A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience
TL;DR: A metaheuristic algorithm, embedding a large neighborhood search heuristic in a multi-directional local search framework, is proposed to solve the home care routing and scheduling problem as a bi-objective problem.
About: This article is published in European Journal of Operational Research.The article was published on 2016-01-16 and is currently open access. It has received 193 citations till now. The article focuses on the topics: Service provider & Job shop scheduling.
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TL;DR: A comprehensive overview of current work in the field of HHC routing and scheduling with a focus on considered problem settings is given and single-period and multi-period problems are differentiated.
320 citations
TL;DR: This paper details a comprehensive overview of recent OR models developed for the HHCRSP, a field that has received a great amount of attention in recent years.
Abstract: The home health care routing and scheduling problem (HHCRSP) consists of designing a set of routes used by care workers to provide care to patients who live in the same geographic area and who must be treated at home. Hence, care activities, i.e., patient visits, must be planned to minimize measures, such as travel costs or to maximize the quality of service delivered to patients while respecting several constraints. The HHCRSP is an extension of the vehicle routing problem (VRP) with unusual side-constraints that make the issues difficult to solve. This paper details a comprehensive overview of recent OR models developed for the HHCRSP, a field that has received a great amount of attention in recent years. To summarize the existing research contributions, we initially identify the most relevant features considered in the HHCRSP models, and then analyze the existing literature according to the way the different studies formulate the constraints and objective functions. We then provide an overview of methods developed to solve the HHCRSP and discuss future research directions.
159 citations
TL;DR: A Green Home Health Care Supply Chain is contributed for the first time by a bi-objective location-allocation-routing model and a set of new modified SA algorithms are proposed to better solve the proposed NP-hard problem.
148 citations
TL;DR: An extensive comparative study confirms the superiority of fourth heuristic along with modification and hybridizing algorithms proposed when solving the large-scale problems as well as the performance of developed model has been evaluated through a set of sensitivity analyses.
115 citations
TL;DR: This study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment and develops a new modified multi-objectives version of SEO by using an adaptive memory strategy, so-called AMSEO.
106 citations
Cites methods from "A bi-objective home care scheduling..."
...Based on this assessment, a modified NPS (MNPS) is the number of Pareto solutions as compared with the exact method that can be classified as the non-dominated solutions [25,48]....
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...[25] – ✓ ✓ – – ✓ – ✓ ✓ – – – – – – Dynamic metaheuristic...
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References
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TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
7,512 citations
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
Abstract: An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.
3,702 citations
TL;DR: This paper presents a heuristic for the pickup and delivery problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows that is very robust and is able to adapt to various instance characteristics.
Abstract: The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.
This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.
The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.
The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.
1,685 citations
"A bi-objective home care scheduling..." refers methods in this paper
...As in Shaw (1998) and Ropke and Pisinger (2006), a parameter P ≥ is used in all worst removal operators to introduce some randomess in the selection of jobs, thereby avoiding the same jobs to be emoved over and over again....
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TL;DR: A unified heuristic which is able to solve five different variants of the vehicle routing problem and shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger.
1,282 citations
Additional excerpts
...For a deailed discussion of these operators, the reader is referred to Ropke nd Pisinger (2006) and Pisinger and Ropke (2007). Furthermore, an ther greedy operator, denoted random greedy, is applied....
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ILOG1
TL;DR: In this paper, a local search method called Large Neighbourhood Search (LNS) is used to solve vehicle routing problems, analogous to the shuffling technique of job shop scheduling.
Abstract: We use a local search method we term Large Neighbourhood Search (LNS) to solve vehicle routing problems. LNS is analogous to the shuffling technique of job-shop scheduling, and so meshes well with constraint programming technology. LNS explores a large neighbourhood of the current solution by selecting a number of "related" customer visits to remove from the set of planned routes, and re-inserting these visits using a constraint-based tree search. Unlike similar methods, we use Limited Discrepancy Search during the tree search to re-insert visits. We analyse the performance of our method on benchmark problems. We demonstrate that results produced are competitive with Operations Research meta-heuristic methods, indicating that constraint-based technology is directly applicable to vehicle routing problems.
1,207 citations