Model-driven decision support to facilitate efficient fresh food deliveries

The delivery of fresh food is challenged by various uncertainties present in daily logistics operations. To facilitate successful operations, this work reviews the recent work on model-driven decision support systems to identify research gaps and derive implications. Introduced systems in literature mainly employ simulation or optimization methods and focus on the consideration of industry specifics such as short shelf lives and the importance of efficient temperature control. Therefore, food quality models are often integrated to enable one to monitor quality throughout supply chain operations and adjust planning procedure respectively. To strengthen research, future work focusing on a stronger consideration of customer-related factors and holistic approaches considering various interdependencies present in fresh food logistics operations are required

A decision support system to facilitate collaborative supply of food cooperatives

Food cooperatives are gaining popularity due to consumers’ desire to eat healthy and source locally. Mainly run by private citizens, such groups present an interesting additional income source to regional organic farmers. However, small order quantities and substantial logistics efforts challenge operations. To facilitate efficient and sustainable food transports from farms to food cooperatives, this work investigates impacts of collaborative logistics activities through the development of a simulation and optimization-based decision support system. Results of computational experiments considering fresh food transports in Austria highlight potentials of such joint activities. Particularly, if orders are infrequent and quantities small, collaboration results in a substantial reduction of travel distances and reduces the number of required vehicles. Nevertheless, delivered food quality may deteriorate as consolidating shipments results in longer travel durations as well as additional loading and unloading activities

A simulation model to investigate impacts of facilitating quality data within organic fresh food supply chains

Demand for and production of organic fresh food play an increasing role worldwide. As a result, a growing amount of fresh fruits and vegetables has to be transported from predominantly rural production regions to customers mostly located in urban ones. Specific handling and storage conditions need to be respected along the entire supply chain to maintain high quality and product value. To support organic food logistics operations, this work investigates benefits of facilitating real-time product data along delivery and storage processes. By the development of a simulation-based decision support system, sustainable deliveries of organic food from farms to retail stores are investigated. Generic keeping quality models are integrated to observe impacts of varying storage temperatures on food quality and losses over time. Computational experiments study a regional supply chain of organic strawberries in Lower Austria and Vienna. Results indicate that the consideration of shelf life data in supply chain decisions allow one to reduce food losses and further enables shifting surplus inventory to alternative distribution channels

A Decision Support System for Efficient Last-Mile Distribution of Fresh Fruits and Vegetables as Part of E-Grocery Operations

Efficient last-mile distribution of fresh fruits and vegetables is a major challenge within e-grocery operations. This work presents a decision support system to jointly investigate the impact of various service offers on customer preferences and logistics operations. Results from a conjoint analysis surveying 531 end consumer are incorporated within an agent-based simulation. Delivery days, fees, time windows and discounts as well as guaranteed remaining shelf life of products at delivery are considered. To model shelf life and schedule deliveries, food quality models and vehicle routing procedures are further integrated within the system. Based on an e-grocery provider operating in Vienna, Austria, computational experiments investigate the impact of the offered delivery service on fulfilled demand, order volume and customer utility. Results indicate the importance of incorporating shelf life data within e-grocery operations and various potentials of considering customer preferences in logistics decision support systems

A decision support system to investigate dynamic last-mile distribution facilitating cargo-bikes

<p>This work presents a decision support system to facilitate efficient urban last-mile distribution. Orders are collected and delivered by a fleet of both conventional vehicles owned by a logistics provider and cargo-bikes operated by freelancers. Additionally, micro-hubs are operated to perform transshipments between multiple vehicles. To investigate the corresponding problem setting, an agent-based simulation is developed, which uses dynamic optimisation procedures to generate and select vehicle routes and transshipment points. Experiments motivated by dynamic real-world urban restaurant delivery services investigate the impact of cargo-bikes, urban consolidation and guaranteed delivery times. Potentials are discussed and implications for successful implementations are provided. Results highlight the importance of having a sufficient number of active cargo-bikes available and benefits of incorporating consolidation strategies to guarantee timely deliveries.</p

An Analysis of Compounding Factors of Epidemics in Complex Emergencies: A System Dynamics Approach

Purpose: This research describes compounding factors in a complex emergency which exacerbate a cholera epidemic among vulnerable populations due to supply chain disruptions. Basic needs such as food, medicine, water, sanitation, and hygiene commodities, are critical to reduce the incidence rate of cholera and control the spread of infection. Conflicts cause damage to infrastructure, displace vulnerable populations, and restrict the flow of goods from both commercial and humanitarian organizations. This work assesses the underlying internal and external factors which either aggravate or mitigate the risk of a cholera outbreak in such settings, using Yemen as a case study. Design/Methodology/Approach: This study adopts a system dynamics methodology to analyze factors which influence cholera outbreaks in the context of the Yemeni Civil War. A causal loop diagram with multiple components was constructed to represent the complexities of humanitarian situations which require critical decision-making. The model was built using data from humanitarian organizations, NGOs, and practitioners, along with literature from academic sources. Variables in the model were confirmed through semistructured interviews with a field expert. Findings: Compounding factors which influenced the cholera outbreak in Yemen are visualized in a causal loop diagram, which can improve understanding of relationships where numerous uncertainties exist. A strong link exists between humanitarian response and the level of infrastructure development in country. Supply chains are affected by constraints deriving from the Yemeni conflict, further inhibiting the use of infrastructure, which limits access to basic goods and services. Aligning long-term development objectives with short-term humanitarian response efforts can create more flexible modes of assistance to prevent and control future outbreaks. Research limitations/implications: The model focuses on the qualitative aspects of system dynamics to visualize the logistics and supply chain-related constraints that impact cholera prevention, treatment, and control through humanitarian interventions. The resulting causal loop diagram is bounded by the Yemen context, thus an extension of the model adapted for other contexts is recommended for further study. Practical implications: This research presents a systematic view of dynamic factors existing in complex emergencies which have cause and effect relationships. Several models of cholera outbreaks have been used in previous studies, primarily focusing on the modes and mechanisms of transmission throughout a population. However, such models typically do not include other internal and external factors which influence the population and context at the site of an outbreak. This model incorporates those factors from a logistics perspective to address the distribution of in-kind goods and cash and voucher assistance. Social implications: This research has been aligned with six of the United Nations Sustainable Development Goals, using their associated targets in the model as variables which influence the cholera incidence rate. Recognizing that the SDGs are interlinked, as are the dynamic factors in complex humanitarian emergencies, we have chosen to take an interdisciplinary approach to consider social, economic, and environmental factors which may be impacted by this research. Originality/Value: This paper provides an insight into the underlying interrelations of internal and external factors present in the context of a cholera outbreak in a complex emergency. Supply chains for food, WASH, and health commodities are crucial to help prevent, control, and treat an outbreak. The model exposes vulnerabilities in the supply chain which may offer guidance for decision makers to improve resilience, reduce disruptions, and decrease the severity of cholera outbreaks. Keywords: Humanitarian logistics, complex emergency, cash and voucher assistance, epidemics, in-kind assistance, system dynamics, resilience, cholera outbreak, disruptions, casual loop diagrampeerReviewe

Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation-optimization

Waste collection is one of the most critical logistics activities in modern cities with considerable impact on the quality of life, urban environment, city attractiveness, traffic flows and municipal budgets. Despite the problem's relevance, most existing work addresses simplified versions where container loads are considered to be known in advance and served by a single vehicle depot. Waste levels, however, cannot be estimated with complete certainty as they are only revealed at collection. Furthermore, in large cities and clustered urban areas, multiple depots from which collection routes originate are common, although cooperation among vehicles from different depots is rarely considered. This paper analyses a rich version of the waste collection problem with multiple depots and stochastic demands by proposing a hybrid algorithm combining metaheuristics with simulation. Our 'simheuristic' approach allows for studying the effects of cooperation among different depots, thus quantifying the potential savings this cooperation could provide to city governments and waste collection companies

Facilitating Resilience during an African Swine Fever Outbreak in the Austrian Pork Supply Chain through Hybrid Simulation Modelling

This study aimed to simulate the impact of an African swine fever (ASF) outbreak in Austria. ASF is one of the most significant and critical diseases for the global domestic pig population. Hence, the authors evaluated control strategies and identified bottlenecks during an ASF outbreak. A hybrid approach was selected, including discrete-event and agent-based simulation. An extended Susceptible-Exposed-Infectious-Recovered (SEIR) model (within a pig farm) and a standard SEIR model (between pig farms) were used to simulate the chain of infection. A total of 576 scenarios with several parameter variations were calculated to identify the influence of external factors on key performance indicators. The main results show a comparison between two control strategies anchored in law: a standard strategy (SS) and a preventive culling strategy (SC). The calculated scenarios show a difference between these strategies and indicate that with SC during an outbreak, fewer farms would be infected, and fewer pigs would be culled. Furthermore, specific geographical areas were identified, which&mdash;due to their density of pigs and farms&mdash;would be severely affected in case of an ASF outbreak. The analysis of bottlenecks in rendering plants (RPs) showed an increase in the number of days RPs were overutilized as the transmission rate increased. In addition, SS caused more days of overutilized RPs than SC