Plates-formes en centre ville pour la Logistique Urbaine: étude sur la ville de Marseille

International audienceCette étude, conduite dans le cadre du projet PLUME, se propose d'évaluer l'intérêt de la mise en œuvre de systèmes de distribution urbaine à partir de Zones Logistiques Urbaines. Nous visons à définir d'un point de vue organisationnel et fonctionnel les atouts économiques, environnementaux et sociétaux de ces systèmes; le but étant de fournir un cadre méthodologique pour guider leur mise en place. Un premier terrain d'analyse pour notre étude sera la ville de Marseille qui possède la particularité de disposer d'une ZLU en cœur de centre-ville avec la plate-forme logistique d'ARENC (41362 m2 d'entrepôts et de bureaux). Dans cet article, nous proposons de définir plus précisément notre problématique avant de donner un bref état de l'art des problèmes classiques de la Recherche Opérationnelle se rattachant à notre étude (Facility Location Problem, Network Design Problem et Green Logistics). Nous établissons enfin une liste d'éléments que nous chercherons à prendre en compte dans un modèle général

Optimization of logistics and distribution of the supply chain, taking into account transport costs, inventory and customer demand

PURPOSE: The aim of the article is to develop an algorithm to optimize logistics and distribution of the supply chain, taking into account transport costs, inventory and customer demand.DESIGN/METHODOLOGY/APPROACH: To solve the problem, the class of optimization problems and the traveling salesman problem were used. The boundary conditions and the objective function were determined. The optimization criterion was to minimize the total transport costs, kilometers traveled and the time needed to complete the task.FINDINGS: As a result of the analyzes and calculations performed, the optimization task was performed. With the help of the prepared software, a map of goods delivery points was determined, the total route and the route with individual points marked, and various solution methods were tested.PRACTICAL IMPLICATIONS: The model presented in the article can be used in a supply chain application to optimize routes, costs and delivery time.ORIGINALITY/VALUE: A novelty is the preparation of algorithms and universal software using the R language, which was used in the application of an intelligent IT system in a distributed model, controlling the supply chain, enabling personalization and identification of products in real time.peer-reviewe

Combined Location-Inventory Optimization of Deteriorating Products Supply Chain Based on CQMIP under Stochastic Environment

The design and optimization of combined location-inventory model for deteriorating products are a main focus in supply chain management. There were many combined location-inventory design models in this field, but these models are under the assumptions of adequate capacity facilities, invariable lead time, unique product, and uncorrelated retailer’s demands. These assumptions have a big gap in the practical situation. In this paper, we design a combined location-inventory model for deteriorating products under capacitated facilities, stochastic lead time, multiple products, and correlated retailers’ stochastic demands assumptions. These constraints are near to actual supply chain circumstance. The problem is modeled as conic quadratic mix-integer programming (CQMIP) to minimize the total expected cost. We explain how to formulate these problems as conic quadratic mixed-integer problems, and in order to obtain better computational results we use extended cover cuts. Simultaneously we compare our method with the previous Lagrange methods; the result is that the new CQMIP method can get better solution

Capacity Planning and Resource Acquisition Decisions Using Robust Optimization

This dissertation studies strategic capacity planning and resource acquisition decisions, including the facility location problem and the technology choice problem. These decisions are modeled in an integrative manner, and the main purpose of the proposed models and numerical experiments is to examine the effects of economies of scale, economies of scope, and the combined effects of scale and scope under uncertain demand realizations using robust optimization. The type of capacities, or technology alternatives, that a firm can acquire can be classified on two basic dimensions. The first dimension relates to the effects of scale via distinction between labor-intensive (less automated) technologies and capital-intensive (more automated) technologies. The second dimension relates to the effects of scope via distinction between product-dedicated and flexible technologies. Moreover, each of the product-dedicated and flexible technologies can have different levels of labor or capital-intensiveness, leading to the joint effects of economies of scale and economies of scope. Each of the technology alternatives possesses certain cost structures. Labor-intensive technologies are characterized by low fixed costs and high variable costs, whereas capital-intensive technologies are characterized by just the opposite cost structure, i.e., high fixed costs and low variable costs. Flexible technologies cost more than product-dedicated technologies, both in terms of fixed and variable costs. Robust optimization methodology is used to investigate how different levels of robustness, and facility and technology costs affect the quantities, types and allocation of technologies to facilities. Results show that specific technology choice patterns emerge depending on various cost structures and different levels of model robustness specified to accommodate uncertain demand realizations. The results obtained by the two-stage robust optimization approach are compared to the results obtained by a non-robust approach and a stochastic programming approach

Modeling inventory and responsiveness costs in a supply chain

Evaluation of supply chain performance is often complicated by the various interrelationships that exist within the network of suppliers. Currently many supply chain metrics cannot be analytically determined. Instead, metrics are derived from monitoring historical data, which is commonly referred to as Supply Chain Analytics. With these analytics it is possible to answer questions such as: What is the inventory cost distribution across the chain? What is the actual inventory turnover ratio? What is the cost of demand changes to individual suppliers? However, this approach requires a significant amount of historical data which must be continuously extracted from the associated Enterprise Resources Planning (ERP) system. In this dissertation models are developed for evaluating two Supply Chain metrics, as an alternative to the use of Supply Chain Analytics. First, inventory costs are estimated by supplier in a deterministic (Q , R, δ )2 supply chain. In this arrangement each part has two sequential reorder (R) inventory locations: (i) on the output side of the seller and (ii) on the input side of the buyer. In most cases the inventory policies are not synchronized and as a result the inventory behavior is not easily characterized and tends to exhibit long cycles. This is primarily due to the difference in production rates ( δ), production batch sizes, and the selection of supply order quantities (Q) for logistics convenience. The (Q , R, δ )2 model that is developed is an extension of the joint economic lot size (JELS) model first proposed by Banerjee (1986). JELS is derived as a compromise between the seller\u27s and the buyer\u27s economic lot sizes and therefore attempts to synchronize the supply policy. The (Q , R, δ )2 model is an approximation since it approximates the average inventory behavior across a range of supply cycles. Several supply relationships are considered by capturing the inventory behavior for each supplier in that relationship. For several case studies the joint inventory cost for a supply pair tends to be a stepped convex function. Second, a measure is derived for responsiveness of a supply chain as a function of the expected annual cost of making inventory and production capacity adjustments to account for a series of significant demand change events. Modern supply chains are expected to use changes in production capacity (as opposed to inventory) to react to significant demand changes. Significant demand changes are defined as shifts in market conditions that cannot be buffered by finished product inventory alone and require adjustments in the supply policy. These changes could involve a ± 25% change in the uniform demand level. The research question is what these costs are and how they are being shared within the network of suppliers. The developed measure is applicable in a multi-product supply chain and considers both demand correlations and resource commonality. Finally, the behavior of the two developed metrics is studied as a function of key supply chain parameters (e.g., reorder levels, batch sizes, and demand rate changes). A deterministic simulation model and program was developed for this purpose

Control of Supply Chain Systems by Kanban Mechanism.

This research studies the control mechanism of a supply chain system to operate it efficiently and economically under the just-in-time (JIT) philosophy. To implement a JIT system, kanbans are employed to link different plants\u27 production processes in a supply pipeline. Supply chain models may be categorized into single-stage, multi-stage, and assembly-line types of production systems. In order to operate efficiently and economically, the number of kanbans, the manufacturing batch size, the number of batches, and the total quantity over one period are determined optimally for these types of supply chains. The kanban operation at each stage is scheduled to minimize the total cost in the synchronized logistics of the supply chain. It is difficult to develop a generalized mathematical model for a supply chain system that incorporates all its salient features. This research employs two basic models to describe the supply chain system: a mathematical programming model to minimize the supply chain inventory system cost and a queuing model to configure the kanban logistic operations in the supply pipeline. A supply chain inventory system is modeled as a mixed-integer nonlinear programming (MINLP) that is difficult to solve optimally for a large instance. A branch-and-bound (B&B) method is devised for all versions of it to solve the MINLP problems. From the solution of MINLP, the number of batches in each stage and the total quantity of products are obtained. Next, the number of kanbans that are needed to deliver the batches between two adjacent stages is determined from the results of the MINLP, and kanban operations are fixed to efficiently schedule the dispatches of work-in-process. The new solutions result in a new line configuration as to the number and size of kanbans that led to simpler dispatch schedules, better material handling, reduction in WIP and delivery time, and enhancement of the overall productivity. These models can help a manager respond quickly to consumers\u27 need, determine the right policies to order the raw material and deliver the finished goods, and manage the operations efficiently both within and between the plants

Mathematical Programming Formulations of the Planar Facility Location Problem

The facility location problem is the task of optimally placing a given number of facilities in a certain subset of the plane. In this thesis, we present various mathematical programming formulations of the planar facility location problem, where potential facility locations are not specified. We first consider mixed-integer programming formulations of the planar facility locations problems with squared Euclidean and rectangular distance metrics to solve this problem to provable optimality. We also investigate a heuristic approach to solving the problem by extending the $K$-means clustering algorithm and formulating the facility location problem as a variant of a semidefinite programming problem, leading to a relaxation algorithm. We present computational results for the mixed-integer formulations, as well as compare the objective values resulting from the relaxation algorithm and the modified $K$-means heuristic. In addition, we briefly discuss some of the practical issues related to the facility location model under the continuous customer distribution

Models for designing the production-distribution system in supply chains of the Finnish nursery industry.

Verkkojulkaisu. Painettuna ISBN 951-40-1948-2 (nid.