Scheduled Commit

data/retrieval_battle-76c7b5f4-5fbc-4311-bf05-8defcdc1ba4e.jsonl

@@ -44,3 +44,5 @@
 {"tstamp": 1722925504.6514, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "81716936957c4959a0184ced3c23b968", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who is first PM of india?", "0_output": [["who is first PM of india?", "Manmohan Singh\n\nManmohan Singh (; born 26 September 1932) is an Indian retired politician, economist, academician and bureaucrat who served as the 13th Prime Minister of India from 2004 to 2014. He is the fourth longest-serving prime minister after Jawaharlal Nehru, Indira Gandhi and Narendra Modi. A member of the Indian National Congress, Singh was the first Sikh prime minister of India. He was also the first prime minister since Jawaharlal Nehru to be re-elected after completing a full five-year term.\nBorn in Gah, West Punjab, in what is today Pakistan, Singh's family migrated to India during its partition in 1947. After obtaining his doctorate in economics from Oxford, Singh worked for the UN during 1966–1969. He subsequently began his bureaucratic career when Lalit Narayan Mishra hired him as an advisor in the Ministry of Commerce and Industry. During the 1970s and 1980s, Singh held several key posts in the Government of India, such as Chief Economic Advisor (1972–1976), governor of the Reserve Bank (1982–1985) and head of the Planning Commission (1985–1987)."]], "0_corpus": "wikipedia", "1_conv_id": "2623f5c18a1d4d8db6babab7e15aced9", "1_model_name": "embed-english-v3.0", "1_prompt": "who is first PM of india?", "1_output": [["who is first PM of india?", "List of prime ministers of India\n\nSince 1947, India has had 14 prime ministers. Jawaharlal Nehru was India's first prime minister, serving as prime minister of the Dominion of India from 15 August 1947 until 26 January 1950, and thereafter of the Republic of India until his death in May 1964. (India conducted its first post-independence general elections in 1952). Earlier, Nehru had served as prime minister of the Interim Government of India during the British Raj from 2 September 1946 until 14 August 1947, his party, the Indian National Congress having won the 1946 Indian provincial elections. Nehru was succeeded by Lal Bahadur Shastri, whose 1 year 7-month term ended in his death in Tashkent, then in the USSR, where he had signed the Tashkent Declaration between India and Pakistan. Indira Gandhi, Nehru's daughter, succeeded Shastri in 1966 to become the country's first female prime minister. Eleven years later, her party the Indian National Congress lost the 1977 Indian general election to the Janata Party, whose leader Morarji Desai became the first non-Congress prime minister. After Desai resigned in 1979, his former associate Charan Singh briefly held office until the Congress won the 1980 Indian general election and Indira Gandhi returned as prime minister. Her second term as prime minister ended five years later on 31 October 1984, when she was assassinated by her bodyguards. Her son Rajiv Gandhi was sworn in as India's youngest premier. Members of NehruGandhi family have been prime minister for approximately 38 years."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722925589.3373, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "45c2e0a964cd4ad6b902f731f30da5db", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Open-access dataset for training AI on instruction-following tasks in 114 languages.", "0_output": [["Open-access dataset for training AI on instruction-following tasks in 114 languages.", "Title: The Structures of Distant Galaxies - III: The Merger History of over 20,000 Massive Galaxies at z < 1.2\n\nAbstract: Utilizing deep Hubble Space Telescope imaging from the two largest field galaxy surveys, the Extended Groth Strip (EGS) and the COSMOS survey, we examine the structural properties, and derive the merger history for 21,902 galaxies with M_*>10^{10} M_0 at z<1.2. We examine the structural CAS parameters of these galaxies, deriving merger fractions, at 0.2<z<1.2, based on the asymmetry and clumpiness values of these systems. We find that the merger fraction between z=0.2 and z=1.2 increases from roughly f_m=0.04+/-0.01 to f_m=0.13+/-0.01. We explore several fitting formalisms for parameterising the merger fraction, and compare our results to other structural studies and pair methods within the DEEP2, VVDS, and COSMOS fields. We also re-examine our method for selecting mergers, and the inherent error budget and systematics associated with identifying mergers using structure. For galaxies selected by M_*>10^{10} M_0, the merger fraction can be parameterised by f_m = f_0*(1+z)^m with the power-law slope m=2.3+/-0.4. By using the best available z = 0 prior the slope increases to m=3.8+/-0.2, showing how critical the measurement of local merger properties are for deriving the evolution of the merger fraction. We furthermore show that the merger fraction derived through structure is roughly a factor of 3-6 higher than pair fractions. Based on the latest cosmological simulations of mergers we show that this ratio is predicted, and that both methods are likely tracing the merger fraction and rate properly. We calculate, utilising merger time scales from simulations, and previously published merger fractions that the merger rate of galaxies with M_*>10^{10} M_0 increases linearly between z = 0.7 and z = 3, and that a typical M_*>10^{10} M_0 galaxy undergoes between 1-2 major mergers at z<1.2."]], "0_corpus": "arxiv", "1_conv_id": "8a819022d7b640b48ced37afcdb98934", "1_model_name": "embed-english-v3.0", "1_prompt": "Open-access dataset for training AI on instruction-following tasks in 114 languages.", "1_output": [["Open-access dataset for training AI on instruction-following tasks in 114 languages.", "Title: Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning\n\nAbstract: Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the finetuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and translating existing datasets across 114 languages. In total, we contribute four key resources: we develop and open-source the Aya Annotation Platform, the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as a valuable framework for future research collaborations that aim to bridge gaps in resources."]], "1_corpus": "arxiv"}
 {"tstamp": 1722927476.8423, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "562d4f2d900a482dbae0a1164fde5eb6", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "0_output": [["how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "Mathematica Stackexchange\n\nQ: Is there any better way to create {a, a, a, b, b, b, b, b, c, c} from {{a, 3}, {b, 5}, {c, 2}}? My attempt:\ndata = {{a, 3}, {b, 5}, {c, 2}};\n\noutput = Table[#[[1]], #[[2]]] & /@ data // Flatten\n\n\n{a, a, a, b, b, b, b, b, c, c}\n\n\nA: Join @@ Table @@@ data\n\n\n{a, a, a, b, b, b, b, b, c, c}\n\n\nor\nJoin @@ ConstantArray @@@ data\n\n\n{a, a, a, b, b, b, b, b, c, c}\n\n\n## & @@ PadRight[{}, #2, {#}] & @@@ data\n\n\n{a, a, a, b, b, b, b, b, c, c}\n\n\n## & @@ PadRight[{#}, #2, \"Fixed\"] & @@@ data\n\n\n{a, a, a, b, b, b, b, b, c, c}\n\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "eacef2155e1b40c1ab710b48af60d343", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "1_output": [["how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "Stackoverflow Stackexchange\n\nQ: Using VirtualBox for Mobile App Development? I keep wanting to use VitualBox for Mobile App Development, but I can't seem to get the emulator to run fast enough inside the already-emulated 32-bit machine.\nIs there any way around this?\nNow while it doesn't seem to me like there is an answer to this question, I figured I'd ask it anyway.\nP.S. I have a 64-bit machine.\n\nA: This should do the trick: 4 (or more) core CPU, plenty of ram (>4GB) and install the virtual operating system on a disk other than the one your main OS uses (e.g. firewire/esata external drive).\nAnother virtualization software might be faster too, but that I don't know.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722927629.7055, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "179f00b150c44afdaa92de74eda5ecf1", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "are you a man or a mouse?", "0_output": [["are you a man or a mouse?", "Stackoverflow Stackexchange\n\nQ: Regular expression for changing spaces to tabs in Notepad++ I'm trying to use regular expression in Notepad++ to change spaces to tabs in something like this\n\n01 fsdfsd\n01 01 fsdfsd\n01 01* fsdfsd\n01 01 01 fsdfsd\n01 01 01* fsdfsd\n\nHow can I keep spaces between numbers and change only the last space?\nThanks.\n\nA: Search for:\n[ ]([a-zA-Z])\n\n(Note that there is a space in front of the character class.) And replace with:\n\\t$1\n\nAn alternative that might be better suited if you also have lines that are of a different format, or if fsdfsd may contain spaces, is this:\n^((?:\\d+\\*?)(?:[ ]\\d+\\*?)*)[ ]\n\nNow replace with\n$1\\t\n\nThis matches any space after the longest possible string of digits with optional asterisks separated by spaces.\n"]], "0_corpus": "stackexchange", "1_conv_id": "ba137b3f33354ec08b92797840f60928", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "are you a man or a mouse?", "1_output": [["are you a man or a mouse?", "Ell Stackexchange\n\nQ: What does \"Of\" mean in the beginning of \"Of mice and men\"? The title of one of John Steinbeck's novels:\n\nOf Mice and Men\n\nIn Iran this novel has been translated to Persian word by word without considering the Of many years ago. It is just Mice and Humans not Of Mice and Men.\nIs it only the taste of translator or had s/he been right in translating it like this? Does Of easily only mean From or something else?\nI have to add the men in the title has been translated to humans in Persian. I know man or men can mean human in English but it would be better to be sure about that. \n\nA: Of, in this context, means regarding. So a \"translated\" title might be, \"Regarding Mice and Men.\"\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722927635.269, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "90fc5a9a37e644fdaa454b00d5df4dfe", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Route planning with multi depot and multi drone with assignment problem", "0_output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Vehicle Routing Problems for Drone Delivery\n\nAbstract: Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multi-trip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding sub-optimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs."]], "0_corpus": "arxiv", "1_conv_id": "11311fc4873f4c8085040af85d96c55a", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Route planning with multi depot and multi drone with assignment problem", "1_output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions\n\nAbstract: In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations."]], "1_corpus": "arxiv"}

data/retrieval_individual-76c7b5f4-5fbc-4311-bf05-8defcdc1ba4e.jsonl

@@ -138,3 +138,6 @@
 {"tstamp": 1722927243.6279, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722927241.4772, "finish": 1722927243.6279, "ip": "", "conv_id": "eacef2155e1b40c1ab710b48af60d343", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "output": [["how can I do {\"a\":4, \"b\":1, \"c\":3} -> {\"b\":1, \"c\":3, \"a\":4}", "Stackoverflow Stackexchange\n\nQ: Using VirtualBox for Mobile App Development? I keep wanting to use VitualBox for Mobile App Development, but I can't seem to get the emulator to run fast enough inside the already-emulated 32-bit machine.\nIs there any way around this?\nNow while it doesn't seem to me like there is an answer to this question, I figured I'd ask it anyway.\nP.S. I have a 64-bit machine.\n\nA: This should do the trick: 4 (or more) core CPU, plenty of ram (>4GB) and install the virtual operating system on a disk other than the one your main OS uses (e.g. firewire/esata external drive).\nAnother virtualization software might be faster too, but that I don't know.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722927578.1363, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722927577.9637, "finish": 1722927578.1363, "ip": "", "conv_id": "179f00b150c44afdaa92de74eda5ecf1", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are you a man or a mouse?", "output": [["are you a man or a mouse?", "Stackoverflow Stackexchange\n\nQ: Regular expression for changing spaces to tabs in Notepad++ I'm trying to use regular expression in Notepad++ to change spaces to tabs in something like this\n\n01 fsdfsd\n01 01 fsdfsd\n01 01* fsdfsd\n01 01 01 fsdfsd\n01 01 01* fsdfsd\n\nHow can I keep spaces between numbers and change only the last space?\nThanks.\n\nA: Search for:\n[ ]([a-zA-Z])\n\n(Note that there is a space in front of the character class.) And replace with:\n\\t$1\n\nAn alternative that might be better suited if you also have lines that are of a different format, or if fsdfsd may contain spaces, is this:\n^((?:\\d+\\*?)(?:[ ]\\d+\\*?)*)[ ]\n\nNow replace with\n$1\\t\n\nThis matches any space after the longest possible string of digits with optional asterisks separated by spaces.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722927578.1363, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722927577.9637, "finish": 1722927578.1363, "ip": "", "conv_id": "ba137b3f33354ec08b92797840f60928", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are you a man or a mouse?", "output": [["are you a man or a mouse?", "Ell Stackexchange\n\nQ: What does \"Of\" mean in the beginning of \"Of mice and men\"? The title of one of John Steinbeck's novels:\n\nOf Mice and Men\n\nIn Iran this novel has been translated to Persian word by word without considering the Of many years ago. It is just Mice and Humans not Of Mice and Men.\nIs it only the taste of translator or had s/he been right in translating it like this? Does Of easily only mean From or something else?\nI have to add the men in the title has been translated to humans in Persian. I know man or men can mean human in English but it would be better to be sure about that. \n\nA: Of, in this context, means regarding. So a \"translated\" title might be, \"Regarding Mice and Men.\"\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722927626.9082, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722927624.6916, "finish": 1722927626.9082, "ip": "", "conv_id": "90fc5a9a37e644fdaa454b00d5df4dfe", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Route planning with multi depot and multi drone with assignment problem", "output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Vehicle Routing Problems for Drone Delivery\n\nAbstract: Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multi-trip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding sub-optimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs."]], "corpus": "arxiv"}
+{"tstamp": 1722927626.9082, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722927624.6916, "finish": 1722927626.9082, "ip": "", "conv_id": "11311fc4873f4c8085040af85d96c55a", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Route planning with multi depot and multi drone with assignment problem", "output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions\n\nAbstract: In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations."]], "corpus": "arxiv"}
+{"tstamp": 1722927676.5352, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722927676.2011, "finish": 1722927676.5352, "ip": "", "conv_id": "83d24709e1b54eb7a1d0c832286283ee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Route planning with multi depot and multi drone with assignment problem", "output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Vehicle Routing Problems for Drone Delivery\n\nAbstract: Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multi-trip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding sub-optimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs."]], "corpus": "arxiv"}

data/retrieval_single_choice-76c7b5f4-5fbc-4311-bf05-8defcdc1ba4e.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1722927684.2395, "task_type": "retrieval", "type": "downvote", "models": "Alibaba-NLP/gte-Qwen2-7B-instruct", "ip": "", "conv_id": "83d24709e1b54eb7a1d0c832286283ee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Route planning with multi depot and multi drone with assignment problem", "output": [["Route planning with multi depot and multi drone with assignment problem", "Title: Vehicle Routing Problems for Drone Delivery\n\nAbstract: Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multi-trip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding sub-optimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs."]], "corpus": "arxiv"}