This article considers a two-stage assembly scheduling problem (TSASP) with batch setup times, time-dependent deterioration, and preventive maintenance activities (PMAs). The objective of this problem is to simultaneously determine the optimal component-manufacturing sequence (CMS), product-assembly sequence (PAS), number of setups, and number and position of PMAs (PPMA). First, to determine the optimal solution, a novel mixed integer linear programming model (MILP) for the proposed problem is derived. Then, a standard genetic algorithm (SGA), hybrid genetic algorithm (HGA), standard harmony search (SHS), hybrid harmony search (HHS), and harmony-search-based evolutionary algorithm (HSEA) were proposed owing to the intractability of the optimal solution for large-scale problems. SGA and SHS provide a chromosome to represent a complete solution including three decisions (CMS, PAS, and PPMA). HGA, HHS, and HSEA provide a chromosome to represent a partial solution including PAS. CMS and PPMA are found by an effective local search heuristic based on the partial solution. A computational experiment is then conducted to evaluate the impacts of the factors on the performance of the proposed algorithms.

The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time.

This article addresses an integrated problem of one batching and two scheduling decisions between a manufacturing plant and multi-delivery sites. In this problem, two scheduling problems and one batching problem must be simultaneously determined. In the manufacturing plant, jobs ordered by multiple customers are first manufactured by one of the machines in the plant. They are grouped to the same delivery place and delivered to the corresponding customers using a set of delivery trucks within a limited capacity. For the optimal solution, a mixed integer linear programming model is developed and two variable neighborhood search algorithms employing different probabilistic schemes. We tested the proposed algorithms to compare the performance and conclude that the variable neighborhood search algorithm with dynamic case selection probability finds better solutions in reasonable computing times compared with the variable neighborhood search algorithm with static case selection probability and genetic algorithms based on the test results.