Production planning and scheduling optimization model: a case of study for a glass container company

Based on a case study, this paper deals with the production planning and scheduling problem of the glass con-tainer industry. This is a facility production system that has a set of furnaces where the glass is produced in order to meet the demand, being afterwards distributed to a set of parallel molding machines. Due to huge setup times involved in a color changeover, manufacturers adopt their own mix of furnaces and machines to meet the needs of their customers as flexibly and efficiently as possible. In this paper we proposed an optimization model that maximizes the fulfillment of the demand considering typical constraints from the planning production formulation as well as real case production constraints such as the limited product changeovers and the minimum run length in a machine. The complexity of the proposed model is assessed by means of an industrial real life problem

Advanced Planning Concepts in the Closed-Loop Container Network of ARN

In this paper we discuss a real-life case study in the optimization of the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands.Advanced planning concepts like dynamic assignment of dismantlers to logistic service providers are analyzed by a simulation model.In this model, we periodically solve a vehicle routing problem to gain insight in the long-term performance of the system.The vehicle routing problem considered is a multi depot pickup and delivery problem with alternative delivery locations.We solve this problem with a heuristic based on route generation and set partitioning.Reverse logistics;Closed-loop supply chain mmanagement;vehicle routing;set partitioning;distribution planning

Reverse logistics

This paper gives an overview of scientific literature that describes and discusses cases of reverse logistics activities in practice. Over sixty case studies are considered. Based on these studies we are able to indicate critical factors for the practice of reverse logistics. In addition we compare practice with theoretical models and point out research opportunities in the field

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem

In many production processes, a key material is prepared and then transformed into different final products. The lot sizing decisions concern not only the production of final products, but also that of material preparation in order to take account of their sequence-dependent setup costs and times. The amount of research in recent years indicates the relevance of this problem in various industrial settings. In this paper, facility location reformulation and strengthening constraints are newly applied to a previous lot-sizing model in order to improve solution quality and computing time. Three alternative metaheuristics are used to fix the setup variables, resulting in much improved performance over previous research, especially regarding the use of the metaheuristics for larger instances

Production planning in different stages of a manufacturing supply chain under multiple uncertainties

This thesis focuses on designing stochastic programming models for production planning at different stages in a manufacturing supply chain under multiple sources of uncertainties. Various decision makers along the manufacturing supply chain often have to make planning decisions with embedded risks and uncertainties. In an effort to reduce risks and to ensure that the customer demand is met in the most efficient and cost effective way, the production plans at each stage need to be strategically planned. To assist production planning decisions, a two-stage stochastic programming model is developed with the objective of minimizing the total cost including production, inventory, and backorder costs. The proposed framework is validated with case studies in an automobile part manufacturer with real data based on literature. The results demonstrate the robustness of the stochastic model compared with various deterministic models. Sensitivity analysis is performed for the production capacity parameter to derive managerial insights regarding lot-sizing and scheduling decisions under different scenarios

Closing Loops in Supply Chain Management: Designing Reverse Supply Chains for End-of-Life Vehicles.

In this thesis, the focus is on the design of reverse supply chains for end-of-life products, in particular end-of-life vehicles. For long-term success of end-of-life management, more economic stimuli are needed than is currently the case. Legislation as a single driving force is insufficient for companies to achieve closed loop supply chains. The key issue is to find eco-efficient solutions, i.e. design and operate an economically low cost network without violating applicable targets imposed by environmental legislation. In this thesis, a case study research methodology is adopted to develop design principles for network design and assess the consequences on the operations research models. Three case studies, which stem from the network of Auto Recycling Nederland, are described in detail.

Lotsizing and scheduling in the glass container industry

Manufacturing organizations are keen to improve their competitive position in the global marketplace by increasing operational performance. Production planning is crucial to this end and represents one of the most challenging tasks managers are facing today. Among a large number of alternatives, production planning processes help decision-making by tradingoff conflicting objectives in the presence of technological, marketing and financial constraints.Two important classes of such problems are lotsizing and scheduling. Proofs from complexity theory supported by computational experiments clearly show the hardness of solving lotsizing and scheduling problems.Motivated by a real-world case, the glass container industry production planning and scheduling problem is studied in depth. Due to its inherent complexity and to the frequent interdependencies between decisions that are made at and affect different organizational echelons, the system is decomposed into a two-level hierarchically organized planning structure: long-term and short-term levels.This dissertation explores extensions of lotsizing and scheduling problems that appear in both levels. We address these variants in two research directions. On one hand, we develop and implement different approaches to obtain good quality solutions, as metaheuristics (namely variable neighborhood search) and Lagrangian-based heuristics, as well as other special-purpose heuristics. On the other hand, we try to combine new stronger models and valid inequalities based on the polyhedral structure of these problems to tighten linear relaxations and speed up the solution process.Manufacturing organizations are keen to improve their competitive position in the global marketplace by increasing operational performance. Production planning is crucial to this end and represents one of the most challenging tasks managers are facing today. Among a large number of alternatives, production planning processes help decision-making by tradingoff conflicting objectives in the presence of technological, marketing and financial constraints.Two important classes of such problems are lotsizing and scheduling. Proofs from complexity theory supported by computational experiments clearly show the hardness of solving lotsizing and scheduling problems.Motivated by a real-world case, the glass container industry production planning and scheduling problem is studied in depth. Due to its inherent complexity and to the frequent interdependencies between decisions that are made at and affect different organizational echelons, the system is decomposed into a two-level hierarchically organized planning structure: long-term and short-term levels.This dissertation explores extensions of lotsizing and scheduling problems that appear in both levels. We address these variants in two research directions. On one hand, we develop and implement different approaches to obtain good quality solutions, as metaheuristics (namely variable neighborhood search) and Lagrangian-based heuristics, as well as other special-purpose heuristics. On the other hand, we try to combine new stronger models and valid inequalities based on the polyhedral structure of these problems to tighten linear relaxations and speed up the solution process

BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level

The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system, based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.Comment: 11 pages, double column, 4 figures, 3 table

Reinforcement learning approaches for the stochastic discrete lot-sizing problem on parallel machines

This paper addresses the stochastic discrete lot-sizing problem on parallel machines, which is a computationally challenging problem also for relatively small instances. We propose two heuristics to deal with it by leveraging reinforcement learning. In particular, we propose a technique based on approximate value iteration around post-decision state variables and one based on multi-agent reinforcement learning. We compare these two approaches with other reinforcement learning methods and more classical solution techniques, showing their effectiveness in addressing realistic size instances