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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. [ABSTRACT FROM AUTHOR]

This paper considers the assignment of heterogeneous workers to workstations of an assembly line in order to minimise the total production time. As the structure of the system implies that each of the workstations needs at least one worker, thus the problem can be considered as a generalised assignment problem (GAP). The objective is to perform an efficient human resource planning for a specified horizon consisting of several periods. Hence, we present an extension of the generalised assignment problem, consisting of a set of GAPs (one for each planning period) in which each GAP depends on the previous ones. A mixed integer mathematical model is presented for this sequencing assignment problem. The model is solved by an exact algorithm using Gurobi solver. It is proved that the problem is NP-hard and solving the medium and large size instances is not possible by the exact algorithms. Hence, two matheuristic approaches based on the disaggregated formulation of GAP are proposed. The first approach solves the problem through two sub-problems as the transportation formulation and assignment formulation. The second approach solves the problem by decomposition of the problem into several classical GAPs. The approaches are examined by a total of 27 instances. The results illustrate the efficiency of the proposed algorithms in the computational time and accuracy of the solutions. [ABSTRACT FROM AUTHOR]

This paper studies an integrated scheduling problem for a single-item, make-to-order supply chain system consisting of one supplier, one capacitated transporter and one customer. Specifically, we assume the existence in the production stage of an intermediate inventory that works as a buffer to balance the production rate and the transportation speed. Jobs are first processed on a single machine in the production stage, and then delivered to the pre-specified customer by a capacitated vehicle in the delivery stage. Each job has a due date specified by the customer, and must be delivered to the customer before its due date. Moreover, it is assumed that a job that is finished before its departure date or arrives at the customer before its due date will incur a stage-dependent corresponding inventory cost (WIP inventory, finished-good inventory or customer inventory cost). The objective is to find a coordinated production and delivery schedule such that the sum of setup, delivery and inventory costs is minimised. We formulate the problem as a nonlinear model in a general way and provide some properties. We then derive a precise instance from the general model and develop a heuristic algorithm for solving this precise instance. In order to evaluate the performance of the heuristic algorithm, we propose a simple branch-and-bound (B&B) approach for small-size problems, and a lower bound based on the Lagrangian relaxation method for large-size problems. Computational experiments show that the heuristic algorithm performs well on randomly generated problems. [ABSTRACT FROM PUBLISHER]