Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

Optimization Models for Cost Efficient and Environmentally Friendly Supply Chain Management

This dissertation aims to provide models which will help companies make sustainable logistics management and transportation decisions. These models are extensions of the economic lot sizing model with the availability of multiple replenishment modes. The objective of the models is to minimize total replenishment costs and emissions. The study provides applications of these models on contemporary supply chain problems. Initially, the impact of carbon regulatory mechanisms on the replenishment decisions are analyzed for a biomass supply chain under fixed charge replenishment costs. Then, models are extended to consider multiple-setups replenishment costs for age dependent perishable products. For a cost minimization objective, solution algorithms are proposed to solve cases where one, two or multiple replenishment modes are available. Finally, using a bi-objective model, tradeoffs in costs and emissions are analyzed in a perishable product supply chain

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research

Mathematics in the Supply Chain

[no abstract available

Integrated production-distribution systems : Trends and perspectives

During the last two decades, integrated production-distribution problems have attracted a great deal of attention in the operations research literature. Within a short period, a large number of papers have been published and the field has expanded dramatically. The purpose of this paper is to provide a comprehensive review of the existing literature by classifying the existing models into several different categories based on multiple characteristics. The paper also discusses some trends and list promising avenues for future research

Integrating MRP in production systems simulation tools

Literature review suggests concentrating on the development of new reference model for manufacturing system simulation, which may implement an operation logic much closer to real industrial contexts. A production system modelling tool should be designed with the aim of standardizing and simplifying the simulation of manufacturing processes and to widespread this approach in SMEs. With this aim, the authors got committed in designing a reference model for providing a structural framework to support shop-floor simulation and optimization. This paper presents the basic framework logic and structure of the simulation tool, showing how it is possible to represent it in Business Process Modelling Notation (BPMN). On top of this, the efforts of implementing an MRP module on top of a simulation took which was originally conceived to embed look-back material handling policies area described, together with the operative solutions chosen to reach the integration

Dynamic warehouse optimization using predictive analytics.

The forward area is a small area of a warehouse with a low picking cost. Two approaches that are investigated for selecting the SKUs of this area and the allocated space are the static and the dynamic approaches. In the case that decisions about the forward area are made periodically (e.g. yearly) and the products\u27 demand patterns are completely ignored, the FRP is static. We developed two heuristics that solve the large discrete assignment, allocation, and sizing problem simultaneously. Replenishing the same product in the same place of the forward area brings about a ``Locked layout of the fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick operations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity arises. The primary objective of this dissertation is to formally define the dynamic FRP. One main mission of this research is to define a generic dynamic slotting problem while also demonstrating the strengths of this approach over the static model. Dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics. Applying different order data with different demand volatility, we show that the dynamic model always outperforms the static model. The benefits attained from the dynamic model over the static model are greater for more volatile warehouses

Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria