Bolsi, B.; de Lima, V. L.; Iori, M.; Kramer, A.
abstract

In this work, we deal with a dynamic problem arising from outpatient healthcare facility systems. Patients in need of service arrive during the day at the facility. Their requests are expected to be satisfied within a given target time, otherwise, tardiness is incurred. The facility has multiple identical servers that operate simultaneously and are in charge of providing the patients with the requested services. Each server can provide only a finite subset of services, and each subset is called a configuration. The objective is to assign to each server a configuration selected from a set of predefined configurations, aiming at minimizing total tardiness. Assignments are not fixed statically, but they can be dynamically changed over time to better cope with the requested services. As the problem nature is dynamic, we propose a re-optimization algorithm that periodically optimizes the assignments with a Reduced Variable Neighborhood Search (RVNS). The RVNS works on neighborhood structures based on changing the assignments of one or more servers. The RVNS has been extensively tested on realistic instances. The results prove its efficiency in reaching low-tardiness solutions under low computing time.

2022 - An Iterated Local Search for a Pharmaceutical Storage Location Assignment Problem with Product-cell Incompatibility and Isolation Constraints [Relazione in Atti di Convegno]
Mendes, N. F. M.; Bolsi, B.; Iori, M.
abstract

In healthcare supply chain, centralised warehouses are used to store large amounts of products close to hospitals and pharmacies in order to avoid shortages and reduce storage costs. To reach these objectives, the warehouses need to have efficient order retrieval and dispatch procedures, as well as a storage allocation policy able to guarantee the safe keeping of items. Considering this scenario, we present a Storage Location Assignment Problem with Product-Cell Incompatibility and Isolation Constraints, that models the targets and restrictions of a storage policy in a pharmaceutical product warehouse. In this problem, we aim to minimise the total distance travelled by the order pickers to recover all products required in a set of orders. We propose an Iterated Local Search algorithm to solve the problem, and present numerical experiments based on simulated data. The results show a relevant improvement with respect to a greedy full turnover procedure commonly adopted in real life operations.

2022 - Heuristic algorithms for integrated workforce allocation and scheduling of perishable products [Articolo su rivista]
Bolsi, B.; de Lima, V. L.; Alves de Queiroz, T.; Iori, M.
abstract

We study a problem from a real-world application, in which a daily set of orders must be processed following two stages, consisting of preparing perishable products on benches and allocating them to conveyors to be packed in disposable trays. Daily decisions must be made regarding the number and start time of working shifts, the number of workers and their allocation to machines, and the scheduling of orders in a two-stage flexible flow shop environment. The flow shop environment of the studied problem is common in many industries of perishable products, making the problem very general. The problem involves a number of operational constraints, and three objective functions that are minimised in a lexicographic way. To solve the problem, we implement a constructive heuristic and embed it within three metaheuristics: a Random multi-start algorithm (MR), a Biased random key genetic algorithm (BRKGA), and a Variable neighbourhood search (VNS) based one. We perform computational experiments over a set of realistic instances, and present a lower bound obtained from a constraint programming model for the scheduling counterpart. The results of the experiments show that the BRKGA is the most effective in practice for the integrated problem of workforce allocation and scheduling.

2022 - Optimizing a Dynamic Outpatient Facility System with Multiple Servers [Capitolo/Saggio]
Bolsi, B.; Kramer, A.; de Queiroz, T. A.; Iori, M.
abstract

The management of queues is a complex problem, and it requires special attention in dynamic environments where information changes over time. This work focuses on an outpatient facility system where patients are attended by identical parallel servers offering different services. Each patient requires service and expects to receive it within a given target time, after which, a tardiness is created. The objective of the problem is to minimize the total tardiness while defining which services each server will offer during the working hours. The arrival of patients is dynamic, and the server’s configurations of services can be updated from time to time. To solve the problem, we propose a local search-based heuristic that locally assigns a configuration to each server based on the improvement reached in terms of total tardiness. The heuristic is tested on realistic instances, considering different settings, showing its superiority over the solution currently implemented on the facility system.

2021 - Integrated Workforce Scheduling and Flexible Flow Shop Problem in the Meat Industry [Relazione in Atti di Convegno]
Bolsi, B.; de Lima, V. L.; de Queiroz, T. A.; Iori, M.
abstract

We address a problem from a meat company, in which orders are produced in two stages, consisting of preparing meats on benches and allocating them to conveyors to be packed in disposable trays. In an environment where machines are unrelated, the company has to take daily decisions on the number and start time of working periods, the number of workers and their allocation to machines, and the scheduling of activities to satisfy the required orders. The objective of the problem is to minimize, in a lexicographic way, the number of unscheduled activities, the weighted tardiness, and the total production cost. To solve the problem, we propose a multi-start random constructive heuristic, which tests different combinations of number of workers in the machines and for each combination produces many different schedules of the orders. The results of our computational experiments over realistic instances show that the heuristic is effective and can support the company on its daily decisions.