An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company

Mohammadi, Shahriar; Al-e-hashem, S.M.J. Mirzapour; Rekik, Yacine

doi:10.1016/j.ijpe.2019.05.017

Cited by 89 publications

(21 citation statements)

References 29 publications

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“…A Hybrid Particle Swarm Optimization (H-PSO) was described for IPPS and delivery route planning. This was implemented utilizing multi-purpose machines to minimize the cost and earliness and tardiness of the jobs [26]. A logic-based Benders decomposition (LBBD) algorithm mainly separates the decision variables into two sub-categories, i.e., master problem deals with the process plan and sub-problem deals with the sequencing has been emphasized that can solve the IPPS problem for finding an exact solution [27].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

et al. 2021

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Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

et al. 2021

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“…Mohammadi et al [25] studied an integrated flexible job-shop scheduling-vehicle routing problem with a time window. They modelled the problem as a novel biobjective mixed integer, in which the first objective function tries to minimize a sum of the production and distribution scheduling costs, and the second objective function aims to minimize a weighted sum of delivery earliness and tardiness.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They considered a furniture manufacturing company producing customized goods as a case study. In this study, an Ɛ-constraint method is applied to solve small-sized real data and medium-and large-sized problems are solved by a Hybrid Particle Swarm Optimization algorithm [25].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Integrated Production and Distribution Scheduling Problem with Outsourcing Allowed

Izadi

Ahmadizar

Arkat

2020

IJE

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In this paper, we studied a new integrated production scheduling, vehicle routing, inventory and outsourcing problem. The production phase considers parallel machine scheduling including setup times with outsourcing allowed and the distribution phase considered batch delivery by a fleet of homogenous vehicles with respect to holding cost of completed jobs. The objective of the Mixed Integer Linear Programming (MILP) formulated model is to minimize the total costs including production, outsourcing, holding, tardiness and distribution fixed and variable costs. Due to the nondeterministic polynomial time (Np)-hardness of the problem, we derive a number of dominance properties for the optimal solution and combine them with a Genetic Algorithm (GA) to solve the problem. To assess the efficiency and effectiveness of the proposed hybrid algorithm, we conduct the computational study on randomly generated instances. Sensitivity analyses showed the impacts of the parameters on the objective function were incorporated. In order to evaluate the significance of the differences among the results obtained by GA and GADP one-tailed paired t tests were performed and interval plots were depicted.

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“…Studies of scheduling and vehicle routing problems with time windows were initiated by Schrage [30] and Bodin et al [5]. Recent publications include Sarasola and Doerner [29], Gnegel and Fügenschuh [15], Mohammadi et al [25], Chen et al [8] and Lera-Romero et al [23], among others.…”

Section: Problemmentioning

confidence: 99%

Fixed interval scheduling with third‐party machines

et al. 2020

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We study a problem of scheduling n jobs on machines of two types: in‐house machines and third‐party machines. Scheduling on in‐house machines incurs no additional costs, while using third‐party machines implies costs depending on their number and the time of usage. Each job has a fixed time interval for being processed which can be divided and allocated among several machines, as long as there is only one machine processing the job at any time. No machine can process more than one job at a time. Jobs can be rejected, and they are of different importance that is reflected in the weight of each job. The objective is to find a subset of the jobs and the number of third‐party machines for any period of time so that the accepted jobs can be feasibly scheduled, the total weight of the accepted jobs is maximized, and the total machine usage costs does not exceed a given upper bound. We also study a similar problem in which the objective is to maximize the total time at which at least one job is processed. Both problems are encountered in situations in which certain activities with given start and completion times have to be serviced by human operators. Examples are air traffic control and the monitoring safe vehicle unloading. Other examples are the employment of subcontractors in agriculture, construction or transportation. We will present NP‐hardness proofs, polynomial and pseudo‐polynomial optimal algorithms and an approximation algorithm for these problems and their special cases. These problems admit graph‐theoretical interpretations associated with finding independent sets and a proper vertex coloring in interval graphs.

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An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company

Cited by 89 publications

References 29 publications

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

A Hybrid Genetic Algorithm for Integrated Production and Distribution Scheduling Problem with Outsourcing Allowed

Fixed interval scheduling with third‐party machines

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