Modeling industrial lot sizing problems: a review

Coordinated deterministic dynamic demand lot-sizing problem: A review of models and algorithms

The aim of this work is to propose a solution approach for a capacitated lot sizing and scheduling real problem with parallel machines and shared buffers, arising in a packaging company producing yoghurt. The problem has been formulated as a hybrid Continuous Set-up and Capacitated Lot Sizing Problem (CSLP–CLSP). A new effective two stage optimisation heuristic based on the decomposition of the problem into a lot sizing problem and a scheduling problem has been developed. An assignment of mixture to buffers is made in the first stage, and therefore the corresponding orders are scheduled on the production lines by performing a local search. Computational tests have been performed on the real data provided by the company. The heuristic exhibits near-optimal solutions, all obtained in a very short computational time.

In this study, we investigated the time-varying capacitated lot-sizing problem under a fast-changing production environment, where production factors such as the setup costs, inventory-holding costs, production capacities, or even material prices may be subject to continuous changes during the entire planning horizon. Traditional lot-sizing theorems and algorithms, which often assume a constant production environment, are no longer fit for this situation. We analyzed the time-varying environment of today’s agile enterprises and modeled the time-varying setup costs and the time-varying production capacities. Based on these, we presented two mixed-integer linear programming models for the time-varying capacitated single-level lot-sizing problem and the time-varying capacitated multi-level lot-sizing problem, respectively, with considerations on the impact of time-varying environments and dynamic capacity constraints. New properties of these models were analyzed on the solution’s feasibility and optimality. The solution quality was evaluated in terms of the entropy which indicated that the optimized production system had a lower value than that of the unoptimized one. A number of computational experiments were conducted on well-known benchmark problem instances using the AMPL/CPLEX to verify the proposed models and to test the computational effectiveness and efficiency, which showed that the new models are applicable to the time-varying environment. Two of the benchmark problems were updated with new best-known solutions in the experiments.

An integrated approach for lot-sizing and storage assignment

Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches

A modified variable neighborhood search algorithm for dynamic lot-sizing with supplier selection under varying delivery time quotation

A goal programming model for joint decision making of inventory lot-size, supplier selection and carrier selection

Inventory control in supply chains: Alternative approaches to a two-stage lot-sizing problem

A strategic model using structural equation modeling and fuzzy logic in supplier selection

The quality of such processed agrifood products as dehydrated apple is related to the quality and variety of fresh harvested products and connected with wastage reduction throughout the agrifood supply chain. For this purpose, cold‐storage management is important to avoid or mitigate the quality decay of fresh products stored in refrigerated systems. This paper explores the benefits of a two‐stage stochastic programming model for product quality through the selection of producers and the management of cold storage to mitigate deterioration and guarantee the maintenance of quality. A case study with real data from an agribusiness company is presented in the case study to illustrate and assess the suitability of the stochastic approach. Uncertainty regarding the conversion rate of fresh apples into the final dehydrated product and the purchase cost of the apples in the system are represented through scenarios generated from historical data. Recourse actions include the purchase of additional fruit and renting of additional cold stores to meet the demand. Based on the different scenarios, the value of the stochastic solution shows that modeling and solving the proposed stochastic model minimizes costs by an average of around 6.4%. In addition, the expected value of perfect information demonstrates that using a proactive strategy could reduce costs by up to 9%. These results ensure the applicability of this model in practice before and during the harvesting season for planning and replanning as uncertainty is revealed under a rolling horizon.

A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

A matheuristic solution approach for the production routing problem with visit spacing policy

A spatial computer model is being developed to assist in the strategic and tactical planning of helicopter logging operations. The strategic model operates in two phases. First it searches among alternative paths between log pickup points and landings to identify the shortest flight time from each pickup point to each landing considering topography and helicopter operating characteristics. Second it searches for the combination of roads and landings that minimizes total construction plus yarding costs. The tactical model uses information from the strategic model to determine the sequence of yarding during a day to maximize yarding productivity.

Forest management and planning is a major task faced by private timber companies and public agencies. As an example, the U.S. Forest Service is responsible for managing over 190 million acres of forests and grasslands in the U.S., an area that is almost twice the size of Germany. Thus, it is not surprising that the U.S. Forest Service uses many types of analysis tools including the use of input-output models to assess the impact of forest operations on local and regional economies. The most common approach to plan activities for a forested region over decades involves decomposing the decision making process into three hierarchically-defined components: strategic, tactical, and operational (Weintraub and Cholaky, 1991; Nelson, Broadie, and Sessions, 1991). Each level of management involves the development of decision support models, either optimization or simulation. These models aid in exploring alternatives and generating tradeoffs among objectives. Strategic models help identify overall targets for harvesting and conservation activities over a long planning horizon and over large tracts of land. Tactical models help specify where the activities should be accomplished in order to meet strategic targets. Finally, operational models help translate tactical level decisions into detailed operations plans. This type of hierarchical decomposition has been used in forest planning by governmental agencies and by private industry (Church, Murray and Barber, 1994).

Impact analysis of flexibility on the integrated lot sizing and supplier selection problem