A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

Biopharmaceutical manufacturing requires high investments and long-term production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lot-sizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution). The sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space.     

Optimal production plans and shipment schedules in a supply-chain system with multiple suppliers and multiple buyers

This research addresses an optimal policy for production and procurement in a supply-chain system with multiple non-competing suppliers, a manufacturer and multiple non-identical buyers. The manufacturer procures raw materials from suppliers, converts them to finished products and ships the products to each buyer at a fixed-interval of time over a finite planning horizon. The demand of finished product is given by buyers and the shipment size to each buyer is fixed. The problem is to determine the production start time, the initial and ending inventory, the cycle beginning and ending time, the number of orders of raw materials in each cycle, and the number of cycles for a finite planning horizon so as to minimize the system cost. A surrogate network representation of the problem developed to obtain an efficient, optimal solution to determine the production cycle and cycle costs with predetermined shipment schedules in the planning horizon. This research prescribes optimal policies for a multi-stage production and procurements for all shipments scheduled over the planning horizon. Numerical examples are also provided to illustrate the system.     

Multiproduct,Three-Stage Production Inventory Systems

This paper deals with production systems with three stages in series and producing multiple products. A method of determining a near optimal production plan taking into consideration the demands, the production rates, the set-up costs and all the relevant inventory carrying costs is discussed. This heuristic approach gives the method of determining the production batch quantities and the sequence in which the different products are to be taken up in a production cycle. A numerical example is also included. The results obtained for 21 test problems by the use of the heuristic are compared with the exact optimum values, and the power of the heuristic is demonstrated.     

Optimal production scheduling for the dairy industry

The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility and an efficient coordination of existing resources. Especially in industrial yogurt production, there is a wide variety of products that differ in features like fat content, the whey used to produce the mixture, the flavor, the size of the container or the language on the label. The great diversification and the special features that characterize yogurt production lines (satisfaction of multiple due dates, variable processing times, sequence-dependent setup times and costs and monitoring of inventory levels), render generic scheduling methodologies impractical for real-world applications. In this work we present a customized Mixed Integer Linear Programming (MILP) model for optimizing yogurt packaging lines that consist of multiple parallel machines. The model is characterized by parsimony in the utilization of binary variables and necessitates the use of only a small pre-determined number of time periods. The efficiency of the proposed model is illustrated through its application to the yogurt production plant of a leading dairy product manufacturing company in Greece.     

Scheduling parallel machines with single vehicle delivery

We investigate the integrated production and distribution scheduling problem in a supply chain. The manufacturer’s production environment is modeled as a parallel machine system. A single capacitated vehicle is employed to deliver products in batches to multiple customers. The scheduling problem can also be viewed as either parallel machines with delivery considerations or a flexible flowshop. Different inventory holding costs, job sizes (volume or storage space required in the transportation unit), and job priorities are taken into account. Efficient mathematical modeling and near-optimal heuristic approaches are presented for minimizing total weighted completion time.     

Optimal production planning in pull flow lines with multiple products

The paper is concerned with the problem of optimal production planning in deterministic pull flow lines with multiple products. The objective is to specify the production policy that minimizes the total inventory and backlog costs overtime. Assuming constant product demands and non-decreasing unit holding costs along the flow, an algorithm which obtains the optimal production policy is developed. This algorithm works for the discounted-cost function as well. The HJB equation is used to verify the optimality of the policy, and the computational complexity of the algorithm is discussed. Some illustrative examples are also included.     

Synchronized routing of seasonal products through a production/distribution network

This paper presents a multi-period vehicle routing problem for a large-scale production and distribution network. The vehicles must be routed in such a way as to minimize travel and inventory costs over a multi-period horizon, while also taking retailer demands and the availability of products at a central production facility into account. The network is composed of one distribution center and hundreds of retailers. Each retailer has its demand schedule representing the total number of units of a given product that should have been received on a given day. Many high value products are distributed. Product availability is determined by the production facility, whose production schedule determines how many units of each product must be available on a given day. To distribute these products, the routes of a heterogeneous fleet must be determined for a multiple period horizon. The objective of our research is to minimize the cost of distributing products to the retailers and the cost of maintaining inventory at the facility. In addition to considering product availability, the routing schedule must respect many constraints, such as capacity restrictions on the routes and the possibility of multiple vehicle trips over the time horizon. In the situation studied, no more than 20 product units could be carried by a single vehicle, which generally limited the number of retailers that could be supplied to one or two per route. This article proposes a mathematical formulation, as well as some heuristics, for solving this single-retailer-route vehicle routing problem. Extensions are then proposed to deal with the multiple-retailer-route situation.     

A decomposition approach for the General Lotsizing and Scheduling Problem for Parallel production Lines

This paper presents a novel solution heuristic to the General Lotsizing and Scheduling Problem for Parallel production Lines (GLSPPL). The GLSPPL addresses the problem of simultaneously deciding about the sizes and schedules of production lots on parallel, heterogeneous production lines with respect to scarce capacity, sequence-dependent setup times and deterministic, dynamic demand of multiple products. Its objective is to minimize inventory holding, sequence-dependent setup and production costs. The new heuristic iteratively decomposes the multi-line problem into a series of single-line problems, which are easier to solve. Different approaches for decomposition and for the iteration between a modified multi-line master problem and the single-line subproblems are proposed. They are compared with an existing solution method for the GLSPPL by means of medium-sized and large practical problem instances from different types of industries. The new methods prove to be superior with respect to both solution quality and computation time.     

Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran

In this paper, dynamic dairy facility location and supply chain planning are studied through minimizing the costs of facility location, traffic congestion and transportation of raw/processed milk and dairy products under demand uncertainty. The proposed model dynamically incorporates possible changes in transportation network, facility investment costs, monetary value of time and changes in production process. In addition, the time variation and the demand uncertainty for dairy products in each period of the planning horizon is taken into account to determine the optimal facility location and the optimal production volumes. Computational results are presented for the model on a number of test problems. Also, an empirical case study is conducted in order to investigate the dynamic effects of traffic congestion and demand uncertainty on facility location design and total system costs.     

Tactical level planning in float glass manufacturing with co-production,random yields and substitutable products

We investigate tactical level planning problems in float glass manufacturing. Float glass manufacturing is a process that has some unique properties such as uninterruptible production, random yields, partially controllable co-production compositions, complex relationships in sequencing of products, and substitutable products. Furthermore, changeover times and costs are very high, and production speed depends significantly on the product mix. These characteristics render measurement and management of the production capacity difficult. The motivation for this study is a real life problem faced at Trakya Cam in Turkey. Trakya Cam has multiple geographically separated production facilities. Since transportation of glass is expensive, logistics costs are high. In this paper, we consider multi-site aggregate planning, and color campaign duration and product mix planning. We develop a decision support system based on several mixed integer linear programming models in which production and transportation decisions are made simultaneously. The system has been fully implemented, and has been in use at Trakya Cam since 2005.     

A robust optimization model for production planning of perishable products

In this study, a robust optimization model is developed to solve production planning problems for perishable products in an uncertain environment in which the setup costs, production costs, labour costs, inventory costs, and workforce changing costs are minimized. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilize the resources. By adjusting penalty parameters, decision-makers can determine an optimal production loading plan and better utilize resources while considering different economic growth scenarios. A case from a Hong Kong plush toy company is studied and the characteristics of perishable products are discussed. Numerical results demonstrate the robustness and effectiveness of the proposed model. An analysis of the trade-off between solution robustness and model robustness is also presented.     

Production lot-sizing with dynamic capacity adjustment

In this paper we study a single-item lot-sizing model in which production capacity can be adjusted from time to time. There are a number of different production capacity levels available to be acquired in each period, where each capacity level is assumed to be a multiple of a base capacity unit. To reduce the waste of excess of capacity but guarantee meeting the demand, it is important to decide which level of capacity should be acquired and how many units of the item should be produced for every period in the planning horizon. Capacity adjustment cost incurs when capacity acquired in the current period differs from the one acquired in the previous period. Capacity acquisition costs, capacity adjustment costs, and production costs in each period are all time-varying and depend on the capacity level acquired in that period. Backlogging is allowed. Both production costs and inventory costs are assumed to be general concave. We provide optimal properties and develop an efficient exact algorithm for the general model. For the special cases with zero capacity adjustment costs or fixed-plus-linear production costs, we present a faster exact algorithm. Computational experiments show that our algorithm is able to solve medium-size instances for the general model in a few seconds, and that cost can be reduced significantly through flexible capacity adjustment.     

Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design

In collaborative manufacturing, the supply chain scheduling problem becomes more complex according to both multiple product demands and multiple production modes. Aiming to obtain a reasonable solution to this complexity, we analyze the characteristics of collaborative manufacturing and design some elements, including production parameters, order parameters, and network parameters. We propose four general types of collaborative manufacturing networks and then construct a supply chain scheduling model composed of the processing costs, inventory costs, and two penalty costs of the early completion costs and tardiness costs. In our model, by considering the urgency of different orders, we design a delivery time window based on the least production time and slack time. Additionally, due to the merit of continuously processing orders belonging to the same product type, we design a production cost function by using a piecewise function. To solve our model efficiently, we present a hybrid ant colony optimization (HACO) algorithm. More specifically, the Monte Carlo algorithm is incorporated into our HACO algorithm to improve the solution quality. We also design a moving window award mechanism and dynamic pheromone update strategy to improve the search efficiency and solution performance. Computational tests are conducted to evaluate the performance of the proposed method.

     

A production scheduling heuristic for an electronics manufacturer with sequence-dependent setup costs

In this paper, we develop a three-step heuristic to address a production scheduling problem at a high volume assemble-to-order electronics manufacturer. The heuristic provides a solution for scheduling multiple product families on parallel, identical production lines so as to minimize setup costs. The heuristic involves assignment, sequencing, and time scheduling steps, with an optimization approach developed for each step. For the most complex step, the sequencing step, we develop a greedy randomized adaptive search procedure (GRASP). We compare the setup costs resulting from the use of our scheduling heuristic against a heuristic previously developed and implemented at the electronics manufacturer that assumes approximately equal, sequence-independent, setup costs. By explicitly considering the sequence-dependent setup costs and applying GRASP, our empirical results show a reduction in setups costs for an entire factory of 14–21% with a range of single production line reductions from 0% to 49%.     

Solving the capacitated lot-sizing problem with backorder consideration

In this research, we formulate and solve a type of the capacitated lot-sizing problem. We present a general model for the lot-sizing problem with backorder options, that can take into consideration various types of production capacities such as regular time, overtime and subcontracting. The objective is to determine lot sizes that will minimize the sum of setup costs, holding cost, backorder cost, regular time production costs, and overtime production costs, subject to resource constraints. Most existing formulations for the problem consider the special case of the problem where a single source of production capacity is considered. However, allowing for the use of alternate capacities such as overtime is quite common in many manufacturing settings. Hence, we provide a formulation that includes consideration of multiple sources of production capacity. We develop a heuristic based on the special structure of fixed charge transportation problem. The performance of our algorithm is evaluated by comparing the heuristic solution value to lower bound value. Extensive computational results are presented.     

Design of mathematical models for the integration of purchase and production lot-sizing and scheduling problems under demand uncertainty

This study addresses the multi-level lot-sizing and scheduling problem with complex setups and considers supplier selection with quantity discounts and multiple modes of transportation. The present research proposes a mixed-integer linear programming (MILP) model in which the purchase lot-sizing from multiple suppliers, production lot-sizing with multiple machines and scheduling of various products of different families are accomplished at the same time. However, these decisions are not integrated in traditional environments and are taken separately. In this study, two different types of lot-sizing models called aggregated and disaggregated are developed for the problem to evaluate and compare the computational efficiency of them under deterministic and stochastic demands and provide some managerial insights. To deal with the stochastic demands, Chance-Constrained Programming (CCP) approach is applied. Based on the results of this study, the average profit of the separated (purchase from production) lot-sizing model under demand choice flexibility and stochastic demand is 24% and 22% less than the integrated model, respectively. Moreover, the results also confirm the effect of discount structure on the amount of purchases, productions, revenues and costs.     

Group technology in a hybrid flowshop environment: A case study

This paper addresses the problem of products grouping in the tile industry. This production system can be classified as a three-stage hybrid flowshop with sequence dependent and separable setup times. Main objective has been to identify a set of families integrated by products with common features. This classification would help Production Managers to minimize changeover time, allowing them to further reduce production times. The basic concept of “exploiting similarities”, taken from the Group Technology (GT) philosophy, has been used to address the problem in a creative way. A new “coefficient of similarity” between each of the products, has been defined and used as a parameter, allowing products to be grouped through a heuristic method. This research has already been applied in the framework of a real case, getting quite positive results (actual reduction in both setup and production costs, easier long-horizon planning and short-horizon scheduling, more accurate set up time estimates for new products, etc.).     

Dynamic pricing with uncertain production cost: An alternating-move approach

This article studies a two-firm dynamic pricing model with random production costs. The firms produce the same perishable products over an infinite time horizon when production (or operation) costs are random. In each period, each firm determines its price and production levels based on its current production cost and its opponent’s previous price level. We use an alternating-move game to model this problem and show that there exists a unique subgame perfect Nash equilibrium in production and pricing decisions. We provide a closed-form solution for the firm’s pricing policy. Finally, we study the game in the case of incomplete information, when both or one of the firms do not have access to the current prices charged by their opponents.     

A just-in-time three-level integrated manufacturing system for linearly time-varying demand process

This paper considers a just-in-time (JIT) manufacturing system in which a single manufacturer procures raw materials from a single supplier, process them to produce finished products, and then deliver the products to a single-buyer. The customer demand rate is assumed to be linearly decreasing time-varying. In the JIT system, in order to minimize the suppliers as well as the buyers holding costs, the supply of raw materials and the delivery of finished products are made in small quantities. In this case, both the supply and the delivery may require multiple installments for a single production lot. We develop a mathematical model for this problem, propose a simple methodology for solving the model, and illustrate the effectiveness of the method with numerical examples.