A comparison of two lot sizing-sequencing heuristics for the process industry

Production Models;produktie

Quality issues impacting production planning

Among the various problems affecting production processes, the unpredictability of quality factors is one of the main issues which concern manufacturing enterprises. In make-to-order or in perishable good production systems, the gap between expected and real output quality increases product cost mainly in two different ways: through the costs of extra production or reworks due to the presence of non-compliant items and through the costs originating from inefficient planning and the need of unscheduled machine changeovers. While the first are relatively easy to compute, even ex-ante, the latter are much more difficult to estimate because they depend on several planning variables such as lot size, sequencing, deliveries due dates, etc. This paper specifically addresses this problem in a make-to-order multi-product customized production system; here, the enterprise diversifies each production lot due to the fact that each order is based on the customer specific requirements and it is unique (in example, packaging or textiles and apparel industry). In these contexts, using a rule-of-thumb in overestimating the input size may cause high costs because all the excess production will generate little or no revenues on top of contributing to increasing wastes in general. On the other hand, the underestimation of the lots size is associated to the eventual need of launching a new, typically very small production order, thus a single product will bear twice the changeover costs. With little markups, it may happen that these extra costs can reduce profit to zero. Aim of this paper is to provide a critical analysis of the literature state-of-art while introducing some elements that can help the definition of lot-sizing policies considering poor quality costs

Paints supply chain optimisation

Imperial Users onl

Quality issues impacting production planning

Among the various problems affecting production processes, the unpredictability of quality factors is one of the main issues which concern manufacturing enterprises. In make-to-order or in perishable good production systems, the gap between expected and real output quality increases product cost mainly in two different ways: through the costs of extra production or reworks due to the presence of non-compliant items and through the costs originating from inefficient planning and the need of unscheduled machine changeovers. While the first are relatively easy to compute, even ex-ante, the latter are much more difficult to estimate because they depend on several planning variables such as lot size, sequencing, deliveries due dates, etc. This paper specifically addresses this problem in a make-to-order multi-product customized production system; here, the enterprise diversifies each production lot due to the fact that each order is based on the customer specific requirements and it is unique (in example, packaging or textiles and apparel industry). In these contexts, using a rule-of-thumb in overestimating the input size may cause high costs because all the excess production will generate little or no revenues on top of contributing to increasing wastes in general. On the other hand, the underestimation of the lots size is associated to the eventual need of launching a new, typically very small production order, thus a single product will bear twice the changeover costs. With little markups, it may happen that these extra costs can reduce profit to zero. Aim of this paper is to provide a critical analysis of the literature state-of-art while introducing some elements that can help the definition of lot-sizing policies considering poor quality costs

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

Clips: a capacity and lead time integrated procedure for scheduling.

We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Lagrangian Dual Decision Rules for Multistage Stochastic Mixed Integer Programming

Multistage stochastic programs can be approximated by restricting policies to follow decision rules. Directly applying this idea to problems with integer decisions is difficult because of the need for decision rules that lead to integral decisions. In this work, we introduce Lagrangian dual decision rules (LDDRs) for multistage stochastic mixed integer programming (MSMIP) which overcome this difficulty by applying decision rules in a Lagrangian dual of the MSMIP. We propose two new bounding techniques based on stagewise (SW) and nonanticipative (NA) Lagrangian duals where the Lagrangian multiplier policies are restricted by LDDRs. We demonstrate how the solutions from these duals can be used to drive primal policies. Our proposal requires fewer assumptions than most existing MSMIP methods. We compare the theoretical strength of the restricted duals and show that the restricted NA dual can provide relaxation bounds at least as good as the ones obtained by the restricted SW dual. In our numerical study, we observe that the proposed LDDR approaches yield significant optimality gap reductions compared to existing general-purpose bounding methods for MSMIP problems