Capacity planning in supply chains of mineral resources

Fung, Joey; Singh, Gaurav; Zinder, Yakov

doi:10.1016/j.ins.2014.11.015

Cited by 22 publications

(8 citation statements)

References 29 publications

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“…Proof Let n = 6, and let the resource requirements of the jobs be (ω j ) 6 j=1 = (6, 6, 6, 5, 5, 2). Let the resource availability be described by a function Ω(t) given by the sequence (6,12,6,7,7,11,6,6,6,6,6,6). An optimal schedule of length 8 is presented in Fig.…”

Section: Lemma 3 Any Two Conjugate Instances Of the F2|storage ω J mentioning

confidence: 99%

“…Since p 1 ≥ p 2 , this means that no two jobs are executed on the same machine at the same time. Each job starts exactly once by (7). Inequalities (8) guarantee that all jobs are completed by time C max .…”

Section: Heuristicsmentioning

confidence: 99%

“…A number of applications, ranging from multimedia systems [14] and star data gathering networks [1] to supply chains of mineral resources [7], motivated the recent interest in the two-machine flow shop with limited storage space, also referred to as the two-machine flow shop with job dependent buffer requirements. This direction of research focuses on the situations with two distinct characteristics: (1) each job requires some storage space and the storage requirements vary from job to job; and (2) each job seizes the required portion of the storage space (buffer) from the start of its first operation till the completion of its second operation.…”

Section: Introductionmentioning

confidence: 99%

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Two-machine flow shop with dynamic storage space

2020

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The publications on two-machine flow shop scheduling problems with job dependent storage requirements, where a job seizes a portion of the storage space for the entire duration of its processing, were motivated by various applications ranging from supply chains of mineral resources to multimedia systems. In contrast to the previous publications that assumed that the availability of the storage space remains unchanged, this paper is concerned with a more general case when the availability is a function of time. It strengthens the previously published result concerning the existence of an optimal permutation schedule, shows that the variable storage space availability leads to the NP-hardness in the strong sense even for unit processing times, and presents a polynomial-time approximation scheme together with several heuristic algorithms. The heuristics are evaluated by means of computational experiments.

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Section: Lemma 3 Any Two Conjugate Instances Of the F2|storage ω J mentioning

confidence: 99%

Section: Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-machine flow shop with dynamic storage space

2020

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“…Mohring (2014) NC code spindle speed feed rate, number of cutting edges diameter of milling tool, initial geometry forces, torques, temperatures, vibration, acceleration surface conditions, tool wear condition Mori et al (2014) Setup-related issues Zheng et al (2014) Sequence-dependent setup time, time to complete setup activities comprising resources' assembly and disassembly, temperature change, software download, calibration in testing Hao et al (2014) New product allocation, multiple orders per job, a large number of processing steps, random equipment failures, waiting time constraints Meng et al (2014) Modularity Costa et al (2014) Product-mix, seasonality, working shifts Kouedeu et al (2015) Planning policies, random failures rates and repair time, components fails, imperfect repairs Shaik et al (2015) Modularity product design, variety, quality Jain et al (2015) Breakdowns speed losses, and quality defects Gontarz et al (2015) Machine environment and operations knowledge Piechnicki et al (2015) TPM implementation MacCarthy (2015) Shape, dimension, point of customization Mwanzaa and Mbohwaa (2015) Availability, spare shortage, operator involvement, raw material short, power problems. Nakajima losses Andersson and Bellgran (2015) Availability, performance, quality, production pace considering cycle time Mourtzis (2015) The craft approach offers a less efficient process, at least for commodity products, but remains necessary for technologies that are still new Mourtzis et al (2015) Supplier network efficiency Deteriorating production environment, machine breakdown Mokhtari and Dadgar (2015) Time varying failure rates, realistic environments, where failure rate of a machine is variable when environmental situations like shop temperature, shop light, shop humidity or even worker skill change significantly Kull (2015) Setup time Mesa et al (2015) Changeover time Fung et al (2015) Transportation and infrastructure of transport Reyes (2015) Labor, logistics, performance, availability, quality, setup, adjustment, stoppage, breakdown, scheduled and planned maintenance, official production breaks, process improvement initiatives or equipment tests, maintenance performed by machine operators (e.g. equipment cleaning), operators training, actual operating speed of the equipment (i.e.…”

Section: Machine Efficiency In Mass Customizationmentioning

confidence: 99%

A review of machine efficiency in mass customization

James

2019

BIJ

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Purpose The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies. Design/methodology/approach The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review. Findings The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized. Research limitations/implications Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries. Practical implications This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia. Originality/value This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.

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“…Thus, the interest of one of the coauthors of this paper in the PP-problem was originally inspired by the scheduling situations in supply chains where the change of the means of transportation involves unloading and loading, using certain storage space. Such situations arise, for example, in a supply chain of mineral resources [9].…”

mentioning

confidence: 99%

On a borderline between the NP-hard and polynomial-time solvable cases of the flow shop with job-dependent storage requirements

Kononov¹,

Memar²,

Zinder³

2021

Preprint

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The paper is concerned with the two-machine flow shop, where each job requires an additional resource (referred to as storage space) from the start of its first operation till the end of its second operation. The storage requirement of a job is determined by the processing time of its first operation. At any point in time, the total consumption of this additional resource cannot exceed a given limit (referred to as the storage capacity). The goal is to minimise the makespan, i.e. to minimise the time needed for the completion of all jobs. This problem is NP-hard in the strong sense. The paper analyses how the parameter -a lower bound on the storage capacity specified in terms of the processing times, affects the computational complexity.This paper considers the two-machine flow shop that is comprised of the firststage machine M 1 , the second-stage machine M 2 , and an additional resource referred to as a buffer or storage space. The machines must process a set of jobs N = {1, ..., n}. In order to be processed a job j ∈ N must be processed on

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Capacity planning in supply chains of mineral resources

Cited by 22 publications

References 29 publications

Two-machine flow shop with dynamic storage space

Two-machine flow shop with dynamic storage space

A review of machine efficiency in mass customization

On a borderline between the NP-hard and polynomial-time solvable cases of the flow shop with job-dependent storage requirements

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