The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty☆

Abstract

To the best of our knowledge, research in the Dynamic Plant Layout Problem (DPLP) assumes that the planning horizon is fixed and that material flows are known with certainty. But in practice, many companies use rolling planning horizons. Further, they have to deal with the effect of uncertainty in material flow forecasts. This paper investigates the performance of algorithms under fixed and rolling horizons, under different shifting costs and flow variability, and under forecast uncertainty. Nearly 1800 problems were run using different algorithms. The results show that algorithms that dominated under fixed horizons may not work as well under rolling horizons. Also it is difficult to identify an algorithm that performs well under all situations. Thus the development of efficient and effective heuristics might be useful in solving the rolling horizon problem. It also appears that increasing the planning horizon under rolling plans does not offer any advantage. Further forecast uncertainty may not significantly affect the performance of algorithms and in some cases may be beneficial.

SimSum1: a general optimisation via simulation approach for 0-1 programming models

“…The 4 th and the 5 th columns are the number of constraints and variables in the mathematical model. The 6 th column indicates that the graph has the vertex-magic total labelling or not. The 7 th and the 8 th columns are the CPU time reported by [29] and the proposed algorithm, respectively.…”

“…Recently, the concept of dynamic layout problems has been introduced by several researchers. According to [6][7], dynamic layout problems take into account possible changes in the material handling flow over multiple periods. In the dynamic facility layout problem (DFLP), the planning horizon is generally divided into certain periods which may be defined in weeks, months, or even years.…”

Formulating and solving sustainable stochastic dynamic facility layout problem: a key to sustainable operations

“…McKendall et al (2006) have addressed the need for building dynamic facility layout problem (DFLP) due to demand uncertainty and supply uncertainty. Balakrishnan and Cheng (2009) have further argued to develop DFLP algorithms so that demand uncertainty does not influence the algorithms performance. Lieckens et al (2015) have argued the need for sustainable aspect, which includes moral hazards while locating the maintenance services with remanufacturing unit location and its layout design.…”

The Heuristic Methods of Dynamic Facility Layout Problem

“…After that, it is popular to study the heuristic methods for solving DFLP. For examples, Venkatamanan [2] used genetic algorithm to solve DFLP, and Balakrishnan [3][4][5] developed two heuristic algorithms to improve the downhill pair exchanged heuristic algorithm. The first algorithm is the same heuristic algorithm that Urban [6] used to generate the feasible solutions for DFLP, but he proposed a tracing pairing exchanged heuristic algorithm to improve the solutions, which generated by the forecast window, then choose the best solution; the second heuristic algorithm combined Urban's algorithm with dynamic programming.…”

“…In order to simplify the problem, while a dynamic facility layout problem is modeled, the assumptions will be considered as [12]: (1) Each facility has the same area; (2) The number of the facilities is less than or equal to the number of the location area(if less than, assuming the virtual facilities); (3) The same cost of the unit transportation between the facilities; (4) The facility moving cost is irrelative with the layout (assuming a constant); (5) The distances between the facilities in the same row or column are calculated by measuring their centers of these facilities, else the rectangular distances.…”