Abstract
This paper aims at presenting a customer order scheduling environment in which the setup times are explicit and depend on the production sequence. The considered objective function is the total tardiness minimization. Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and Perez-Gonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO). The algorithms under comparison have been compared on an extensive benchmark of randomly generated test instances, considering two performance measures: Relative Deviation Index (RDI) and Success Rate (SR). For the small-size evaluated instances, the SPAM is the most efficient algorithm, presenting the better values of RDI and SR. For the large-size evaluated instances, the hybrid matheuristic SPAM-JPO and MILP model are the most efficient methods.
Highlights
Consumer demand, combined with global competition among firms, has been introducing new production paradigms in a crescent quest for quality, resulting in the allocation of manufacturing of components of specialized goods to several plants or production environments (Fernandez-Viagas & Framinan, 2015)
Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and PerezGonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO)
Two mixed-integer linear programming models are developed for this problem, along with two matheuristics that reduce the number of decision variables
Summary
Consumer demand, combined with global competition among firms, has been introducing new production paradigms in a crescent quest for quality, resulting in the allocation of manufacturing of components of specialized goods to several plants or production environments (Fernandez-Viagas & Framinan, 2015). In recent years, production scheduling researchers have focused on the assembly scheduling problems, such as the customer order scheduling environment (Framinan et al, 2018). The setup times are embedded in the processing times in the current literature on customer order scheduling (Prata et al, 2021a). This may not be a realistic assumption in many practical situations since the machines allow for some versatility in handling various items, which typically necessitates some set-up operations. The remaining sections of this paper are structured as follows: Section 2 addresses some related approaches, Section 3 describes the problem under study, Section 4 presents the proposed solution approaches, Section 5 summarizes the computational results.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
