OR PRACTICE—Production Planning with Patterns: A Problem from Processed Food Manufacturing

Abstract

Based on our work with ConAgra Foods (http://www.conagrafoods.com), a leading U.S. food manufacturer, we study a large-scale production-planning problem. The problem incorporates several distinguishing characteristics of production in the processed-food industry, including (i) production patterns that define specific combinations of weeks in which products can be produced, (ii) food groups that classify products based on the allergens they contain, (iii) sequence-dependent setup times, and (iv) manufacture of a large number of products (typically, around 200–250) on multiple production lines (typically, around 15–20) in the presence of significant inventory holding costs and production setup costs. The objective is to obtain a minimum-cost four-week cyclic schedule to resolve three basic decisions: (a) the assignment of products to each line, (b) the partitioning of the demand of each product over the lines to which it is assigned, and (c) the sequence of production on each line. We show that the general problem is strongly NP-hard. To develop intuition via theoretical analysis, we first obtain a polynomially solvable special case by sacrificing as little of its structure as possible and then analyzing the impact of imposing production patterns. A mixed-integer programming model of the general problem allows us to assess the average impact of production patterns and production capacities on the cost of an optimal schedule. Next, to solve practical instances of the problem, we develop an easy-to-implement heuristic. We first demonstrate the effectiveness of the heuristic on a comprehensive test bed of instances; the average percentage gap of the heuristic solution from the optimum is about 3%. Then, we show savings of about 28% on a real-world instance (283 products, 17 production lines) by comparing the schedule obtained from the heuristic to one that was in use (at ConAgra) based on an earlier consultant's work. Finally, we discuss the IT infrastructure implemented to enable the incorporation of optimized (or near-optimized) solutions for ongoing use.

Product Wheels for Scheduling in the Baking Industry: A Case Study

“…Such results show that this manual heuristic did accommodate the need for mix flexibility and minimisation of production costs in Baking Company. The theoretical savings calculated for the production cycles at Baking Company are slightly higher than other research studies which used production cycles, such as the 15% reduction in setup and inventory costs found by Mehrotra et al (2011) using their optimisation model. A study of product wheels in the process industry showed mixed results as to the impact of scheduling on changeover time, increasing the time in some cases and decreasing time in others (Wilson & Ali, 2014).…”

“…However, based on the application example of Baking Company in this study, it can be concluded that the method should be reserved for small problems where few MTS items are required to be integrated into the product wheel. The other optimisation models presented in Table 1 which utilised operations research methods to solve the issues of natural sequencing via schedule blocks might be more suitable for solving applications with higher variety (Bilgen & Günther, 2010;Günther et al, 2006;Mehrotra et al, 2011;Mendéz & Cerdá, 2002;Pinedo, 2009). Just like the product wheel, the operations research production cycles aim to increase production efficiency by using pre-defined sequences of production orders.…”

A price-directed heuristic for the economic lot scheduling problem

“…The sequence-dependent ELSP Ever since the ELSP was first introduced by Rogers (1958), a large number of heuristics, among them the socalled common-cycle, basic-period, and varying-lot-sizes approaches, have been proposed (see, e.g., the reviews given in Elmaghraby, 1978;Davis, 1995;and Carstensen, 1999). Although sequence-dependent setup times and costs are reported to be prevalent in most practical applications (see, e.g., Monkman et al, 2008 andMehrotra et al, 2011), the vast majority of the ELSP-related research concentrates on the special case of sequence-independent setup times and costs. Early exceptions like Maxwell (1964), Sing and Foster (1987), and Inman and Jones (1993) considered very restrictive forms of setup times.…”

Scheduling of production and logistics operations of steam production systems in food industries: a case study of the tomato processing industry

“…Both topics are relevant for food and agroindustries, particularly to the first as the industrial capacity and cost can be substantially modified due to changes in the production planning. Some studies have addressed specifically the operations scheduling in food and agro-industries, for instance: Doganis and Sarimveis (2007) proposed a mixed integer model for production scheduling in a single production line of yogurt; Simpson and Abakarov (2009) optimized the scheduling of thermal process in canned food industry; Toso et al (2009) approached the production scheduling in an animalfeed company; Ferreira et al (2010) dealt with the production lot-sizing and scheduling in the beverage industry; Kopanos et al (2011Kopanos et al ( , 2012 coped with the production planning in semicontinuous food industries; Mehrotra et al (2011) conducted a study of production planning with patterns in a large company in USA; Amorim et al (2012) formulated models integrating production and distribution planning of perishable goods; Wauters et al (2012) also approached the production scheduling for the food industry in real contexts.…”