Multi-objective due-date setting in a make-to-order environment

Abstract

This paper presents a new dual-objective problem of due-date setting over a rolling planning horizon in make-to-order manufacturing and proposes a bi-criterion integer programming formulation for its solution. In the proposed model the due-date setting decisions are directly linked with available capacity. A simple critical load index is introduced to quickly identify the system bottleneck and the overloaded periods. The problem objective is to select a maximal subset of orders that can be completed by the customer requested dates and to quote delayed due dates for the remaining acceptable orders to minimise the number of delayed orders or the total number of delayed products as a primary optimality criterion and to minimise total or maximum delay of orders as a secondary criterion. A weighted-sum program based on a scalarisation approach is compared with a two-level due-date setting formulation based on the lexicographic approach. In addition, a mixed-integer programming model is provided for scheduling customer orders over a rolling planning horizon to minimise the maximum inventory level. Numerical examples modeled after a realworld make-to-order flexible flowshop environment in the electronics industry are provided and, for comparison, the single-objective solutions that maximise total revenue subject to service level constraints are reported.

Tactical capacity planning in a real-world ETO industry case: An action research

“…Some authors have conducted hypothetical problem-solving research (Markus et al, 2003;Sawik, 2009;Monostori et al, 2010;Tolio and Urgo, 2007;Tunali et al, 2011), and only two papers applied the model to a real industrial environment (Alfieri et al, 2011(Alfieri et al, , 2012. This lack of real-world cases corroborates the academic literature (e.g., Ramezanian et al, 2012) and…”

“…It refers to an aggregate production planning (APP) that typically encompasses a time horizon from 3 to 18 months and focuses on determining the optimum production, workforce and inventory levels for each period of the planning horizon for a given set of production resources and constraints (Ramezanian et al, 2012;Jamalnia and Feili, 2013;Wang and Yeh, 2014). In this context, many authors recognize that a capacity planning system that quickly analyses the impact of potential orders on capacity plans is of major importance during the order acceptance phase to determine reliable due dates and price quotations (Giebels, 2000;Gademann and Schutten, 2005;Hans et al, 2007;Sawik, 2009;Montreuil et al, 2013;Nobibon et al, 2015). These decisions are crucial in the ETO context and are related to setting important milestones for each project and for bid preparation (Bertrand and Muntslag, 1993;Gademann and Schutten, 2005;Hans et al, 2007;Zorzini et al, 2008;Grabenstetter and Usher, 2013;Montreuil et al, 2013).…”

An optimisation approach for capacity planning: modelling insights and empirical findings from a tactical perspective

“…At this planning level, project acceptance or rejection decisions are made (Giebels, 2000;Zorzini et al, 2008;Aslan et al, 2012) and adequate capacity planning methods that assess the consequences of these decisions for the production system are crucial (Giebels, 2000;Gademann & Schutten, 2005;Hans et al, 2007;Sawik, 2009;Montreuil et al, 2013). It is a common practice that organisations accept projects without sufficiently assessing the impact of incoming projects on the resource capacity, leading to the overload of resources and affecting delivery performance and the profitability of the production system (Hans et al, 2007).…”

Solution of Co-evolution Self-adaptive Genetic Algorithm to Production Scheduling Problem of Flexible Manufacturing System

“…Not only work pieces, machine tool, cutter, checking-tool, pallet and other factors, but also disturbance information inside and outside the system must also be integrated into system in the real-time manner in the process of scheduling, researchers conducted researches on FMS scheduling from multiple aspects, and existing scheduling theories were supplemented, perfected and improved while new basic theories were proposed. Researches with variable process paths, diversified objectives, scheduling dynamics and algorithm complex [1] were initiated; constantly improved self-adaptive GA, reactive and other intelligent scheduling strategies were built; processing objects, object grouping, equipment load balance and reasonable processing sequence suitable for FMS and others issues were researched in literatures [2] ; dynamics with different processing paths and identical processing under the premise that different machine tools are selectable are researched in literatures to conduct machine tool selection and process allocation [3] ; documentary [4] research changes the sequencing method of the fewest parts in order through effective management on free time of equipment and rearrangement of task sequence; documentary [5] study the way of multi-objective optimization in the production planning and scheduling. Researches on effects of longitudinal and transverse comprehensive state of on scheduling in evolutionary process are seldom though fruitful and comprehensive achieved are made.…”