Machine scheduling with sequence-dependent setup times using a randomized search heuristic

Montoya‐Torres, Jairo R.; Soto-Ferrari, Milton; González-Solano, Fernando; Alfonso-Lizarazo, Edgar

doi:10.1109/iccie.2009.5223742

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…To solve the problem with the total weighted completion time minimization objective, Fowler et al [5] developed a hybridized genetic algorithm with dispatching rules. To solve the problem with the makespan minimization objective, Montoya-Torres et al [6] established a randomized search heuristic. Lin et al [7] proposed a genetic algorithm to solve the minimization of the maximum lateness.…”

Section: P R S Cmentioning

confidence: 99%

Identical Machine Scheduling Problem with Sequence-Dependent Setup Times: MILP Formulations Computational Study

Yalaoui¹,

Nguyen²

2021

AJOR

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This work aims to give a systematic construction of the two families of mixed-integer-linear-programming (MILP) formulations, which are graphbased and sequence-based, of the well-known scheduling problem | , | m j ij j P r s C∑ . Two upper bounds of job completion times are introduced.A numerical test result analysis is conducted with a two-fold objective 1) testing the performance of each solving methods, and 2) identifying and analyzing the tractability of an instance according to the instance structure in terms of the number of machines, of the jobs setup time lengths and of the jobs release date distribution over the scheduling horizon.

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Section: P R S Cmentioning

confidence: 99%

Identical Machine Scheduling Problem with Sequence-Dependent Setup Times: MILP Formulations Computational Study

Yalaoui¹,

Nguyen²

2021

AJOR

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“…Previously, Montoya‐Torres et al. (, ) implemented an efficient random‐based algorithm for the single‐ and parallel‐machine scheduling problem with both sequence‐dependent setup times and job release times. Despite these outlined applications, as far as we know, this is the first time that a BR technique is implemented into a solution procedure for the capacitated LRP (CLRP).…”

Section: Related Literaturementioning

confidence: 99%

A biased‐randomized metaheuristic for the capacitated location routing problem

Quintero-Araújo

Caballero-Villalobos

Juan

et al. 2016

Int Trans Operational Res

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The location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departing from and finishing at each facility to serve the associated customers' demands. In this paper, a two-phase metaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisions must be made taking into account limited capacities of both facilities and vehicles. In the first phase (selection of promising solutions), we determine the depots to be opened, perform a fast allocation of customers to open depots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed several times in order to keep the most promising solutions. In the second phase (solution refinement), for each of the selected solutions we apply a perturbation procedure to the customer allocation followed by a more intensive routing heuristic. Computational experiments are carried out using well-known instances from the literature. Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect to the best-known solutions (BKSs) for all tested sets in short computational times.

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“…Exact solution methods were programmed using X-press IVE while the proposed heuristic was programmed using Visual Basic for Applications (VBA) in MS Excel spreadsheets. Data was generated using the same data sets of Montoya-Torres et al (2009). Integer travel costs/times were generated from a uniform distribution [0, min ], with being integer service times generated from a uniform distribution [1,100].…”

Section: Data Setsmentioning

confidence: 99%

Using randomization to solve the deterministic single and multiple vehicle routing problem with service time constraints

Montoya‐Torres¹,

Alfonso-Lizarazo²,

Franco³

et al. 2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

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This paper considers the deterministic vehicle routing problem with service time requirements for delivery. Service requests are also available at different times known at the initial time of route planning. This paper presents an approach based on the generation of service sequences (routes) using randomness. Both the single-vehicle and the multiple-vehicle cases are studied. Our approach is validated using random-generated data and compared against the optimal solution obtained by mathematical programming for small-sized instances, as well as against known lower bounds for medium to large-sized instances. Results show that our approach is competitive with reference to the value of the objective function, and requires less computational time in comparison with the exact resolution procedure. INTRODUCTIONThe vehicle routing problem (VRP) has received an immense attention from the scientific community during the last three to four decades. Since the first time the problem was presented in the scientific community by Dantzig and Ramser (1959), it plays a vital role in the design of distribution systems. Basically, the classical VRP consists of designing routes for a set of vehicles that are to requested to service at the lowest cost a set of geographically dispersed customers. In order to approach the basic version of the problem to more real-life contexts, several restrictions have been added: service time windows, pickup and deliveries, backhauls, etc. (Cordeau et al. 2005). The most widely studied vehicle routing problems are the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). These are surveyed by Laporte and Semet (2002), and Cordeau et al. (2002a). This paper considers the classical Vehicle Routing Problem (VRP) with multiple uncapacitated homogenous vehicles (Laporte 1992, Toth and Vigo 2001, Golden, Raghavan and Wasil 2008). Formally, the classical VRP is defined on an undirected graph = ( , ) where = { 0 , 1 , … , } is a vertex set and = {� , �: , ∈ , < } is an edge set. Vertex 0 is a depot at which are basedidentical (homogeneous) vehicles of capacity , while the remaining vertices represent customer or clients. A non-negative cost, distance or travel time matrix = ( ) is defined on . Each customer has a nonnegative demand and a non-negative service time . The VRP consists on designing a set of vehicle routes of least cost, each starting and ending at the depot, and such that each customer is visited exactly once by a vehicle, and the total demand of any route does not exceed the load capacity of vehicles. The VRP is a hard combinatorial optimization problem. Exact solution procedures have been proposed (Naddef and Rinaldi 2002, Baldacci, Hadjiconstantinou and Mingozzi 2004) but they can only solve relatively small instances and their computational time are highly variable. To this day, heuristics remain the only reliable approach for the solution of practical instances. Several literature surveys have been recently published on the use of heuristics a...

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Machine scheduling with sequence-dependent setup times using a randomized search heuristic

Cited by 7 publications

References 36 publications

Identical Machine Scheduling Problem with Sequence-Dependent Setup Times: MILP Formulations Computational Study

Identical Machine Scheduling Problem with Sequence-Dependent Setup Times: MILP Formulations Computational Study

A biased‐randomized metaheuristic for the capacitated location routing problem

Using randomization to solve the deterministic single and multiple vehicle routing problem with service time constraints

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