Pedro Amorim

The impact of food perishability issues in the vehicle routing problem

Highly perishable food products can lose an important part of their value in the distribution process. We propose a novel multi-objective model that decouples the minimization of the distribution costs from the maximization of the freshness state of the delivered products. The main objective of the work is to examine the relation between distribution scenarios and the cost-freshness trade-off. Small size instances adapted from the vehicle routing problem with time windows are solved with an @?-constraint method and for large size instances a multi-objective evolutionary algorithm is implemented. The computational experiments show the conflicting nature of the two objectives.

Supplier selection in the processed food industry under uncertainty

This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed-integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.

Influence of consumer purchasing behaviour on the production planning of perishable food

This paper addresses the impact of consumer purchasing behaviour on the production planning of perishable food products for companies operating in the fast moving consumer goods using direct store delivery. The research presented here builds on previous marketing studies related to the effects of expiry dates in order to derive mathematical formulae, which express the age dependent demand for different categories of perishable products. These demand expressions take into account both customer willingness to pay and product quality risk. The paper presents deterministic and stochastic production planning models, which incorporate the customer’s eagerness to pick up the fresher products available. Results indicate that model approximations neglecting the fact that customers pick up the fresher products or considering that all products have the same product quality risk have a reduced impact on profit losses. On the other hand, not considering the decreasing customer willingness to pay has an important impact both on the profit losses and on the amount of spoiled products.

An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products

Production and distribution problems with perishable goods are common in many industries. For the sake of the competitiveness of the companies, the supply chain planning of products with restricted lifespan should be addressed with an integrated approach. Particularly, at the operational level, the sizing and scheduling of production lots have to be decided together with vehicle routing decisions to satisfy the customers. However, such joint decisions make the problems hard to solve for industries with a large product portfolio. This paper proposes an adaptive large neighbourhood search (ALNS) framework to tackle the problem. This metaheuristic is well known to be effective for vehicle routing problems. The proposed approach relies on mixed-integer linear programming models and tools. The ALNS outperforms traditional procedures of the literature, namely, exact methods and fix-and-optimize, in terms of quality of the solution and computational time of the algorithms. Nine in ten runs of ALNS yielded better solutions than traditional procedures, outperforming on average 12.7% over the best solutions provided by the latter methods.

A decision support system for the operational production planning and scheduling of an integrated pulp and paper mill

Abstract Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with regard to optimization models and methods, and several research works that do not address industrial issues. We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and from a thorough review of the literature on process systems engineering. The DSS incorporates relevant industrial features (which motivated the development of a specific model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.

Hierarchical scheduling of continuous casters and hot strip mills in the steel industry: a block planning application

This work addresses the joint scheduling of continuous caster and hot strip mill processes in the steel industry. Traditionally, slab yards are used to decouple these two stages. However, the rising importance of energy costs and reduced logistic effort gives motivation for a combined scheduling. For each of the processes, a mixed-integer linear optimisation model based on the block planning principle is presented. This approach develops production schedules that take technological sequences of steel grades and milling programmes into account. We consider the integrated steel plant of an international steel company as a case study. Numerical results demonstrate the practicability of this approach under experimental conditions, which reflect typical settings from an industrial application in the steel industry.

INDUSTRIAL INSIGHTS INTO LOT SIZING AND SCHEDULING MODELING

Lot sizing and scheduling by mixed integer programming has been a hot research topic in the last 20 years. Researchers have been trying to develop stronger formulations, as well as to incorporate real-world requirements from different applications. This paper illustrates some of these requirements and demonstrates how small- and big-bucket models have been adapted and extended. Motivation comes from different industries, especially from process and fast-moving consumer goods industries.

Annual Distribution Budget in the Beverage Industry: A Case Study

Unicer, a major Portuguese beverage company, improved its tactical distribution planning decisions and study alternative scenarios for its supply strategies and network configuration as result of an operations research OR-driven process. In this paper, we present the decision support system responsible for this new methodology. At the core of this system is a mathematical programming-based heuristic that includes decision variables that address transportation and inventory management problems. Unicer runs a set of production and distribution platforms with various characteristics to fulfill customers demand. The main challenge of our work was to develop a tactical distribution plan, which Unicer calls an annual distribution budget, as realistically as possible without jeopardizing the nature of the strategic and tactical tool. The company had a complex tactical distribution planning problem because of the increasing variety of its stock-keeping units and its need for a flexible distribution network to satisfy its customers, who demand a very fragmented set of products. Atypical flows of finished products from Unicers distribution centers to its production platforms are a major cause of this complexity, which yields an intricate supply chain. The quality of the solutions we provided and the implementation of a user-friendly interface and editable inputs and outputs for our decision support system motivated company practitioners to use it. Unicer saves approximately two million euros annually and provides better information to its decision makers. As a result, these decision makers now view their operations from a more OR-based perspective.

A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Abstract This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

A long-haul freight transportation problem: Synchronizing resources to deliver requests passing through multiple transshipment locations

This research aims at tackling a real-world long-haul freight transportation problem where tractors are allowed to exchange semi-trailers through several transshipment points until a request reaches its destiny. The unique characteristics of the considered logistics network allow for providing long-haul services by means of short-haul jobs, drastically reducing empty truck journeys. A greater flexibility is achieved with faster responses. Furthermore, the planning goals as well as the nature of the considered trips led to the definition of a new problem, the long-haul freight transportation problem with multiple transshipment locations. A novel mathematical formulation is developed to ensure resource synchronization while including realistic features, which are commonly found separately in the literature. Considering the complexity and dimension of this routing and scheduling problem, a mathematical programming heuristic (matheuristic) is developed with the objective of obtaining good quality solutions in a reasonable amount of time, considering the logistics business context. We provide a comparison between the results obtained for 79 real-world instances. The developed solution method is now the basis of a decision support system of a Portuguese logistics operator (LO).