Abstract:Choosing between Less Than Truckload (LTL) and Full Truckload (FTL) is based on a multicriteria decision because it takes into account aspects such as costs and operational efficiency in transportation, handling and stock of goods. The aim of this article is to provide a detailed analysis of the criteria that comprise the LTL and FTL transport decision using a multicriteria methodology that considers decision makers´ preferences. The SMARTS method was applied in a telecommunications company that outsources its… Show more
Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of the food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to food spoilage and food waste; therefore, data-based decision models are needed to aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) of commercially produced boneless chicken breast filet trays. The pallets were exposed to 24 h of simulated LTL TA (cyclic 2 h at 4°C, then 2 h at 23 ± 2°C). Filet temperatures were recorded for all 20 boxes using dataloggers with thermocouple wires. Additionally, microbiological sampling of filets [aerobic plate counts (APC) and psychrotrophic plate counts (PSY)] was conducted before (0 h of LTL TA) and after (24 h of LTL TA) the TA experiment for select boxes of the pallet and compared to control filets (maintained at 4°C). After TA, a shelf-life experiment was conducted by storing filets from predetermined boxes at 4°C until spoilage (7 log CFU/ml). Temperature and microbiological data were augmented using Monte Carlo simulations (MC) to build decision making models using two methods; (1) the risk of each box on the pallet reaching the bacterial “danger zone” (>4°C) was determined; and (2) the risk-of-loss (shelf-life < 4 days; minimum shelf-life required to prevent food waste) was determined. Temperature results indicated that boxes on the top and bottom layers reached 4°C faster than boxes comprising the middle layers while the perimeter boxes of each layer reached 4°C faster than centrally located boxes. Shelf-life results indicate simulated LTL TA reduced shelf-life by 2.25 and 1.5 days for APC and PSY, respectively. The first MC method showed the average risk of boxes reaching 4°C after 24 h of simulated LTL TA were 94.96%, 43.20%, 27.20%, and 75.12% for layers 1–4, respectively. The second MC method indicated that exposure at >4°C for 8 h results in a risk-of-loss of 43.8%. The findings indicate that LTL TA decreases shelf-life of chicken breast filets in a heterogenous manner according to location of boxes on the pallet. Therefore, predictive models are needed to make objective decisions so that a “First Expire, First Out” method can be implemented to reduce food wastes due to TA during the last mile.
Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of the food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to food spoilage and food waste; therefore, data-based decision models are needed to aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) of commercially produced boneless chicken breast filet trays. The pallets were exposed to 24 h of simulated LTL TA (cyclic 2 h at 4°C, then 2 h at 23 ± 2°C). Filet temperatures were recorded for all 20 boxes using dataloggers with thermocouple wires. Additionally, microbiological sampling of filets [aerobic plate counts (APC) and psychrotrophic plate counts (PSY)] was conducted before (0 h of LTL TA) and after (24 h of LTL TA) the TA experiment for select boxes of the pallet and compared to control filets (maintained at 4°C). After TA, a shelf-life experiment was conducted by storing filets from predetermined boxes at 4°C until spoilage (7 log CFU/ml). Temperature and microbiological data were augmented using Monte Carlo simulations (MC) to build decision making models using two methods; (1) the risk of each box on the pallet reaching the bacterial “danger zone” (>4°C) was determined; and (2) the risk-of-loss (shelf-life < 4 days; minimum shelf-life required to prevent food waste) was determined. Temperature results indicated that boxes on the top and bottom layers reached 4°C faster than boxes comprising the middle layers while the perimeter boxes of each layer reached 4°C faster than centrally located boxes. Shelf-life results indicate simulated LTL TA reduced shelf-life by 2.25 and 1.5 days for APC and PSY, respectively. The first MC method showed the average risk of boxes reaching 4°C after 24 h of simulated LTL TA were 94.96%, 43.20%, 27.20%, and 75.12% for layers 1–4, respectively. The second MC method indicated that exposure at >4°C for 8 h results in a risk-of-loss of 43.8%. The findings indicate that LTL TA decreases shelf-life of chicken breast filets in a heterogenous manner according to location of boxes on the pallet. Therefore, predictive models are needed to make objective decisions so that a “First Expire, First Out” method can be implemented to reduce food wastes due to TA during the last mile.
The study aims to provide a structured and flexible analysis for the selection of transportation types when solving a cargo distribution problem. When solving this problem, it is necessary to use tools and methods for selecting the best distribution method, taking into account the multicriteria nature of the problem
The paper provides a comparative analysis of approaches to solving multicriteria problems, based on advanced methods that take into account the preferences of decision makers
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.