Employee or labor scheduling is associated with assigning an appropriate number of workers to the jobs during each day of work. It requires determining when staff members will work and when part-time, full-time workers will be needed to work. It is obvious that the number of employees wanted on duty throughout the week may fluctuate depending on health or family issues of the labor and the employer's requirement for a particular job; Scheduling forces us to systematically identify and analyze about all the tasks that need to be done on a project, the expected time each task might take, the expected requirement of the workforce for the job in terms of size and quality of employee personnel and the expected labor expenses. As the availabilities of the employees may vary and change from week to week; hence the scheduling becomes more essential for the smooth running of a project or shift. This paper focuses on a constructive method for solving Labor Scheduling problem encountered in a construction company, suggesting an estimated labor cost over a week and the requirement of part-time labors in each shift, using linear programming techniques, thus, providing a logical way to organize these tasks and produce a new schedule each week, by the virtue of the changing demand for service while minimizing labor cost and maximizing labor preferences.

<abstract> <p>Retail supply chains are intended to empower effectiveness, speed, and cost-savings, guaranteeing that items get to the end client brilliantly, giving rise to the new logistic strategy of cross-docking. Cross-docking popularity depends heavily on properly executing operational-level policies like assigning doors to trucks or handling resources to doors. This paper proposes a linear programming model based on door-to-storage assignment. The model aims to optimize the material handling cost within a cross-dock when goods are unloaded and transferred from the dock area to the storage area. A fraction of the products unloaded at the incoming gates is assigned to different storage zones depending on their demand frequency and the loading sequence. Numerical example considering a varying number of inbound cars, doors, products, and storage areas is analyzed, and the result proves that the cost can be minimized or savings can be intensified based on the feasibility of the research problem. The result explains that a variation in the number of inbound trucks, product quantity, and per-pallet handling prices influences the net material handling cost. However, it remains unaffected by the alteration in the number of material handling resources. The result also verifies that applying direct transfer of product through cross-docking is economical as fewer products in storage reduce the handling cost.</p> </abstract>

Space and labor are the two internal resources within a warehouse or cross-dock center which seek attention. Meaningful efforts in optimizing these two resources can reduce the operational cost or time of the goods delivery. The timely allocation of resources to order picking not only reduces the makespan and operational time but can also evade delay. In decentralized settings, where all the information is not properly shared between the players of the supply chain, miscommunication results in delays in product delivery. In this study, efforts were made to determine the pallet quantity of different product types in an order quantify when there is a gap in information shared and, based on that, the allocation of material handling devices or pickers was conducted. Each handling device is bounded by a workload to eliminate the option of idle resources and ensure it is utilized properly. A mixed integer linear programming model was formulated for this study and was solved using Lingo. Numerical experiments were performed under varying resource numbers and pallet quantities to investigate the circumstances where the number of pallet types and allocation of machines have the highest benefit. The results confirm that a change in the pallet quantity of the products increases the total picking time. However, an increase in the number of handling devices minimizes the level of over-utilization of a particular machine.