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This paper addresses a routing and scheduling problem from two different perspectives: economic and environmental. From economic perspective, we aim to optimize the vehicle routing plan to reduce the operating cost, but from environmental perspective, we aim to optimize the vehicle routing and speed decisions to reduce the carbon emissions. This research can provide two different decision plans under these two different perspectives, and the comparison of the results from the two different perspectives will be very meaningful and helpful to the logistics decision-makers. We formulate the problem using two mixed-integer programming (MIP) models with different objectives. However, this problem is very challenging, with medium-sized instances already difficult for the MIP solver. In order to solve it with larger scale instances, we propose an exact branch-and-price (BAP) algorithm. The BAP algorithm relies on efficiently solving the pricing sub-problem with different objectives. We design two different tailored labeling algorithms to solve it. Extensive computational experiments demonstrate the effectiveness of the proposed BAP algorithm, comparing with the MIP formulation solved by CPLEX with a time limit of 2 h.

Home health care structures provide care for the elderly, people with disabilities as well as patients with chronic conditions. Since there has been an increase in demand, organizations providing home health care are eager to optimize their activities. In addition, the increase in patient numbers has led organizations to expand their geographical reach. As a result, home health care structures tend to be located in different offices to limit their travel time and, consequently, caregivers employed by these various structures must be assigned to one of the offices so they start and end their workday at their associated office. Unlike the existing literature where an upstream assignment of caregivers is performed to become a parameter of the model, the assignment of caregivers to offices is solved during the resolution of the problem in order to obtain the best possible combinations. Thus, we suggest a mixed-integer programming model of the multi-depot home health care assignment, routing, and scheduling problem without prior assignment of caregivers to the home health care offices. In addition, we propose an original matheuristic-based approach with different assignment strategies to assign visits and caregivers to the home health care offices in order to solve the problem. The experiments are conducted on a set of 56 heterogeneous instances of various sizes. Results are compared with best solutions obtained by a commercial solver, and with a lower bound obtained by Lagrangian relaxation. The results highlight the efficiency of the matheuristic-based approach since it provides a low deviation ratio with a faster computational time.

Over the years, Specialized Education and Home Care Services (SEHCS) have found their place in the French social in order to support people with visual, hearing or physical disabilities and the overall aging of the population. The development of these activities has brought to light new issues within SEHCS establishments. In this paper, we proposed an mixed-integer linear programming mathematical model to solve the assignment, scheduling and routing problems of a SEHCS. The model is validated through small and middle size instances inspired by a real-life SEHCS example and the solver Gurobi is applied to find the optimal solution. The experimental results highlight the efficiency of our approach since the optimal results show that the solutions perform well as the SEHCS assignment, scheduling and routing problem can be solved in a reasonable time with optimal solution.

The growing concern about the influences of anthropogenic pollutions has forced researchers and scholars to study the environmental concerns. This paper addresses a green routing and scheduling problem in home health care (HHC) with the constraints of synchronized visits and carbon emissions. In this work, the objective is to design a reasonable logistics route meanwhile reduce the effect on the environment for the HHC company. The formulated mixed-integer programming (MIP) model is solved for a set of small scale instances using Gurobi solver with a time limit of 1 hour. An efficient two-phase heuristic approach through decomposing the studied problem into a routing problem and a speed optimization problem is proposed. The heuristic approach is based on two exact methods using Gurobi solver and dynamic programming (DM) method. The proposed heuristic approach is examined by a total of 19 instances with different scales. The experimental results for the studied problem highlight the effectiveness and efciency of the proposed heuristic approach.

This work addresses a home health care routing and scheduling problem with time window and synchronization constraints. Each patient is associated with a period of availability according to their preferences while some visits may require the presence of two staff members simultaneously, which requires the synchronization of two visits. In this paper, the problem is studied with hard and soft patients time window and synchronization constraints. We developed a mixed integer programming model and a memetic algorithm featuring two original crossover operators. Experiments are conducted on benchmark instances from the literature as well as new instances based on real life data from a home health care provider in France. The results highlight the efficiency of the memetic algorithm since it provides great results while being flexible to the instance type. Indeed, the memetic algorithm is efficient whether the problem is studied with hard or soft time window and synchronization constraints, various caregivers qualification or several home health care offices.

In this paper, we propose an Efficient Tabu Search based Procedure (ETSP) to solve the Vehicle Routing Problem with Simultaneous Delivery and Pick-up with Time Window (VRPSDPTW). We are given a set of identical vehicles (each having a capacity), a depot, and a set of customers with deterministic delivery and pick-up demands and time windows. The objective is to minimize the number of used vehicles and the total traveling costs related to the performed routes. ETSP is composed of two types of tabu search strategies, which are called TS I and TS II respectively. TS I only considers recording the shifted customers between the different vehicles by a two-dimensional tabu list. This strategy benefits a fast decreasing of the objective searching, but easily traps into the local optimum. On the other hand, TS II utilizes a three-dimensional array to record the detailed movement of each customer. This strategy is quite slow but easy to get rid of local optima. The search is performed by shifting between TS I and TS II. These two strategies complement each other’s strengths and weaknesses, and the algorithm terminates when the two search methods cannot further improve the objective values. Computational experiments on benchmark instances from the literature show that the proposed algorithm can produce several better solutions than the previously published methods.

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the Home Health Care industry, it is of importance to research the optimization of the Home Health Care logistics. This paper considers a Home Health Care Routing Problem with stochastic travel and service time, which comes from the logistics practice of the home health care company. A stochastic programming model with recourse (SPR) is proposed, the Hybrid Genetic Algorithm (HGA) and stochastic simulation method are integrated to solve the proposed model. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, experimental results for the related instances validate the effectiveness and efficiency of the proposed HGA. Then HGA and stochastic simulation are integrated to solve the stochastic model. Comparisons between the solutions obtained by stochastic model and deterministic one validate the reasonable and robustness of considering the stochastic travel and service time. This research will help HHC companies to make appropriate decisions when arranging their vehicle routes.

There is a large number of papers which studies the hospital operating theater management and more precisely the operating room planning and scheduling. The purpose of these researches is to find methods to organize the prescribe surgical procedures and determine a planning which improves a chosen performance criterion while considering the hospital context constraints. In this paper, we focus on the importance of the medical devices sterilization step in operating theater scheduling and propose a method to reduce the number of medical devices boxes needed to respect a planning. From this standpoint, we develop several met a-heuristics (genetic algorithm, particle swarm optimization and tabu search) to solve the problem. With this work, we hope to lay the foundation for a transverse logistic between the hospital pharmacy and the operating theater. Such a logistic may be applied in a multi-site context with a central pharmacy providing surgical devices to several hospital complexes.

In this paper, we study a real-industrial case of an integrated lot sizing and delivery scheduling problem with multi-capacitated transporters, distinct due dates considerations and batch-size-dependent delivery times and costs. The objective is to find a coordinated lot sizing and scheduling scheme so that the total cost is minimised while guaranteeing a certain service level at the manufacturers. A mathematical formulation of the problem is given as a nonlinear model in a general way. We prove dominance properties among the solutions of the problem and propose two solving methods. The first one is a generalised dynamic programming scheme to solve this problem in the case of homogeneous and heterogeneous transporters, and the second one is a mixed integer programming formulation proposed to evaluate the performance of the dynamic programming method. Computational experiments show that the proposed approach is very efficient for solving from small to medium-sized instances, especially for the identical transporter case.

This article deals with an integrated scheduling problem for a multi-item capacity-constrained production and delivery system. The compatible jobs are firstly processed on a batching machine, and then delivered to a customer by one capacitated transporter. Each job has to be delivered to the customer before its due date. It is assumed that the job which arrives to the customer before its due date will occur an earliness penalty. The problem is to find an integrated schedule such that the total logistics cost is minimised while guaranteeing a certain customer service level. We formulate the problem as a nonlinear model, and show that this problem is intractable. Then we develop a hybrid-coding simulated annealing algorithm for solving the problem. At last, we derive a lower bound to verify performance of this proposed algorithm. Experiments show the efficiency in terms of both solution quality and running time of the proposed algorithm.

This Design Structure Matrix is a method and a tool used to capture, analyze and optimize iterations in the design process. It has been widely used over the past two decades by researchers from both academia and industry. DSM is well suited to identify iterations in the design process. Many heuristics were developed to deal with DSM in order to reduce the number of iterations by rescheduling the tasks of the design process. These heuristics are based on a process called partitioning. These heuristics give generally an approached solution but not the optimal one. In this paper we introduce a new algorithm in order to get the exact solution of the partitioning process using linear programming.

There are many practical production systems with distribution operations where finished jobs are transferred from supplier to the customer by transporters. In these systems, the coordination of production and logistics operations can help to improve overall system performance and reduce the total operation cost. This paper focuses on two categories of integrated scheduling problems. One is Integrated Scheduling of Production–Distribution Problems (ISPDP) and the other is Integrated Scheduling of Production–Inventory–Distribution Problems (ISPIDP). This paper presents a review of the state–of–the–art articles and suggests the corresponding further research directions for the above two categories of integrated scheduling problems.

This paper chooses a supply chain which involves multiple supply links as the studied object. In each supply link, we consider an integrated scheduling problem in which a set of identical jobs are first processed on a single machine, and then batch delivered to a customer by a transporter. Each job has a due date specified by the customer in each supply link. It is supposed that a job which is finished before its departure date or delivered to the customer before its due date will incur a corresponding inventory cost. The objective is to find a joint schedule for each supply link such that the total cost of the supply chain is minimised. We show that this problem is NP-hard in the maximum capacity of the transporters, and then propose a dominance-related greedy algorithm and a genetic algorithm. Computational results illustrate the efficiency of the proposed heuristics.