Location and layout planning

Domschke, Wolfgang; Krispin, Gabriela

doi:10.1007/bf01545586

Cited by 50 publications

(3 citation statements)

References 128 publications

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“…A review of location-allocation models in a traditional production-distribution context can be found in Domschke and Krispin (1997). The use of location-allocation models in reverse logistics is described in a number of studies, mostly related to cases.…”

Section: Literaturementioning

confidence: 99%

Business case Océ: Reverse logistic network re-design for copiers

Krikke¹,

1999

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The introduction of extended producer responsibility forces Original Equipment Manufacturers to set up a logistic network for take back, processing and recovery of discarded products. In this paper, we discuss a business case study carried out at Océ, a copier firm in Venlo (NL). It concerns the installment of remanufacturing processes. There is a choice from two locations in Venlo (NL) and one in Prague (Czech Republic), where assignments are subjected to managerial constraints. The study is meant to verify whether the strategic decision of Océ to move remanufacturing activities to the Czech Republic is also economically feasible. We limit ourselves to an optimisation of the HV02-machine network. We follow our general approach, in which we first determine how return products are processed (recovery strategy) and subsequently optimise the reverse logistic network design. We optimise on total operational costs over all possibilities and also compare three pregiven managerial solutions (=network designs) with a Mixed Integer Linear Programming model. Differences in economic costs appear to be very small, hence installing recovery activities in Prague for the HV02-machine must be well motivated from a strategic point of view. Moreover, we argue that besides cost minimisation, Océ should include performance indicators, such as JIT, reliability, in logistic optimisation to support its quality oriented business strategy. In addition, we discuss aspects regarding specific modelling elements in this case situation, the definition of cost functions, the possibility of optimising the forward and reverse logistic network and the use of LP-versus MILP-models in this kind of situations.

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Section: Literaturementioning

confidence: 99%

Business case Océ: Reverse logistic network re-design for copiers

Krikke¹,

1999

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“…These distance values are essential for computing the assignment cost of the permutation, as they help determine the overall cost associated with different facility assignments for the given problem. The assignment cost of the permutation may then be calculated using the formula: (3,4)×distance (3,4)…”

Section: Fig 2 Facility Location Problem With Four Facilitiesmentioning

confidence: 99%

“…Since 1957, when Koopmans and Beckmann [1] first presented the quadratic assignment problem as a combinatorial optimization problem, it has attracted much attention and research from researchers in mathematics, computer science, and many other applications. On the one hand, quadratic assignment problems are widely used in practice, and many real-world problems can be formalized as quadratic assignment problems, such as integrated circuit wiring [2][3], factory location layout [4], typewriter keyboard design, task scheduling [5][6], etc. On the other hand, some classical NP-hard combinatorial optimization problems, such as the traveling salesman problem, the triangulation problem, and the Max Clique problem, can also be transformed into quadratic assignment problems [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Discrete Rat Swarm Optimization Algorithm for the Quadratic Assignment Problem

Mzili,

Riffi

et al. 2023

FU Mech Eng

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The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP.

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