Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

An optimal-control based integrated model of supply chain

Problems of supply chain scheduling are challenged by high complexity, combination of continuous and discrete processes, integrated production and transportation operations as well as dynamics and resulting requirements for adaptability and stability analysis. A possibility to address the above-named issues opens modern control theory and optimal program control in particular. Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of complexity, dynamics, uncertainty, and adaptivity. Supply chain schedule generation is represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. With the developed model, important categories of supply chain analysis such as stability and adaptability can be taken into consideration. Besides, the dimensionality of operations research-based problems can be relieved with the help of distributing model elements between an operations research (static aspects) and a control (dynamic aspects) model. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes.supply chain, model of supply chain scheduling, optimal program control theory, Pontryagin’s maximum principle, operations research model,

Hierarchical production planning

Includes bibliographical references.Partially supported by the Leaders for Manufacturing Program.Gabriel R. Bitran, Devanath Tirupati

Activity-Based Costing in Supply Chain Cost Management Decision Support Systems

Activity-based costing and management (ABC/M) is an accounting and management approach that enhances the level of understanding about business operation costs, especially the overhead costs. ABC/M generates more reliable and precise cost information compared to those of traditional cost accounting (TCA) systems. The integration of ABC/M in supply chain (SC) mathematical decision support models can elucidate the managerial aspects of ABC/M more as an accounting and management tool. Most of the supply chain (SC) order management decision support systems (DSSs) developed so far are based mainly on the material flow and capacity constraints without considering the profitability factor. This thesis first presents a profitable-to-promise (PTP) multi-objective mixed-integer programming (MIP) model which considers profitability in order to effectively manage order acceptance decisions in supply chains, subject to capacity constraints by using ABC/M. The proposed model fulfills a desirable amount of orders completely and accepts a selective number of orders partially having the objective of minimizing the amount of residual capacity and increasing the profitability simultaneously. Because of the common disadvantages that traditional operations research (OR) approaches have such as, complexity in modeling, impossibility of integrating qualitative factors, and inability of on-time model result analysis, the thesis presents a new generic DSS modeling methodology with system dynamics (SD) and based on ABC/M cost structure. The approach presented results a novel real-time cost monitoring and analysis system. SD is a dynamic simulation approach with learning ability to investigate the status changes in the system that correspond to the system variables’ changes as well as their interactions amongst them. Subsequently, the thesis elaborates on both models by integrating them and introducing them as hybrid (MIP-SD) decision support system. In the hybrid system, MIP model generates the order management policy and SD model monitors the cost behavior of each implemented policy during the implementation process. The main purpose is to show how ABC/M acts as a common cost accounting, information, and managerial approach to synchronize the two mentioned models and to introduce the combination as a hybrid DSS system. In general, the approach provides the order fulfillment optimal mix aligned with the implementation strategy considering the factors such as, minimizing the residual capacity, considering the customer satisfaction level, selling price, the cost of resources incurred for each order fulfillment policy, and the share of each product and/or order from manufacturing overhead costs. Such an approach can assists management to analyzing and foreseeing the consequences and outcome of each order fulfillment strategy chosen besides finding the optimal order fulfillment combination

Systems Analysis by Multilevel Methods: With Applications to Economics and Management

This book presents a survey of usable multilevel methods for modeling and solving decision problems in economics and management. The methods are largely extensions of linear programming and fall within the realm of column generation and decomposition. About one third of the book is concerned with methods and the rest describes case studies where these methods have actually been used. They are taken from areas such as national and regional economic planning, production planning, and transportation planning

Applying stochastic programming models in financial risk management

This research studies two modelling techniques that help seek optimal strategies in financial risk management. Both are based on the stochastic programming methodology. The first technique is concerned with market risk management in portfolio selection problems; the second technique contributes to operational risk management by optimally allocating workforce from a managerial perspective. The first model involves multiperiod decisions (portfolio rebalancing) for an asset and liability management problem and deals with the usual uncertainty of investment returns and future liabilities. Therefore it is well-suited to a stochastic programming approach. A stochastic dominance concept is applied to control the risk of underfunding. A small numerical example and a backtest are provided to demonstrate advantages of this new model which includes stochastic dominance constraints over the basic model. Adding stochastic dominance constraints comes with a price: it complicates the structure of the underlying stochastic program. Indeed, new constraints create a link between variables associated with different scenarios of the same time stage. This destroys the usual tree-structure of the constraint matrix in the stochastic program and prevents the application of standard stochastic programming approaches such as (nested) Benders decomposition and progressive hedging. A structure-exploiting interior point method is applied to this problem. Computational results on medium scale problems with sizes reaching about one million variables demonstrate the efficiency of the specialised solution technique. The second model deals with operational risk from human origin. Unlike market risk that can be handled in a financial manner (e.g. insurances, savings, derivatives), the treatment of operational risks calls for a “managerial approach”. Consequently, we propose a new way of dealing with operational risk, which relies on the well known Aggregate Planning Model. To illustrate this idea, we have adapted this model to the case of a back office of a bank specialising in the trading of derivative products. Our contribution corresponds to several improvements applied to stochastic programming modelling. First, the basic model is transformed into a multistage stochastic program in order to take into account the randomness associated with the volume of transaction demand and with the capacity of work provided by qualified and non-qualified employees over the planning horizon. Second, as advocated by Basel II, we calculate the probability distribution based on a Bayesian Network to circumvent the difficulty of obtaining data which characterises uncertainty in operations. Third, we go a step further by relaxing the traditional assumption in stochastic programming that imposes a strict independence between the decision variables and the random elements. Comparative results show that in general these improved stochastic programming models tend to allocate more human expertise in order to hedge operational risks. The dual solutions of the stochastic programs are exploited to detect periods and nodes that are at risk in terms of expertise availability