Comparing mathematical formulations for customer order scheduling with dedicated machines

The Customer Order Scheduling (COS) problem arises when performance measures are defined at the order level, and each order consists of multiple jobs. This article studies the COS problem with dedicated machines, aiming to minimize the total completion time of orders. Four mathematical formulations are examined and compared: three Mixed-Integer Linear Programming (MILP) models based on positional, precedence and time-indexed variables, and a Constraint Programming (CP) formulation, which is introduced for the first time in the literature. Computational experiments on benchmark instances show that the models provide good balances between solution quality and implementation complexity.

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Publication Details

Journal
Engineering Optimization
Published
2026-07-27
DOI
https://doi.org/10.1080/0305215x.2026.2691603
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Comparing mathematical formulations for customer order scheduling with dedicated machines

Erica Pastore, Fabio Salassa, Andrea Grosso, Arianna Alfieri
Engineering Optimization
Scheduling and Optimization Algorithms
article

Comparing mathematical formulations for customer order scheduling with dedicated machines

Erica Pastore, Fabio Salassa, Andrea Grosso, Arianna Alfieri
article en

Abstract

The Customer Order Scheduling (COS) problem arises when performance measures are defined at the order level, and each order consists of multiple jobs. This article studies the COS problem with dedicated machines, aiming to minimize the total completion time of orders. Four mathematical formulations are examined and compared: three Mixed-Integer Linear Programming (MILP) models based on positional, precedence and time-indexed variables, and a Constraint Programming (CP) formulation, which is introduced for the first time in the literature. Computational experiments on benchmark instances show that the models provide good balances between solution quality and implementation complexity.

Engineering Optimization
Politecnico di Torino (IT), University of Turin (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Scheduling and Optimization Algorithms
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Comparing mathematical formulations for customer order scheduling with dedicated machines — Erica Pastore, Fabio Salassa, et al. · Engineering Optimization (2026) | TGRS Research Map | TGRS