Tabu search and lower bounds for a combined production–transportation problem

Condotta, Alessandro; Knust, Sigrid; Meier, Dimitri V.; Shakhlevich, Natalia V.

doi:10.1016/j.cor.2012.08.017

Cited by 28 publications

(9 citation statements)

References 29 publications

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“…They verified the impacts of purchasing, manufacturing and transportation lead time on the supply chain configuration and total cost. Condotta et al (2013) developed a combined production-distribution problem in which transportation schedule dominates the production. Different from other studies, Hajiaghaei-Keshteli and Aminnayeri (2014) considered both local transportation and rail transportation in an integrated scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits

Sun

Chung

Chan

2015

Transportation Research Part E: Logistics and Transportation Re

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Section: Literature Reviewmentioning

confidence: 99%

Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits

Sun

Chung

Chan

2015

Transportation Research Part E: Logistics and Transportation Re

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“…Constraint set (6) specifies the last P k th part of each batch is processed after its previous parts. The starting and completion times of a batch in the manufacturer are defined by constraint sets (7) and (8). Constraint set (9) makes sure that the set-up time is needed before a new batch is processed on the manufacture's machine.…”

Section: Problem Formationmentioning

confidence: 99%

“…Another characteristic is the release time of jobs, and this situation has also been investigated in many other scheduling problems (see, e.g. [3,8,18]). …”

Section: Introductionmentioning

confidence: 99%

Preemptive scheduling in a two-stage supply chain to minimize the makespan

Pei

Fan

Pardalos

et al. 2014

Optimization Methods and Software

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This paper deals with the problem of preemptive scheduling in a two-stage supply chain framework. The supply chain environment contains two stages: production and transportation. In the production stage n jobs are processed on a manufacturer's bounded serial batching machine, preemptions are allowed, and set-up time is required before a new batch is processed. In the transportation stage each batch is delivered to a customer by a single vehicle. The objective is to minimize the makespan by making decisions for both mutually coordinated stages. Specifically, two versions are studied. The first one is that all jobs are available to be processed at time zero, and the second one is that jobs have different release times. An O(n log n) time algorithm is developed for the first version, and we show that it can produce the optimal schedule for the entire problem. For the second version, based on some useful properties we have designed an O(n log n) time heuristic and a novel lower bound. The worst-case performance ratio of our algorithm is bounded by 2. Our computational study with random instances of different scales shows high-quality solutions for either small-scale or large-scale instances returned in a reasonable PC times.

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“…This problem has recently been addressed using IP and a heuristic algorithm (Garcia and Rabadi, 2013) as well as a metaheuristic method (Garcia and Rabadi, 2011). Perhaps most closely related to the problem studied in this research, however, is a recent paper by Condotta et al (2013) which examines a combined production planning and transportation problem. This problem involves simultaneously scheduling a set of jobs with release times on a single-machine processor at the production plant, and then delivering the finished products to customers under due dates.…”

Section: Related Literaturementioning

confidence: 99%

A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity

Garcia

2014

Kybernetes

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Purpose – The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem. Design/methodology/approach – The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required. Findings – The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions. Practical implications – The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement. Originality/value – This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

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Tabu search and lower bounds for a combined production–transportation problem

Cited by 28 publications

References 29 publications

Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits

Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits

Preemptive scheduling in a two-stage supply chain to minimize the makespan

A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity

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