A hybrid heuristic for the generalized assignment problem

Amini, Mehdi; Racer, Michael

doi:10.1016/0377-2217(94)00154-5

Cited by 36 publications

(25 citation statements)

References 3 publications

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“…There are a wide range of suitable meta‐heuristics for solving the subproblems for and , including simulated annealing and tabu search [ Osman , 1995; Higgins , 2001], genetic algorithms [ Chu and Beasley , 1996], and hybrid heuristics [ Amini and Racer , 1995]. Any of these methods could have been applied and a comparison between methods is beyond the scope of this paper.…”

Section: Solution Methodsmentioning

confidence: 99%

“…[53] Algorithm 1 Set the decision variables X best , H best , Z best ¼ 0 Initialise X ¼ 0, H ¼ midpoint weir heights Repeat Solve for X using Algorithm 2 Solve for H using Algorithm 3 UNTIL There is no further improvement in the solution [54] There are a wide range of suitable meta-heuristics for solving the subproblems for x m i and h m l , including simulated annealing and tabu search [Osman, 1995;Higgins, 2001], genetic algorithms [Chu and Beasley, 1996], and hybrid heuristics [Amini and Racer, 1995]. Any of these methods could have been applied and a comparison between methods is beyond the scope of this paper.…”

Section: Solution Methodsmentioning

confidence: 99%

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Integrated modelling of cost‐effective siting and operation of flow‐control infrastructure for river ecosystem conservation

Higgins

Bryan

Overton

et al. 2011

Water Resources Research

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[1] Wetland and floodplain ecosystems along many regulated rivers are highly stressed, primarily due to a lack of environmental flows of appropriate magnitude, frequency, duration, and timing to support ecological functions. In the absence of increased environmental flows, the ecological health of river ecosystems can be enhanced by the operation of existing and new flow-control infrastructure (weirs and regulators) to return more natural environmental flow regimes to specific areas. However, determining the optimal investment and operation strategies over time is a complex task due to several factors including the multiple environmental values attached to wetlands, spatial and temporal heterogeneity and dependencies, nonlinearity, and time-dependent decisions. This makes for a very large number of decision variables over a long planning horizon. The focus of this paper is the development of a nonlinear integer programming model that accommodates these complexities. The mathematical objective aims to return the natural flow regime of key components of river ecosystems in terms of flood timing, flood duration, and interflood period. We applied a 2-stage recursive heuristic using tabu search to solve the model and tested it on the entire South Australian River Murray floodplain. We conclude that modern meta-heuristics can be used to solve the very complex nonlinear problems with spatial and temporal dependencies typical of environmental flow allocation in regulated river ecosystems. The model has been used to inform the investment in, and operation of, flow-control infrastructure in the South Australian River Murray.

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Section: Solution Methodsmentioning

confidence: 99%

Section: Solution Methodsmentioning

confidence: 99%

Integrated modelling of cost‐effective siting and operation of flow‐control infrastructure for river ecosystem conservation

Higgins

Bryan

Overton

et al. 2011

Water Resources Research

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“…Among recent exact algorithms for GAP are a branch-and-price algorithm by Savelsbergh [38] and a branch-and-cut algorithm by Nauss [23], where exact optimal solutions to many benchmark instances with up to 200 jobs and 20 agents were obtained by Nauss [23]. Among various heuristic and metaheuristic algorithms developed for GAP are a combination of the greedy method and local search by Martello and Toth [20,21]; a tabu search and simulated annealing approach by Osman [26]; a genetic algorithm by Chu and Beasley [7]; variable depth search methods by Amini and Racer [4,31]; a tabu search approach by Laguna et al [16]; a set partitioning heuristic by Cattrysse et al [6]; a relaxation heuristic by Lorena and Narciso [18]; a GRASP and MAX-MIN ant system combined with local search and tabu search by Louren co and Serra [19]; a linear relaxation heuristic by Trick [41]; a tabu search algorithm by DÃ az and FernÃ andez [8]; an ejection chain approach and a path relinking approach by Yagiura et al [42,43]; and so on. Motivated by practical applications, various generalizations of GAP have been proposed, e.g., the multi-level generalized assignment problem by Laguna et al [16]; the dynamic multi-resource generalized assignment problem by Shtub and Kogan [39]; the generalized multi-assignment problem by Park et al [27]; the multi-resource generalized assignment problem with additional constraints by Privault and Herault [30]; and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…are 72 instances, i.e., one instance for each combination of the type (C, D or E), n (100 or 200), m (5, 10 or 20) and s(1, 2, 4 or 8). The generated MRGAP instances are available at our web site 4. …”

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confidence: 99%

A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

Yagiura

Iwasaki

Ibaraki

et al. 2004

Discrete Optimization

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We propose a metaheuristic algorithm for the multi-resource generalized assignment problem (MRGAP). MRGAP is a generalization of the generalized assignment problem, which is one of the representative combinatorial optimization problems known to be NP-hard. The algorithm features a very large-scale neighborhood search, which is a mechanism of conducting the search with complex and powerful moves, where the resulting neighborhood is e ciently searched via the improvement graph. We also incorporate an adaptive mechanism for adjusting search parameters, to maintain a balance between visits to feasible and infeasible regions. Computational comparisons on benchmark instances show that the method is e ective, especially for types D and E instances, which are known to be quite di cult.

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“…In the constant search for optimal solutions to the GAP, the use of heuristics is most important, as shown in the works by Cattrysse and Van Wassenhove [58], Cattrysse, Salomon [59], Amini and Racer [60] and Lorena and Narciso [61], where it has accelerated the search for solutions to the optimisation problem. By developing the HC route assignment algorithm presented herein, we built on the results of Ribeiro and Pradin [62], who relied on a two-phase method: firstly, selecting and assigning similar HC assistance tasks (Phase 1); secondly, establishing a new division and reallocation to minimise possible inefficiency (Phase 2), similarly to the work presented by Hiermann and Prandtstetter [31].…”

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confidence: 99%

Sustainable Urban Homecare Delivery with Different Means of Transport

et al. 2018

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Abstract:Due to the increasing number of requests for homecare services, care institutions struggle to perform in urban traffic, which eventually makes travel times longer and less predictable and, therefore, leads to a declining service quality. Homecare delivery scheduling and planning tools must lead to efficient reliable routes that allow the nursing crew to make the least efforts and use the fewest institutional resources, and that consider urban sustainability goals. For the case study, a European city was selected with 58,000 people of whom 73 patients received long-term care at homes provided by 11 homecare nurses. While maximising patient satisfaction, a homecare planning algorithm considered many means of transport and minimised travel times. The study reduced the total nurses' working hours/day by a bus and walking combination, and by comparing if nurses ride e-bikes, which respectively reduced~35-44% of the total time that nurses spent travelling. This result is applicable to an urban environment where the public transport network is sufficient and biking is allowed on a reasonable number of roads. Better homecare management can support the efficient use of resources of health care institutions, high-quality home care and aspirations towards livable communities and sustainable development.

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A hybrid heuristic for the generalized assignment problem

Cited by 36 publications

References 3 publications

Integrated modelling of cost‐effective siting and operation of flow‐control infrastructure for river ecosystem conservation

Integrated modelling of cost‐effective siting and operation of flow‐control infrastructure for river ecosystem conservation

A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

Sustainable Urban Homecare Delivery with Different Means of Transport

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