A Practical Application of Market-Based Mechanisms for Allocating Harvesting Tasks

Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection

Sklar

2022

Self Cite

The food production industry faces the complex challenge of scheduling both production and hygiene tasks. These tasks are typically scheduled manually. However, due to the increasing costs of raw materials and the regulations factories must adhere to, inefficiencies can be costly. This paper presents the initial findings of a survey, conducted to learn more about the hygiene tasks within the industry and to inform research on how multi-agent task allocation (MATA) methodologies could automate and improve the scheduling of hygiene tasks. A simulation of a heterogeneous human workforce within a factory environment is presented. This work evaluates experimentally different strategies for applying market-based mechanisms, in particular Sequential Single Item (SSI) auctions, to the problem of allocation hygiene tasks to a heterogeneous workforce.

Section: Adaption Of Mata Methodologies To Food Factory Hygiene Tasksmentioning

confidence: 99%

Section: Adaption Of Mata Methodologies To Food Factory Hygiene Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards the Application of Multi-Agent Task Allocation to Hygiene Tasks in the Food Production Industry

Owen

Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection

Sklar

2022

Self Cite

“…Agents bid on these tasks and an auction manager assigns each task to the agent that presents the bid with the lowest cost; the cost is computed based on an approximated duration to complete the task. The work presented in this section builds on our prior work ( Harman and Sklar, 2021a , b ). This paper presents an evaluation of our approach using the teams created as per the previous section, a comparison of the different task allocation mechanisms and an additional performance metric.…”

Section: Methodsmentioning

confidence: 99%

“…Our strategy takes into consideration the practical challenges associated with transferring a laboratory approach into a real-world setting. Our previous work ( Harman and Sklar, 2021a , b ) evaluated different ratios of pickers to runners. An overview of our team allocation method appeared in an extended abstract ( Harman and Sklar, 2022a ).…”

Section: Introductionmentioning

confidence: 99%

Multi-agent task allocation for harvest management

Sklar²

2022

Front. Robot. AI

Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that are ready to be harvested and team leaders who manage the workers in the fields. Creating these assignments is a dynamic and complex problem, as the skill of the workforce and the yield (quantity of ripe fruit picked) are variable and not entirely predictable. The work presented here posits that multi-agent task allocation methods can assist farm managers and team leaders to manage the harvesting workforce effectively and efficiently. There are three key challenges faced when adapting multi-agent approaches to this problem: (i) staff time (and thus cost) should be minimised; (ii) tasks must be distributed fairly to keep staff motivated; and (iii) the approach must be able to handle incremental (incomplete) data as the season progresses. An adapted variation of Round Robin (RR) is proposed for the problem of assigning workers to fields, and market-based task allocation mechanisms are applied to the challenge of assigning tasks to workers within the fields. To evaluate the approach introduced here, experiments are performed based on data that was supplied by a large commercial soft fruit farm for the past two harvesting seasons. The results demonstrate that our approach produces appropriate worker-to-field allocations. Moreover, simulated experiments demonstrate that there is a “sweet spot” with respect to the ratio between two types of in-field workers.

Multi-Agent Task Allocation Techniques for Harvest Team Formation

Lecture Notes in Computer Science

Sklar

2022

Self Cite

With increasing demands for soft fruit and shortages of seasonal workers, farms are seeking innovative solutions for efficiently managing their workforce. The harvesting workforce is typically organised by farm managers who assign workers to the fields that are ready to be harvested. They aim to minimise staff time (and costs) and distribute work fairly, whilst still picking all ripe fruit within the fields that need to be harvested. This paper posits that this problem can be addressed using multi-criteria, multi-agent task allocation techniques. The work presented compares the application of Genetic Algorithms (GAs) vs auctionbased approaches to the challenge of assigning workers with various skill sets to fields with various estimated yields. These approaches are evaluated alongside a previously suggested method and the teams that were manually created by a farm manager during the 2021 harvesting season. Results indicate that the GA approach produces more efficient team allocations than the alternatives assessed.