A hybrid approach for the capacitated lot sizing problem with setup carryover

Gören, Hacer Güner; Tunalı, Semra; Jans, Raf

doi:10.1080/00207543.2011.559486

Cited by 28 publications

(14 citation statements)

References 38 publications

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“…Lang and Shen [21] applied hybrid RF&FO to solve CLSP with sequence dependent setups with time decomposition approach. Goren et al [14] combined Genetic Algorithm (GA) with FO to solve CLSP. Seeanner et al [31] combined variable neighborhood search (VNS) with FO to solve GLSP.…”

Section: Fix-and-optimizementioning

confidence: 99%

“…To evaluate the performance of presented heuristics, the problems are solved by FR, FR&FO and the CPLEX solver directly. The time limit for CPLEX solver is 3600 s. Goren et al [14] introduce the index GAP to compare solution quality between different heuristics. GAP is the relative difference between the heuristic solution and the best feasible solution by the CPLEX solver within the CPU time limitation.…”

Section: The Heuristic Solution Qualitymentioning

confidence: 99%

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General lot-sizing and scheduling for perishable food products

et al. 2020

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General lot-sizing and scheduling is a well-studied problem in the literature, but for perishable or time-sensitive products is less investigated. Also, most of studies on perishable product supply chains focus on strategic and tactical decision levels rather than operational decision level and integrated operational and tactical decision levels. We focus on a general lot-sizing and scheduling problem faced by perishable food products. The lifespan and shelf life are two important key features of perishable products that are considered in the problem. This problem can be described as a multi-product, multi-parallel line, multi-period general lot-sizing and scheduling problem with sequence dependent change over time. The objective function is sum of production costs, inventory holding costs, waste costs, and lifespan related cost function. We apply two mixed-integer programming based heuristics to solve generated instances. The heuristics are compared in terms of solution quality and computational time. Also, the sensitivity analysis is presented to analyze the effects of parameters' changes.Mathematics Subject Classification. 90B30, 90C11, 90C59.

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Section: Fix-and-optimizementioning

confidence: 99%

Section: The Heuristic Solution Qualitymentioning

confidence: 99%

General lot-sizing and scheduling for perishable food products

et al. 2020

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“…The pioneering study of Helber and Sahling [24] was to solve a multi-level CLSP with positive lead times using F&O approach. Inspired from this work, different problems such as stochastic CLSP [25], cooperative lot-sizing problem [14], and CLSP with setup carryover [9,19,20] have been solved using F&O heuristic. Some other applications of F&O heuristic to solve lot-sizing problems include CLSP with backlogging [59,60], CLSP with sequence dependent setup [37,58], and multi-level CLSP [17].…”

Section: Literature On Fix-and-optimize Heuristicmentioning

confidence: 99%

“…Thus, with the novel application area of F&O, performance analysis of decomposition schemes, and two hybrid heuristics, we contribute to fill the research gap. [24] F&O heuristic Drechsel and Kimms [14] F&O heuristic to obtain upper bound Goren et al [20] F&O embedded into GA procedure Lang and Shen [37] Relax & Fix (R&F) with F&O heuristic Helber et al [25] F&O heuristic Toledo et al [59] F&O embedded into multipopulation GA Chen [9] F&O heuristic with Variable neighborhood search (VNS) strategy Goren and Tunali [19] GA to obtain initial solution followed by F&O heuristic Tempelmeier and Copil [58] F&O and Fix & Relax (F&R) heuristic Toledo et al [60] Integrated R&F and F&O heuristic Furlan and Santos [17] Hybrid bee and fix-and-optimize algorithm Lot-sizing and scheduling James and Almada-Lobo [29] R&F for initial solution, F&O heuristic along with iterative neighborhood search procedure Guimarães et al [21] Relax-price-fix and Fix-priceoptimize iterative heuristics Seeanner et al [53] F&O hybridized with metaheuristic Variable Neighborhood Decomposition Search (VNDS) Stadtler and Sahling [55] F&O heuristic Xiao et al [67] R&F for initial solution, F&O heuristic along with iterative neighborhood search procedure Baldo et al [5] R&F as a construction heuristic followed by F&O as an improvement heuristic Health-care facility location problem Ghaderi and Jabalameli [18] Greedy…”

Section: Literature On Fix-and-optimize Heuristicmentioning

confidence: 99%

A hybrid fix-and-optimize heuristic for integrated inventory-transportation problem in a multi-region multi-facility supply chain

Tanksale

Jha

2020

RAIRO-Oper. Res.

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In this work, we study an integrated inventory-transportation problem in a supply chain consisting of region-bound warehouses located in different regions. The supply chain deals with multiple items that compete for storage space and transportation capacity with multi-modal transportation considering regional capacity constraint for each mode of transportation. The objective is to determine an optimal storage and transportation plan to satisfy the demand of all regions without shortages for known procurement plan for all items. The problem is formulated as a mixed integer programming (MIP) model for minimizing the total costs over a finite planning horizon. An MIP-based fix-and-optimize (F&O) heuristic with several decomposition schemes is proposed to solve the problem efficiently. The performance of the decomposition schemes is investigated against the structure of the sub-problems obtained. To enhance the performance, F&O is crossbred with two metaheuristics -genetic algorithm (GA) and iterated local search (ILS) separately, which lead to hybrid heuristic approach. Extensive numerical experiments are carried out to analyze the performance of the proposed solution methodology by randomly generating several problem instances built using data collected from the Indian Public Distribution System. The proposed solution approach is found to be computationally efficient and effective, and outperforming state of the art MIP solver Cplex for practical size problem instances. Also, the hybridization of F&O heuristic with GA and ILS boosts its performance although with a justified increase in the computational time.Mathematics Subject Classification. 90B05, 90B06, 90B90, 90C11.aspects are practiced independently which may result in suboptimal performance of a supply chain. Therefore, the need for close coordination between inventory control and transportation planning is highlighted in the literature [35].Along with the traditional supply chain, the challenge of competitive performance through efficient management of inventory and transportation is equally applicable to a non-profit supply chain such as public distribution system (PDS). PDS is practiced in most of the countries for distribution of essential commodities, particularly foodgrains, at subsidized prices to the needy people of society to ensure the food security. In the Indian PDS, foodgrains, mainly wheat and rice, are procured from farmers and distributed to the beneficiaries at subsidized prices to ensure availability and affordability of foodgrains. Serving to more than 813.5 million beneficiaries, the Indian PDS is the largest in the world. The supply chain of the Indian PDS has some special characteristics. It consists of multiple storage facilities (warehouses) located across the country to store the foodgrains to meet the demand of different regions. Each region has a procurement and demand strategy that is aggregated over all the warehouses located in that region. Consequently, it is necessary to decide which warehouse to store the procured items and w...

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“…They solved the problem via an iterative heuristic called the fixand-optimize algorithm. To solve the same problem, Goren et al (2012) developed a hybrid approach by combining GAs and a fix-and-optimize heuristic. Furlan & Santos (2015) addressed the MLCLSP to find a production plan that satisfies the demand on time and minimizes the sum of inventory holding costs, setup costs and overtime costs.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimisation model for cooperative supply chain planning

Yahia

Cheikhrouhou

Ayadi

et al. 2017

IJSOM

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Generally, each member of a supply chain (SC) optimizes his own individual objective and accordingly, plans his activities (e.g. production operations, inventories) without considering a global perspective. The goal of this work is the development of a multiobjective optimization model for cooperative planning between different manufacturing plants belonging to the same SC. The model aims at minimizing simultaneously the total production cost and the average of inventory level for several items and over a multi-period horizon. To solve this problem, a non-dominated sorting elitist genetic algorithm (NSGA-II) is developed to derive the Pareto front solutions. Several tests are developed to show the performance of the solution method and the behavior of the cooperative planning model with respect to different demand patterns. The proposed model shows high performance in the tested cases with comparison to the literature.

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A hybrid approach for the capacitated lot sizing problem with setup carryover

Cited by 28 publications

References 38 publications

General lot-sizing and scheduling for perishable food products

General lot-sizing and scheduling for perishable food products

A hybrid fix-and-optimize heuristic for integrated inventory-transportation problem in a multi-region multi-facility supply chain

A multi-objective optimisation model for cooperative supply chain planning

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