A data driven approach to reducing household food waste

Woolley, Elliot; Luo, Zhengfan; Jellil, Aicha; Simeone, Alessandro

doi:10.1016/j.spc.2021.11.004

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Although both the authors and this study seem to propose a DSU to recommend a menu for the household kitchen, the problem structure and the algorithms used are different. The authors of [26] use modified particle swarm optimization which relies on the size of the problem for optimality, while in this study a novel RL algorithm integrated with mathematical optimization is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets

Turgut

2024

Preprint

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Lack of proper planning is pointed out as one of the main culprits behind the significant consumer-end food waste. This observation can be used to build effective solutions, especially in digitalized societies. This paper proposes a reinforcement learning (RL) based decision support unit (DSU) in order to prevent food waste specifically for household and restaurant kitchens. The proposed RL model is improved with the help of mathematical modeling. The DSU assumes a generic kitchen environment for improving three important aspects of food management: the total shopping budget, the unused amount of ingredients, and the nutrition level offered by the selected menu. The RL-based algorithm is built to generate menu recommendations in a volatile market. All the practical contributions of the proposed planning tool to “monitor”, “prevent” and “reuse” levers of sustainable food strategy are summarized in a generic kitchen example. Results indicate that the proposed DSU can save around 30% from monthly shopping costs concurrently improving unused ingredients and nutritional intake. More insights about industrial processes such as package sizing and integration with “food sharing” services are also highlighted. Offering only one extra package size option for each ingredient in a market with volatile prices can help to diminish unused amounts completely and cut total shopping costs by 68% in a planned kitchen. Furthermore, the contribution of integrating mathematical optimization results into basic reinforcement learning implementation is observed as a 40% more reduction in the unused ingredients amount especially when the volatility of the market increases due to any disruption.

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Section: Literature Reviewmentioning

confidence: 99%

“…The methods used in DS-based studies range from discrete event simulation [20] to Markov decision processes [9]. To the best of our knowledge, the closest counterpart of the proposed DSU belongs to Woolley et al [26]. The authors propose another unit for planning the kitchen menu.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets

Turgut

2024

Preprint

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“…As seen in Fig 5 the different clusters are overlapped, hence in this group there are some factors regarding household FW. Specifically, consumer's habits [104][105][106]; wrong interpretation of the expiry date increases the FW [27,107]; low cooking skills negatively affect the reduction of FW [108,109]; purchasing and preparing correct portions of food can decrease FW [27,110]; the weight given to social behaviours and reputation can influence food management at the household level [86,[111][112][113][114].…”

Section: Green Cluster: "Economic Impacts and Different Types Of Inte...mentioning

confidence: 99%

Factors affecting food waste: A bibliometric review on the household behaviors

2023

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Sustainability issues such as food insecurity, climate change, land degradation, economic development and food waste are the actual most important challenges at the global level. Among them, the food waste (FW) challenge has a great magnitude, emphasizing the importance of examining this issue. Specifically, there is a need to focus on the household level. Thus, this study aims to investigate and identify the main factors influencing FW household behaviors on which policymakers and stakeholders could outline specific and sustainable strategies. Starting from a large number of published studies on this subject with a similar aim but focusing on specific Countries or contexts, the goal of our study is achieved through the implementation of a systematic literature review followed by a bibliometric review using the VOSviewer software. The selected query generated a total of 235 matching papers from which only 111 papers were collected for the bibliometric review because of the inclusion criteria. The analysis showed the existence of four major research strands: the largest one analyses the antecedents of behavior during food management, including the implementation of the Theory of Planned Behavior (TPB). Other detected topics are the economic impact of FW, the effects generated by the Covid-19 pandemic on consumer behaviors, and finally, the environmental and social effects of FW. The objective of this study is to investigate and identify the main factors influencing FW household behaviors. The obtained output represents useful information for policymakers and stakeholders to outline specific and sustainable strategies to reduce FW.

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“…Food waste can be minimized by creating a weekly shopping list of necessary goods. Individuals that implemented effective food management practices such as shopping lists and meal plans saw a reduction in food waste during the Covid-19 shutdown (Principato et al, 2020;Woolley et al, 2022). Whereas, the low level of wasting is caused by a lot of different things, but thrift and selfprovisioning are two of the most important (Sosna et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge about the negative consequences of wasting food, as well as the ability to interpret information on food product labels, were linked to lower levels of household food waste (Abu Hatab et al, 2022). Woolley et al (2022) discovered that knowledge about the negative consequences of wasting food was related to lower levels of household food waste. Population and children in a household greatly influence the quantity and composition of household food waste, while socioeconomic considerations and neighborhood food environment characteristics such as retail food availability, density, and closeness play a little role (Everitt et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Awareness of Household Food Waste Management with A Zero-Waste Concept: A Preliminary Study

Aprilia

Haryati

Dewi

et al. 2022

HBT

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This is pilot study designed to ascertain college students' understanding of domestic food waste management using an online questioner. The descriptive results section of the report is used to describe the central tendency of the data collected for each variable. Meanwhile, the people who took part in the research were all part of a webinar on waste management that was led by people from universities and environmental groups. The findings indicated that students' knowledge of domestic food waste management remained diverse. Some of them continue to view waste management as a time-consuming and demanding activity. Community understanding about waste management, especially food, should be given early and started at home. As a result, environmentally friendly activities must be regularly promoted and implemented at the school, university, government, and community levels in order to improve people's quality of life.

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A data driven approach to reducing household food waste

Cited by 9 publications

References 13 publications

Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets

Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets

Factors affecting food waste: A bibliometric review on the household behaviors

Awareness of Household Food Waste Management with A Zero-Waste Concept: A Preliminary Study

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