Solving discrete lot-sizing and scheduling by simulated annealing and mixed integer programming

Ceschia, Sara; Gaspero, Luca Di; Schaerf, Andrea

doi:10.1016/j.cie.2017.10.017

Cited by 24 publications

(11 citation statements)

References 21 publications

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“…Another well known meta-heuristic is the Simulated Annealing (SA) algorithm. Ceschia et al [30] introduced a SA approach to cope with the multi-item single-machine LSS problem. The search space was a vector V (size: number of periods) with values v t representing the items to produce.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is a limited number K d of each type of drum d in the assembling shop. Constraint (30) ensures that for each drum d, the overall production planned in curing process can be absorbed by the assembling shop, where ∆ idt represents the number of drums d necessary for the production of item i planned in the curing workshop in period t. Constraints (31) to (37) help to define ∆ idt variable that can take three values: 0, 1 or 2. These constraints are linearisation constraints.…”

Section: Indexes and Setsmentioning

confidence: 99%

“…Saturation of upstream assembling workshop is also dealt with in the LSSP without consideration of curing presses in Constraint (64). The assembling workshop resources (drums) saturation Constraints (30) to (37) are taken into account as well and adapted through Constraints (65) to (67). On the contrary, the minimum production quantity after a mold setup on a given curing press (Constraints (38) to (40)) are not considered in the LSSP.…”

Section: Decision Variablesmentioning

confidence: 99%

See 2 more Smart Citations

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

Koch,

Arbaoui,

Ouazene

et al. 2022

Preprint

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This paper introduces an integrated lot sizing and scheduling problem inspired from a real-world application in off-the-road tire industry. This problem considers the assignment of different items on parallel machines with complex eligibility constraints within a finite planning horizon. It also considers a large panel of specific constraints such as: backordering, a limited number of setups, upstream resources saturation and customers prioritization. A novel mixed integer formulation is proposed with the objective of optimizing different normalized criteria related to the inventory and service level performance. Based on this mathematical formulation, a problem-based matheuristic method that solves the lot sizing and assignment problems separately is proposed to solve the industrial case. A computational study and sensitivity analysis are carried out based on real-world data with up to 170 products, 70 unrelated parallel machines and 42 periods. The obtained results show the effectiveness of the proposed approach on improving the company's solution. Indeed, the two most important KPIs for the management have been optimized of respectively 32% for the backorders and 13% for the overstock. Moreover, the computational time have been reduced significantly.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Indexes and Setsmentioning

confidence: 99%

Section: Decision Variablesmentioning

confidence: 99%

See 1 more Smart Citation

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

Koch,

Arbaoui,

Ouazene

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To minimize the total cost, they also applied a variant of particle swarm optimization. Ceschia et al [13] studied the discrete single-machine, multi-item lot-sizing and scheduling problem. After modeling the problem as a MIP model, to solve it, they proposed a simulated annealing algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Capacitated lot-sizing and production sequence problem with setups complexity

Behnamian,

Fatemi Ghomi,

Karimi

et al. 2022

Scientia Iranica

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This paper considered the multi-product multi-level multi-period capacitated lot-sizing and sequencing problem with setup carry-over and sequence-dependent family setup times and costs. A formulation of the problem was provided as a mixed-integer nonlinear programming model. To propose this formulation, first, the mixed-integer nonlinear of the problem was linearized, and then converted to a mixed-integer linear program. To solve large-size instances of the problem, then, a lower bound was provided. The results confirmed the efficiency of the proposed model compared to previous models in terms of the runtime and the number of defined variables and constraints. Since this problem is NP-hard and adding other factors such as family setups, setup carry over and sequence-dependent setups increase its complexity, in this paper, a genetic algorithm (GA) was applied in large-size dimensions and its results were compared with the proposed lower bound. The numerical results showed that there is no significant difference between the results of the proposed GA and lower bound, and, so, the GA had been able to approach the optimal solution.

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“…Some strategies are developed for specific problems, such as Park & Kim (2005) where the authors present proposes a method to solve the scheduling problem of a port berth and quay cranes and Biagio et al (2012) which proposes a heuristic to solve capacitor allocation problems in electric energy radial distribution networks. New applications of preexisting strategies can also be found, like Ceschia et al (2017) which proposes a Simulated Annealing application for the discrete single-machine, multi-item lot-sizing scheduling problem and obtained good results.…”

Section: Bibliography Reviewmentioning

confidence: 99%

A New Strategy to Solve Linear Integer Problems With Simplex Directions

2022

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In this paper, we propose a new heuristic strategy to solve linear integer mathematical problems. The strategy begins by finding the optimal solution of the continuous associated problem and the simplex directions from that optimal solution. A total of n − m problems are generated and solved by the strategy, where m is the number of constraints and n is the number of variables of the problem. The best solution to those problems is the solution to the original problem. Several instance problems were randomly generated to validate the strategy, and our strategy finds good solutions in 80% of the instances. We also tested the proposed strategy with instances of the MIPLIB 2017. We compare a total of 39 instances and on average our strategy performed 47% fewer iterations than the solver, in 34 of 39 instances, the strategy found good solutions.

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Solving discrete lot-sizing and scheduling by simulated annealing and mixed integer programming

Cited by 24 publications

References 21 publications

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

Capacitated lot-sizing and production sequence problem with setups complexity

A New Strategy to Solve Linear Integer Problems With Simplex Directions

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