Designing sustainable cold chains for long-range food distribution: Energy-effective corridors on the Silk Road Belt

Modern food production-distribution processes represent a critical stressor for the environment and for natural ecosystems. The rising flows of food across growing and consumption areas couple with the higher expectations of consumers for the quality of products and compel the intensive use of refrigerated rooms and transport means throughout the food supply chain. In order to aid the design of sustainable cold chains that incorporate such aspects, this paper proposes a mixed integer linear programming model to minimize the total energy consumption associated with the cold operations experienced by perishable products. This model is intended for food traders, logistics practitioners, retail managers, and importers collaboratively called to design and plan a cost and environmentally effective supply strategy, physical channels, and infrastructures for cold chains. The proposed model is validated with a case study inspired by the distribution of two example food products, namely fresh apples and ice cream, along the New Silk Road connecting Europe and China. The illustrated analysis investigates the effect of alternative routes and transport modes on the sustainability of the cold chain. It is found that the most energy-efficient route for ice cream is via rail over a northern route and, for apples, is via a southern maritime route, and, for these two routes, the ratios of the total energy consumed to the energy content of the food are 760 and 913, respectively. By incorporating the energy lost due to the food quality decay, the model identifies the optimal route to adopt in accordance with the shelf life and the conservation temperature of each product

Machine learning methods to improve the operations of 3PL logistics

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Post pandemic strategic planning of food catering production and distribution networks: A regional case study

Due to the Covid19 outbreak, the food catering industry faces disruption of demand traits and great uncertainty about the future development of market segmentation. The need for a re-design of production and logistic networks faces the lack of knowledge about cost drivers, rendering the application of mathematical optimization models challenging. In this paper, a cost components analysis is carried out to quantify each cost item's impact on the full meal cost. Cost analysis aims to formalize the relationship between meal cost and parameters such as productivity, meal conservation regime, customer typology, portioning method, and customer-plant distance. The cost parameters are adjusted through empirically driven correction factors to include operational and management complexities that would otherwise be neglected. The obtained parameters feed a total cost minimization model for a productive and distributive catering network. The location-allocation model chooses the production capacity to activate in each production plant for every meal-type and achieves the customer-production plant pairing. The framework is applied in an Italian regional case study to compare the BAU scenario to two different To-Be scenarios. The As-Is scenario considers four different production facilities serving the pre-pandemic demand of 2019, while the To-Be scenarios are based upon a demand forecast enforcing a more resilient network. The analysis shows how re-designing production and distribution networks enables meeting uncertain demand while keeping FMCs under control within a regional environment

A Supporting Decisions Platform for the Design and Optimization of a Storage Industrial System

Warehouses are one of the most critical resources in production systems, whose performance significantly depend on the availability of materials in the right location, in the right quantity and at the right time. Literature presents many contributions for the design and control of a storage system, but a few of them discuss on the importance of an integrated approach based on the adoption of different supporting decisions models and tools, from mixed integer linear programming (MILP) to visual interactive simulation (VIS), passing through heuristic procedures and cluster analysis (CA). This chapter presents a conceptual and integrated framework for the design, management, control and optimization of both manual, i.e. man-on-board, picker to part and automated, i.e. part to picker, storage systems, both unit-load and less than unit-load order picking systems (OPS), by the development and application of different models and tools. The proposed framework integrates the management decisions in order to find not a system configuration as a result of local optima, but the minimal cost warehousing system as a result of the following integrated decisions: the space allocation to the forward area and the bulk area in a OPS, the system layout, the storage allocation within each area, i.e. the determination of the storage level devoted to a stock keeping unit (sku) both in fast pick area and in reserve area, the storage locations assignment, i.e. the determination of the warehousing system location to be assigned to a sku, the routing policies, the operating procedures, etc. A discussion on supporting decisions models and tools useful for practitioners of industry to face these critical problems is presented and finally a case study illustrated

Economic and environmental optimization of packaging containers choice in Food Catering Supply Chain

Disposable containers are widely employed throughout food supply chains (FSCs) to transport, sell, or store perishable products. These containers are made of several materials, like plastic and cardboard. Albeit the widespread use of such containers, not all the materials are suitable for food contact, and a barrier layer between perishable products and the container is needed. Moreover, a high percentage of waste along the FSC consists of primary and secondary packages. Food Catering Supply Chain (FCSC), made of multi-stage logistic networks, represents a challenging scenario for minimizing packaging disposal. Chosen for reducing waste, reusable plastic containers (RPCs) are gaining ground within the food supply chain network. We propose a multi-objective optimization model to improve the business as usual (BAU) of FCSC, quantifying saving in terms of cost and environmental impact due to the employment of RPCs. The model virtualizes the logistic and operational costs as well as the transportation and disposal impact of reusable and recyclable plastic containers. This paper evaluates the use of RPCs by comparing the performances of as-is and to-be scenarios derived from an industrial case study. The analyzed network comprises perishable product suppliers, RPC poolers, cross-docking facilities, customers, and collectors. The results show the reduction of environmental impacts and logistic, handling, and operational costs in the proposed FCSC topology. A new network configuration and insights for future research investigations are presented

Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

Sustainability assessment of transport operations in local Food Supply Chain networks

As food supply chains represent a hotspot of climate change, a rapid transition toward more sustainable processes and operations is expected. Whilst research provides decision-support models to optimize food ecosystems, the application of these techniques in practice is often discouraged by a lack of knowledge and visibility on the hidden food networks’ performance and impacts. This paper overviews a case study on a regional fruit and vegetable supply chain characterized by broad fragmentation of supplies, a wide number of actors involved, multiple stages, and limited visibility on the routes traveled by a generic food order. This work analyzes the perishable flows from the growers to the retailer under the lens of environmental externalities in order to promote sustainable supply chain management strategies. Logistic flows throughout the stages are tracked and mapped to aid integrated decision-making, resulting in food miles and transport externalities assessment. A multi-scenario what-if analysis is illustrated to compare and assess transportation costs, food miles, and carbon footprint resulting from more integrated supply chain decisions and configurations. The To-Be scenario results in significant savings in terms of carbon emissions, traveling, and transportation costs. Moreover, the reduction of transported volumes reflects how multiple supply chain stages compel double/triple-handling of food and avoidable travelin

A Supporting Decision Tool for the Integrated Planning of a Logistic Network

none4Design, management and control of a logistic distribution system are very critical issues in supply chain management. They involve a large number of interdependent decisions, such as the determination of the best location and capacity of a distribution center (DC), a production plant, a wholesaler etc., the allocation of customer demand to suppliers, e.g. regional DC (RDC), the adoption of a transportation mode, e.g. rail and truck, the vehicles routing adopting/not adopting a groupage strategy. This chapter presents an original and automatic supporting decisions platform for the integration of strategic (long-term), tactical (mid-term) and operational (short-term) decisions in the design, management and control of a logistic network including up to four operating levels: sources (production plants), central distribution centers (CDCs), RDCs, and customers. A case study is illustrated and obtained results discussed in presence of different problem settings and operating hypotheses.openMANZINI R.; BORTOLINI M.; GAMBERI M.; MONTECCHI M.MANZINI R.; BORTOLINI M.; GAMBERI M.; MONTECCHI M

Design, Management and Control of Logistic Distribution Systems

Nowadays global and extended markets have to process and manage increasingly differentiated products, with shorter life cycles, low volumes and reducing customer delivery times. Moreover several managers frequently have to find effective answers to one of the following very critical questions: in which kind of facility plant and in which country is it most profitable to manufacture and/or to store a specific mix of products? What transportation modes best serve customer points of demand, which can be located worldwide? Which is the best storage capacity of a warehousing system or a distribution center (DC)? Which is the most suitable safety stock level for each item of a company's product mix? Consequently logistics is assuming more and more importance and influence in strategic and operational decisions of managers of modern companies operating worldwide. The Council of Logistics Management defines logistics as "the part of supply chain process that plans, implements and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers' requirements". Supply Chain Management (SCM) can be defined as "the integration of key business processes from end-user through original suppliers, that provides product, service, and information that add value for customers and other stakeholders" (Lambert et al., 1998). In accordance with these definitions and with the previously introduced variable and critical operating context, Figure 1 illustrates a significant conceptual framework of SCM proposed by Cooper et al. (1997) and discussed by Lambert et al. (1998). Supply chain business processes are integrated with functional entities and management components that are common elements across all supply chains (SCs) and determine how they are managed and structured. Not only back-end and its traditional stand-alone modelling is addressed, but the front-end beyond the factory door is also addressed through information sharing among suppliers, supplier's suppliers, customers, and customers' customers. In the modern competitive business environment the effective integration and optimization of the planning, design, management and control activities in SCs are one of the most critical issues facing managers of industrial and service companies, which have to operate in strongly changing operating conditions, where flexibility, i.e. the ability to rapidly adapt to changes occurring in the system environment, is the most important strategic issue affecting the company success. As a consequence the focus of SCM is on improving external integration known as "channel integration" (Vokurka & Lummus, 2000), and the main goal is the optimization of the whole chain, not via the sum of individual efficiency maximums, but maximising the entire system thanks to a balanced distribution of the risks between all the actors. The modelling activity of production and logistic systems is a very important research area and material flows are the main critical bottleneck of the whole chain performance. For this reason in the last decade the great development of research studies on SCM has found that new, effective supporting decisions models and techniques are required. In particular a large amount of literature studies (Sule 2001, Manzini et al. 2006, Manzini et al. 2007a, b, Gebennini et al. 2007) deal with facility management and facility location (FL) decisions, e.g. the identification of the best locations for a pool of different logistic facilities (suppliers, production plants and distribution centers) with consequent minimization of global investment, production and distribution costs. FL and demand allocation models and methods object of this chapter are strongly associated with the effective management and control of global multi-echelon production and distribution networks.A few studies propose operatio..